geoinformation technology and data models

Piotr Cichociński, Agnieszka Dawidowicz, Monika Mika, Marek Ogryzek, Tomasz Salata, Monika Siejka, Marek Ślusarski, Ada Wolny





GEOINFORMATION TECHNOLOGY AND DATA MODELS

Zagreb, Croatia, 2015







Reviewers Jarosław Bydłosz Ryszard Źrobek Scientific Editors Agnieszka Dawidowicz Ada Wolny

Published by: Croatian Information Technology Society, GIS Forum 10 000 Zagreb, Ilica 191e, Croatia



Copyright © Croatian Information Technology Society, GIS Forum, Croatia All rights reserved Number of copies: 100

ISBN 978‐953‐6129‐44‐7 Nacionalna knjižnica, Zagreb, Croatia

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CONTENTS INTRODUCTION ........................................................................................................................... 4 1. MODEL OF DATA QUALITY COLLECTED IN THE TOPOGRAPHIC DATABASE .. 6 1.1. Database of topographic objects (BDOT10k) as a source of spatial information ....... 8 1.2. Data quality model BDOT10k .......................................................................................................... 11 1.3. Evaluation of the quality of data collected in BDOT10k – experimental studies ..... 16 1.4. Conclusions ............................................................................................................................................. 20 2. ANALYSIS OF FREE SOFTWARE CAPABILITIES IN ENSURING TOPOLOGICAL CORRECTENESS OF SPATIAL DATA .................................................................................... 22 2.1. Geometry issues .................................................................................................................................... 23 2.2. Topology ................................................................................................................................................... 24 2.3. The procedure ........................................................................................................................................ 25 2.4. Free software .......................................................................................................................................... 27 2.4.1. QGIS ................................................................................................................................................... 27 2.4.2. gvSIG CE ........................................................................................................................................... 28 2.4.3. OpenJUMP ....................................................................................................................................... 28 2.5. Implementation of the procedure in featured programs .................................................... 29 2.5.1. OpenJUMP ....................................................................................................................................... 29 2.5.2. GvSIG CE .......................................................................................................................................... 32 2.5.3. QGIS ................................................................................................................................................... 37 2.6. Conclusions ............................................................................................................................................. 40 3. UPDATING OF LOCAL DATABASES AT THE COMMUNE LEVEL USING GPS TOOLS ......................................................................................................................................................... 42 3.1. Observations and methods ............................................................................................................... 45 3.2.Results and discussion ........................................................................................................................ 54 3.3. Conclusions ............................................................................................................................................. 56 4. ANALYSIS OF POLISH SDI WITHIN THE CONTEXT OF NEEDS OF REAL ESTATE DEVELOPERS ............................................................................................................................... 59 4.1. Methodology ........................................................................................................................................... 60 4.2. SDI as a network and an enabling platform .............................................................................. 62 4.2.1. GEOPORTAL.GOV.PL .................................................................................................................. 66 4.2.2. Atlas of Warmia and Mazury .................................................................................................. 69 4.2.3. MSIPMO ............................................................................................................................................ 70 4.2.4. SIP Stawiguda ................................................................................................................................ 74 4.3. Assessment of the NSDI ..................................................................................................................... 77 4.4. The use of GIS systems for real estate market investors .................................................... 81 4.4. Conclusions ............................................................................................................................................. 83 5. SOFTWARE, TOOLS AND INSTRUMENTS USED FOR THE PRESENTATION (VISUALIZATION) OF RESULTS OF SPATIAL ANALYSIS IN GIS .................................... 85 5.1. Materials and methods ....................................................................................................................... 86 5.2. Results and discussion ....................................................................................................................... 88 5.3. Conclusions ............................................................................................................................................. 96 REFERENCES ............................................................................................................................... 98 LIST OF FIGURES ..................................................................................................................... 105 LIST OF TABLES ...................................................................................................................... 107 NOTES ON THE AUTHORS .................................................................................................... 109 

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INTRODUCTION As the demand for spatial information grows rapidly there is a need for utilizing, improving geoinformation systems and adapting tools based on IT. This book discusses application of information collected in SDI systems like spatial data quality, topicality of information and topological correctness of spatial data. It also presents SDI systems development as well as helpful tools such as GIS software and GPS tools for analysis within the use of spatial data. Spatial information is used in decision‐making processes concerning the functioning of states and quality of life of citizens. The number of institutions recording data and amount of collected information is increasing. Topographic objects database is one of the basic databases covering the whole country area. That is why a key component of any official spatial database is the management of the quality of data collected there. Wherefore the quality of data elements is characterized by varying degrees of significance and by assigning weights to them. The use of weights is particularly important when using the data sets characterized by internal diversity of the criteria for assessing the quality of data. With the aim of improving full use of different data in geographic information systems, this book introduces also a procedure of utilizing tools available in free GIS software to convert CAD drawings into fully‐fledged spatial data sets. This requires finding and testing tools verifying topological correctness of entered data and determining the order of their launching. Indicating ways to transform text elements into attributes of objects is also necessary. The use of free software enhances possibilities of broad implementation of proposed procedures and thus can speed up the process of converting data into useful formats. Moreover, this study presents the practical aspects concerning the creation and updating of databases on the commune administrative level, that is, the development of the highest possible accuracy for GIS data. Such created databases can be widely used, for example in administrative procedures performed before or during building development and in the coordination of crisis intervention staffs in the area of development. The creation of such an infrastructure within the use of GPS tools enables access to spatial data and services relating to a given area as well as provides resource management and use of geoinformation. Having regard to diversity of SDI systems this book includes a demonstration of the evaluation of activity in developing local and regional GIS. As local and regional GIS may be used for different purposes, there is a variety of participants involved in their creation and users interested in obtaining information for their needs. That is why another purpose of this elaboration is to show usefulness of geographic information systems within the context of needs of real estate investors ‐ spatial information essential on different stages of development process. Finally, this monograph presents the possible application environment GIS software for a variety of spatial analysis, achieved by assigning the target groups of different instruments GIS software. With the aim of modernization of existing records map by

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defining a set of required features on new layers, the occurrence of which affects the needs of a variety of spatial analysis. Analysis are carried out such tools as in ArcGIS, MapInfo, EWMAPA, AutoCAD, which belongs to a group of software GIS. Available Web‐ based versions of GIS software allow performing analysis and presentation the results of spatial analysis. Enjoy the reading.

Scientific Editors Agnieszka Dawidowicz Ada Wolny



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1. MODEL OF DATA QUALITY COLLECTED IN THE TOPOGRAPHIC DATABASE Computer revolution in the second half of the twentieth century initiated the era of information, in which the collected data resources are held by computer systems. These systems allow easy sharing of data, and perform complex analyzes in order to provide processed data. After a brief period of unconsciousness, that computer databases store reliable information, the research on the computer data quality in the broad sense began. According to REDMAN (2001) the data are of high quality if they can be used in operational processes, decision‐making and planning. The features of good quality data are: accessibility, comprehensiveness, consistency and accuracy, completeness and usefulness. The data should have the appropriate metrics. Data quality metrics must be characterized by readability, measurability, ease of obtaining and comparability of results. Metrics of data sets are commonly called metadata. A compendium of spatial data infrastructure ‐ The SDI Cookbook (NEBERT, 2015) ‐ distinguishes metadata of recognition, which allow the assessment of the quality of data set and determination of the set data in terms of user requirements. The main elements of the standard CSDGM (US Federal Geographic Data Committee’s Content Standard for Digital Geospatial Metadata) include in a systematic way in order of importance inter alia the following elements: basic information about the dataset, information about data quality (overall assessment of the quality of data in the file), the way of the arrangement of the spatial data in the set, and others (LONGLEY AT ALL, 2006). Characterizing the quality of spatial data, it is possible to use several different properties. The origin of the data, positional accuracy, attribute accuracy, logical consistency, completeness, semantic accuracy, and temporal quality are the key elements of data quality (OORT, 2005) and (DEVILLERS, 2010). According to GAŹDZICKI (2008) the quality of the data is described by the following features: completeness, logical consistency, positional accuracy, temporal accuracy, thematic accuracy, semantic precision and the origin. The completeness is defined as the presence of all the desired data without omission and commission. The logical consistency is the lack of a logical inconsistency in the data set. The positional accuracy refers to the geodetic accuracies – expressed by the coordinates of the objects position. The temporal accuracy is associated with the data changes over time, and thematic accuracy is the correctness of determining, for example, the qualitative properties. Semantic accuracy, represented as a set of data, recreates space of considerations (universe of discourse). The origin describes the method and time of data acquisition and source materials, methods and techniques. The European Parliament adopted in 2007 the directive, establishing an Infrastructure for Spatial Information in the European Community (INSPIRE). The Directive determines the mechanisms and processes of interaction, access and use of spatial data. Infrastructure for spatial information is understood as a spatial data set described with metadata and services and processes associated with this undertaking (DIRECTIVE, 2007/2/WE). Metadata should include information on, inter alia, the quality and validity of spatial data sets. The Implementing Regulation of the INSPIRE Directive (REGULATION,1205/2008) specifies the record of the history and spatial resolution as metadata elements describing the quality and reliability of spatial data. The European standards ISO 19100 series contain a wide range of concepts relating to spatial information and are characterized by rich conceptual apparatus. The

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comprehensive methodology of the data quality description is included in the standard: ISO 19157: Geographic information – Data quality (ISO, 2013). According to the essence of the standard the quality is “totality of characteristics of a product, that bear on its ability to satisfy stated and implied needs”. Complete identification of the quality information should include the "non‐quantitative" and "quantitative" quality information. Non‐quantitative quality information are: purpose, lineage and usage. Quantitative include: completeness, logical consistency, positional accuracy, temporal accuracy and thematic accuracy. The completeness is defined by the presence and absence of attributes, their features and relationships. It consists of two elements of data quality: commission and omissions in the set of data. The logical consistency is defined as the degree of adherence to logical data structures, attributes, and relationships. The structure of data can be conceptual, logical or physical. The logical consistency consists of four components of data quality:  the conceptual consistency ‐ the conformity with the rules of the conceptual framework,  the domain consistency ‐ the consistency of the values with their domain,  the format consistency ‐ the degree of compliance of the data record with the physical structure of data set,  the topological consistency ‐ the correctness of explicitly stored topological characteristics of the data set. The positional accuracy is defined as the positional accuracy of features in a spatial reference system. It consists of three elements of the quality:  the absolute or external accuracy ‐ the proximity of the presented coordinate values to the values considered true or being true,  the relative or internal accuracy ‐ the proximity of the relative positions of objects in the set of data to their respective relative positions recognized as true or being true,  girded data positional accuracy ‐ the proximity of the girded data to the values considered true or being true. The temporal accuracy is defined as the value of temporal attributes and temporal relationships and characteristics. It consists of three components of data quality:  the accuracy of a time measurement – the correctness of the temporal references to values accepted as the true,  the temporal consistency ‐ the correctness of arrangement of events in time,  the temporal validity ‐ the correctness of the data with respect to time (up to date). The thematic accuracy is defined as the accuracy of quantitative attributes and the correctness of non‐quantitative attributes and classification of features and their relationships. It consists of three elements of the quality:  classification correctness ‐ comparison of classes assigned to objects or their attributes to the space of considerations (e.g. the actual value or a set of reference),  non‐quantitative attributes correctness ‐ comparison of classes assigned to objects or their attributes with the scope of interest,  quantitative attributes accuracy ‐ the proximity of the quantitative attribute value to the true value.

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1.1. Database of topographic objects (BDOT10k) as a source of spatial information The primary objective of the creation of databases of topographic objects (BDOT10k) is to provide access of official specialist spatial information systems to the up‐to‐date and high quality topographic data. In this way, the data collected in BDOT10k provide a starting point for the construction of spatial information systems for various government and local government institutions, as well as for the private sector (GUIDELINES TBD, 2003; GOVERNMENT REGULATION 2011) and (GOTLIB 2013). The implementation of the presented objective is possible since the topographic database is the primary sources of information their spatial location, characteristics, cartographic codes and also metadata (GOVERNMENT REGULATION, 2011). This information is obtained from multiple reference data sources. As the primary source, the public records should be mentioned which are National Geodetic and Cartographic Resource. In contrast, the complementary sources are the records collected by other agencies and institutions such as municipal offices, boards of roads, water management, etc. An example of BDOT10k in the cartographic form is shown in figure 1.

Fig. 1. An example of a BDOT10k database in the cartographic form. Source: Head Office of Geodesy and Cartography

According to (GOVERNMENT REGULATION, 2011), (GOTLIB, 2013); (MARMOL and BUCZEK, 2013); (ŁABAJ, 2013) and (BIELECKA, 2010) BDOT10k consists of classes of objects for which the spatial information was obtained from the following reference databases. As the primary database should be considered cadastre of land and buildings, maintained by the county geodetic and cartographic documentation centres. It is a source of information for obtaining and updating the geometry and attributes of the following classes of objects:

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

building, symbol: OT_BUD_A; information concerning the identifier of the building, status of the building, number of floors, type of building according to the Polish Classification of Types of Construction,  another construction, symbol: OT_BUIB_A; information on the geometry and the attribute ‐ the type of construction,  area of grass vegetation or agricultural crops, symbol: OT_PTTR_A; information regarding the soil science classification of land and the type of land use,  locality, symbol: OT_ADMS; information on the locality borders, based on the borders of registration precincts. Using these data sources must be preceded by a study in terms of completeness, timeliness and topological consistency. The state registry of boundaries and areas of the state territory division units (PRG) is a database maintained by the Central Office of Geodesy and Cartography Documentation. This database is used in obtaining and updating the geometry and attribute of topographic object classes in the range of local administrative district referenced with the symbol: OT_ADJA_A. The obtained information includes the name of the unit and the code of territorial division unit. But for cartographic studies, they have additional space infrastructure object identifier and the identifier of the border point. The course of borders is taken automatically from the state registry of boundaries database and is not subject to control of the course with the topological boundaries of other objects (rivers, lakes, roads, railways, etc.). Therefore, if the information will be used for the construction of spatial information systems or the production of maps, the topological control should be carried out by the user. The reference database in the range of geographical names, correctness of their sound and spelling, is a database of state registry of geographical names maintained by the Central Office of Geodesy and Cartography Documentation. This registry has spatial and descriptive data for administrative units and physiographic objects. These data are available in the SHP, XLS, XML, GML, TXT formats, so they can be used on other layers of topographic objects database. The registry of towns, streets and addresses (EMUiA) is a database kept in electronic form by the authorities of cities and communes. It is a reference base for the layer locality marked with symbol OT_ADMS_A. This database contains information on the identifier of the object, the place name, the type of locality, the number of residents, TERYT identifier, PRNG identifier, and the street name and street type. The data contained in the database EMUiA originate from the database of the State Registry Of Geographical Names (PRNG) in terms of the names and types of the locality, while the course of borders originates from the database of the state register of borders and the cadastre of land and buildings. Aerial and satellite imaging as well as orthophoto and digital terrain model are databases used to obtain and verify the objects geometry and also their classification and initial interpretation. Objects, which cannot be clearly identified on basis of the available materials are subject to verification in the field. The supplement of data on orthophotomap is a digital terrain model (DTM). It allows accurate verification of objects located in areas difficult to access e.g. it enables digitizing and verification of the geometry of water brook in wooded areas. DTM plays an important role in the construction of the contents of cartographic elaborations. This applies to the terrain relief objects such as: escarpments, contours, mounds, ravines, elevation points etc. In case of this reference base, to ensure the appropriate quality of the data contained in BDOT10k, it is important to monitor the updates of existing source materials.

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The database of topographic objects BDTO500 concerning the details of maps at scales from 1: 500 to 1: 5000, is a large‐scale numerical elaboration of the content of the base map. These databases are maintained in urban and rural areas, but only for built‐up areas or designated for development. On the day of the elaboration in Poland there are no data of this type. National Official Registry of Territorial Division of the Country (TERYT), is a database maintained by the Central Statistical Office. This database is a database of reference in the field of territorial units for all records and systems of public administration. The information there contains identifiers and names of territorial division units, identifiers and names of places, names and symbols of streets. This registry provides unambiguous identification of territorial units at different levels of detail (state, county, municipality, city, street, etc.). Thanks to this, it is possible to integrate data between different systems. In the database BDOT10k the following classes of objects can be distinguished:  the unit of administrative division, the symbol: OT_ADJT_A; containing the territorial identifier of the superior unit and the identification of the administrative unit  locality, symbol: OT_ADMS_A; containing an identifier of the commune and locality identifier in the TERYT registry  list of streets, symbol: OT_Ulica; including the identifier of the street coming from central streets directory maintained by the Central Statistical Office. Database registry of immovable monuments is created and maintained by the voivods. It is created on the basis of a decision on the entry of the object into the register of monuments. This is the basic form of protection of monuments in Poland. Documentation of the national register of immovable monuments is collected by the National Heritage Board. Objects can be entered in the register of monuments in two basic forms of protection. One is to recognize the object as a monument to history. The second form are the objects listed on the UNESCO World Heritage List. These are the objects protected under the Convention on the Protection of the World Cultural and Natural Heritage. The database of topographic objects BDOT10k, contains the following classes of objects:  buildings, symbol: OT_BUBD_A; information on the number of general and specific functions of the building,  antique and historical complex, symbol: OT_KUZA_A,  object, symbol: OT_OIOR_A; information regarding the type of object. Registers of monuments have often incomplete address information due to the lack of updates of changes of the names of towns or changes of the object address. Therefore, the information coming from the register should be verified with other sources. Reference register for BDOT in the range of water network is the Map of Hydrographical Division of Poland run by the National Water Management and the Institute of Meteorology and Water Management. This map was based on military topographic maps at the scale of 1: 50 000, therefore it has a low accuracy. In this situation, the geometry of the hydrographical network is derived from the current orthophotomap; a reference to the Map of Hydrographical Division of Poland is achieved by giving the identifier to the correct objects. Examples of objects on layers: 1. surface water, symbol: OT_PTWP_A, 2. river and stream, symbol: OT_SWRS_L, 3. channel, symbol: OT_SWKN_L,

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4. drainage ditch, symbol: OT_SWRM_L. Descriptive and localization data about roads and bridges are acquired from managers of these objects. Depending on the class of the object the management can be performed by the central, provincial, county or municipal unit. BDOT10k database contains the following classes of objects: 1. road, symbol: OT_SKDR_L; information concerning the category of management and class of road attribute, 2. roadway, symbol: OT_SKJZ_L; information concerning the category of management and class of road attribute, 3. road trail, symbol: OT_SzlakDrogowy; information concerning the road number attribute, 4. roundabout, road junction, symbol: OT_SKRW_P; information concerning the road junction type attribute, 5. communication complex, symbol: OT_KUKO_A; information concerning the communication complex type attribute e.g. MOP‐ passenger service place. These data on roads and bridges are run in the form of analogue maps and tables containing descriptive information. The enter these data into the BDOT10k database will require inspection of continuity of attributes and the correctness of the entered data. The presented data relating to objects and their attributes when entered into BDOT10k will be able to provide a systematic source of terrain information for various specialized elaborations only then when they are characterized by the high quality. The main components of the quality include completeness and timeliness of the data. In this situation BDOT databases must be successively updated at specified time intervals and be subject to the technical and substantive control (MARMOL and BUCZEK, 2013). The rules of spatial data quality management must result from the adopted model of data quality. The quality model should be designed and formulated before the actual production of spatial data to take into account user requirements and expected quality objectives. The model should include two basic correlated parts. The first part refers to the definition of objectives and quality requirements resulting from the database specification. The second part deals with the evaluation process of spatial data. In the proposed model of the data quality BDOT10k data quality elements have been defined. They are described by measures of data quality, evaluation of data quality and the result of data quality. The model also defines the scope and manner of reporting the results of the quality evaluation. 1.2. Data quality model BDOT10k National Management System of Topographic Objects Database (KSZBDOT) which is being built now is the project aimed at the purposes of obtaining, control, storage and sharing of topographic information. This will be the information and communication system managing topographic and general geographic databases, from which standard cartographic elaborations can be created (ŁABAJ, 2013). The concept is to build a system of several components comprising KSZBDOT. One of the main areas of KSZBDOT functioning will be the module of topographic information data quality management. The module of system data quality management BDOT10k will control the errors detected during data validation and manage the quality model and the set of metadata. Checking the accuracy of the BDOT10k database data sets will be performed automatically using templates of data control. The elements of the control templates

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are following: controlled database definition and specification of the rules of the control parameters. Database definition defines, what class of objects, and additional files should be in the set. Specification of the rules of the control parameters is a register of control rules stored in a standard form that facilitates the interpretation of a control template by the application executing data control. In the control process the internal consistency of a set of data in the temporary buffer and consistency with the data in the BDOT10k store will be verified. The next step of data correctness checking will be office control. This check will be performed on a random selected data sets samples and will allow the estimation of data quality indicators. The basis for the BDPT10k storage update will be the situation when quality indicators do not exceed the limit values specified in the model data quality (GUIDELINES (WYTYCZNE), 2012). On the basis of the assumptions of the National Management System of Topographic Objects Database project and implemented in 2012‐2013 undertakings concerning the creation of BDOT10k, the scheme of quality control of topographic data can be formulated. Fragment of the diagram of data quality control process is shown in figure 2. The data sets are subject to the quantitative and qualitative control. Quantitative control verifies the correctness of files saving and their structure, completeness and nomenclature. Quality control is carried out by means of three components: automatic, office and field control. Automatic data control of BDOT10k consists of five basic components: data BDOT10k validation with the GML scheme attribute control in GML files, geometry control, topology control and additional checking. The second step of the quality control is the office control. Its purpose is to examine the detail substantive compliance of reported data with source materials. Area under the control is selected by the selection of the data sample consisting of representative areas and objects. The data are verified due to the: completeness and accuracy of obtaining data from other registers, the correctness of the position of the introduced objects, the correctness of entering of the values of attributes and the correctness of the identification of BDOT10k objects. The third step of the quality control is the field control. The purpose of this control is to examine the substantive compatibility of the transferred data with the real situation on the ground. Subject to the control are all objects entered to the BDOT10k in the area of sample data selected for control. Quantitative and qualitative controls can be performed in several iterations until a positive result is obtained (ZAPALSKA and STUGLIK, 2013). Currently, the only legal act regulating the rules of creating and updating BDOT10k is the Regulation of November 17, 2011 on the topographic objects database and general geographical objects database, and also standard cartographic elaborations (GOVERNMENT REGULATION, 2011). The provisions of the Regulation do not explicitly define the principles of data quality management. In § 19 is written only that system supporting BDOT10k should provide, inter alia, data quality control. Data quality elements are contained in the Annexes to the Regulation. Annexes provide, inter alia, a catalogue of objects with their attributes, relationships and constraints, classification of objects at three levels of detail, UML and GML application diagrams and guidance concerning the rules of entering the objects to BDOT10k. The Regulation does not determine the scope of the data control, office and field control rules taking into account the size of the data samples and the acceptable level of error for the data set.

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Control of data sets should include validation of database creation and technological and substantial aspects of content. Technological control is mainly analysis of the method of data recording, the topology and conformity to the standards of data exchange. Substantive correctness is the data completeness, the fulfilment of the required accuracy and compatibility of data with real terrain situation (BIELECKA, 2010). Quantity control Kontrola ilościowa + completeness of files + kompletności + correctness of record plików + file saving structure + poprawności + . . .

zapisu

yes

Data Dane correct? poprawne

Supplement of data

no

? Correction of the elaboration

Automatic quality Kontrola control jakościowa

automatyczna

+ validation of BDOT10k data with + walidacji danych GML scheme BDOT10k ze + attribute schematem + geometric GML + atrybutowa + . . .

+

Office quality control + Substantial conformity with source data

Field control + Substantial conformity with the field situation

t

Acceptance of BDOT10k Przyjęcie BDOT10k to National Geodetic and do Państwowego Cartographic Resource Zasobu Geodezyjnego

yes

Data correct?

no

Fig. 2. The diagram of data quality control process (part). Source: SIEJKA and ŚLUSARSKI own study based on (GUIDELINES, 2012) The principles of BDOT10k data quality management must result from the adopted model of data quality. The quality model should be designed and formulated

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before the actual production of spatial data to take into account user requirements and expected quality objectives. The model should include two basic interdependent parts. The first part refers to the definition of objectives and quality requirements resulting from the database specification. The second part deals with the evaluation process of spatial data. The construction of data quality model includes defining its components. The main components of the model are:  data quality elements,  measures of data quality,  methods of evaluation of data quality,  result of data quality,  metaquality,  scope of data quality,  metadata. For BDOT10k elements of data quality concern the five major components. Investigation of completeness is called quantitative and qualitative control. Quantitative control verifies the correctness of files saving and their structure, completeness and nomenclature. Quality control of completeness is mainly based on examining the occurrence of excess or deficiency of objects and their attributes and relationships. Logical consistency is the degree of compliance with the logical rules applicable to data structures, attributes and relationships. Here the co linearity and continuity of objects conditions are checked, preserving of the spatial rules and correctness of linear objects segmentation. Positional accuracy is assessed by geometric accuracy of objects. This feature focuses on the proximity of the coordinate values to values accepted as true and proximity to the relative position of objects to their correct relative position. The temporal accuracy will be implemented using information on timeliness and the date of the dataset creation. The thematic accuracy is examined in terms of correctness of classification, non‐quantitative attributes correctness and the accuracy of quantitative attributes. Examination of the quality of BDOT10k data should be based on several measures easy to understand by the user of the database. The main measures of data quality are: number of incorrect objects, attributes, and relationships, and the error coefficient, that is, the ratio of false objects, attributes, and relationships to their total number. In the assessment of the geometric accuracy of database objects understandable to interpret measures are: mean square error, circular error (CE95) and linear error (LE95). CE95 error is the radius circumscribing the circle, in which the real point is located with the probability of 95%. Linear accuracy of map with a 95% level of significance (LE95) is half the length of the interval defined by the upper and lower limit, in which the true value is located with the probability of 95%. CE95 error is applicable to the study of one‐dimensional properties, such as the corners of buildings or network nodes. Linear error (LE) determines the likelihood of misplacement of two‐dimensional features, such as border lines or centre line (ESDIN, 2010). The result of data quality assessment should be presented separately for each item of data quality. The evaluation results will be given in different units, depending on the nature of used data quality measures. Geometrical accuracy will be expressed in the unit of distance, and temporal accuracy (timeliness of data) in unit of time. Database user interpreting the results must have knowledge of these values and units. The results of the data quality will be easier to understand, if all the results are

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presented by a single scale or units. It can be done by creating a level of compliance for each data quality measure. Then it is necessary to specify for each result of data quality, in which conformation class is located the result. Determination and gradation of conformation classes for BDOT10k can be based on the classification of objects contained in Regulation on the database of topographic objects (GOVERNMENT REGULATION, 2011). First class (I ‐ the highest) includes communication networks, infrastructure networks and buildings, structures and equipment. Next class (II) includes land‐use complexes and territorial division units. The third class (III) includes a network of waters, land cover and protected areas. In addition, the characteristics of data quality can be described by a metaquality. Metaquality elements include a set of quantitative and qualitative measures of quality assessment and its result. Metaquality can be described in three meanings: as a confidence, representativity and homogeneity. The confidence means the credibility of the results of data quality. Representativity is the level at which the used data samples reached the result, which is representative for the data in terms of data quality. Homogeneity is expected or tested uniformity of the results obtained during the evaluation of data quality (ISO, 2013). For BDOT10K Metaquality should inform the user of data about the level of reliability of the obtained evaluation results. Statistical measures such as standard deviation and coefficient of variation may be useful here. Description of the method of sampling shows degree of representation of the data set. Homogeneity assessment will be needed mainly in the case of entering to one database, information from various sources. It is necessary then to carry out comparison of the results of the quality assessment for data from different segments of a given set. The scope of data quality determines spatial and temporal characteristics, identifying the data on which the quality of data is to be assessed. This characteristics include: the geographical coverage, the time scope , the data set or data series. In the model of BDOT10k data quality, the scope of quality is defined by defining a set of data, class of objects, attributes, relationships, and sample sizes. Spatial data users have now widespread access to unlimited resources of these data. The valuable data are those that have the appropriate metrics describing data sets called metadata. Metadata as quantifiers describing spatial data sets should among others include information on location and type of objects, their attributes, origin, accuracy, details and timeliness of the data set (BIELAWSKI, 2013). Quality information of BDOT10k database may be given in the form of standard quality reports or be a part of the collection of metadata. Presentation of the information about the data quality in the form of metadata simplifies their analysis and will facilitate access to them for producers and users of data, because the metadata have a strictly formalized structure. Conceptual proposal of BDOT10k data quality model is shown in figure 3.

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Data quality scope + the set of data + classes of objects + attributes + relations + sizes of l

defined by

Data quality BDOT10k

is reported

is expressed

Metadata + . . . . + information of data quality + . . . .

Data quality element + completeness + logical consistency + positional accuracy + temporal accuracy + thematic accuracy is described

Data quality measure + number of incorrect objects, attributes and relationships + error index + geometrical accuracy

Data quality evaluation + direct and internal + external (full control and sampling) + . . . .

Data quality result + the expected level of compliance + . . . .

Metaquality + confidence + representativity + homogeneity



Fig. 3 The conceptual model of data quality BDOT10k. Source: SIEJKA and ŚLUSARSKI own study based on (ISO, 2013). 1.3. Evaluation of the quality of data collected in BDOT10k – experimental studies Experimental studies of spatial data quality were performed by analyzing the information contained in the BDOT10k database for the Zielonki community in the Malopolskie Voivodeship. Zielonki is a rapidly growing community, which is adjacent to Krakow. It covers an area of 48.4 square km, and the number of its inhabitants in 2013 exceeded 18 thousands of people. Built‐up and urban areas account for about 15% of the commune area. In The District Geodetic and Cartographic Resource for the community there are available the digital land and buildings cadastre map and the digital base map. Experimental studies of spatial data quality were performed, paying particular attention to the needs of the user of the data set. Examination of the completeness is mainly quality control investigating the occurrence in the database of excess or deficiency of objects and their attributes and relationships. For BDOT10k of Zielonki commune the field inspections were performed and additional verification tests based on digital base map and orthophotomap. As a result of the carried out studies significant deficiencies in the database objects were

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found, mainly in the categories of buildings, structures and equipment, infrastructure and utility networks. The deficiencies are estimated at 40%. The reasons for this situation may be two. The first is the lack of base map in the digital geodetic resource during creating the database. The second one is the use of an outdated orthophotomap. Additionally, a control of attribute values of objects was performed. Considerable deficiencies in attribute values were found, for example the height and number of floors of the building. With regard to the logical consistency of BDOT10k is the degree of compliance with the logical rules of data structures, attributes and relationships. For the test database co linearity and continuity conditions of objects were checked and the accuracy of the segmentation of linear objects. The study was performed using the QGIS tools. Practically there were no weaknesses in the logical consistency, analyzing the criteria relevant to the user of spatial databases. Positional accuracy is assessed by geometric accuracy of objects. An error of positions of objects in BDOT10k should not exceed 1.5 m. The accuracy of the database objects is affected by the quality of the source materials, correctness of interpretation and accuracy of digitizing. For BDOT10k of Zielonki commune checks of the accuracy based on the reference material, which is the digital base map, were performed. As a result of this analysis it was found, that more than half of the analyzed object does not fulfil the required parameters of the geometric accuracy. The temporal accuracy is defined as the value of temporal attributes and temporal relationships and characteristics. For the user of BDOT10k, the most important is the temporal importance, or timeliness of the data set. BDO10k is currently not subject of present updates, and changes in the land use in urbanized areas affect the progressive obsolescence of the database. Analysis of temporal accuracy of the data collection of the study area showed its timeliness at 80% level. Thematic accuracy is the precision of the objects classification correctness, non‐quantitative attributes correctness or accuracy of quantitative attributes. The correctness of the classification and the correctness and accuracy of the attributes were rated as a result of the comparison with source materials. Practically there were not found any weaknesses in the thematic accuracy, analyzing the criteria relevant to the users of spatial database. The results of the assessment of the BDOT database quality carried out for the Zielonki community are shown in table 1. The results are shown using point values in the range from 1 to 100. Table 1. The results of the data quality evaluation. No

Element of data quality

1 2 3 4 5

Completeness Logical consistency Position accuracy Temporal accuracy Thematic accuracy

The evaluation point values 60 99 40 80 95

Source: SIEJKA and ŚLUSARSKI own study. Data quality elements are characterized by varying degrees of significance, therefore during the BDOT10k database qualitative assessment it is necessary to assign to them appropriate weights of validity. The sizes of the weights were determined by the Analytic Hierarchy Process (AHP). The AHP is one of the methods

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used to solve complex multivariate tasks through the creation of a hierarchy structure. At every level of the hierarchy a matrix is created, resulting from the pairwise comparisons of individual elements of the hierarchical structure. (PIASEK and SIEJKA, 2003). In this way, elements of matrix A’ meet the following conditions: ‐ all elements aij > 0 ‐ diagonal elements aii = 1 ‐ symmetrical elements aij = aji‐1 Matrix A’ has always real and positive eigenvalue λ, which has the following properties (SAATY 1977), (SAATY 1980): 1. it is simple root of the characteristic equation of the matrix, 2. it is the largest (as regards the module) eigenvalue of the matrix, and corresponding to this eigenvalue eigenvector w has always all the components positive (wi>0). Therefore, in order to obtain a solution it is necessary to determine for each matrix, the maximum eigenvalue λmax and associated with this value eigenvector w, which is a vector of priorities. After setting partial priorities for all levels the solution of the task is a vector: k

C[1, k ]T   Bi  Bk  Bk 1  B2 i 2

where: C[1,k] ‐ the vector of results of priorities attached to the elements of the hierarchical level k with respect to the thesis, Bi ‐ the matrix of results for the level i, which columns are the vectors of priorities of elements of this level relative to elements of the level i – 1. In order to verify the correctness of the results two indicators were introduced (SAATY, 1980): 1. consistency index – CI

CI 

2.

 max  n n 1

 0,10



where: n – the dimension of the matrix λmax – the maximum eigenvalue of the matrix consistency ratio – CR

CR 

CI  0,10 RI





where: RI (random index) depends on the size of the matrix n, the table 2. Table 2. The value of RI depending on the dimension of the matrix n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0,58 0,90 1,12 1,24 1,32 1,44 1,45 1,49 1,51 1,53 1,56 1,57 1,59

Source: SAATY, 1980.

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When the consistency ratio CR>0,1 or consistency index CI>0,1, the assessment of the dependence of the elements of the matrix must be repeated. The first step of determining the validity weights in the study of BDOT10k is to create matrix of pairwise comparisons for the five elements of data quality (completeness, logical consistency, positional accuracy, temporal precision and thematic accuracy). Preferences are expressed using a scale from 1 to 9, where 1 is the equivalence of comparable elements, and 9 ‐ extreme preference of one component relative to another. Considering the quality of the BDOT10k data from the level of the user's needs, A weight matrix of validity will look like this: 1 9/1 5/1 3/1 3/3 1/9 1 1/7 1/7 1/2 A= 1/5 7/1 1 2/1 3/3 1/3 7/1 1/2 1 3/1 1/3 2/1 1/3 1/3 1 The next step is to calculate the eigenvector of the matrix of preferences. SAATY (1980) proved that this approach is optimal in order to find the final ranking of the considered criterion. As a result of the calculations the weight vector was obtained. Vector w is a vector of weights in relating to the five elements of the quality of data representing user preferences. w = [0.49,0.04,0.21,0.18,0.08] In order to verify the results of pairwise comparisons of matrix A elements, consistency index and consistency ratio were calculated. The calculated values of the coefficients were respectively 0,068 and 0,061. Table 3 indicates the point values of data quality assessment for the five features described by the point values on a scale from 1 to 100, on the basis of table 1. The weights of individual data quality criteria come from the matrix w. Table 3. The point values of data quality evaluation and the values of calculated estimators No 1 2 3 4 5 Calculated estimates

Criteria of data quality

The evaluation point values

Weights

Completeness Logical consistency Position accuracy Temporal accuracy Thematic accuracy

60

0.49

Point values for assessment ‐ weighted 29

99

0.04

4

40

0.21

8

80

0.18

14

95

0.08

8

The average value

75

The coefficient of variation

0.33

The average weighted value The coefficient of variation

63 0.16

Source: SIEJKA and ŚLUSARSKI own study. Overall evaluation of the quality of the BDOT10k data was performed by calculating two estimators: the average value and the coefficient of average variation.

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The average value of all data quality criteria shows, how big part of the data meets the established criteria of 100 points. The difference of 12‐points between the values of average and weighted average shows the merits of the application of validity weights in the study of spatial data quality. In addition, the values of coefficients of variation for the average and the weighted average are 0.33 and 0.16 respectively. They show the consistency of the result of the data quality evaluation calculated using weights. A conceptual model is one in which the coefficient of averages variation is zero. 1.4. Conclusions In recent years, demand for spatial information is growing rapidly. They are used in decision making processes concerning the functioning of state and quality of life of citizens. Increases also the number of institutions collecting the data, as well as the amount of information collected. In Poland, are created and maintained the official databases collecting spatial information. BDOT10k is one of the basic databases covering the whole country. A key component of the official spatial the database is quality management of the collected data. BDOT10k due to its complexity of structures and a wide range of topics of collected data requires the use of advanced techniques for data quality control. Currently there is no official solution for quality management purposes in BDOT10k. There are no official control templates and software applications for data control. Control templates shall be essentially consistent with current legislation and assumed as independent from the particular commercial development. The proposed in the work concept of data quality model for BDOT10k will be useful for creating National Management System of Topographic Objects Database. Experimental study of the quality of the data of BDOT10k database was carried out in Zielonki commune, Malopolskie Voivodeship. Performing a qualitative assessment, particular attention was paid to the needs of the user the data set. Criteria for assessing the quality of data set were based on five criteria: completeness, logical consistency, positional accuracy, temporal accuracy and thematic accuracy. As a result of this analysis it was stated that, more than half of the analyzed object does not meet the required geometric accuracy parameters. Studies of completeness have shown deficiencies of the data at the level of 40%. Research of the temporal accuracy of the data set for the study area showed its timeliness at 80%. Practically there were no weaknesses found in the thematic accuracy and logical consistency, analyzing the criteria relevant to the user of spatial databases. For thematic accuracy 95% met this criterion and 99% for logical consistency. Based on these results an average data quality assessment for the BDOT10k database is 75%. Doing the research of data quality from the point of view of the user it is necessary to remember, that data quality elements are characterized by varying degrees of significance. For this reason, during the qualitative assessment the appropriate weights should be assigned to them. The sizes of the weights were calculated using AHP method in the range of data quality elements representing user preferences. The most important quality criterion is the completeness of the data set ‐ 49 points (at a scale from 1 to 100), further geometrical accuracy ‐ 21 points and the temporal accuracy ‐ 18 points. Thematic accuracy ‐ 8 points and logical consistency ‐ 4 points, are of minor importance for the qualitative assessment of the spatial database from the point of view of the user's needs. The weighted average assessment of BDOT10k database data quality is 63%.

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Result of data quality assessment of the BDOT10k database calculated using weights is lower by 12 percentage points compared to the result obtained without the use of weights. This fact points to the validity of the use of weights in the study of spatial data quality. In particular, the use of validity weights is important when using the data sets characterized by internal diversity of the criteria values for assessing the quality of data.





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2. ANALYSIS OF FREE SOFTWARE CAPABILITIES IN ENSURING TOPOLOGICAL CORRECTENESS OF SPATIAL DATA The increasing demand for the data and information of a spatial nature in the past three decades as well as the developments in the area of information technologies and informatics, has led to the development of automated tools for efficient storage, analysis and presentation of geographic data. This rapidly evolving technology has become to be known as Geographic Information Systems (GIS). Geographically referenced data separates GIS from other information systems. Simple vector models in GIS build upon points and lines. Areas (polygons) are modelled by closed loops of lines – there may be inner loops to exclude “islands” (INTERNATIONAL ORGANIZATION FOR STANDARDIZATION, 2004). Simple geometric elements: point, line and polygon are furnished with semantic attributes, thereby creating features. This must be done explicitly. Therefore, a vector model is also called geo‐relational. Geographical feature, representing ground element, is made up of two components (GOMARASCA, 2009):  positional, which graphically and geometrically defines the position and the shape of the objects represented by geometric primitives like points, lines and polygons (e.g. poles, roads, parcels);  descriptive, expressed by alpha‐numerical declarations, aimed at qualifying some non‐spatial properties of the geometrical features by means of attributes (numbers, strings, date); i.e. pole height, road surface, parcel number, etc. Attributes are arranged in tables and each feature has a database table record. Geographic features are organized into thematic layers, allowing segregating different kinds of features. Additionally each GIS layer has a homogeneous geometric type (all points, lines, or polygons). In the era when GIS software was not yet so popular and widely used Computer Aided Design (CAD) software and other vector graphics programs were used for map creation. CAD software has powerful functions of graphics drawing and graphics editing. It can draw many different geometric shapes, such as points, lines, polygons, circles, arcs, ellipses etc. Therefore it is well suited to draw digital maps. Nowadays CAD files such as AutoCAD drawing files (DWG), MicroStation design files (DGN), and Autodesk's drawing exchange format (DXF) can serve as good sources of GIS data sets (ESRI, 2001). CAD files contain some textual and mainly vector data that can be used to populate GIS data sets. In general terms CAD files contain a collection of autonomous geometric objects (also called entities or elements) that are defined by static graphic properties, such as colour, line style, and line weight, and are loosely organized by level or layer. Layering is the most widely used technique for managing the complex information contained in larger drawings and CAD models (INTERNATIONAL ORGANIZATION FOR STANDARDIZATION, 1998a). This consists of assigning graphical drawing elements of the same type to invisible layers, which can be turned on and off, both on the screen and in paper printouts, to help the user to focus on only that which is essential to her work, hiding the rest of the information. Since Layering is a widely used method for structuring data in CAD models, during the last few years national standardization organizations, professional associations, user groups for particular CAD systems, individual companies etc. have

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issued numerous standards and guidelines for the naming and structuring of layers, especially in building design. In order to increase the integration of CAD data in the industry as a whole ISO decided to define an international standard for layer usage – ISO 13567 (INTERNATIONAL ORGANIZATION FOR STANDARDIZATION, 1998b). Although CAD layer is often used to organize and define default symbols for a category of objects in a CAD file, in practice CAD layer can be used to hold objects from many different categories, or CAD objects in a category can be spread out over many CAD layers. In some sense CAD layers are nothing more than other entity properties, such as colour or line type. This causes that there is a great deal of flexibility within CAD to organize data. In a CAD file, enforcement of data organization is the sole responsibility of the CAD standards implemented by the organization and subsequently adhered to by the CAD operator. CAD standards do not always separate object systems by layer – objects could be differentiated just as well by colour or line style. Sometimes data organization is dictated by an application that creates the data. The use of well‐defined data standards is essential in avoiding ambiguity and poor data quality in a CAD file (ESRI, 2003). In addition to the purely geometric data CAD drawings can also contain descriptions of objects. There are a variety of methods used within AutoCAD and MicroStation to maintain descriptive attributes on CAD objects. Fortunately in case of map data usually simple labelling is used by means of texts located on maps. 2.1. Geometry issues Because of presented above different data models and the different intended uses of the CAD drawings, the process of using CAD data in a GIS or converting CAD data into a GIS data set requires conversion form CAD data to GIS data. The conversion process includes two parts: one is graphical data conversion, and the other is attribute data conversion. Data migration is often the most challenging and expensive step in the GIS implementation. Importing data from external sources requires some necessary treatment and management such as separating and classifying features, editing geometric elements and modifying the attributes. Recent advances in GIS software have made it simpler to move CAD data into GIS databases, however, many quirks still exist in file transfer due to the intrinsic differences in the file formats and structure. It is common in CAD or general‐purpose vector drawing software to have a network of line segments that are used to define the visual boundaries of polygons and to use separate point objects, such as text entities, to identify the “would‐be” polygons. This particularly happens when boundaries of parcels have been drawn using lines and numbers have been presented using texts. It is much easier to draft polygons as a collection of line segments than it is to draw closed polygons (ESRI, 2004). But, unfortunately, they do not include necessary information for executing even the most elementary operations and calculations on given geometric data (ŽALIK, 1999). For instance:  it is not possible to fill individual parcels,  it is not possible to determine which parcels are neighbours to a specified parcel,  it is not possible to calculate the area of a desired parcel.

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Obviously, such representation of geometric data is not sufficient to automate aforementioned operations. So, it is necessary to create polygons from a collection of CAD lines that are not drawn as closed polygons. When there is a high degree of confidence in the data, they can be used “as is” to generate polygons from lines and points. But this can be done only under the assumption that each parcel is surrounded by boundary lines and exactly one number is located inside that identifies it. To be able to build a polygon all border lines must touch each other only at their ends and form closed rings. Moreover, it should be possible to convert texts representing parcel numbers into parcel attributes, stored in attribute table. But it may happen, that maps drawn in CAD software look perfectly correct and complete only when displayed by a computer. This way, dangerous illusion of “a correct” computer‐based maps is achieved whereas in fact there are so‐called topological inconsistencies. 2.2. Topology Originally and in the wider sense the term topology referred to the branch of mathematics dealing with the properties of geometric figures that are not subject to change under geometric transformations (MAGNUSZEWSKI, 1999). However, in the field of Geographic Information Systems (GIS) topology is understood as a description of the spatial relationships between adjacent or located near each other objects (THEOBALD, 2001). Topological consistency describes the trustworthiness of relationships between the dataset segments (JOKSIĆ and BAJAT, 2004). These relations typically involve spatial data inconsistencies such as incorrect line intersections, polygons not properly closed, duplicate lines or boundaries, or gaps in lines. It deals with the structural integrity of a given data set based on formal framework for modelling of spatial data and relationships among objects. These types of errors must be corrected to avoid incomplete features and to ensure data integrity. Unites States Census Bureau was a pioneer in the field of application of topology to reduce the number of errors made in the process of collecting large data sets at the turn of sixties and seventies of the last century. Environmental Systems Research Institute (ESRI) has also made major achievements in the field of topology applications. In the early eighties they developed Coverage format allowing for the storage of large data sets and efficient spatial analyses taking into account limited capacities of data storage media and relatively low processing capability of computers in those days (ESRI, 2005). The operation principle of this format was to store only information about the components of objects and their relationships (topology) and construction of these objects “on‐demand” for the purpose of presentation or analysis. Thus, storing data in this format was synonymous with the verification of topological correctness. It assumes that the objects are located on the a plane and are represented by nodes (zero‐dimensional), edges, also called arcs (one‐dimensional) and polygons (two‐dimensional). Because edges are located on a plane they are not allowed to intersect, but they have to touch each other at their ends, represented by nodes, finally forming non‐overlapping and filling the entire area of polygons (ZADRAVEC and ŽALIK, 2009). The development of computer hardware in terms of processing power and data storage capacity in the nineties of the twentieth century resulted in the change of a view on how to store the vector data. It turned out that it was easier to store objects

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in the “ready” form of so‐called simple features (OPEN GEOSPATIAL CONSORTIUM, 2011). This is particularly important in the case of polygons, because it resulted in redundant double storage of the same boundary points. Shapefile format was then created (ESRI, 1998). This approach to storage of simple objects was also used at the end of this decade, when the first attempts to store geographic data (both geometry and attribute) in relational database structures were undertaken (ZEILER, 1999). This way storage of topology together with the data was abandoned. It turned out easier and more efficient to generate or check the topology “on‐demand” than to create objects by that means. 2.3. The procedure The above considerations allow to propose a step‐by‐step procedure for data transfer from CAD to GIS and subsequent construction of polygons from a set of line segments or polylines. Particular emphasis is placed on the possibility to use free software for this purpose (MICHALAK, 2007). Issues described here were previously described in various publications, for example (AL RAWASHDEH BALQIES SADOUN and AL FUKARA, 2012), (ŽALIK, 1999), but so far no one has proposed a comprehensive and detailed solution to this problem. Interested reader will find here some kind of guide, but it does not cover every technical issue and likely situation in CAD to GIS transfer. The proposed procedure is comprised of the following steps: Step 1. CAD data import When converting CAD drawings, it is necessary to be able to isolate objects that can be candidates for GIS features. If building of polygons is anticipated, it is necessary to obtain the information about their boundaries and identifiers. CAD data is commonly grouped by CAD layer name or CAD graphic properties such as colour or line style. GIS software has several methods for categorizing and isolating data contained in CAD files. The key to successful use of CAD data as GIS features is the ability to uniquely and consistently categorize different objects within the CAD file that can be used within GIS layers. Although there are several standards on how the layers should be organized, mostly in architectural design (DAVIES, 2011), (INTERNATIONAL ORGANIZATION FOR STANDARDIZATION, 1998b) and (NATIONAL INSTITUTE OF BUILDING SCIENCES, 2014), CAD applications do not force users to follow any of these standards. Therefore it is possible to find CAD drawings where the information regarding polygon boundaries and their identifiers is mixed with other data or divided into several layers. The second situation is easily solved by mixing the contents of the layers that keep the desired information, whereas the first situation is still an open problem, because typically there is no additional data (apart from line attributes like colour or thickness) to support automatic extraction of primitives from the layers without involving the user. Once the correct features have been successfully identified in CAD file GIS can directly use the geometry of CAD objects as the GIS feature geometry. GIS uses the geometric entity type as the primary organization tool. When a CAD drawing is imported into GIS, it interprets all elements into the three primitive geometric GIS features: points, lines and polygons. Their characteristics such as layer name, colour, or line style are stored in individual columns of attribute table created specifically for

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them. On this basis, an attribute query utilizing any of the CAD graphic properties can be used to further isolate essential objects within a CAD file. Step 2. Detection of line inconsistencies Before running the polygon construction tool, all inconsistencies at the input data have to be eliminated. Because the user of the CAD software does not need to follow any other limits than a correct visual effect, coordinate and topology errors may occur. Topological errors exist due to violation of predefined topology rules. The most common topology errors in map data include (SEHRA et al., 2014):  Intersections:  two line segments intersect at exactly one point – this situation is the most common  one line only touches the other, not actually intersecting it  self‐intersection, which is a special case  Dangling nodes (line ends) or lines:  undershoots (lines being too short)  overshoots (lines being too long), which in specific cases can also result in creation of intersection errors  Duplicate lines:  two line segments completely overlap  two line segments overlap partially  Lines, which do not create a border between two polygons (the same polygon is on their both sides). Step 3. Correction of detected inconsistencies Automatic correction of detected errors are generally not recommended (ESRI, 2004). Instead, a more controlled method of fixing any errors should be established rather than relying on the automated snapping that occurs when features are snapped using the cluster tolerance. Using the cluster tolerance to modify or mitigate geometry errors does not provide adequate control that one might want in the creation of polygons from lines. Manual editing tools should be considered to fix errors when accuracy is a concern. There are cases in which the automatic action may cause worsening of the situation rather than its improvement. ŽALIK (1999) presents some such examples. In his opinion, problems may also result from the wrong order of correction of individual topological errors. Before the editing, it is necessary first to specify which layer will be edited, i.e. to start so called “Editing session”. It is important to mention that usually editing is possible only on one layer. The aforementioned types of errors should be corrected as follows:  Two intersecting lines should be replaced with three or four lines touching each other at their ends.  Undershoots (lines being too short) should be extended, whereas overshoots (lines being too long) should be shortened, without forgetting about correction of the possible intersection errors.  One of the two overlapping (identical) lines must be removed. If the lines overlap only partially, the common fragments should be separated from both lines and one of them removed.  Lines, which do not create a border between two polygons (the same polygon is on their both sides) have to be removed.

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Step 4. Creation of temporary polygons only on the basis of lines After obtaining correct lines, polygons should be created using tool available in chosen GIS software. Step 5. Determination of the number of points in polygons GIS software is usually equipped with Spatial join tool which purpose is to transfer attributes from one feature to another based on the spatial relationship. In addition to the conventional use this tool often has an additional option, which allows to determine statistical parameters such as total, average and standard deviation for the selected attribute in case of multiple Join Features inside Target Features. Kind of a side effect of this action is to create an additional column in the attribute table indicating how many join features match each target feature . Step 6. Correction of point errors In the case of more than one point in the polygon user needs to determine which one is correct. If the point is missing then user has to obtain information from an additional source what attributes it should have. Only a human can perform such activities. They cannot be automated in any way. At most thematic (choropleth) map can be created, where polygons are shaded or patterned in proportion to the number of internal points. Step 7. Creation of final polygons After obtaining correct polygons and points it is necessary to include the attributes of the point features as attributes on the new polygons. Described above Spatial join can be used for this purpose 2.4. Free software The development of free and open source software has experienced a boost over the last few years. The variety of Free/Libre and Open Source Software (FLOSS) that can be found on desktop computers ranges from word processors (e.g. LibreOffice), through web browsers (e.g. Mozilla Firefox) to vector drawing (e.g. Inkscape). In the GIS domain, the widespread use of FLOSS is apparent as well (STEINIGER and BOCHER, 2009). Three FLOSS GIS have been selected, tested and analyzed regarding their capabilities to be used as tools for spatial data conversion from CAD drawings. Vector editing functions to create polygons, topology validation and support for common vector and CAD data formats are considered essential characteristics of GIS desktop software used for this task. These and other characteristics are compared for QGIS, gvSIG and OpenJUMP. They have been selected out of six which have been introduced and compared in (PIEPER ESPADA, 2010). They are considered by the author of this paper as some of the most advanced and sophisticated FLOSS GIS currently available. 2.4.1. QGIS QGIS might be the best known FLOSS GIS software, which owns one of the largest user communities. It is a user friendly desktop GIS which can be used to manage, edit, visualize, analyze data and compose printable maps (QGIS DEVELOPMENT TEAM, 2015). The programming language is C++ and GUI functionality is based on the Qt4 library. Its development started in 2002 and the original aim was to provide an easy to use and fast geographic data viewer for Linux‐based systems. However, as the QGIS

27

project evolved the idea emerged to use QGIS as a simple Graphical User Interface (GUI) for GRASS. The QGIS development team reached its initial objectives and started working to extend the functionality beyond data viewing. Now QGIS includes powerful analytical functionality through integration with SAGA, OTB, MMGIS, fTools and GRASS. It runs on Linux, Unix, Mac OSX, and Windows and supports numerous vector, raster and database formats as well as data delivered from web servers. In particular it can read CAD files: DXF and DGN. An attractive feature for other programmers is the option to write QGIS extensions (called plug‐ins) in Python to add custom functionality. 2.4.2. gvSIG CE gvSIG (Generalitat Valenciana Sistemas de Información Geográfica) project has been founded by the Regional Council for Infrastructures and Transportation (CIT) of Valencia (Spain) to produce software of similar functionality as ESRI’s ArcView for municipal authorities (ANGUIX, DÍAZ, 2008). The goals of the project are to provide an open source tool that utilizes open standards and is platform independent. gvSIG wraps a number of the Java libraries, including GeoTools and JTS. One of the goals was unifying CAD and GIS worlds through integration of CAD tools within FLOSS GIS to get rid of the proprietary software and license costs. The goal to provide software with the functionality of ESRI’s ArcView (3.x) has almost been reached, and, in some aspects, is exceeded. gvSIG is known for its user‐friendly interface and being able to access all common vector and raster formats. gvSIG is a very useful GIS product with extensive vector editing functions. The development of gvSIG started in 2003 and was led by the company IVER S.A. (Spain). Currently project is managed by international non‐profit organization gvSIG Association. In 2011 a group of programmers unhappy with direction of program development forked gvSIG creating gvSIG Community Edition (CE). This version is characterized by rich set of topology tools that can be used to validate geometry of vector layers. Advanced analytical functionality is provided by powerful SEXTANTE library (OLAYA, 2010), offering currently more than 300 geoalgorithms. The SEXTANTE project has successfully developed a Java‐based framework for the analysis and processing of vector and raster data. The framework also includes graphical components that enable the creation of workflows similar to the ESRI’s ModelBuilder. 2.4.3. OpenJUMP The JUMP (JAVA Unified Mapping Platform) project was founded in 2002 by a consortium of two Canadian provincial ministries and two companies. The objective was to develop a GIS specifically for data editing and data conflation (CICHOCIŃSKI, 2007). JUMP was designed to be a generic and pluggable environment into which the complex algorithms required for spatial data conflation could be embedded. Spatial data conflation usually requires a human input element, and as a result JUMP was built with a number of generic user interface and GIS viewer features. A forerunner and part of that project was also the enhancement of the geometry library Java Topology Suite (JTS), which attempts to implement the OpenGIS Simple Features Specification (SFS) (OPEN GEOSPATIAL CONSORTIUM, 2011) for geometric operations as accurately as possible. In some cases the SFS is unclear or omits a specification. In this case JTS attempts to choose a reasonable and consistent alternative. JTS is intended to be used in the development of applications that support validation, cleaning, integration and querying of spatial datasets (VIVID SOLUTIONS, 2003). When

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JUMP development activities almost stopped in 2004 due to the loss of financial support, a group of volunteers founded the JUMP‐Pilot Project and continued the software development under the name OpenJUMP. They quickly added multilingual support and numerous small interface improvements as well as some analytical plug‐ ins. The initial application focus on data conflation and editing is responsible for the fact that the JUMP GIS family has a strong focus on vector data creation and analysis (offering good topology validation tools and vector editing functions), while it provides no raster analysis functionality. Only recently OpenJUMP has been integrated with SEXTANTE (also used by gvSIG), which adds extensive raster analysis tools to the software. 2.5. Implementation of the procedure in featured programs The small fragment of cadastral map, covering 31 parcels, was used as the sample data. Apart from boundaries and designations of land use forms, ALE.DXF file contained lines representing boundaries of parcels on GPE layer and texts representing parcel numbers on GNE_R layer. This drawing contained examples of all of the aforementioned topological errors that potentially could interfere with the construction of correct polygons. DXF drawing format was used due to the fact that not all of featured programs support all CAD formats, and DXF format can be considered as the most versatile and the simplest. It has some restrictions on the possibility of storing more advanced elements, however, in the case of map data it would not matter as generally only lines (polylines) and texts are used. The DXF file has become the de facto intermediate file format for vector data. DXF files are supported by a wide variety of vector‐based geometric editing software. The DXF file is generally an ASCII file. 2.5.1. OpenJUMP OpenJUMP is able to read only DXF files. After loading the whole drawing is presented in its entirety as one layer, in one colour. The attribute table indicates the geometry type of each feature – line or point (converted from text), respectively. Additionally it contains universal columns FID (feature identifier), LAYER, LTYPE (line type), ELEVATION, THICKNESS, COLOR, TEXT, TEXT_HEIGHT, TEXT_ROTATION, TEXT_STYLE. On the basis of the content of the attribute table, selection of required drawing elements can be made using Attribute query tool (Fig. 4). OpenJUMP allows immediate creation of a new layer for the results. It is user responsibility to save such layer, for instance in popular shapefile format. Topological correctness is verified in two consecutive steps. The first may be but does not have to be, Validate Selected Layers (Fig. 5). This allows to check basic topology and that geometries are simple (do not self‐intersect).

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Fig. 4. Attribute query tool in OpenJUMP. Source: CICHOCIŃSKI own study.

Fig. 5. Validate Selected Layers tool in OpenJUMP. Source: CICHOCIŃSKI own study. However, the ultimate test of the correctness of the borders is the creation of polygons using Polygonize tool (Fig. 6). Selecting the option Node input before polygonizing allows to automatically split intersecting lines into parts, touching each other at the ends. The results of this operation are polygons which could be created and two types of lines that do not form the boundaries of polygons.

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Dangling lines (at least one of their ends are not in contact with another line) are presented in red (although dangling ends are not indicated), while blue colour represents lines, called Cuts, that although linked to other, do not create a border between two polygons (the same polygon is on their both sides). Therefore it is written above, that the first step does not have to be conducted because errors such as self‐ intersecting lines and overlapping lines does not interfere with the Polygonize tool (they are automatically corrected). OpenJUMP has many editing tools collected in Editing Toolbox, which allow manual correction of signalled errors. Their description, however, goes (for each of the presented programs) outside the scope of this text.

Fig. 6. Polygonize tool in OpenJUMP.

Source: CICHOCIŃSKI own study.

Fig. 7. Join Attributes Spatially tool in OpenJUMP. Source: CICHOCIŃSKI own study.

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Fig. 8. Spatial Join tool in OpenJUMP. Source: CICHOCIŃSKI own study. After obtaining correct polygons it is necessary to assign them attributes of the points that are inside. Such operation is unambiguous only when there is only one point in one polygon. To check if this is the case, Join Attributes Spatially tool can be used with parameters set as presented in Fig. 7. The requirement for this mechanism to operate properly is that there must be at least one column of numeric type (integer, double) in attribute table (although it can be empty). In this case column COLOUR was used. The result is new polygon layer, with column named COUNT in attribute table, which provides information about number of points in every polygon. One (1) is the desired value, zero (0) indicates a lack of a point, while two (2) or more is the evidence of excess. At this stage again human intervention is required, who has to verify and correct the excess or lack of points. In order to facilitate this task, the resulting polygons can be presented in the form of thematic map (choropleth map), where areas are shaded or patterned in proportion to the number of internal points. After correcting points final polygons (parcels) are obtained by transferring attributes from points to polygons using Spatial Join command (Fig. 8). It is important to effectively verify the number of points in polygons, because every additional point inside generates additional resulting polygon, and the lack of a point in the polygon is synonymous with a lack of the resulting polygon. 2.5.2. GvSIG CE The gvSIG CE recognizes two CAD file formats: DXF and DGN. After loading data the program tries to present them in the form the most similar to the form in which they could be seen in CAD. It uses for this purpose properties of the graphical elements stored in the columns of the attribute table: ID, FShape (in this case containing values FPolyline2D and FPoint2D), Entity (values Line and Text), Layer, Colour, Elevation, Thickness, Text, HeightText, RotationText. In particular, it uses the content of the Colour column to specify colour of the elements and labels points originating from texts using content of the Text column, utilizing also parameters stored in columns HeightText and RotationText. However, probably because of an error, it does not present texts in appropriate colours, although according to layer properties it should.

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Fig. 9. Selection Query tool in gvSIG CE. Source: CICHOCIŃSKI own study. On the basis of the content of the attribute table, selection of needed drawing elements can be made, using Selection Query tool. It opens the wizard, which simplifies the process of query building by indicating appropriate columns and displaying their content (Fig. 9). Launching the query by pressing New Selection button causes highlighting of appropriate elements, which can then be saved to a new dataset, using Export Data As option. Because gvSIG does not analyze the geometry type of selected elements, it actually saves 3 files, automatically giving them suffixes indicating whether they contain points, lines or polygons. In case of selection of parcel boundaries they are saved to file with “line” suffix. Similarly, points representing texts are saved in a file with “point” suffix. Both in query wizard and in the next step it can be seen that gvSIG user interface is inspired by solutions from ESRI, ArcView producer. Checking the correctness of the course and the relationships between individual segments of borders consists in creating topology. In the subsequent windows presented by the wizard one need to specify the layers that will be included in the topology (there may be more than one) (Fig. 10), and provide the topological rules to be verified. In case of lines they are respectively (Fig. 11):

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Fig. 10. Layers that will be included in the topology in gvSIG CE. Source: CICHOCIŃSKI own study.

Fig. 11. Topological rules to be verified in gvSIG CE. Source: CICHOCIŃSKI own study. 

All geometries of A must pass JTS validation – Geometries must meet Simple Feature Specification requirements.

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

No geometry of A may have duplicate coordinates – Vertices of geometries must not have identical coordinates.  No line of A may self‐intersect – Lines in the same layer must not cross or overlap themselves. They are allowed to intersect and overlap other lines.  All line geometries of A must be free of dangling nodes – No dangling lines allowed. The last vertex of a line must touch a segment of the same or another line.  Layer A must not contain any duplicate geometries – No duplicate geometries allowed in layer. It should be noted that none of the available rules does allow for analysis of the presence of intersections between two different lines. The next step is topology validation, which results in detecting and flagging up places in which the rules are violated. As expected gvSIG has detected dangling nodes, duplicate geometries and self‐intersections, but unfortunately, just as one could suspect it has not signalled intersection errors. Therefore, it appears that the process of building the topology and correcting found errors must be preceded by launching geoprocessing tool Clean (Fig. 12). By using a minimum tolerance value operation of this tool should be limited to correction of intersection errors.

Fig. 12. Geoprocessing tool Clean in gvSIG CE. Source: CICHOCIŃSKI own study.

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Fig. 13. Geoprocessing tool Build polygons in gvSIG CE. Source: CICHOCIŃSKI own study. In most cases, this tool indeed has worked as expected, but has not worked properly in places where one line only touches the other, not actually intersecting it. Therefore, Build polygons tool (Fig. 13) has been eventually used, acting in a similar manner to the previously presented Polygonize tool from OpenJUMP. The only difference in favour of gvSIG is signalling not only dangling lines, but also their ends – dangling nodes.

Fig. 14. Spatial join tool in gvSIG CE. Source: CICHOCIŃSKI own study.

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After correction of detected errors and re‐construction of polygons validation of points representing parcel numbers can begin. Spatial join tool can be used for this purpose (Fig. 14). This tool usually transfers attribute table field values from an overlay layer to an input layer. But if Transfer from nearest feature option is not selected, then NUM_RELA column of output layer attribute table receives the number of overlay layer objects contained inside every input layer object. Just as in OpenJUMP this information can be used to verify and manually correct excess or lack of points. In the last step Spatial join tool is run again, but this time actually to transfers attribute table field values from an overlay layer to an input layer. The results are final polygons (parcels) with attribute table enriched with parcel numbers. 2.5.3. QGIS After opening CAD file (DXF or DGN) in QGIS user can select one or more geometry types that she wants to load. In this case there are two of them: Point and LineString (Fig. 15). They are loaded respectively as entities Point and entities LineString layers. Their attribute tables contain only two usable columns: Layer and Text. On this basis, needed elements can be selected. It can be done using Extract by attribute tool (Fig. 16) accessible in Processing Toolbox. Like OpenJUMP, QGIS is equipped with Polygonize tool, but it has limited functionality: it does not indicate anomalies detected in the course of building polygons. In particular, one type of error is relevant: dangling lines. Other errors: overlapping (duplicate) lines and intersections do not interfere are automatically corrected. To control the correctness of relationships between geometric elements in QGIS there is, installed by default, Topology Checker plug‐in and it can be used to detect dangling lines. Appropriate rule must be specified for that purpose (Fig. 17) and then validated (Fig. 18). Detected errors can then be indicated on the list, which causes them to automatically highlight on the map and thus simplifies manual correction. After obtaining correct polygons one can proceed to check points corresponding to texts in CAD drawing. The number of points in each polygon can be explored using Points in Polygon tool (Fig. 19). After determining the correct number of points, point attributes can be transferred to the surrounding polygons using Join Attributes by Location tool (Fig. 20) thereby obtaining final polygons (parcels).

Fig. 15. Geometry types available to load in QGIS. Source: CICHOCIŃSKI own study.

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Fig. 16. Extract by attribute tool in QGIS. Source: CICHOCIŃSKI own study.

Fig. 17. Topology Rule Settings in Topology Checker plugin in QGIS. Source: CICHOCIŃSKI own study.

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Fig. 18. Topology Checker plugin in QGIS. Source: CICHOCIŃSKI own study.

Fig. 19. Points in Polygon tool in QGIS. Source: CICHOCIŃSKI own study.

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Fig. 20. Join Attributes by Location tool in QGIS. Source: CICHOCIŃSKI own study. 2.6. Conclusions Conducted research shows that there exists a group of free software tools that allow to perform the entire procedure of CAD data import, and then convert them into full‐ fledged GIS data. Some difficulty may result in the fact that currently free software do not read DWG files, but it is usually possible to make earlier conversion to DXF format. Not all tools worked as expected, but every time they could be replaced by others, which use was not originally planned. In each of the programs only one path leading to the goal was selected, but there were other possible solutions. Particularly noteworthy are extensive capabilities of GRASS GIS in the area of auto‐correction of topological errors. These tools can be launched from all the analyzed programs and, although auto‐correction is generally not recommended, situations could be imagined in which the efficiency of data correction is more important than the quality of the result. It was noted that the final result and the amount of work designed to achieve it depends not only on the capabilities of the software, but also on the quality of acquired data. Therefore, obtained research results allow to formulate guidelines for persons creating CAD drawings intended for use in GIS software.

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The simplest suggestion for making CAD data more useful as GIS content is to implement and adhere to a CAD layering standard. Implementing a CAD standard improves the quality of the CAD data and its usefulness as GIS content. A CAD standard is the closest thing in CAD to a GIS database schema. Being able to reliably identify different object categories by layer name will ensure that various object categories can consistently be identified in CAD files. This is particularly important if one wish to use CAD drawings in QGIS, because this software is able to use only this property. The closer a CAD file's data constructs are to those of GIS software's features, the easier and more useful that geometry will be for use in GIS. Not only the schematic, pictorial representation of the data should be considered but also the geometric interpretation of connectivity when the drawing is used as a GIS data source. For example, there should be no lines stopping short of connecting to make, for cartographic reasons, room for the node symbol. Using more than one piece of text inside a polygon to denote a parcel number should also be avoided, at least in situations when these text object are on the same layer, have the same colour and text style, and have no other distinguishing characteristics.





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3. UPDATING OF LOCAL DATABASES AT THE COMMUNE LEVEL USING GPS TOOLS Today, the administration and space management processes are carried out using information technology. Previous work tool in this direction were thematic cartography tools in the traditional attitude. The work consisted largely on the manual drawing of thematic maps, which were later used for other branches studies. The scope of the elaborated data and their accuracy has always depended on specifics and the method of obtaining of the baseline data. Along with the development of information technologies using geolocation and spatial attributes also expectations towards their creators and designers increased. They are mainly focused on the effective use of geomatic methods to acquire and develop spatial data in order to obtain reliable information. To meet this requirement it was necessary to create tools ensuring adequate accuracy and speed of action. This solution proved to be computer Geographical Information Systems (GIS), also called spatial information systems. According to (GOTLIB et al., 2007) GIS provides collecting and advanced analysis processes of geographic data and their attributes. In the broad range an essential factor that contributes to the development of spatial information systems is a group of people directly creating and using this system, as well as the organizational, technological and legal procedures in force, enabling its operation. The potential of GIS technology is used in many sectors including the planning, administration, monitoring of pollution, location systems, health care, and many others. The Information Systems discipline is 50 years old, in Australia, in Scandinavia, the U.S.A., the U.K. and Germany (CLARK, 2006). In Poland, the beginnings of information systems date back to the eighties of the twentieth century, where the first attempts to create them using the information technology appeared. Earlier they were replaced by the cartographic and descriptive presentations in classical forms ‐ maps and tabular data sets. With the passage of years the need has increased for access to accurate and complete information, also acquired at a rapid pace. Information plays an important role in every area of modern life. The pace of life in today's society generates the need to create mechanisms for access to information in a quickly way and unlimited by place and time. The value of information depends on its credibility, up to date character, the speed of access, the method of sharing and the form of presentation. Geographic Information System (GIS) is used as the name of the field dealing with spatial information – geoinformation, and the methods and data processing techniques which have geometric character, often referred to as spatial. These methods are referred to in this paper as GIS tools. These relate to the acquisition, collecting, verifying, integration, analysis, transfer and sharing of spatial data in the broad sense it includes methods and technical measures. (GAŹDZICKI, 2003) GIS is used to describe, explain and predict the spatial distribution of geographical phenomena. It provides both software as well as science sections, and the developed methodology to solve research problems. GIS is a proven method of spatial data processing, providing tools such as cartometric measurements and spatial analysis. Scientific basis for GIS are developed by geoinformatics, which takes advantage of new opportunities associated with the development of computer networks. This system is strengthened in Poland due to the growing importance of spatial data and worked out methods of analysis. In the information systems field there is a great need for different theories. Theory Development can be performed in different ways – deductively and/or

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inductively. Different approaches with their pros and cons for theory development exists. A Combined approach, which builds on inductive as well as deductive thinking, has been put forward – a Multi‐Grounded Theory approach. Important is the knowledge of the limits of information systems: “Knowledge created within scientific disciplines are often codified and structured in theories. Information systems (IS) research, there are growing efforts in developing theories. One approach often taken is to use an established theory from a reference discipline and redevelop and adapt it to the information systems context” (LIND, 2006). According to (SMYTH and GABLE, 2006) There is a body of knowledge that suggests that many of the characteristics of Information Systems are consistent with those observed across emerging disciplines in the early stages of their development. For example, in the early evolution of Management as a discipline, some of the characteristics that manifested themselves at that time have been seen more recently in the development of Information Systems. GIS tools enable acquisition and collection of data, their processing, analyze and elaboration of results in appropriate formats, useful for the systems of mass presentations (mapservers and geoservers) and convenient for users to interpret in the form of resulting numerical thematic maps. By combining the geometric characteristics and location of the object with their description in contrast to analogue maps, GIS maps contain a large amount of information. The versatility and usefulness of GIS tools caused that they are used in many areas. The subject of the paper is to show the usefulness of spatial information systems, created in accordance with the requirements of the GIS in order to develop local databases for the purposes of local government administration tasks in neighbouring communes. Creation of such sets of information has practical justification thanks to the consistency of decision‐making by units at the same level of administration. This solution based on the law (Act, 2010), ensures the implementation of the main objectives of spatial policy. Currently, a part of the decisions taken by local authorities is based on spatial information of GIS databases, which define the location of the phenomenon or the object together with its description. Thanks to the content of local spatial data sets some of the communes have complex characteristics of their land and use it in the process of analyzing, monitoring, management, local planning. This is a good direction of changes, what can be a sign of the development of social awareness in the time of the necessity of obtaining complete and recent data in a short time. Modern information and communication methods can greatly simplify the use of large amounts of information. As an example can serve the procedure for the selection of land for investment, which using GIS tools can take just a few minutes (BAJTEK, 2007). These systems can be used anywhere where one of the characteristics of an object is its geographical location. The creation of local databases is a part of the complex process of integration of databases at the district and state levels. Thanks to the common use of Internet networks there are new opportunities for obtaining spatial information. Unfortunately, this fact also carries certain risks regarding the protection of such information. In turn ‐ integration in the network of units and institutions having data results in the determination of the responsibility for the completeness, validity and availability of data by the institutions and persons concerned. The Directive of the European Parliament and of the Council of the European Union ‐INSPIRE (INSPIRE Infrastructure for Spatial Information in Europe) deals with it, establishing the Infrastructure for Spatial Information in the European Community. It aims to build a spatial data infrastructure enabling the sharing of environmental spatial data by

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public sector organizations and facilitating of public access to spatial information in Europe (DIRECTIVE, 2007). In Poland, on administrative levels of communes, binding rules regarding the spatial management are The Local Spatial Development Plans. They determine, among others, trends of changes in allocation of land, housing development, and the development of technical infrastructure. To accomplish this, communes are more and more often analyzing the social phenomena, which distribution in space is extremely important for sustainable development. Spatial management is therefore one of the areas of local government activity, which requires strong support from GIS tools. Because this opens up the way not only to support the planning process, but also to performing of advanced spatial analyses. GIS tools enable providing information about the proposed plans to potential investors and local communities, actively contributing to building the information society. In this paper, the authors present a practical aspect of spatial data collection for the greation of local spatial databases, using the most recent measurement technology and GIS informatic tools. In the specialist literature, according to (GOTLIB et al., 2007) we can read that „Geoinformation systems allow to record spatial data in a logical structure, their comprehensive analysis and visualization. They are also used to describe, explain and predict the spatial distribution of geographical phenomena. The GIS system consists mainly of appropriate software and hardware, collected data, applied algorithms and procedures for processing and sharing of information.” The subject of the work includes the following topics: 1. Description and brief characteristics of detailed data collection method for the needs of local databases together with an assessment of their accuracy (base materials – EGiB (cadastre) maps, GPS/GNSS tools, statistical data on the basis of Central Statistical Office and other resources); 2. Description of the methodology of creating of the Spatial Information Systems (SIP) elements in the phase of office works of spatial data processing in the program QGIS; 3. Presentation of the effect of data processing in selected areas of the elaboration of SIP for selected communes in accordance with their expectations and local needs; 4. Components of developed spatial information systems:  map of economic activity of the commune; target designation and use: the development of the service sector, promotion of the commune as a unit of local government,  map of the distribution and intensity of building,  identification of fees impact areas due to commune investment activities on the value of a given property,  communication network update (stops, local connections, exits and intersections of a lower category roads),  update of maps of infrastructural networks, including water and fire fittings (fire hydrants, wells) possibilities of using in the local fire services,  survey of the status of selected objects (buildings) and giving them the appropriate characteristics relating to the degree of their technical wear.

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3.1. Observations and methods The following section presents the methodology of creation and visualization of GIS components created for the selected objects. They are a few communes of the Małopolskie Voivodship in Poland. This methodology has been developed by the Authors of the publication and used partly for BSc and MSc works shown in the references. In addition, most of the elements of GIS has been implemented in the respective communes and can be used at the moment as the basis of planning activities. In the whole elaboration the base materials were used (maps of Land and Buildings Cadastre (EGiB) in the raster and vector form, data of Geodetic Register of Infrastructure Networks (GESUT), Local Spatial Development Plans (MPZP) and others) obtained from local geodetic resources. Using integrated measurement techniques commonly applied in geodesy, these materials have been updated, and in further technical processing were used as a new sources of information for further analysis in the communes areas. All the work was divided into the surveying part and the office processing of data. In the surveying part both classic and satellite measurement techniques and GPS instruments were used to update the content of selected elements of thematic maps, such as: location selected groups of buildings, buses for public transportation, public utilities objects. The information obtained in the form of files in gpx or shapefile formats were properly processed and converted for further technical elaboration and processing on layers with giving them the relevant references. During the realization of the elaboration object a key role played GIS tools, enriching the thematic cartography methods and significantly shortening time of analyzes development of the phenomena and processes in the areas of communes. They made it possible to carry out spatial analysis and visualization of the final result in the form of maps. Analysis of the spatial data is the essence of GIS, it helps to discover the rules, trends or anomalies in the areas of research, which could not be identified by statistical analysis or the use of thematic cartography methods. In the desk job development of data, digital elevation models (DEM) were used for the elaboration of the characteristics volatility maps in the range of the research. In addition, the image of basic and cadastral map was used, having georeference given in the ETRS system: Poland 2000 ‐ EPSG 2178. Input materials, obtained from District Geodetic Documentation Centre in Kraków and locally appropriate, within the area of development, Town and Commune Councils were raster layers in the GeoTIF format. They contained information concerning the thematic of point objects, lines and surfaces, concerning:  cadastral objects from the EGiB maps, administrative borders of communes divided into cadastre precincts, borders of plots, location of buildings,  GESUT networks(course of lines, fittings etc.),  elements of MPZP in communes. These maps required in the first place update of the contents. For this purpose, three types of handheld GPS receivers were used: GPSmap 76 (GARMIN), GPSmap62st (GARMIN) and Nautiz X7. Manual GPS receivers have already been the subject of numerous publications, among others (KWINTA, 2010), (MIKA, 2011), (MIKA, 2014), (SIEJKA, 2006), (PLEWAKO, 2010), (SIEJKA, 2013). They described the basic features and work modes, comparing and determining the accuracy of subsequent models of the GARMIN family and demonstrating both the advantages and disadvantages of working with their use. In (PLEWAKO, 2010) it was concluded that: „The accuracy of handheld GPS signals receivers can be regarded as the same as the error (of determination with

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their use) of geodetic point position for the II or III class of the network. Accepting this criterion repeatedly, in different regions of the southern Polish and for different reference systems, a systematic factor of significant size was observed to exist. Removal of such factor from the set of observations allows for a reduction in the value of the position error. This error is at the level of about ± 1.5 m for nearly optimal measurement conditions. This accuracy drops to approximately ± 3 m for the area where there are few obstacles in the way of signals from satellites. When access to signals is limited by the high buildings, dense forest, etc. the error increases to ± 10 meters and more. Access to signals from EGNOS satellite system significantly improves and adds credibility to the measurement results.” In elaborations (MIKA, 2011), (MIKA, 2014) and (SZOSTAK et al. 2014) the ability of use of a wider range of handheld receivers than land or water navigation was also demonstrated. Fig. 21 shows the selected types of handheld GPS receivers, which functions can be used to some geodetic works, although their primary function is navigation.

Fig. 21. Nautiz x7, Garmin GPSmap 62st, Garmin GPSmap 76 Source: http://www.trekkinn.com/outdoor‐gory/garmin‐gpsmap‐76/6633/p Source: http://www.smallgis.pl Field works, using GPS receivers shown in Fig. 21 consisted in the location of selected objects on the ground and saving their coordinates. Another element of the field works was to give localized objects properly prepared characteristics (descriptive attributes). On the basis of field measurements of selected elements of map content recorded using GPS devices, on the underlay of vectorised land and buildings cadastre maps, a thematic base map was created in a shapefile format using the QGIS free software. An important advantage of this software is the ability to use modular construction. The software components can function in it separately, they can also be combined. For the work were used, inter alia,: modules of data import from text files, transmission of routes and waypoints from the GPS devices, rasters calibration, group statistics, geoprocessing, spatial queries and multilayer system construction . By adopting this methodology the system of spatial information for the selected communes of the Małopolskie Voivodship was created. It is currently being

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used at the level of the tasks of commune as a base material for the decision‐making action. In the following figures the iconographic material concerning development of SIP for selected local government units (communes) is shown. In the first place (Fig. 22) the vector map of the selected commune is shown. It serves as the core (georeference material) of subsequently developed thematic layers. The commune was divided into zones to allow carrying out the characteristics of the variability in a finite number of objects.

Fig. 22. Municipalities zonal division. Source: MIKA and SALATA own study. An example of such a study is shown in the following figure (Fig. 23), illustrating the building intensity in different zones, using the dasymetric cartogram.

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Fig. 23. Dasymetric cartogram of development intensity in the municipality area. Source: MIKA and SALATA own study. The next step in the development of information system in the selected municipality was to illustrate the intensity and location of the service sector (MADRZYK, 2013). Spatial information systems within the scope of their activities include visualization of demographic phenomena and the attributes associated with running a business. In the services sector it makes possible to perform the analysis of the acquisition of potential customers and development of a strategy relating to competitive threats. Methods of analysis offered by the GIS are very useful in marketing research, because they allow among other things to select the optimal location of given objects, determination of an appropriate assortment for customers and also determination of the priorities for further development (URBAŃSKI, 1997). Economic activity is, therefore, an important element used in spatial planning and in determination of the development strategy of the commune, and also the entire country. In Poland, the National Court Register is sharing the information on this area of research through the Court and Commercial Gazette, which collects information on the business activities in the area, then files by codes of Polish Classification of Activities. In the above‐mentioned classification the economic activities are divided into 21 main branches. Each of them contains successively class, the subclass and the clustering. In the era of globalization, when the dynamic development of local

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governments takes place and the economic activity gradually plays an increasingly important role in spatial planning, in the space management processes necessary is to draw up a clear elaboration of business activities and its analysis on the basis of mentioned above Polish Classification of Activities. According to (Act of 2 July 2004, on freedom of economic activity, Chapter 1, Art. 2) „Economic activity is a profit generation, construction, trade or service activity and prospecting, exploration and extraction of minerals from deposits, as well as the professional activity carried out in organized and continuous manner.” The main legal act in this area is Council of Ministers Regulation of 24 December 2007 on the Polish Classification of Activities (PKD). It sets out the rules for the implementation of the Polish Classification of Activities (PKD), hereinafter referred to as PKD 2007 for use in statistics, records and documentation and accounting, as well as in the official registers and information systems. The regulation specifies that from the date of entry into force of the Regulation, i.e. from 01 January 2008 PKD 2007 classification system covers all entries of entities starting economic activity. It is also highlighted that, until 31 December 2009 economic activities registered before the date of entry into force of this Regulation will be reclassified in accordance with the classification of PKD 2007. In the process of creation of SIP elements for a commune, successive changes of this provision were taken into consideration. The legal basis from Council of Ministers Regulation of 1 April 2009 is making changes in the regulation of the Polish Classification of Activities (PKD). The Regulation emphasizes, that in the case of entities of economic activity, that will not be reclassified according to the standards of PKD 2007 to 30 September 2009. they will be subject to the classification ex officio by a public statistics officials. Fig. 24 shows the map of economic activity of the subjects developed in the commune according to the described methodology.

Fig. 24. Map of economic activity in Zielonki commune. Source: MIKA and SALATA own study.

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An additional element of the created system was a thematic layer concerning determination of the impact areas of community investment activities on the value of properties (UNIWERSAŁ, 2014). In Poland, in accordance with the provisions in force of (The Act, 2003) as a result of the development of local plans the internal revenue of the commune can increase. Through a well‐functioning land information system the system of local fees and taxes can be inspected currently. So ‐ the separation of the betterment levy impact area and the planning fee will help in better execution of these fees. Each new investment in technology infrastructure can be co‐financed by the inhabitants of the commune. Imposition of multiple charges on a single plot makes, that it becomes less attractive to potential buyers – the owner to compensate for the charges raises the price of the plot. One of the objectives of the development of the GIS system for the selected commune was therefore the creation of thematic layers aimed at the target for automatic or semi‐automatic identifying the areas, in which planning fee and betterment levy have an area of activities. For the realization of the aim the program QGIS was used. The starting thematic map, as in the other described cases of created spatial information systems in communes, contains all the updated surface elements in the commune. To identify those elements the following thematic layers were used: precincts, plots, roads, buildings, fixture elements and GESUT networks (upgraded with the help of GPS), MPZP (land use plans). Each of these layers has other attributes describing the layer. The attributes were chosen to fully identify the item. The main attribute is the ID of the object. As a result, after generating the base map, shown in the next figures (Fig. 25 and Fig. 26). The buffer zones of influence impacts of the fees for a given property were designated.

Fig. 25. Determination of the parcels within the scope of residential areas. Source: MIKA and SALATA own study. The following figures present visualizations of the results of studies of areas belonging to the sphere of betterment levy influence and others in the selected commune. Software tools QGIS were used for the elaboration.

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Fig. 26. Buffers and parcels located in their influence on plots. Source: MIKA and SALATA own study.

Fig. 27. The creation of the resulting layer for the betterment levy due to the construction of technical infrastructure. Source: MIKA and SALATA own study.

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In technical sense of studies, layer shows plots qualified for the betterment levy due to the construction of technical infrastructure, it is the difference of the layer with parcels which are in the influence of buffers and a layer with plots already equipped with given network. For this purpose the spatial query equals was used and in the attribute table selections were changed (Fig. 27). In the presentation of selected applications and elements of land information systems surveyed and updated items of technical infrastructure should also be taken into account. This issue is illustrated in Fig. 28 (POSIAK, 2014).

Fig. 28. Layers of buildings, technical infrastructure, fittings and precincts. Source: MIKA and SALATA own study. An interesting element of the created system was the analysis of the rate of development intensity in another municipality. This problem is further illustrated by successive figures 30 and 29. Permanent monitoring of areas designated for housing in land use plan is important in the process of spatial planning. The share of developed plots in building areas is the main aim of the control. Moreover, its location and investment rate in each region has been monitored, too. Fig. 29 shows an analysis of the scattering of developed plots. Individual plots with buildings and finished investments are marked with a darker colour.

52

Fig. 29. Share of developed plots in Local Development Plan. Source: MIKA and SALATA own study. Plots designated for housing and not yet developed are marked with lighter colour. Such analyses help to determine the need to increase building areas in the future. Another example is the management of road exits from municipal roads. The location and quality of the road exits are monitored and the location of objects such as bridges and bus stops are placed on the map. The orthophotomap was used as a background (Fig. 30).

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Fig. 30. Share of developed plots in Local Development Plan. Source: MIKA and SALATA own study.



3.2.Results and discussion In the discussion of the results it should be noted that the adopted methodology proved to be fully helpful in the creation of local databases of spatial information systems in selected communes. Both the costs and the quality of the elaboration proved to be satisfactory from the point of view of the recipients of the system. For the purposes of update of the contents of available maps handheld GPS receivers were applied. Their cost ranges 1000‐5000 zł. Personnel costs were reduced by commissioning this task to the University of Agriculture students as a part of production traineeships in following subjects “Practical usage of GIS” and “Advanced techniques of GIS” carried out at the University of Agriculture in Krakow, in turn the office works were based mainly on the QGIS program under open source license. In the world software is commonly used under open source license. The paper (STARK et al., 2008) presents the conclusions of the survey on „The use of open source software in the geospatial environment in Switzerland ". Opinions and conclusions set forth on this basis seem interesting. A great knowledge of the subject in the associations and organizations operating in the geo‐information environment was demonstrated. Studies have shown that the OSS (OpenSource Software) is used in at least 20% of the works in the field of widely understood geoinformation. On this basis, can be concluded that this also applies to the creation of the SIP (Spatial Information Systems). 40% of the respondents reported the widespread use of OSS. Depending on the studied branch and the software requirements (mapping, development and generalization of internet databases etc.) percentage results of the OSS application differ. They are located between 14% and 36% of responses in relation to the study population. In the group of respondents dominated the view that in order to use OSS in

54

practice detailed knowledge about it is not required. This software is designed intuitively and practically everyone who earlier carried out work in graphic programs is able to quickly learn how to use OSS. Correlations have been shown at the level of 36‐38% between knowledge and its practical use and the involvement of the respondents in the work in the OSS environment. In addition, it was stated that OSS is being promoted mainly in sectors such as training and administrative sector. In the private sector OSS seems to be much less present. The reasons for the use of OSS are satisfactory results in terms of functionality and quality of the obtained results. The lack of a license fee is also a strong argument. Disadvantages in the range of OSS applications involve cases when it is necessary to change the system at some stage and carry out associated data migrations. On thus prepared thematic maps analyzes of the technical background in the housing and investment areas in the commune were also carried out. Thematic layers containing Local Spatial Development Plan or technical infrastructure networks in the commune were used. The following figures 31 and 32 show the results of selected analyzes, determining the level of the land investment and the accessibility to particular GESUT (polish abb.: geodesic evidence underground utility, author’s annotation) network elements (KULESA, 2013).





Fig. 31. The ratio of the invested area surface to those labeled MN in the Local Spatial Development Plan in different villages. Source: KULESA, 2013.

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Fig. 32. Percentage chart of easy access of residential buildings to the infrastructure by villages. Source: KULESA, 2013. On the basis of developed detailed spatial information systems in selected communes, the indicators of quality of life and its spatial differentiation were determined. They were presented on a high level of detail, because the relationship of each residential building with individual service points was examined. It should be noted here, that using traditional methods of statistical analysis it is only possible to examine the total quality of life for the entire commune. The methodology used to develop complex and detailed land information systems in the communes, allowed to perform analysis in this area, using GIS tools. Thanks to them, it was possible to verify areas requiring investments improving commercial facilities, educational, communication, and health service. Furthermore, this analysis indicates the recipient sites that are attractive in terms of future investments, or the possibility of inhabitancy. Analysis of differentiation in quality of life, proved to be an extremely valuable source of information for commune. Thanks to the conclusions flowing from similar analyzes local governments can significantly improve the living conditions of their citizens, taking into account in their activities the necessity of proper spatial development. 3.3. Conclusions In the publication (DALE and MCLAREN, 1999) was shown that effective and efficient management of the land and its resources depends on the availability of good information about the area. Many countries already have or are in the process of creating a computerized national database of cadastral data. Data collected in computer systems thanks to information technology are integrated, analyzed and disseminated in a way that until recently was not possible. The article discusses among others, the issues related to the operation of such datasets. In addition, it mentions examples of well‐functioning land information systems. The authors emphasize the institutional, organizational and business issues, that need to be resolved, to create full value spatial information systems on the foundation of cadastral data, using GIS tools.

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In addition, the authors point out the special role of the cadastral system in the process of land management: „A cadastre is a distinguished form of a land registration system in that the latter has been exclusively concerned with ownership. A land register must operate within a strict legal framework and may not, in practice, cover a whole country since not all citizens may choose to register their lands. The cadastre, however, should be based on complete coverage of a country since it may be used for the purposes of land taxation”. The advantages of introducing spatial databases refer to all potential users of the system, from the private sector to administration:”The creation of data in digital form is necessary, but not sufficient, for effective land administration to occur. Experience to date suggests that it is essential that the legal, political, economic, and social issues also be addressed. Given that any inherent problems can be overcome, significant benefits should ensue. Private citizens seeking to move house will be able to locate properties that meet their needs more easily while conveyance will be cheaper and more secure. Planners will find it easier to locate suitable places for development and determine the constraints on their use. There will be clearer protection for sites of special scientific interest. Property developers and investors will be more secure in their analysis of sites while banks and other mortgage lending organisations will have more information on land and property values and hence be able to reduce their risks in lending money. Architects and builders will have more certain and detailed information about sites. Governments will be able to tax land and property more equitably and make more informed judgments where there are competing proposals for.” (DALE and MCLAREN, 1999). GIS software in recent years has changed to a small extent. Functions used today were known previously. Only the public pressure changes to provide spatial information in a simple way. To accurately hit the needs and provide solutions. This causes that the spatial data must be collected and processed at a high level of detail. Spatial information is valuable and credible when every inhabitant of the region is identified separately. Then there is a higher probability, that he would be interested in conclusions obtained using GIS. There are two levels of geomatics work with data: the spatial data model ‐ intricate, complex and unfamiliar to most people (at this level advanced visualization tools, specialized software 2D, 2.5D are used), and a simple and easy to interpret set of the results of spatial analyses (3D visualization systems and programs for animation). In the standard range, GIS usually provide tools for map edition for the needs of the presentation on a computer screen or preparation of maps to print (SZCZEPANEK 2013). It enables not only the collection, but also analyzing of geographic data – data associated with geographical space and assigned to them descriptive attributes such as estimation of the intensity ratios of inhabitancy, or regional development trends. According to (GOTLIB et al. 2007) ‐ GIS systems enable recording of spatial data in a logical structure, and their comprehensive analysis and visualization. They also offer the possibility of description, explanation and prediction of the spatial distribution of geographical phenomena. Geoinformation systems primarily consist of the appropriate software and hardware, collected data, the algorithms and procedures for processing and sharing of information. This example shows, that using the described methodology it is possible to apply cheaper integrated research and measuring tools and get a satisfactory result. The presented methodology is consistent with the definition of GIS technology (GOTLIB et al. 2007) understood as a set of methods and techniques for the construction of geographic information systems. GIS technology capabilities are used, among others,

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in administration, nature conservation, spatial planning, pollution monitoring, health care, geomarketing, localization systems, education, science, crisis management. The range of applications continues to grow. Currently, it is difficult to find a sphere of life, in which they are not applicable . The advantage of created by the described methodology spatial information systems is the possibility of their extension for a further layer, and thus openness to variation in needs and analyzes trends in a given area. In subsequent actions in implemented spatial information systems it is planned to perform next steps in order to allow the determination in areas of individual communes. In the future, the developed system can be extended by further thematic layers containing, inter alia: ‐ supplementing local databases with information promoting the region, ‐ interesting places and tourist curiosities, ‐ specification sites at risk, e.g. with flood, landslides, etc..



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4. ANALYSIS OF POLISH SDI WITHIN THE CONTEXT OF NEEDS OF REAL ESTATE DEVELOPERS In the last ten years there has been observed a significant activity in creating regional and local GIS in Poland. These activities are the result of the global trend formation of the Spatially Enabled Society (FIG, 2012). It is linked with the need to strive for sustainable development. The concept of Spatially Enabled Society derives from the concept of the information society. It is worth recalling one of its many definitions, where the Information Society is understood as a society that not only has a strong means of information processing and communication, but these measures are the basis of national income and provide livelihoods of the majority of society" (GOBAN‐KLAS and SIENKIEWICZ, 1999). STEUDLER and RAJABIFARD (Fig. 33.) modified the concept of information society in the direction of society spatially informed and literally "Spatially Enabled Society" (SES), (FIG, 2012). The SES concept implies adding to the existing information these on location in space, thereby releasing the wealth of existing knowledge about the land and the water, their legal and economic situation, resources, availability and potential applications and hazards. Spatially enabled society uses the concept of place and location to organize information and processes. Currently it is one of the main aim of consistent many government programs development strategies. Fig. 33. Cover of elaboration on „Spatially Enabled Society”. Source: FIG, 2012. Spatially enabled encourages the development of innovation, transparency and democracy in the country. In connection with the chosen development direction we can talk about the onset of spatial information revolution. Citizens and their governments must be spatially enabled, have the right tools and information within easy reach to make the right decisions.

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The concept of a spatially enabled society offers new opportunities for the state and its citizens. Its intention is to lead to the effective use and delivery of spatial data and services. SES benefits from a wide range of spatial data, information and services as a means to organize activities related to land and water. SES is now part of the global development goals pursued by governments in many countries. This indicates the importance of increasing the spatial information is also in the development strategies of local and regional policies and make the right decisions in the public sector. SES increasingly active in the virtual world, but these activities must go hand in hand with the institutional and structural reforms in the real world in the use of spatial information and Spatial Data Infrastructures as an access platforms. Local and regional SDI may be used for different purposes. Created systems include different kind of information especially cadastral data. We also may observe variety of actors involved in their creation as well as users interested in obtaining information for their needs. There is a large group of investors in real estate market who expect that these developing systems may take a role of main sources of information on real estate. For them it is important to develop local and regional GIS with the aim of usefulness in realization of investments in the real estate market. This elaboration is to show usefulness of geographic information systems within the context of needs of real estate investors. Having regard to diversity of the systems there has been made a research in a form of comparative analysis of information presented by state, regional and local systems, which are essential on different stages of investment process. 4.1. Methodology One of the main objectives of this study is to assess the functioning of four selected portals with spatial data as a part of local, regional and state level of National Spatial Data Infrastructures (NSDI) in the context of the recommendations of the Inspire Directive and guidelines of European Interoperability Framework. The analysis covered the organizational and technical geoportals action area. The authors analyzed four different level of SDI geoportals:  Geoportal.gov.pl (state level),  Atlas of Warmia and Mazury (regiolan level),  MSIPMO (province level),  SIP Stawiguda (municipality level). The choice of these geoportals was not accidental. All these geoportals except Geoportal.gov.pl (national level) have database from the north‐east of the Warmia and Mazury Region (one of the poorest regions in Poland), (Fig. 34).

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Fig. 34. Warmia and Mazury Region. Source: DAWIDOWICZ A. on the basis of KSNG (2014). In Poland there are 16 regions (in Polish: województwa). Each region contains provinces ‐ districts (in Polish: powiat). Municipality (in Polish: gmina) is the smallest administrative division of the country. Figure 35 and table 4 presents administrative division of Republic of Poland.

Fig. 35. Administrative division of Republic of Poland – as at January 1, 2011. Source: Commission on Standardization of Geographical Names, 2010

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Table 4. List of territorial units of Poland ‐ as at 01.01.2015.

1571

602

908

602

18

19

36 17 20 9 18 14 35 3 16 13 25 49 5 16 19 11

78 92 171 41 133 121 229 36 110 78 81 96 71 67 117 50

55 35 22 33 26 47 50 32 34 27 17 22 26 33 90 53

91 52 42 42 44 61 85 35 50 40 42 71 31 49 109 64

55 35 22 33 26 47 50 32 34 27 17 22 26 33 90 53

‐ ‐ ‐ ‐ ‐ ‐ 18 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐

5 ‐ ‐ ‐ 5 4 ‐ ‐ ‐ ‐ ‐ ‐ ‐ ‐ 5 ‐

Total

Delegations

306

169 144 213 83 177 182 314 71 160 118 123 167 102 116 226 114

Districts

2479

4 4 4 2 3 3 5 1 4 3 4 19 1 2 4 3

Urban Rural

66

26 19 20 12 21 19 37 11 21 14 16 17 13 19 31 18

Rural

314

Urban

In urban‐rural communities

Municipalities

Auxiliary units

Total

Dolnośląskie Kujawsko‐pomorskie Lubelskie Lubuskie Łódzkie Małopolskie Mazowieckie Opolskie Podkarpackie Podlaskie Pomorskie Śląskie Świętokrzyskie Warmińsko‐mazurskie Wielkopolskie Zachodniopomorskie

Cities

Cities with district status

Poland 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Territorial units of Poland

District (province)

Specification: country and regions

Source: DAWIDOWICZ based on the data from TERYT (National Register of Country Territorial Division). The aim of the study is to compare the thematic resources, available services, tools and skills to use some of the national SDI portals in every level of country administrative division. Examination of the SDI portals technical conditions was more complex. The basic assumption in the research part was an evaluation of NSDI from the point of view an ordinary user. Have been taken into account the general range of thematic data and metadata provided by portals, available tools, and services related to the use of these data. In view of the variety of solutions offered by SDI portals and increasingly larger needs of citizens in the use of these data, were also examined possible services offered by the analyzed portals in the range of personalization. Particular attention was paid to the possibility of creating a user account on the site and the ability to customize the tool palette to suit user needs. Due to the constantly evolving branch services designed for mobile devices, the assessment also included the ability to download applications from the SDI portals to mobile devices. 4.2. SDI as a network and an enabling platform The starting point for the construction of SDI initiative was to formulate the creation of economic prosperity, stability (balance), social, environmental protection can be facilitated through the development of products and services based on the spatial data collected at all levels of administration and country territorial division to easy use by government bodies, the private sector and individuals. In this context, access to data and spatial information integrated on a single platform play a key role. The first works of construction of the National Spatial Data Infrastructure was launched in 1990 in the United States. The idea of sharing of spatial data spread to other continents. In 2001

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was born an initiative to construction of European Spatial Information Infrastructure. Then also formed the foundations of the INSPIRE Directive (Infrastructure for Spatial Information in the European Community), which was adopted in 2007 by the European Parliament and the Council of the Commission of the European Community (INSPIRE, 2007). The concept of SDI so far received a number of definitions. In one of them the spatial data infrastructure is understood as "a set of legal, organizational, economic and technical conditions: ensure universal access to sustained spatial data from the territory of the country and geospatial services, contribute to the efficient use of geoinformation for increasing the competitiveness of the economy, taking into account the principles of sustainable development of the country, allow the rational management of geoinformation managed by the government and self‐government, and contribute to the development of the information society" (http://gisplay.pl/gis/krajowy‐system‐informacji‐przestrzennej.html15). In accordance with the definition adopted in the INSPIRE Directive (2007) by SDI is understood described metadata and spatial data sets for their services, technology, processes and procedures that are used and shared by co‐creating infrastructure for spatial information leading authorities, other authorities and third parties. INSPIRE applies to geographical and environmental information that are stored in electronic form by public authorities of the country concerned or on their behalf. Spatial information refer to the areas in which Member State has and/or exercises jurisdictional rights. Spatial information, in accordance with the Directive, should be included in the national 'geoportals', are listed in Annexes I, II and III of the Directive 2007/2/EC. The Annex I sets out the basic spatial data, such as administrative boundaries, geographic names, cadastral parcels, hydrographs and transport networks. However, in Annexes II and III are specific data on, inter alia, orthoimages, geology, soil use, human health and safety, environmental monitoring facilities, and distribution of public service or industrial facilities. Spatial information listed in the Annexes to Directive INSPIRE information are mandatory for NSDI in all Member States. NSDI of the Member States can be enhanced with additional thematic sections or modules of spatial information. The article 11 paragraph 1 of the INSPIRE Directive indicates that all member States shall establish and operate a network of the following services for the spatial data sets and services for which metadata have been created in accordance with this Directive: a) discovery services making it possible to search for spatial data sets and services on the basis of the content of the corresponding metadata and to display the content of the metadata; b) view services making it possible, as a minimum, to display, navigate, zoom in/out, pan, or overlay viewable spatial data sets and to display legend information and any relevant content of metadata; c) download services, enabling copies of spatial data sets, or parts of such sets, to be downloaded and, where practicable, accessed directly; d) transformation services, enabling spatial data sets to be transformed with a view to achieving interoperability; e) services allowing spatial data services to be invoked.

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Annex III

Annex II

Annex I

No

Annex

The data integrated in the SDI can be flexibly expanded despite initially planned database topics covered in the Annexes of the INSPIRE Directive covering subject matter described in Table 5. Table 5. Spatial data themes. Theme

1

Coordinate systems

reference

2

Geographical grid systems

3

Geographical names

4

Administrative units

5

Addresses

6 7

Cadastral parcels Transport networks

8

Hydrograph

9

Protected sites

1

Elevation

2

Land cover

3

Orthoimagery

4

Geology

1 2 3

Statistical units Buildings Soil

4

Land use

5

Human health and safety

Contents Systems for uniquely referencing spatial information in space as a set of coordinates (x, y, z) and/or latitude and longitude and height, based on a geodetic horizontal and vertical datum. Harmonised multi‐resolution grid with a common point of origin and standardised location and size of grid cells Names of areas, regions, localities, cities, suburbs, towns or settlements, or any geographical or topographical feature of public or historical interest Units of administration, dividing areas where Member States have and/or exercise jurisdictional rights, for local, regional and national governance, separated by administrative boundaries Location of properties based on address identifiers, usually by road name, house number, postal code Areas defined by cadastral registers or equivalent Road, rail, air and water transport networks and related infrastructure. Includes links between different networks. Also includes the trans‐European transport network as defined in Decision No 1692/96/EC of the European Parliament and of the Council of 23 July 1996 on Community Guidelines for the development of the trans‐European transport network (1) and future revisions of that Decision Hydrographical elements, including marine areas and all other water bodies and items related to them, including river basins and sub‐basins. Where appropriate, according to the definitions set out in Directive 2000/60/EC of the European Parliament and of the Council of 23 October 2000 establishing a framework for Community action in the field of water policy (2) and in the form of networks Area designated or managed within a framework of international, Community and Member States' legislation to achieve specific conservation objectives Digital elevation models for land, ice and ocean surface. Includes terrestrial elevation, bathymetry and shoreline Physical and biological cover of the earth's surface including artificial surfaces, agricultural areas, forests, (semi‐)natural areas, wetlands, water bodies Geo‐referenced image data of the Earth's surface, from either satellite or airborne sensors Geology characterized according to composition and structure. Includes bedrock, aquifers and geomorphology Units for dissemination or use of statistical information Geographical location of buildings Soils and subsoil characterized according to depth, texture, structure and content of particles and organic material, stoniness, erosion, where appropriate mean slope and anticipated water storage capacity Territory characterized according to its current and future planned functional dimension or socio‐economic purpose (e.g. residential, industrial, commercial, agricultural, forestry) Geographical distribution of dominance of pathologies (allergies, cancers, respiratory diseases, etc.), information indicating the

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6

Utility and governmental services

7

Environmental monitoring facilities

8

Production and industrial facilities

9

Agricultural and aquaculture facilities Population distribution — demography

10 11

Area management/ restriction/regulation zones and reporting units

12

Natural risk zones

13

Atmospheric conditions

14

16

Meteorological geographical features Oceanographic geographical features Sea regions

17

Bio‐geographical regions

18

Habitats and biotopes

19

Species distribution

20

Energy resources

21

Mineral resources

15

effect on health (biomarkers, decline of fertility, epidemics) or well‐being of humans (fatigue, stress, etc.) linked directly (air pollution, chemicals, depletion of the ozone layer, noise, etc.) or indirectly (food, genetically modified organisms, etc.) to the quality of the environment Includes utility facilities such as sewage, waste management, energy supply and water supply, administrative and social governmental services such as public administrations, civil protection sites, schools and hospitals Location and operation of environmental monitoring facilities includes observation and measurement of emissions, of the state of environmental media and of other ecosystem parameters (biodiversity, ecological conditions of vegetation, etc.) by or on behalf of public authorities Industrial production sites, including installations covered by Council Directive 96/61/EC of 24 September 1996 concerning integrated pollution prevention and control (1) and water abstraction facilities, mining, storage sites Farming equipment and production facilities (including irrigation systems, greenhouses and stables) Geographical distribution of people, including population characteristics and activity levels, aggregated by grid, region, administrative unit or other analytical unit Areas managed, regulated or used for reporting at international, European, national, regional and local levels. Includes dumping sites, restricted areas around drinking water sources, nitrate‐ vulnerable zones, regulated fairways at sea or large inland waters, areas for the dumping of waste, noise restriction zones, prospecting and mining permit areas, river basin districts, relevant reporting units and coastal zone management areas Vulnerable areas characterised according to natural hazards (all atmospheric, hydrologic, seismic, volcanic and wildfire phenomena that, because of their location, severity, and frequency, have the potential to seriously affect society), e.g. floods, landslides and subsidence, avalanches, forest fires, earthquakes, volcanic eruptions Physical conditions in the atmosphere. Includes spatial data based on measurements, on models or on a combination thereof and includes measurement locations Weather conditions and their measurements; precipitation, temperature, evapotranspiration, wind speed and direction Physical conditions of oceans (currents, salinity, wave heights, etc.) Physical conditions of seas and saline water bodies divided into regions and sub‐regions with common characteristics Areas of relatively homogeneous ecological conditions with common characteristics Geographical areas characterized by specific ecological conditions, processes, structure, and (life support) functions that physically support the organisms that live there. Includes terrestrial and aquatic areas distinguished by geographical, abiotic and biotic features, entirely natural or semi‐natural Geographical distribution of occurrence of animal and plant species aggregated by grid, region, administrative unit or other analytical unit Energy resources including hydrocarbons, hydropower, bio‐ energy, solar, wind, etc., where relevant including depth/height information on the extent of the resource

Source: INSPIRE, 2007.

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An essence of SDI contains the five major assumptions INSPIRE (2007) where: a) the data should be collected only once and stored and managed in the most correct and efficient manner by the relevant institutions and services; b) should be ensured the continuity of spatial data so that it is possible to acquire a variety of resources, from a variety of sources, and that they can be made available to multiple users, and for a variety of applications; c) spatial data should be stored at an appropriate (one) level of public administration and made available to those at all other levels; d) spatial data necessary for the proper management of space at all levels of government should be publicly available (i.e. without limiting conditions and / or hinder their free use); e) should be provided access to information about which spatial data are available and under what conditions, as well as information that enables users to evaluate the usefulness of these data for their own purposes. On the basis of the INSPIRE directive all EU Member States have given to the use networking sites that allow searching, viewing and downloading spatial information. All these services are available through the INSPIRE portal, which is connected with the national geoportals of EU countries. The activity of INSPIRE is coordinated at Community level by the European Commission and at national levels by the appropriate structure designated by the authorities of the states. Member States should share the data collected and allow public authorities to access them, their exchange and use for public tasks that affect the environment. Access to data may be payable except in cases where access needs to provide the information in connection with the reporting legislative bodies. Access can also be limited due to the proper functioning of the justice, national defence or international relations. In Poland, there is a tendency to build SDI portals at various levels of country administrative division, adapted to the needs of the public administration but also local investors. Comparative analysis of SDI portals on different levels of the organization is to reveal the desirability and quality of arising portals. 4.2.1. GEOPORTAL.GOV.PL The history of Polish SDI called Geoportal goes back to 2005, when the Head Office of Geodesy and Cartography launched the GEOPORTAL.GOV.PL project. The project has been funded under the Sectoral Operational Programme "Improvement of the Competitiveness of Enterprises" 2004‐2006 (http://geoportal.gov.pl/en/o‐ geoportalu/informacje‐o‐projekcie/informacje‐ogolne ‐ access 01.07.2014). The main goal of the GEOPORTAL.GOV.PL project was to improve competitiveness of enterprises by providing them online access to services based on spatial data, including cadastral data and metadata. Other important goals of the project included:  Development of entrepreneurship as well as increasing innovativeness and competitiveness of enterprises, due to access of spatial data.  Improving decision processes in enterprises, regarding investment decisions.  Modernizing the work of public administration (on central, regional and local level) within the scope of the project, by means of introducing new IT technologies.

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

Increasing the knowledge and importance of spatial data as well as cadastral data among entrepreneurs.  Savings (in terms of time and costs) for entrepreneurs using the geodesy services.  Enriching the offer of enterprises providing services based on public spatial data.  Participation in developing the information society. Under the GEOPORTAL.GOV.PL project we have developed the infrastructure of nodes of the National Infrastructure of Spatial Data (in Polish: Krajowa Infrastruktura Informacji Przestrzennej ‐ KIIP), cooperating and providing services ranging from searching and providing data to data analysis. The network of KIIP nodes has been built on three levels: central, regional and local. The project did also result in development of an internet portal: www.geoportal.gov.pl – acting as a broker, providing users with spatial data and services. The project has been finished in 2008, and it resulted in development of the following data bases:  Cadastral data,  Geographic database,  Database of Topographic Objects,  Orthophotomaps,  Topographic map rasters,  Thematic map rasters,  State Register of Borders (PRG),  State Register of Geographical Names (PRNG),  Numeric Terrain Model,  Metadata of sets and services of spatial data. Once the GEOPORTAL.GOV.PL has been finished, in 2009 we have launched a new project, aimed at continuation and enhancement of previous activities: GEOPORTAL 2 – development of the spatial data infrastructure in the area of georeferential registers and related services. Full image of subject requires the presentation of geoportal interfaces (Fig. 36, Fig. 37).

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Fig. 36. Interface of Polish Geoportal. Source: http://geoportal.gov.pl/ (access 17.07.2014).

Fig. 37. Cadastral data in mapping tab. Source: http://mapy.geoportal.gov.pl/imap/? gpmap=gp0&actions=acShowServices_KATASTER&locale=en (free access 17.07.2014).

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4.2.2. Atlas of Warmia and Mazury Atlas of Warmia and Mazury is a spatial information system implemented within the framework of the project "Construction of the Warmia ‐ Mazury GIS platform for enterprises". Atlas of Warmia and Mazury is a web service publicly available on the Internet. The system provides access to information about the region, published on maps. Such aggregated information can help entrepreneurs in finding areas attractive for investments, assist in determining the impact of the location of investment in the development of urban and rural areas, the impact of population growth on the development of urban and rural areas and also help in the preliminary determination of the complexity of the conduct of investment (eg roads), due to the structure of land ownership in the area. The system also allows access to local zoning plans published by the municipalities, and allows to order map in the Regional Documentation Centre of Geodesy and Cartography (in Polish: Wojewódzki Ośrodek Dokumentacji Geodezyjnej i Kartograficznej WODGiK). Atlas of Warmia and Mazury is a spatial information system for decision support in areas such as urban planning, environmental protection, agriculture, health, natural resources management, crisis management, telecommunications and transport. It can highlight the following objectives to create a system:  increasing the use of telecommunications and Internet tools,  improving the quality and accessibility of e‐services,  promoting synergy between the administration and the entrepreneurs,  providing comprehensive information about the objects,  help entrepreneurs find attractive investment areas,  publication of spatial plans. Atlas of Warmia and Mazury allows you to read the aggregated regional information (for use in the management of province). It also allows authorized users to exchange information in a local area network (data, documents, results of analyzes) needed in the implementation of the current office tasks. Atlas is also unique in the whole country, the system that allows the use of infrastructure created directly by local governments to keep their own records. Local Government Units using the Atlas of Warmia and Mazury do not bear the cost of building data structures, ensuring the technical inspection of processed and published material, the author care costs, costs to fit the portal for the new law and the costs of infrastructure development and promotion of the portal server. These are great savings in the case of such action by a small Local Government Units. Expects the following benefits of building the portal:  Improving the efficiency of the public sector through the creation of rapid access to knowledge and information,  Increasing the number of people interested in geographic information and use of the Internet,  Increasing the level of employment in the areas covered by the project,  Raise awareness of entrepreneurs, for whom a solution is dedicated,  The possibility of placing an order on the selected map and send orders to WODGiK for contract performance (online store),  Improving the quality of life of citizens and the region  Create and publish own maps.

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Built portal consists of two structures. The first is an external portal for business and individuals, who in addition to obtaining extracts of maps and obtaining information about the investment areas having the opportunity to create own thematic maps and placing it on the page and on the websites. Second, the inner panel for office workers who will supply the system data available to the office. Thanks to local government units receive a tool for analysis to support their work. Portal is an open, i.e. the offices that wish to publish their data on the the portal should contact the Office of the Marshal of Warmia and Mazury at: [email protected] in order to obtain the information needed to begin Atlas of Warmia and Mazury (Fig.38). Atlas of Warmia and Mazury was implemented in Esri technology. System infrastructure is located in the Office of the Marshal of Warmia and Mazury. The system is scalable and its performance monitored by the staff of the office and the contractor ‐ the company SmallGIS Ltd. from Krakow. If necessary, decisions are made concerning the development and improvement of the parameters characterizing the capacity storage and speed of service. Unfortunately portal operates only in Polish version.

Fig. 38. Atlas of Warmia i Mazury. Source: http://atlas.warmia.mazury.pl/mpzp/ access 3.03.2015. 4.2.3. MSIPMO MSIPMO system was introduced in 2009, but preparatory works took few years due to determinants from the Public Procurement Law. Under this circumstances there was prepared a project that includes the design and implementation of organizational‐ technical infrastructure of Municipal Spatial Information System of Olsztyn, which is a part of the national infrastructure for spatial information. The project provides a common (public) access to updated, spatial reference databases of the city of Olsztyn, in particular, the public data registers connected with spatial planning (Studium

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wykonalności projektu “Rozbudowa infrastruktury sieci miejskiej obejmującej jednostki publiczne na terenie miasta Olsztyna” 2013). MSIPMO was made by consortium GIS Partner and Geomatyka Kraków for needs of the city council specially department of surveying, spatial planning and other departments that require spatial information as well as information about real estate ownership. This system presents spatial information within borders of the city. MSIPMO operates in a multi‐layer client ‐ server architecture. MSIPMO consists of several layers and databases. First of all we may find here a layer of orthophotomap – satellite illustration of Olsztyn from 1995, 2005 or 2009. Map coverage from 2009 on the backing of general geographic map is shown on Fig. 39. Besides there is a possibility of showing information from land registry – situation of plots, buildings and precincts (Fig. 40). Moving on to details MSIPMO presents numbers and universal identification of plots, their area and number of registry, where an ordinary user is able to find information about ownership (Fig. 41).

Fig. 39. Chosen layer on a MSIPMO view ‐ orthophotomap from 2009. Source: msipmo.olsztyn.eu.

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Fig. 40. Chosen layer on a MSIPMO view – precinct Kortowo ‐ situation of plots and buildings. Source: msipmo.olsztyn.eu.

Fig. 41. Description of a chosen plot attached to the cadastral map on MSIPMO view. Source: msipmo.olsztyn.eu. This system has more expanded search tool, that helps find:  chosen precinct,  chosen plot with its area,  chosen building with its age and owner,  chosen public utilities.

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On the next figure (Fig. 42) created on the basis of MSIPMO online view there is an example of searching results for public utilities with a kindergarten – its name and location.

Fig. 42. Description of a chosen public utility on a MSIPMO view. Source: msipmo.olsztyn.eu. MSIPMO also provides tools of identifying objects as well as measuring distances and areas. Moreover has a tool of selecting area by drawing lines and polygons. User may sketch on current view an save results for his purposes. Furthermore there is a separated overlap for chosen detailed maps such as:  city plan,  location of land use plans,  investment areas and offers for investors,  acoustic map,  location of historical monuments,  map of ownership and possession. Fig. 43 presents this variety. Wherefore MSIPMO allows its users access the most detailed information as well as carry out analyzes on the basis of information available in different layers and data bases of this system.

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Fig. 43. Variety of detailed maps attached to MSIPMO. Source: msipmo.olsztyn.eu. 4.2.4. SIP Stawiguda Planning information system was introduced in municipality of Stawiguda in 2013. It was developed by MD Project for needs of municipality. This system presents spatial information within borders of the commune. It is a source with variety of spatial information. It combines cadastral and topographic information with orthophotomap as well as current master (land use) plans. “SIP Stawiguda” gives a view of different combined layers like:  boundaries of land use plans and boundaries of plots from cadastral map (Fig. 44),  topographic map and boundaries of plots (Fig. 45),  orthophotomap and land use plans (Fig. 46).

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Fig. 44. Chosen layers on a SIP Stawiguda view ‐ boundaries of land use plans and plots. Source: sip.stawiguda.pl. This system has also a search tool, that helps find:  area of chosen village,  chosen plot,  chosen land use plan (with its whole description attached in a file including symbols and parameters). “SIP Stawiguda” also provides tools of printing current view, saving links, identifying objects as well as measuring distances and areas.

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Fig. 45. Chosen layers on a SIP Stawiguda view ‐ topographic map and boundaries of plots. Source: sip.stawiguda.pl

Fig. 46. Chosen layers on a SIP Stawiguda view ‐ orthophotomap and land use plans. Source: sip.stawiguda.pl

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4.3. Assessment of the NSDI

Annex

To assess national SDI portals adopted specific determinants. The intention of the authors was to identify differences without determining the weight of each attribute. Testing took place on 15 March 2015. The test results are presented in Table 6 and Table 7 and in the Fig. 47 and Fig. 48. An "X" indicates that a factor present in the SDI portal, while "-'' means the absence of the desired item. Table 6. Scope of the data made available in SDI portals by the annexes of INSPIRE Directive. No

Theme

1

Coordinate reference systems Geographical grid systems Geographical names Administrative units Addresses Cadastral parcels Transport networks Hydrography Protected sites Elevation Land cover Orthoimagery Geology Statistical units Buildings Soil Land use Human health and safety

Annex III

Annex II

Annex I

2 3 4 5 6 7 8 9 1 2 3 4 1 2 3 4 5 6 7 8 9

Utility and governmental services Environmental monitoring facilities Production and industrial facilities Agricultural and aquaculture facilities

Geoportal.gov.pl (national level)

Atlas of Warmia and Mazury (regional level)

MSIPMO (provincial level)

SIP Stawiguda (community level)

x

x

x

‐

x

x

x

‐

x x x x x x x x ‐ x ‐ x x ‐ ‐

x x x x x x x x x x ‐ ‐ x x x

x x x x x ‐ x x x x ‐ ‐ x ‐ x

‐ x ‐ x x ‐ ‐ ‐ ‐ x ‐ ‐ x ‐ x

‐

‐

‐

‐

‐

x

x

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

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10 Population distribution demography

No

—

Theme

‐

‐

‐

‐

Geoportal.gov.pl (national level)

Atlas of Warmia and Mazury (regional level)

MSIPMO (provincial level)

SIP Stawiguda (community level)

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐ ‐ ‐

‐ ‐ ‐

‐ ‐ ‐

‐ ‐ ‐

11 Area management/ restriction/regulatio n zones and reporting units 12 Natural risk zones 13 Atmospheric conditions 14 Meteorological geographical features 15 Oceanographic geographical features 16 Sea regions 17 Bio‐geographical regions 18 Habitats and biotopes 19 Species distribution 20 Energy resources 21 Mineral resources

Source: KRUKOWSKA and DAWIDOWICZ own study [2015].

Scope of the data Annex I 9

Annex II

Annex III

9 8

4 3 2

3

3

3 2

2 1

Geoportal.gov.pl Atlas of Warmia and Mazury

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MSIPMO

SIP Stawiguda

Fig. 47. The scope of data rating of the SDI portals. Source: DAWIDOWICZ A. own study

The scope of data in the first three portals is quite similar when it comes to topics of Annex I. With regard to all thematic data the best seems portal Atlas of Warmia and Mazury while the weakest local SIP Stawiguda portal. It can be concluded that local portals contain only data for local development. The higher country administrative division level of portals including a wider range of data for e.g. statistical analysis. Table 7. Functionality of selected SDI portals. Type of geoportal

Tools

Content

Attribute Sectoral module INSPIRE module Statistical module Data from EuroBoundaryMap State Register of Geographical Names The cadastral data Surface relief General Geographic Database Vector map The database of topographic objects Thematic maps Scans of topographic maps Orthophotomap Data on basic control networks Adjust the map to the selected area Adjust the map to the selected selection The form of data presentation [2D / 3D] Thumbnail / Image preview Panel of layers Legend Back / Back to the start page Retry Zoom/Zoom to selected area Zoom to selected object Inserting a class ranges Reduction Moving the map cursor Moving the map by clicking on the frame Centering Removal of selection Selection

Geoportal.gov.pl (national level)

Atlas of Warmia and MSIPMO Mazury (provincial (regional level) level)

SIP Stawiguda (community level)

X X X X

‐ ‐ ‐ ‐

X ‐ ‐ ‐

‐ ‐ ‐ ‐

X

X

X

X

X X X X

X X X X

X X X X

X X X ‐

X

X

‐

‐

X X X X

X ‐ X X

X ‐ X ‐

X X X ‐

X

X

X

X

‐

‐

X

‐

2D

2D

2D

2D

X X X X X X X X X X

X X X X X X X ‐ X X

X X X X X X X X X X

X X ‐ X X ‐ ‐ ‐ X X

X

‐

‐

‐

X X X

X X X

X X X

X X X

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Type of geoportal

Personalization

Services

Attribute

Ruler ‐ measure the distance Adding text characters Measurement of surface Showing the coordinates of the cursor Showing the coordinates of map coverage Clean the measurements Information about specially selected object The choice of scale Search by name Search by keyword Search by parcel number Search by a number of real estate Search by address Search by administrative unit Select a different coordinate system Metadata browser Enter metadata Edit metadata Save the image Print setting Print Export Data Create a Link Send by mail Buying a map Selecting a different language The ability to log on to the website Customizing the Tool Palette Colouring Availability of applications on mobile devices

Geoportal.gov.pl (national level)

Atlas of Warmia and MSIPMO SIP Stawiguda Mazury (provincial (community level) (regional level) level)

X

X

X

X

‐ X

‐ X

X X

‐ X

X

X

X

‐

X

X

X

‐

X

X

X

X

X

X

X

X

X X ‐ X

X X ‐ X

X X ‐ X

X ‐ ‐ X

X

X

X

‐

X

X

X

‐

X

X

X

X

X

X

X

‐

X X X X ‐ ‐ ‐ X ‐

X X X X X X X X ‐

‐ ‐ ‐ X X X X X X

‐ ‐ ‐ ‐ ‐ X ‐ X ‐

‐

X

‐

‐

X

‐

‐

‐

X

X

X

‐

X

‐

‐

‐

X

‐

‐

‐

X

‐

‐

‐

Source: KRUKOWSKA and DAWIDOWICZ [2015]

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Functionality of selected SDI portals Content

Tools

Services

Personalization

22

14

16

20

18

16

14

11

9

8

7

5 1 Geoportal.gov.pl

1

Atlas of Warmia and Mazury

MSIPMO

9

0 SIP Stawiguda

Fig. 48. The functionality rating of the SDI portals. Source: DAWIDOWICZ A. own study. In terms of functionality, the first three analyzed portals similarly present themselves, the most different is SIP Stawiguda portal, which is probably the lowest funding for his conduct. It is functionally adapted to the local users. It can be assumed that is used by local public entities, investors and property owners. An important element of the functionality of portals are services that allow spatial analysis. Most of them have an Atlas of Warmia and Mazury Portal, and a little less MSIPMO and Geoportal.gov.pl. This is due to the fact that regional portals serve not only to provide spatial information to realize the vision of spatially enabled society, but also to the basic spatial analysis, that excel in the regional and provincial development. This phenomenon is beneficial and important for sustainable development. 4.4. The use of GIS systems for real estate market investors According to research made by BEHRENS and HAWRANEK (1993) in the investment process on real estate market we may identify three phases: 1) pre – investment phase, 2) investment phase, 3) operational phase. Each of them may be divided into main activities, studies and analysis which are necessary before, during or after the investment process. While preparing detailed analysis, studies, plans and projects we use variety of sources of information. Some of the traditional sources bound to be replaced by modern GIS systems that combine different sources and make information accessible to an ordinary user. Table 8 presents possibilities of GIS systems implementation on different stages of investment process. There are also noted needs for improvement and development.

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Phase

Table 8. The use of GIS systems on different stages of investment process. Main studies, analysis and activities

Detailed analysis initial investment vision

Pre‐investment phase

location analysis

feasibility study, additional studies, assessment report

market analysis

competition analysis

Investment phase

designing and planning

negotiations, signing agreements, engineering, construction, reception and commissioning

organization of construction works

market exposure

Necessary information

Traditional sources of information

Implementation of GIS systems as a source of information

usage, area, size, number of storeys

architectural concepts

impossible

land use, main roads, neighbourhood, ownership, public utilities

land use plans, general geographical map, land registry, basic map

possible  GEOPORTAL,  Atlas of Warmia and Mazury  SIP Stawiguda,  MSIPMO

supply and demand, number of transactions, average price, types of real estate

registry of real estate prices and values, real estate agents

need for development

differentiation, number of similar objects, distance to similar objects, age of objects

official registers, basic map, statistical registers, internet searchers

possible  GEOPORTAL,  SIP Stawiguda,  MSIPMO (only one with age)

surface, topography, boundaries of plots, infrastructure, ownership,

basic map, cadastral map, land registry, land use plans

possible  GEOPORTAL,  Atlas of Warmia and Mazury  SIP Stawiguda,  MSIPMO

geology, topography, water supply, localization of infrastructure networks, criteria for real estate divisions

basic map, geological studies, geodetic records of public utilities, land use plans, local decisions

need for development

supply and demand, types of real estate

registry of real estate prices and values, agencies

need for development

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Phase

Main studies, analysis and activities

Necessary information

Traditional sources of information

Implementation of GIS systems as a source of information

land use, neighbourhood, ownership

land use plans, geographical map, land registry, basic map

possible  GEOPORTAL,  Atlas of Warmia and Mazury  SIP Stawiguda,  MSIPMO

designing and planning

boundaries of plots, infrastructure, ownership,

basic map, cadastral map, land registry

possible  GEOPORTAL,  SIP Stawiguda,  MSIPMO

analyses of impact on other objects

ownership, land use, building conditions

land registry, land use plans

possible  GEOPORTAL,  SIP Stawiguda,  MSIPMO

Detailed analysis

Operational phase

location analysis

reconstruction, restructuring, expansion, innovation

Source: WOLNY A. own study. As we may notice from Table 8 most of the compared systems are able to replace some of the traditional sources of information on different stages of investment process. Yet none of them contains information that would help investor complete all steps of this process. In some cases like market analysis and exposure or organization of construction works integrating information from different traditional and modern sources might be really helpful. It occurs that the younger GIS system the more adapted it is to investors needs. MSIPMO which was introduced recently has the widest scale of necessary information. 4.4. Conclusions The carried out comparative analyzes allow the following conclusions: 1) The local SDI portals are functionally adapted to the local users, are used by local public entities, investors and property owners. They contain data for local development. 2) The higher country administrative division level of portals including a wider range of data for e.g. statistical analysis. 3) The regional SDI serve not only to provide spatial information to realize the vision of spatially enabled society, but also to the basic spatial analysis, that excel in the regional and provincial development. This phenomenon is beneficial and important for sustainable development. 4) The use of various sources of information on different stages of the investment process can significantly decrease the time of: obtaining the necessary information, analysis and decision‐making by investors. 5) The SDI portals especially on provincial and community level should be adapted to the needs of a wider range of users, particularly real estate market investors and we may notice this direction according to conducted studies. 6) According to analysis MSIPMO seems to be the SDI portal which meets the needs of real estate market investors.

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As the global society becomes more information relying, the concept of Spatially Enabled Society becomes more popular. That is why there is a necessity for developing SDI portals and adapting them for needs of the society. Conducted research confirms this argument as well as important role of SDI portals in creating sustainable development.

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5. SOFTWARE, TOOLS AND INSTRUMENTS USED FOR THE PRESENTATION (VISUALIZATION) OF RESULTS OF SPATIAL ANALYSIS IN GIS Spatial information called the location, the geometric properties and spatial relationships between the elements, which can be applied to the surface of the earth. Information is obtained by interpretation of spatial data, geospatial, geographic (about geographical objects). Information achieved by interpretation of geospatial data (relating to spatial objects related to the Earth's surface) is called Geoinformation. In contrast, harvesting, collecting, verifying , integrating , analyzing , and sharing the transformation of spatial data , and methods , technical resources , including hardware and software, spatial databases, organization and financial resources and the people involved in Geographical Information System . Technical and scientific discipline dedicated to the practical application of Geoinformation (Geoinformation systems) is geomatics (LEKSYKON GEOMATYCZNY, 2002). The sudden development of information management occurred in the eighteenth century, but the first thematic maps were developed (automated production of maps) using computers in the 50's of the previous century. Visualization of the spatial information data processing is made possible by the development of the Harvard Computer Graphics Laboratory and Analysis of spatial raster first model under the name SYMAP. In the 70's a big influence on the development of the SIP had interest in environmental issues and ecology. ESRI was founded institute, where he developed the " ARC / INFO" is used to this day. The breakthrough was the introduction of space technology ‐ LANDSAT satellites. Satellite navigation system (GPS) gradually became a source of spatial data used in geodesy and cartography. The next step was the emergence in the late 80's colour graphics. Then sought to obtain even better image quality, for this purpose developed software that uses the vector model. GIS is a coordinated system for obtaining and providing information about the location, characteristics, and relationships of objects that can be identified with respect to the ground. Object definition is understood very broadly and includes both permanent natural and artificial objects, as well as natural phenomena, social and economic. The space in which objects are identified can be two‐dimensional or three‐ dimensional, depending on the needs of the system. Another distinguishing feature of spatial information systems is the ability to analyze allowing to obtain answers about the real world modelled by the system and its cartographic presentation. Examples of such analysis may be searching for objects that meet certain conditions, measurements or determination of neighbourhood facilities. One of the basic elements of spatial information system is a database of spatial and descriptive information about the objects of the real world represented in the system. To be able to create it and to effectively carry out all the tasks set for the spatial information system is needed yet another of its components, which include the right software and hardware and humans. Speaking of hardware , we mean not only the hardware, but also peripheral devices used for data acquisition (surveying instruments, digitizers, scanners, autographs ), and apparatus for generating tabular and cartographic studies (printers, plotters, image setter). The functioning of the information system is to gather relevant data about real‐world objects that are of concern. These data describe the characteristics of individual objects, and are called attributes. The primary data collection methods include (IZDEBSKI, 2008):

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    

Field measurements, Digitization of maps, Three‐dimensional digitization of photogrammetric Scanning and vectorization of maps. Methods of photogrammetry and remote sensing, Applicability of these methods depends on many factors, including the quality requirements and the technical and economic conditions. Other methods will be used to acquire vector data, and other raster data acquisition. On the basis of information which results from the existence of the Polish GUGiK, at least several systems to keep the data resource. With all of these systems are the most popular software GIS ArsGIS, EWMAPA, GeoInfo and GEO ‐MAP (IZDEBSKI, 2009).

5.1. Materials and methods GIS software can include any program containing functions entering, storing, analyzing and visualizing data geospatial. Current commercial systems using a wide range of GIS software have been applied in many areas of life, including in industry, science, urban planning, agriculture, etc. The data features or methods consist of or should the content of the thematic overlay, digital maps of the area, creating a comprehensive Geographical Information System. The rules for creating, qualifications adequate spatial characteristics, which are something that in modern thematic cartography is called geoinformation, and to develop a methodology of operation of such systems is one of the fundamental tasks of modern geodesy and cartography thematic. Use of cartographic materials and spatial information systems currently on the needs of a variety of spatial analysis, has always constituted, one of the fundamental tasks of geodesy and cartography, as a scientific discipline and practical skills. Map presented in the form of a digital map of the area called the computer a digital presentation of reality. Vector model represents objects using points, lines or polygons (coordinates define the shape and location of the object), while the raster model represents reality, as the area is divisible into cells. The cell stores a numeric value or thematic data. GIS Applications enable you to work with maps by displaying data, symbolization, create and print. In addition, you can edit, analyze data and create charts and reports. The main feature of GIS is to view, create, edit collection, data analysis and transformation, and mapping. GIS software can be divided as follows:  Desktop GIS ‐ programs used to create, edit, manage, analyze and display geospatial data.  DBMS ‐ Spatial database management systems are used to store data, but often also the analysis and data management.  Servers mapping (WebMap) ‐ software for viewing and distribution maps on the internet.  Server GIS ‐ the same functions as a desktop GIS, just that online.  Web GIS ‐ a program designed to display data, and including analysis functions and queries through Web browsers such as Google Maps ‐ display functions and queries, and the creation and editing of data (build your own maps)  Mobile GIS ‐ software for mobile phones and portable computers The result is the transformation of the spatial analysis of geo‐information in thematic maps, which should feed into existing information systems. Development of new layers is possible using software that can be divided according to the general scheme of Fig. 49.

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website

commercial software

map layer On‐Line

Open Source Software

Software

Fig. 49. Schematic GIS software division to create thematic layers.



Source: OGRYZEK own study. To support Gis software skills are required: edit the databases (knowledge of the Statistica software, database, Access) Operating original programs, interfaces, tools and scripts Mapping (knowledge of MapInfo software, EWMAPA, ArcGIS, etc.) Operating Information Systems service portals working in the cloud, the use of a virtual disk BIELECKA (2006) noted that in GIS visualization process is seen as a process of spatial data from the database to graphically visible on the screen. Visualization is therefore a period of transition and the centre of each GIS software is the database and the quality of the data depend on the results obtained. Input modules databases support a very different project planning, and modernization of the existing provisions of the maps is done by selecting the necessary geo‐information presented in a thematic layer. Presentation of the information contained in the database may be by selection of the layer data by attributes such as: the name or position of the objects that is selected from one or more thematic layers based on their spatial relationship with objects other layer. The use of the database can be by:  first method ‐ the development of an interface independent specialist  second method ‐ you can save the results in the form of scanned documents  third method ‐ to supplement the database (tables be merged) However, you should pay attention to how many objects are related to other objects in the database. Joins and relationships are based on the key field, so the field names do not have to have identical names, but the same attribute values in both tables. It is not recommended to use ObjectID and code values TERYT. Therefore, the attribute values must be identical and the primary key data types must be compatible. Fig. 50 shows an example where incompatible database version was transposed possible to join tables. The database came from ARMA and data it used to visualize the software ArcGIS absorption analysis for EU programs in Poland.      

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Fig. 50. Transformation into a compatible database software GIS. Source: OGRYZEK on the basis of data ARiMR 2012. The next important element of visualization is symbolization, i.e. information about the objects expressed by the graphics (colour, shape, size, pattern, direction, etc.), possible to interpret the legend. Symbols are divided on the signature line, fill ‐ you can use a preset or create your own. It applies to both qualitative and quantitative. There are symbolization colour scale, signature grading and proportionate, map crotch and graphs. Depending on the classification methods can be specified intervals such as: natural, equal, etc. 5.2. Results and discussion For several years, steadily increasing popularity of spatial information systems, which we call the systems acquisition, processing and sharing of data containing spatial information and the accompanying descriptive information about the objects featured in the portion of the space covered by the operation of the system. (GAŹDZICKI, 1990). Available Information Systems cities and counties, and portals, as Web‐based versions of GIS software allow you to perform analysis and presentation (visualization) the results of spatial analysis. Using Geographic Information Systems for the needs of a variety of spatial analysis and modernization of existing records in the free software maps on‐line is via a web browser. Fig. 51 Changed practical application of Web GIS software for visualizing the results of different studies:  visualization of "a" Survey of economic activity employed on farms. Kart diagram shows the dependence of the size of farms and the amount of work performed in the Geostatistic Portal.  visualization of "b" ‐ Map of the Local Plan obtained from the Municipal Information System Olsztyn City used in the preparation of the spatial impacts of financial, environmental change parts of the Local Development Plan Olsztyn.

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visualization “a” visualization “b” Fig. 51. Visualization of the results of research in software Web GIS. Source: OGRYZEK own study. A similar tool is a software environment GIS On‐Line, which is usually paid for available data for analysis and analysis tools available in the cloud are free. However, there are geographic information systems offering free access to the software GIS, where registered users can perform a variety of spatial analysis in the environment GIS and the results in the form of maps stored on your own computer provided you have Internet access. An example might be a portal E ‐ government, which developed rapidly since around 2003, and since 2007 the data associated with urban space became a part of it . In time he became one of the world's best public services offered to residents. City of Warsaw has a similar service mapa.um.warszawa.pl where you can find local spatial development plans, plots, offices locations precincts and a few other things. However , there is no current ability to perform spatial analysis and data collection in the form of an Excel file to its own analysis in other software. On Fig. 52 provided an example of the analysis performed for demographics Seoul portal E ‐ government.



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Fig. 52. A demographic analysis made in the software Gis On‐Line Source: http://gis.seoul.go.kr/ Another way to visualize the results are applications of mobile GIS. These applications are used in mobile phones and are associated with integrated and intuitive navigation systems designed for specific groups, e.g. runners, boaters or motorists. Another example of a widely used GIS software can be SailCruiser (Fig. 53) is a unique navigation program designed for sailors. His versatility and intuitiveness makes working with him is easy and at the same time professional. Leading the navigation of any boat quickly and accurately, we can manage all the necessary information.

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Fig. 53. The use of GIS technology in sailing Source: http://www.galeriagps.pl The last group GIS software environment are desktop applications. Can be divided into free and commercial, that is, to which access is possible after buying a license. Quantum GIS (QGIS) is a user friendly open and free (Open Source) GIS software, which runs on the platforms of GNU/Linux, Unix, Mac OSX and MS Windows. QGIS is available for free under the GNU General Public License. QGIS allows you to browse, view, edit and create vector data, raster, and database in different formats, including ESRI shapefile format, MapInfo tab, spatial data Postgre SQL / PostGIS, vector and raster layers GRASS or GeoTiff. Through integration with QGIS GRASS gives the ability to perform advanced analysis. It also has the ability to display layers OGC: WMS and WFS. QGIS functionality can be easily expanded by adding or even the creation of the so‐called. plug‐ins, tailored to individual needs. Plug‐ins are managed by the Manager plug‐in, and written in the language Python or C ++. The program already contains a number of plug‐ins designed, inter alia, to import data from text files, transfer GPS device or calibrating grid (NOWOTARSKA, 2009). ESRI ArcGIS is the most currently used GIS in the world (in 2004 there were 160,000 of his license and have used it more than a million people). ArcGIS consists of three separate programs. ArcMap is used for editing, visualization and analysis of product development. Combined with the ArcToolbox contains tools for data analysis. ArcCatalog acts as a Windows Explorer for geoinformation, and with ArcToolbox tools creates a spatial data management environment. A number of additional enhancements enables specific types of analysis: Spatial Analyst Raster data analysis; Network Analyst analysis of network data; Geostatistical Analyst using geostatistics methods for estimating the continuous fields (values defined at each point in space) of data points, and the 3D Analyst analysis of data in three dimensions. The whole system ArcGIS is offered at three levels. ArcView is the cheapest and poorest version, ArcEditor has full editing capabilities of data, and ArcInfo full of opportunities for both editorial and analytical. The program allows you to create applications integrated with it as a result, there is a number of specialized tools that are created in a variety of research centers and the

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most available free of charge for research purposes (URBAŃSKI, 2008). Pay special attention to the possibility of 3D GIS, which differs from 2D that have primarily coordinates, heights, but also expanded the range of sources, e.g. for satellite photographs . Another important aspect is the ability to automate tasks in the ArcGIS one way to achieve this effect is the use of scripts. ArcGIS scripting language is Python. The interdisciplinary nature of environmental GIS allows the use of tools for analysis and visualization of results of research projects in virtually every area of life. Therefore, the target group of the software is not only commercial users but also the research leading recipient. Regardless of the program, i.e., ArcGIS, MapInfo, EwMapa, AutoCad, etc. They are used, among other things, as a means to visualize the test results. The basic element of the systems are interfaces. These are independent programs to graphical data connection with the descriptive (IZDEBSKI, 2009): 1. Query interface ‐ for information about the specified object. 2. Loopback interface ‐ an indication of the objects that satisfy some condition. It may take place in different ways: as hachure, marker, or by changing the color. 3. Sync interface ‐ allows you to synchronize the database objects from the descriptive part, which ensures the consistency between the internal system objects and external database. 4. Interface Specialist ‐ tailored to a single database, e.g. EWOPIS (the descriptive part of the land and buildings) that when you point the plot displays the data contained in this system. Another example would be the interface to the system CENTRE, allows obtaining more complete information about the Opera and KERG‐in. 5. General Interface ‐  Universal ODBC interface, which allows you to connect to any database for which an ODBC driver exists, such as ORACLE, MS ACCESS  DESCRIPTION universal interface that allows you to link with any object description and photos,  DOCUMENT universal interface that allows you to link with any object scanned documents (e.g. a combination of control points topographical descriptions). Fig. 54 shows the practical application of desktop software for visualizing the results of different studies :  visualization "a" ‐ examine the need for planning works in the municipality of Ghaziabad. Kart diagram shows the number of zoning and land division on zoning decisions and decisions determining the location of a public investment.  visualization of "b" ‐ study the absorption of funds for EU action in areas Warmia and Mazury. This map shows the spatial distribution of the "afforestation of agricultural land."  visualization "c" ‐ test the impact assessment of highway on the environment. On the map filter applied to the visualization of protected areas Nature 2000 as a justification for a variant of the motorway.  visualization "d" ‐ study the impact of EU Programmes on ecological characteristics in Poland. For kart diagram shows the relationship between the amount of the grants received (colour scale) and the coefficients (bar charts) urbanization and agricultural quality in Poland.

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Fig. 54. Visualization of the results of research in a desktop software GIS. Source: OGRYZEK own study. 6. Toolbox ‐ they can be divided into those that change geometry and that change only those attributes. The second group includes those options that perform a table attributes such as calculate field, summary statistic and select by attributes or selects by Location. Tools and functions of the Tables of the group have been converted in Fig. 55 where for the help of applying two layers get the help of a tool for Analyses Tools \ Overlay \ erase, a new layer having attributes and geometry layers combined.

Fig. 55. Toolbox apply for a new layer. Source: OGRYZEK own study.

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Another example of working with GIS tools is to perform the analysis. On Fig. 56 was tested using buffer zones in GIS software, the impact of three factors: the distance from the central site, area, access roads to the city. Converted into vector maps and raster maps using raster algebra maps, GIS technology was achieved in the final version of the map of potential spatial development of the municipality.

Fig. 56. Analysis of the potential of the municipality made in ArcGIS using map algebra. Source: OGRYZEK own study.

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7. Own scripts ‐ is a set of possible programming languages for use in GIS technology. There are solutions: • Languages compiled ‐ Fortran, C, C ++ • Scripting languages ‐ Matlab, R, IDL • Python Python has gained the greatest popularity because of the rich set of diverse libraries, natural language, and the submission of many libraries for purposes other than research. ETHERINGTON (2011) while working on the genetics of the landscape for the first time was surprised that there was no way to visualize the differences pairs of genetic kinship. To fix this, wrote the script kinship links, which will take a series of points and a matrix text file, kinship, and will produce a polyline shapefile links between each combination of pairs of points. Another example shown in the Fig. 57 is the development of wind rose for the presentation of wind direction. The program is written in Python runs as a tool in ArcGIS.

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Fig. 57. WindPRO meteo data report. Source: KOPEĆ A. 5.3. Conclusions GIS is a system of acquiring, processing and sharing of data due to system users, which enables you to analyze the change in time and space phenomena of socio ‐ economic scenario building and forecasting and making decisions based on them. The result of multi‐criteria analysis model solutions space management is the transformation of Geoinformation in thematic maps. The proposed software can be used on a different

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scale and level of detail on a variety of spatial analysis. Entered into a GIS database modules support a variety of project planning and are targeted to a specific audience. The main task of the software GIS visualization test results analysis. The applicability of various instruments GIS depending on your needs, experience, financial capacity and performance objectives determine the target group and indicate the need to use a specific type of software. However, the interdisciplinary nature of GIS makes us closer and closer to the situation that the knowledge and use of GIS software ‐ this is standard in all areas of life.







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ROZPORZĄDZENIE Ministra Spraw Wewnętrznych i Administracji z dnia 9 listopada 2011 r. w sprawie standardów technicznych wykonywania geodezyjnych pomiarów sytuacyjnych i wysokościowych oraz opracowywania i przekazywania wyników tych pomiarów do państwowego zasobu geodezyjnego i kartograficznego. Dz. U. 2011 nr 263 poz. 1572. Regulation of the Minister of Internal Affairs and Administration of 9 November 2011. On technical standards of performing geodetic situational and height measurements and the processing and transmission of the results of these measurements to the national geodetic and cartographic resource. Journal of Laws 2011 No. 263, item 1572 WYTYCZNE, 2012. Architektura Krajowego systemu zarządzania BDOT (Architecture of National Management System BDOT). Wydawnictwo GUGiK, Warszawa: 27‐31.

Web pages: http://gisplay.pl/gis/krajowy‐system‐informacji‐przestrzennej.html15 http://geoportal.gov.pl/ access on 17.07.2014 http://mapy.geoportal.gov.pl/imap/?gpmap=gp0&actions=acShowServices_KAT ASTER&locale=en access on 17.07.2014 http://atlas.warmia.mazury.pl/mpzp/ access on 3.03.2015 http://msipmo.olsztyn.eu access on 20.03.2015 http://sip.stawiguda.pl access on 5.03.2015





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LIST OF FIGURES Fig. 1. An example of a BDOT10k database in the cartographic form. .................... 8  Fig. 2. The diagram of data quality control process (part). ........................................ 13  Fig. 3 The conceptual model of data quality BDOT10k. .............................................. 16  Fig. 4. Attribute query tool in OpenJUMP. ......................................................................... 30  Fig. 5. Validate Selected Layers tool in OpenJUMP. ....................................................... 30  Fig. 6. Polygonize tool in OpenJUMP. ................................................................................... 31  Fig. 7. Join Attributes Spatially tool in OpenJUMP. ........................................................ 31  Fig. 8. Spatial Join tool in OpenJUMP. .................................................................................. 32  Fig. 9. Selection Query tool in gvSIG CE. ............................................................................. 33  Fig. 10. Layers that will be included in the topology in gvSIG CE. ........................... 34  Fig. 11. Topological rules to be verified in gvSIG CE. .................................................... 34  Fig. 12. Geoprocessing tool Clean in gvSIG CE. ................................................................ 35  Fig. 13. Geoprocessing tool Build polygons in gvSIG CE. ............................................. 36  Fig. 14. Spatial join tool in gvSIG CE. .................................................................................... 36  Fig. 15. Geometry types available to load in QGIS. ......................................................... 37  Fig. 16. Extract by attribute tool in QGIS. ........................................................................... 38  Fig. 17. Topology Rule Settings in Topology Checker plugin in QGIS. ................... 38  Fig. 18. Topology Checker plugin in QGIS. ......................................................................... 39  Fig. 19. Points in Polygon tool in QGIS. ............................................................................... 39  Fig. 20. Join Attributes by Location tool in QGIS. ............................................................ 40  Fig. 21. Nautiz x7, Garmin GPSmap 62st, Garmin GPSmap 76 .................................. 46  Fig. 22. Municipalities zonal division. ................................................................................. 47  Fig. 23. Dasymetric cartogram of development intensity in the municipality area. ................................................................................................................................................... 48  Fig. 24. Map of economic activity in Zielonki commune. ............................................. 49  Fig. 25. Determination of the parcels within the scope of residential areas. ...... 50  Fig. 26. Buffers and parcels located in their influence on plots. ............................... 51  Fig. 27. The creation of the resulting layer for the betterment levy due to the construction of technical infrastructure. ........................................................................... 51  Fig. 28. Layers of buildings, technical infrastructure, fittings and precincts. ..... 52  Fig. 29. Share of developed plots in Local Development Plan. .................................. 53  Fig. 30. Share of developed plots in Local Development Plan. .................................. 54  Fig. 31. The ratio of the invested area surface to those labeled MN in the Local Spatial Development Plan in different villages. .............................................................. 55  Fig. 32. Percentage chart of easy access of residential buildings to the infrastructure by villages. ........................................................................................................ 56  Fig. 33. Cover of elaboration on „Spatially Enabled Society”. .................................... 59  Fig. 34. Warmia and Mazury Region. ................................................................................... 61  Fig. 35. Administrative division of Republic of Poland – as at January 1, 2011. 61  Fig. 36. Interface of Polish Geoportal. ................................................................................. 68  Fig. 37. Cadastral data in mapping tab. ............................................................................... 68  Fig. 38. Atlas of Warmia i Mazury. ........................................................................................ 70  Fig. 39. Chosen layer on a MSIPMO view ‐ orthophotomap from 2009. ............... 71 

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Fig. 40. Chosen layer on a MSIPMO view – precinct Kortowo ‐ situation of plots and buildings. ................................................................................................................................ 72  Fig. 41. Description of a chosen plot attached to the cadastral map on MSIPMO view. .................................................................................................................................................. 72  Fig. 42. Description of a chosen public utility on a MSIPMO view. ......................... 73  Fig. 43. Variety of detailed maps attached to MSIPMO. ............................................... 74  Fig. 44. Chosen layers on a SIP Stawiguda view ‐ boundaries of land use plans and plots. ......................................................................................................................................... 75  Fig. 45. Chosen layers on a SIP Stawiguda view ‐ topographic map and boundaries of plots. .................................................................................................................... 76  Fig. 46. Chosen layers on a SIP Stawiguda view ‐ orthophotomap and land use plans. ................................................................................................................................................. 76  Fig. 47. The scope of data rating of the SDI portals. ...................................................... 79  Fig. 48. The functionality rating of the SDI portals. ....................................................... 81  Fig. 49. Schematic GIS software division to create thematic layers. ....................... 87  Fig. 50. Transformation into a compatible database software GIS. ........................ 88  Fig. 51. Visualization of the results of research in software Web GIS. ................... 89  Fig. 52. A demographic analysis made in the software Gis On‐Line ....................... 90  Fig. 53. The use of GIS technology in sailing ..................................................................... 91  Fig. 54. Visualization of the results of research in a desktop software GIS. ........ 93  Fig. 55. Toolbox apply for a new layer. ............................................................................... 93  Fig. 56. Analysis of the potential of the municipality made in ArcGIS using map algebra. ............................................................................................................................................. 94  Fig. 57. WindPRO meteo data report. .................................................................................. 96 

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LIST OF TABLES Table 1. The results of the data quality evaluation. ....................................................... 17  Table 2. The value of RI depending on the dimension of the matrix ...................... 18  Table 3. The point values of data quality evaluation and the values of calculated estimators ....................................................................................................................................... 19  Table 4. List of territorial units of Poland ‐ as at 01.01.2015. ................................... 62  Table 5. Spatial data themes. .................................................................................................. 64  Table 6. Scope of the data made available in SDI portals by the annexes of INSPIRE Directive. ....................................................................................................................... 77  Table 7. Functionality of selected SDI portals. ................................................................ 79  Table 8. The use of GIS systems on different stages of investment process. ...... 82 

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NOTES ON THE AUTHORS Agnieszka Dawidowicz, Ph.D. Department of Real Estate Resources University of Warmia and Mazury Olsztyn, Poland e‐mail: [email protected]







SCIENTIFIC EDITOR An Assistant Professor working in Department of Real Estate Resources, Faculty of Geodesy, Geospatial and Civil Engineering. She holds a PhD in Real Estate Cadastre from the University of Warmia and Mazury. She also holds degrees in Engineering (Land Management) Science (Geodesy and Cartography) from the same institution. From the University of Warmia and Mazury in Olsztyn she holds a university teaching qualification. Her research focuses on technological and functional development of cadastres and land administration systems. She is currently working on methodology for testing the flexibility of the land administration systems. She is an expert in GIS and other spatial information systems. She participated in scientific and teaching trainings in Germany (Leibnitz University of Hannover) and in the Netherlands (University of Twente). She is also a scientific secretary of the board and the editorial team of Journal “Acta Scientiarum Polonorum Administratio Locorum” (eng. Real Estate Management). Agnieszka Dawidowicz acts as a reviewer on many journal and conference series.

Ada Wolny, Ph.D.

Department of Real Estate Resources University of Warmia and Mazury in Olsztyn Olsztyn, Poland e‐mail: [email protected]





SCIENTIFIC EDITOR An Assistant Professor working in Department of Real Estate Resources, Faculty of Geodesy, Geospatial and Civil Engineering. She holds a PhD in Real Estate Management from the University of Warmia and Mazury. She also holds degrees in Engineering (Land Management) Science (Geodesy and Cartography) from the same institution. Her research focuses on application of GIS for real estate management and regional development. She tests capabilities of SDI systems and variety of GIS tools for improving management of suburban areas. She is an expert in GIS. As a licensed real estate broker she identifies needs of different participants of real estate market. Ada Wolny is an author or co – author of scientific publications. She is also a member of Polish Real Estate Scientific Society and she acts as a reviewer on journal and conference series.



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Piotr Cichociński, Prof. Ph.D Department of Geomatics AGH University of Science and Technology Kraków, Poland e‐mail: [email protected]



Piotr Cichociński is employed at the Department of Geomatics, Faculty of Mining Surveying and Environmental Engineering, AGH University of Science and Technology in Kraków. Since 2005, the main field of his scientific and research activity has been related to the broadly defined use of geographic information systems (GIS) in the real estate economy (and the property valuation, in particular). In addition, his research interests include standardization and normalization in GIS, geoinformation modelling and design of spatial databases, web mapping, network analysis, volunteered geographic information. He also focuses on promotion of the idea of open data and free software.





Monika Mika, Ph.D. Department of Land Surveying University of Agriculture in Krakow Kraków, Poland e‐mail: [email protected]





Monika Mika is employed at Department of Land Surveying, Faculty of Environmental Engineering and Land Surveying, University of Agriculture in Krakow. Deals mainly with research and teaching activities in the field of several thematic:  The cadastre, the cadastre history, genesis of the cadastre in Poland and worldwide, modernization of the cadastre, the cadastral information.  The Land and Mortgage Register system in Poland – analysis of existing state in terms of the flow of information about the area and the creation of real estate cadastre.  The use of GNSS measurement techniques and GIS tools in the creation of thematic maps and other cartographic elaborations. Author or co‐author of scientific publications in the field of geodesy and cartography and real estate management.

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Marek Ogryzek, Ph.D. Department of Planning and Spatial Engineering University of Warmia and Mazury Olsztyn, Poland e‐mail: [email protected]



Marek Ogryzek is employed at the Department of Planning and Spatial Engineering Faculty of Geodesy and Land Management, University of Warmia and Mazury in Olsztyn, Poland. His research interests concern mostly GIS. Still in his research there are topics like activity of Agricultural Real Estate Agency, optimal development, spatial planning and EU programs. His field of research includes also the use statistic and geostatistic methods.



Tomasz Salata, Ph.D. Department of Land Management and Landscape Architecture University of Agriculture in Krakow Kraków, Poland e‐mail: rmtsalat@cyf‐kr.edu.pl







Tomasz Salata is academic employed at Department of Land Management and Landscape Architecture, Faculty of Environmental Engineering and Land Surveying, University of Agriculture in Krakow. He mainly deals with spatial information systems (construction and operation in the network) used in the fields of environmental protection, registration of landscape events and phenomena and modelling of spatial and descriptive data in information systems. He is the author of many scientific publications in the field of geodesy and cartography, spatial information systems, GIS and data modelling. In his output he also has many years of experience in the field of informatics implementations in institutions of local government administration in the field of communal space management, municipal property, mailing systems and others.



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Monika Siejka, Ph.D. Department of Land Surveying University of Agriculture in Krakow Krakow, Poland e‐mail: rmwiech@cyf‐kr.edu.pl







Monika Siejka is a research and teaching worker in the Department of Geodesy of the Faculty of Environmental Engineering and Land Surveying at the University of Agriculture in Krakow and real estate appraiser. She is the author and co‐author of numerous publications in journals of national and international range on real estate market, real estate management, real estate cadastre, databases, the concept of real estate information systems. She is the co‐author of a textbook for university students and of a monographs. She leads the research on the use of multi‐criteria methods for the analyses of the real estate market and optimization of the choice of the location for the investment of supralocal character. She is also active in the professional field as a real estate appraiser and member of Malopolskie Association of Property Valuers and Polish Federation of Property Valuers.

Marek Ślusarski, Ph.D. Department of Land Surveying University of Agriculture in Krakow Krakow, Poland e‐mail: rmslusar@cyf‐kr.edu.pl





Graduate of Land Surveying College in Lublin (1987) and the Surveying and Mapping at the University of Agriculture in Krakow (1992). Research and teaching worker in the Department of Geodesy of the Faculty of Environmental Engineering and Land Surveying at the University of Agriculture in Krakow since 1992. Academic degree of doctor of agricultural sciences in shaping the environment (2002) obtained for the work. „Methods of obtaining and selection of data in order to create a uniform system of information on real estate”. Author and co‐author of several original research papers published in national and international journals. Scientific interests: information systems, in particular, investigation of the quality of data in geospatial systems and modern cadastre. A member of the selection committee for the professional certificates in the field of geodesy and cartography.

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