Urban Mobility and Food Ordering Services - A web

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Sep 19, 2017 - Fundación Universitaria Konrad Lorenz, Bogotá, Colombia .... Automating web scrapers for getting updated reports of OFOS web sites. Correa ...
Urban Mobility and Food Ordering Services A web mining perspective

Juan C. Correa1

Phillip Brooker2

Gopal Sakarkar3

1 Consumer Psychology Graduate Program ´ Universitaria Konrad Lorenz, Bogota, ´ Colombia Fundacion [email protected] 2 Department

of Psychology University of Bath, UK

3 Department of Computer Applications Raisoni College of Engineering, Nagpur, India

Conference on Complex Systems, 2017 ´ Mexico Cancun, Correa, Brooker & Sakarkar (2017)

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Agenda

1

Motivation

2

Objectives of Research

3

Materials and Methods

4

Results

Challenges for future research References 5

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Motivation Online Food Ordering Systems (OFOS) are well-known worldwide

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Motivation

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Motivation

Gupta and Paul (2016) noted that OFOS users in Eastern countries (e.g. China or Malaysia) concentrate more on the affordances OFOS offer in terms of convenience and time-saving.

Correa, Brooker & Sakarkar (2017)

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Motivation

Gupta and Paul (2016) noted that OFOS users in Eastern countries (e.g. China or Malaysia) concentrate more on the affordances OFOS offer in terms of convenience and time-saving. Both hedonic motivation and convenience played significant roles in understanding why people use OFOS (Yeo et al., 2017).

Correa, Brooker & Sakarkar (2017)

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Motivation

Gupta and Paul (2016) noted that OFOS users in Eastern countries (e.g. China or Malaysia) concentrate more on the affordances OFOS offer in terms of convenience and time-saving. Both hedonic motivation and convenience played significant roles in understanding why people use OFOS (Yeo et al., 2017). In Western nations (Brazil or England) OFOS are also growing rapidly, providing added convenience to their customers when coupled with the relative ease of access afforded by the ubiquity of mobile Internet devices (Pigatto et al., 2017).

Correa, Brooker & Sakarkar (2017)

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Motivation

Despite the intrinsic connection between urban mobility and OFOS’ operations, we are not aware of any previous research aiming such a relationship. Fortunately, Urban Mobility Online Networks (Waze, Google Maps) provide valuable information that helps us to better understand the impact of traffic dynamics on OFOS’ operations.

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Objectives of Research Describe the observed relationships between Waze traffic dynamics and indicators of Online Fast Food Ordering Services. Indicate the experimental challenges for future research aiming the link between urban mobility and OFOS’ operations from the Web Mining perspective.

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Materials and Methods We employed Chrome DevTools to capture traffic reports from Waze Live Map in Bogota´ City. It provides web developers deep access into the internals of the browser and their web application.

As these reports are set in JSON format, we employed convenient JSON parsers with the aid of the XML R-package (Lang & the CRAN Team, 2017; Munzert, Rubba, Meißner, & Nyhus, 2015). Correa, Brooker & Sakarkar (2017)

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Materials and Methods We also applied standard web scraping techniques (Munzert et al., 2015) to capture relevant information of OFOS indexed at domicilios.com

••••••••

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Materials and Methods We developed ad hoc scripts through Agenty (an advanced web scraper) to build structured data frames containing relevant information publicly available at domicilios.com

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Materials and Methods Our observations took place in two rounds and focused on the following variables. Table 1: Operative Indicators of OFOS

Variable Cost of Delivery (Cod) Comments (Comm) Distance (Dist) Expected delivery time (Edt) Expected travel time (Ett) Minimum Price Ordering (Mpo)

Correa, Brooker & Sakarkar (2017)

Source Domicilios.com Domicilios.com Google Maps Domicilios.com Google Maps Domicilios.com

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Results

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Results

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Results In classifying traffic events as captured by Waze...

We identified that a first classification node considers the latitude, which has a lot of sense in Bogota´ city.

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Results

(a) Northern Bogota´

(b) Southern Bogota´ Figure 1: Correlations between OFOS indicators

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Challenges for future research

Developing handy tools for dealing with high frequency data processing (e.g., Waze).

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Challenges for future research

Developing handy tools for dealing with high frequency data processing (e.g., Waze). Automating web scrapers for getting updated reports of OFOS web sites.

Correa, Brooker & Sakarkar (2017)

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Challenges for future research

Developing handy tools for dealing with high frequency data processing (e.g., Waze). Automating web scrapers for getting updated reports of OFOS web sites. Evaluate the role of other variables that have impacts on traffic dynamics (e.g., weather conditions, non-frequent events, etc.)

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Questions?

Shoot me!

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Thank you!

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References I

Gupta, T., & Paul, K. (2016). Consumer Attitude towards Quick Service Restaurants: A Study Across Select Quick Service Restaurants in Gurgaon. Indian Journal of Applied Research, 6(4), 639-641. Lang, D. T., & the CRAN Team. (2017). Xml: Tools for parsing and generating xml within r and s-plus [Computer software manual]. Retrieved from https://CRAN.R-project.org/package=XML (R package version 3.98-1.9) Munzert, S., Rubba, C., Meißner, P., & Nyhus, D. (2015). Automated data collection with r: A practical guide to web scraping and text mining. West Sussex, UK: John Wiley Sons Ltd. Pigatto, G., Machado, J. G. d. C. F., Negreti, A. d. S., & Machado, L. M. (2017). Have You Chosen your Request? Analysis of Online Food Delivery Companies in Brazil. British Food Journal, 119(3).

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References II

Yeo, V. C. S., Goh, S.-K., & Rezaei, S. (2017). Consumer Experiences, Attitude and Behavioral Intention toward Online Food Delivery (OFD) Services. Journal of Retailing and Consumer Services, 35, 150–162.

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