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Use and validation of location-based services in urban research: An example with Dutch restaurants

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  • Daniel Arribas-Bel

    (Department of Geography and Planning, University of Liverpool, UK)

  • Jessie Bakens

    (Research Centre for Education and the Labour Market, Maastricht University, Netherlands)

Abstract

This article focuses on the use of big data for urban geography research. We collect data from the location-based service Foursquare in The Netherlands and employ it to obtain a rich catalogue of restaurant locations and other urban amenities, as well as a measure of their popularity among users. Because the Foursquare data can be combined with traditional sources of socio-economic data obtained from Statistics Netherlands, we can quantify, document and characterise some of the biases inherent in these new sources of data in the context of urban applications. A detailed analysis is given as to when this type of big data is useful and when it is misleading. Although the users of Foursquare are not representative of the whole population, we argue that this inherent bias can be exploited for research about the attractiveness of urban landscapes and consumer amenities in addition to the more traditional data on urban amenities.

Suggested Citation

  • Daniel Arribas-Bel & Jessie Bakens, 2019. "Use and validation of location-based services in urban research: An example with Dutch restaurants," Urban Studies, Urban Studies Journal Limited, vol. 56(5), pages 868-884, April.
    • Handle: RePEc:sae:urbstu:v:56:y:2019:i:5:p:868-884
    DOI: 10.1177/0042098018779554
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    2. Occhini, Giulia & Tranos, Emmanouil & Wolf, Levi John, 2023. "Measuring a country’s digital industrial structure: commercial websites and weakly supervised classification to the rescue," SocArXiv h572n, Center for Open Science.
    3. Jon Bannister & Anthony O’Sullivan, 2021. "Big Data in the city," Urban Studies, Urban Studies Journal Limited, vol. 58(15), pages 3061-3070, November.
    4. Silver, Daniel & Silva, Thiago H, 2021. "Complex Causal Structures of Neighbourhood Change: Evidence From a Functionalist Model and Yelp Data," SocArXiv wprf8, Center for Open Science.
    5. Olson, Alex & Calderon-Figueroa, Fernando & Bidian, Olimpia & Silver, Daniel & Sanner, Scott, 2020. "Reading the city through its neighbourhoods: Deep text embeddings of Yelp reviews as a basis for determining similarity and change," SocArXiv 8jbvg, Center for Open Science.
    6. Xiaorong Jiang & Wei Wei & Shenglan Wang & Tao Zhang & Chengpeng Lu, 2021. "Effects of COVID-19 on Urban Population Flow in China," IJERPH, MDPI, vol. 18(4), pages 1-14, February.

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