Publication details
Spatial-temporal analysis of retail and services using Facebook Places data
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| Year of publication | 2022 |
| Type | Appeared in Conference without Proceedings |
| Citation | |
| Description | The topic of using location-based network data is currently very popular and widespread among a whole spectrum of scientific disciplines. Social networks are a powerful source of interesting geographic data. However, their use is significantly linked to sufficient use of the network in the studied locality and also to the conditions under which the data are available for download and use. This study analyzes and discusses the potential and limits of using Facebook data to analyse spatial-temporal urban rhythms and city centrality through the availability of services and retail. During September 2020, we harvested Facebook Places data for the area comprising the city of Brno in the Czech Republic. Alongside geographical position (spatial component), place category and name, we also obtained the opening hours (temporal component) of the places. This study provides a detailed description of data collection using Facebook Graph API and further processing in the Geographic Information System (GIS). Further, we focused on the subcategories ‘Food & Beverage’, ‘Shopping & Retail’ and ‘Medical & Health’ and analysed their spatial and temporal distribution in the context of predefined regions of the city. In our pilot study, we present possible methods of spatial-temporal analysis and data visualization. Finally, 131 | P a g e we evaluate the benefits, limitations, and further potential of Facebook Places data for social geography. The main objective of this study is to demonstrate the possibility of using data from social networks such as Facebook concerning the background of social geography concepts. Attention is paid especially to the extraction of spatial data, which are subsequently used for analyses focused on the temporal characteristics (time curves) and intra-urban structures. Results of these analyses are crucial for interpreting the dynamics of post-industrial cities. |