Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times
Extensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through geotemporal information, providing deeper insights...
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| Language: | English |
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MDPI AG
2025-06-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/14/6/221 |
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| author | Mikel Barrena-Herrán Itziar Modrego-Monforte Olatz Grijalba |
| author_facet | Mikel Barrena-Herrán Itziar Modrego-Monforte Olatz Grijalba |
| author_sort | Mikel Barrena-Herrán |
| collection | DOAJ |
| description | Extensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through geotemporal information, providing deeper insights into the underlying causes of urban vibrancy. This study presents a methodology for analyzing the spatiotemporal use of cities and identifying occupancy patterns taking into consideration urban form and function. The analysis relies on data obtained from Google Popular Times (GPT), transforming the relative occupancy of a large number of points of interest (POI) classified into five categories, for estimating the number of people aggregated within urban nodes during a typical day. As a result, this research assesses the utility of this data source for evaluating the changing dynamics of a city across both space and time. The methodology employs geographic information system (GIS) tools and artificial intelligence techniques. The results demonstrate that by analyzing geotemporal data, we can classify urban nodes according to their hourly activity patterns. These patterns, in turn, relate to city form and urban activities, showing a certain spatial concentration. This research contributes to the growing body of knowledge on machine learning (ML) methods for spatiotemporal modeling, laying the groundwork for future studies that can further explore the complexity of urban phenomena. |
| format | Article |
| id | doaj-art-7bd388ac747041bfaa11bc17802789e3 |
| institution | OA Journals |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-7bd388ac747041bfaa11bc17802789e32025-08-20T02:21:12ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-06-0114622110.3390/ijgi14060221Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular TimesMikel Barrena-Herrán0Itziar Modrego-Monforte1Olatz Grijalba2CAVIAR (Quality of Life in Architecture) Research Group, Department of Architecture, University of the Basque Country UPV/EHU, Plaza Oñati 2, 20018 Donostia-San Sebastián, SpainCAVIAR (Quality of Life in Architecture) Research Group, Department of Architecture, University of the Basque Country UPV/EHU, Plaza Oñati 2, 20018 Donostia-San Sebastián, SpainCAVIAR (Quality of Life in Architecture) Research Group, Department of Architecture, University of the Basque Country UPV/EHU, Plaza Oñati 2, 20018 Donostia-San Sebastián, SpainExtensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through geotemporal information, providing deeper insights into the underlying causes of urban vibrancy. This study presents a methodology for analyzing the spatiotemporal use of cities and identifying occupancy patterns taking into consideration urban form and function. The analysis relies on data obtained from Google Popular Times (GPT), transforming the relative occupancy of a large number of points of interest (POI) classified into five categories, for estimating the number of people aggregated within urban nodes during a typical day. As a result, this research assesses the utility of this data source for evaluating the changing dynamics of a city across both space and time. The methodology employs geographic information system (GIS) tools and artificial intelligence techniques. The results demonstrate that by analyzing geotemporal data, we can classify urban nodes according to their hourly activity patterns. These patterns, in turn, relate to city form and urban activities, showing a certain spatial concentration. This research contributes to the growing body of knowledge on machine learning (ML) methods for spatiotemporal modeling, laying the groundwork for future studies that can further explore the complexity of urban phenomena.https://www.mdpi.com/2220-9964/14/6/221spatio-temporal analysistime series clusteringurban dynamicslocation-based social networkgeographic information systemstime geography |
| spellingShingle | Mikel Barrena-Herrán Itziar Modrego-Monforte Olatz Grijalba Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times ISPRS International Journal of Geo-Information spatio-temporal analysis time series clustering urban dynamics location-based social network geographic information systems time geography |
| title | Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times |
| title_full | Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times |
| title_fullStr | Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times |
| title_full_unstemmed | Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times |
| title_short | Revealing Spatiotemporal Urban Activity Patterns: A Machine Learning Study Using Google Popular Times |
| title_sort | revealing spatiotemporal urban activity patterns a machine learning study using google popular times |
| topic | spatio-temporal analysis time series clustering urban dynamics location-based social network geographic information systems time geography |
| url | https://www.mdpi.com/2220-9964/14/6/221 |
| work_keys_str_mv | AT mikelbarrenaherran revealingspatiotemporalurbanactivitypatternsamachinelearningstudyusinggooglepopulartimes AT itziarmodregomonforte revealingspatiotemporalurbanactivitypatternsamachinelearningstudyusinggooglepopulartimes AT olatzgrijalba revealingspatiotemporalurbanactivitypatternsamachinelearningstudyusinggooglepopulartimes |