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Capturing and characterizing human activities using building locations in America
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.ORCID iD: 0000-0003-0794-0110
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Geospatial Sciences.ORCID iD: 0000-0002-2337-2486
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Computer Science.ORCID iD: 0000-0003-0085-5829
2019 (English)In: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 8, no 5, article id 200Article in journal (Refereed) Published
Abstract [en]

Capturing and characterizing collective human activities in a geographic space have become much easier than ever before in the big era. In the past few decades it has been difficult to acquire the spatiotemporal information of human beings. Thanks to the boom in the use of mobile devices integrated with positioning systems and location-based social media data, we can easily acquire the spatial and temporal information of social media users. Previous studies have successfully used street nodes and geo-tagged social media such as Twitter to predict users’ activities. However, whether human activities can be well represented by social media data remains uncertain. On the other hand, buildings or architectures are permanent and reliable representations of human activities collectively through historical footprints. This study aims to use the big data of US building footprints to investigate the reliability of social media users for human activity prediction. We created spatial clusters from 125 million buildings and 1.48 million Twitter points in the US. We further examined and compared the spatial and statistical distribution of clusters at both country and city levels. The result of this study shows that both building and Twitter data spatial clusters show the scaling pattern measured by the scale of spatial clusters, respectively, characterized by the number points inside clusters and the area of clusters. More specifically, at the country level, the statistical distribution of the building spatial clusters fits power law distribution. Inside the four largest cities, the hotspots are power-law-distributed with the power law exponent around 2.0, meaning that they also follow the Zipf’s law. The correlations between the number of buildings and the number of tweets are very plausible, with the r square ranging from 0.53 to 0.74. The high correlation and the similarity of two datasets in terms of spatial and statistical distribution suggest that, although social media users are only a proportion of the entire population, the spatial clusters from geographical big data is a good and accurate representation of overall human activities. This study also indicates that using an improved method for spatial clustering is more suitable for big data analysis than the conventional clustering methods based on Euclidean geometry.

Place, publisher, year, edition, pages
MDPI, 2019. Vol. 8, no 5, article id 200
Keywords [en]
Big data, City-size distribution, Human activities, Scaling, Twitter, US building footprints
National Category
Civil Engineering Other Natural Sciences
Research subject
Sustainable Urban Development
Identifiers
URN: urn:nbn:se:hig:diva-30544DOI: 10.3390/ijgi8050200ISI: 000470965400001Scopus ID: 2-s2.0-85066441533OAI: oai:DiVA.org:hig-30544DiVA, id: diva2:1344933
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2024-05-21Bibliographically approved
In thesis
1. Living Structure for Understanding Human Activity Patterns Using Multi-Source Geospatial Big Data
Open this publication in new window or tab >>Living Structure for Understanding Human Activity Patterns Using Multi-Source Geospatial Big Data
2023 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Geographic space is not neutral or lifeless, but an intricate living structure composed of numerous small features and a few large ones across all scales. The living structure is crucial for comprehending how geographic space shapes human activities. With the emerging geospatial big data, researchers now have unprecedented opportunities to study the relationship between geographic space and human behaviour at a finer spatial resolution. This thesis leverages multisource geospatial big data, including Twitter check-in locations, street networks from OpenStreetMap, building footprints, and night-time light images, to explore the fundamental mechanisms of human activities that underlie geographic space. To overcome the limitations of conventional analytics in this era of big data, we propose the topological representation and living structure based on Christopher Alexander's conception of space.

We utilize scaling and topological analyses to reveal the underlying living structure of geographic space with various big datasets. Our results demonstrate that tweet locations or human activities at different scales can be accurately predicted by the underlying living structure of street nodes. We also capture and characterize human activities using big data and find that building footprints and tweets show similar scaling patterns in terms of sizes of their spatial clusters. We also propose an improved spatial clustering method to increase the processing speed of geospatial big data. Finally, we adopt topological representation to identify urban centres by the fusion of multi-source geospatial big data. The living structure, together with its topological representation can help us better understand human activities patterns in the geographic space at both city and country levels.

Place, publisher, year, edition, pages
Gävle: Gävle University Press, 2023. p. 40
Series
Licentiate thesis ; 16
Keywords
living structure, topological representation, human activities, natural cities, urban centres, complex network, head/tail breaks, big data
National Category
Geosciences, Multidisciplinary
Research subject
Sustainable Urban Development
Identifiers
urn:nbn:se:hig:diva-41341 (URN)978-91-89593-02-2 (ISBN)978-91-89593-03-9 (ISBN)
Presentation
2023-06-09, Kungsbacksvägen 47, Gävle, 10:00 (English)
Opponent
Supervisors
Available from: 2023-05-02 Created: 2023-03-30 Last updated: 2024-05-21Bibliographically approved

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Ren, ZhengJiang, BinSeipel, Stefan

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