A review of spatially-explicit GeoAI applications in Urban Geography
Urban Geography studies forms, social fabrics, and economic structures of cities from a geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban Geography seeks new models and research paradigms to explain urban phenomena and address urban issues. Recent years have witn...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-08-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843222001339 |
| _version_ | 1828356297882337280 |
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| author | Pengyuan Liu Filip Biljecki |
| author_facet | Pengyuan Liu Filip Biljecki |
| author_sort | Pengyuan Liu |
| collection | DOAJ |
| description | Urban Geography studies forms, social fabrics, and economic structures of cities from a geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban Geography seeks new models and research paradigms to explain urban phenomena and address urban issues. Recent years have witnessed significant advances in spatially-explicit geospatial artificial intelligence (GeoAI), which integrates spatial studies and AI, primarily focusing on incorporating spatial thinking and concept into deep learning models for urban studies. This paper provides an overview of techniques and applications of spatially-explicit GeoAI in Urban Geography based on 581 papers identified using a systematic review approach. We examined and screened papers in three scopes of Urban Geography (Urban Dynamics, Social Differentiation of Urban Areas, and Social Sensing) and found that although GeoAI is a trending topic in geography and the applications of deep neural network-based methods are proliferating, the development of spatially-explicit GeoAI models is still at their early phase. We identified three challenges of existing models and advised future research direction towards developing multi-scale explainable spatially-explicit GeoAI. This review paper acquaints beginners with the basics of GeoAI and state-of-the-art and serve as an inspiration to attract more research in exploring the potential of spatially-explicit GeoAI in studying the socio-economic dimension of the city and urban life. |
| first_indexed | 2024-04-14T02:55:51Z |
| format | Article |
| id | doaj.art-7cf4ab15586c4281a457bc3587d8e949 |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-14T02:55:51Z |
| publishDate | 2022-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-7cf4ab15586c4281a457bc3587d8e9492022-12-22T02:16:05ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322022-08-01112102936A review of spatially-explicit GeoAI applications in Urban GeographyPengyuan Liu0Filip Biljecki1Department of Architecture, National University of Singapore, SingaporeDepartment of Architecture, National University of Singapore, Singapore; Department of Real Estate, National University of Singapore, Singapore; Corresponding author at: Department of Architecture, National University of Singapore, Singapore.Urban Geography studies forms, social fabrics, and economic structures of cities from a geographic perspective. Catalysed by the increasingly abundant spatial big data, Urban Geography seeks new models and research paradigms to explain urban phenomena and address urban issues. Recent years have witnessed significant advances in spatially-explicit geospatial artificial intelligence (GeoAI), which integrates spatial studies and AI, primarily focusing on incorporating spatial thinking and concept into deep learning models for urban studies. This paper provides an overview of techniques and applications of spatially-explicit GeoAI in Urban Geography based on 581 papers identified using a systematic review approach. We examined and screened papers in three scopes of Urban Geography (Urban Dynamics, Social Differentiation of Urban Areas, and Social Sensing) and found that although GeoAI is a trending topic in geography and the applications of deep neural network-based methods are proliferating, the development of spatially-explicit GeoAI models is still at their early phase. We identified three challenges of existing models and advised future research direction towards developing multi-scale explainable spatially-explicit GeoAI. This review paper acquaints beginners with the basics of GeoAI and state-of-the-art and serve as an inspiration to attract more research in exploring the potential of spatially-explicit GeoAI in studying the socio-economic dimension of the city and urban life.http://www.sciencedirect.com/science/article/pii/S1569843222001339Urban studiesDeep learningSocio-economicsLocation encoderGraph neural network |
| spellingShingle | Pengyuan Liu Filip Biljecki A review of spatially-explicit GeoAI applications in Urban Geography International Journal of Applied Earth Observations and Geoinformation Urban studies Deep learning Socio-economics Location encoder Graph neural network |
| title | A review of spatially-explicit GeoAI applications in Urban Geography |
| title_full | A review of spatially-explicit GeoAI applications in Urban Geography |
| title_fullStr | A review of spatially-explicit GeoAI applications in Urban Geography |
| title_full_unstemmed | A review of spatially-explicit GeoAI applications in Urban Geography |
| title_short | A review of spatially-explicit GeoAI applications in Urban Geography |
| title_sort | review of spatially explicit geoai applications in urban geography |
| topic | Urban studies Deep learning Socio-economics Location encoder Graph neural network |
| url | http://www.sciencedirect.com/science/article/pii/S1569843222001339 |
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