A big data approach to modelling urban population density functions: from monocentricity to polycentricity
Urban studies have a long tradition of examining the regularity of urban structure by modelling urban population density functions and probing the theoretical or behavioural foundation behind it. Previous studies commonly used census data in areal units such as census tracts or census block groups,...
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Taylor & Francis Group
2025-04-01
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| Series: | Annals of GIS |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19475683.2025.2472769 |
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| author | Cehong Luo Yujie Hu Fahui Wang |
| author_facet | Cehong Luo Yujie Hu Fahui Wang |
| author_sort | Cehong Luo |
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| description | Urban studies have a long tradition of examining the regularity of urban structure by modelling urban population density functions and probing the theoretical or behavioural foundation behind it. Previous studies commonly used census data in areal units such as census tracts or census block groups, which varied a great deal in area size and shape and led to the zonal and scale effects, commonly referred to as the modifiable areal unit problem (MAUP). This study uses big data of individual vehicle trips in Tampa, Florida, to define the precise population and employment distribution locations, and then aggregates them with uniform areal units such as squares, triangles, and hexagons to examine and mitigate the scale and zonal effects. Both monocentric and polycentric models are employed in the analysis of urban population density functions. The results suggest that the exponential density function remains the best fitting monocentric function in most areal units including census units and designed uniform units. The polycentric model reveals two centres (downtown and University of South Florida) exerting influences on the areawide population density pattern. The zonal effect is not significant in the designed uniform units, but the scale effect remains evident in all areal units. |
| format | Article |
| id | doaj-art-2b5090a6fa4e48239fe1388fabfb6717 |
| institution | OA Journals |
| issn | 1947-5683 1947-5691 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Taylor & Francis Group |
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| series | Annals of GIS |
| spelling | doaj-art-2b5090a6fa4e48239fe1388fabfb67172025-08-20T02:30:00ZengTaylor & Francis GroupAnnals of GIS1947-56831947-56912025-04-0131227328610.1080/19475683.2025.2472769A big data approach to modelling urban population density functions: from monocentricity to polycentricityCehong Luo0Yujie Hu1Fahui Wang2Department of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, USADepartment of Geography, University of Florida, Gainesville, FL, USADepartment of Geography and Anthropology, Louisiana State University, Baton Rouge, LA, USAUrban studies have a long tradition of examining the regularity of urban structure by modelling urban population density functions and probing the theoretical or behavioural foundation behind it. Previous studies commonly used census data in areal units such as census tracts or census block groups, which varied a great deal in area size and shape and led to the zonal and scale effects, commonly referred to as the modifiable areal unit problem (MAUP). This study uses big data of individual vehicle trips in Tampa, Florida, to define the precise population and employment distribution locations, and then aggregates them with uniform areal units such as squares, triangles, and hexagons to examine and mitigate the scale and zonal effects. Both monocentric and polycentric models are employed in the analysis of urban population density functions. The results suggest that the exponential density function remains the best fitting monocentric function in most areal units including census units and designed uniform units. The polycentric model reveals two centres (downtown and University of South Florida) exerting influences on the areawide population density pattern. The zonal effect is not significant in the designed uniform units, but the scale effect remains evident in all areal units.https://www.tandfonline.com/doi/10.1080/19475683.2025.2472769Urban population density functionbig datamonocentric modelpolycentric modelmodifiable areal unit problem (MAUP) |
| spellingShingle | Cehong Luo Yujie Hu Fahui Wang A big data approach to modelling urban population density functions: from monocentricity to polycentricity Annals of GIS Urban population density function big data monocentric model polycentric model modifiable areal unit problem (MAUP) |
| title | A big data approach to modelling urban population density functions: from monocentricity to polycentricity |
| title_full | A big data approach to modelling urban population density functions: from monocentricity to polycentricity |
| title_fullStr | A big data approach to modelling urban population density functions: from monocentricity to polycentricity |
| title_full_unstemmed | A big data approach to modelling urban population density functions: from monocentricity to polycentricity |
| title_short | A big data approach to modelling urban population density functions: from monocentricity to polycentricity |
| title_sort | big data approach to modelling urban population density functions from monocentricity to polycentricity |
| topic | Urban population density function big data monocentric model polycentric model modifiable areal unit problem (MAUP) |
| url | https://www.tandfonline.com/doi/10.1080/19475683.2025.2472769 |
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