A big data approach to mitigating the MAUP in measuring excess commuting
Abstract Excess commuting, defined as the inefficiency resulting from spatial mismatches between residential and employment locations, poses significant challenges for urban planning and transportation systems. This study uses big data from individual vehicle trips collected in Tampa, Florida, to qu...
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| Format: | Article |
| Language: | English |
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Springer
2025-03-01
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| Series: | Computational Urban Science |
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| Online Access: | https://doi.org/10.1007/s43762-025-00173-1 |
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| author | Cehong Luo Yujie Hu Fahui Wang |
| author_facet | Cehong Luo Yujie Hu Fahui Wang |
| author_sort | Cehong Luo |
| collection | DOAJ |
| description | Abstract Excess commuting, defined as the inefficiency resulting from spatial mismatches between residential and employment locations, poses significant challenges for urban planning and transportation systems. This study uses big data from individual vehicle trips collected in Tampa, Florida, to quantify excess commuting more accurately than traditional zonal approaches. Through the application of Linear Programming (LP) and Integer Linear Programming (ILP) models, this research measures minimum and actual commuting patterns across different spatial scales—census tract, block group, and individual trip levels. The findings reveal a clear scale effect associated with the Modifiable Areal Unit Problem (MAUP), as smaller spatial units consistently yield shorter minimum commuting distances and times and the ILP model at the individual trip level yields the least. By directly analyzing actual trips rather than simulated data, this approach provides a more precise and realistic assessment of excess commuting. The results underscore the values of methodological improvements and individual-level data in refining our understanding of excess commuting and supporting more efficient urban planning and policymaking. |
| format | Article |
| id | doaj-art-00578f8d3c1c45ff8bd00e45ef3127cf |
| institution | DOAJ |
| issn | 2730-6852 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Springer |
| record_format | Article |
| series | Computational Urban Science |
| spelling | doaj-art-00578f8d3c1c45ff8bd00e45ef3127cf2025-08-20T02:56:09ZengSpringerComputational Urban Science2730-68522025-03-015111410.1007/s43762-025-00173-1A big data approach to mitigating the MAUP in measuring excess commutingCehong Luo0Yujie Hu1Fahui Wang2Department of Geography and Anthropology, Louisiana State UniversityDepartment of Geography, University of FloridaDepartment of Geography and Anthropology, Louisiana State UniversityAbstract Excess commuting, defined as the inefficiency resulting from spatial mismatches between residential and employment locations, poses significant challenges for urban planning and transportation systems. This study uses big data from individual vehicle trips collected in Tampa, Florida, to quantify excess commuting more accurately than traditional zonal approaches. Through the application of Linear Programming (LP) and Integer Linear Programming (ILP) models, this research measures minimum and actual commuting patterns across different spatial scales—census tract, block group, and individual trip levels. The findings reveal a clear scale effect associated with the Modifiable Areal Unit Problem (MAUP), as smaller spatial units consistently yield shorter minimum commuting distances and times and the ILP model at the individual trip level yields the least. By directly analyzing actual trips rather than simulated data, this approach provides a more precise and realistic assessment of excess commuting. The results underscore the values of methodological improvements and individual-level data in refining our understanding of excess commuting and supporting more efficient urban planning and policymaking.https://doi.org/10.1007/s43762-025-00173-1Big dataModifiable Areal Unit Problem (MAUP)Zonal effectScalar effectExcess commutingLinear Programming (LP) |
| spellingShingle | Cehong Luo Yujie Hu Fahui Wang A big data approach to mitigating the MAUP in measuring excess commuting Computational Urban Science Big data Modifiable Areal Unit Problem (MAUP) Zonal effect Scalar effect Excess commuting Linear Programming (LP) |
| title | A big data approach to mitigating the MAUP in measuring excess commuting |
| title_full | A big data approach to mitigating the MAUP in measuring excess commuting |
| title_fullStr | A big data approach to mitigating the MAUP in measuring excess commuting |
| title_full_unstemmed | A big data approach to mitigating the MAUP in measuring excess commuting |
| title_short | A big data approach to mitigating the MAUP in measuring excess commuting |
| title_sort | big data approach to mitigating the maup in measuring excess commuting |
| topic | Big data Modifiable Areal Unit Problem (MAUP) Zonal effect Scalar effect Excess commuting Linear Programming (LP) |
| url | https://doi.org/10.1007/s43762-025-00173-1 |
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