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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2412.02299 |
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| _version_ | 1866915046832996352 |
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| author | Li, Zhenhui Zhang, Hongwei Wu, Kan |
| author_facet | Li, Zhenhui Zhang, Hongwei Wu, Kan |
| contents | Cities play a pivotal role in human development and sustainability, yet studying them presents significant challenges due to the vast scale and complexity of spatial-temporal data. One such challenge is the need to uncover universal urban patterns, such as the urban scaling law, across thousands of cities worldwide. In this study, we propose a novel large-scale geospatial data processing system that enables city analysis on an unprecedented scale. We demonstrate the system's capabilities by revisiting the urban scaling law across 21,280 cities globally, using a range of open-source datasets including road networks, nighttime light intensity, built-up areas, and population statistics. Analyzing the characteristics of 21,280 cities involves querying over half a billion geospatial data points, a task that traditional Geographic Information Systems (GIS) would take several days to process. In contrast, our cloud-based system accelerates the analysis, reducing processing time to just minutes while significantly lowering resource consumption. Our findings reveal that the urban scaling law varies across cities in under-developed, developing, and developed regions, extending the insights gained from previous studies focused on hundreds of cities. This underscores the critical importance of cloud-based big data processing for efficient, large-scale geospatial analysis. As the availability of satellite imagery and other global datasets continues to grow, the potential for scientific discovery expands exponentially. Our approach not only demonstrates how such large-scale tasks can be executed efficiently but also offers a powerful solution for data scientists and researchers working in the fields of city and geospatial science. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02299 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Analysis of Urban Scaling Laws: Leveraging Cloud Computing to Analyze 21,280 Global Cities Li, Zhenhui Zhang, Hongwei Wu, Kan Distributed, Parallel, and Cluster Computing Cities play a pivotal role in human development and sustainability, yet studying them presents significant challenges due to the vast scale and complexity of spatial-temporal data. One such challenge is the need to uncover universal urban patterns, such as the urban scaling law, across thousands of cities worldwide. In this study, we propose a novel large-scale geospatial data processing system that enables city analysis on an unprecedented scale. We demonstrate the system's capabilities by revisiting the urban scaling law across 21,280 cities globally, using a range of open-source datasets including road networks, nighttime light intensity, built-up areas, and population statistics. Analyzing the characteristics of 21,280 cities involves querying over half a billion geospatial data points, a task that traditional Geographic Information Systems (GIS) would take several days to process. In contrast, our cloud-based system accelerates the analysis, reducing processing time to just minutes while significantly lowering resource consumption. Our findings reveal that the urban scaling law varies across cities in under-developed, developing, and developed regions, extending the insights gained from previous studies focused on hundreds of cities. This underscores the critical importance of cloud-based big data processing for efficient, large-scale geospatial analysis. As the availability of satellite imagery and other global datasets continues to grow, the potential for scientific discovery expands exponentially. Our approach not only demonstrates how such large-scale tasks can be executed efficiently but also offers a powerful solution for data scientists and researchers working in the fields of city and geospatial science. |
| title | Scalable Analysis of Urban Scaling Laws: Leveraging Cloud Computing to Analyze 21,280 Global Cities |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2412.02299 |