| id |
caadria2022_325 |
| authors |
Cui, Qinyu, Zhang, Shuyu and Huang, Yiting |
| year |
2022 |
| title |
Retail Commercial Space Clustering Based on Post-carbon Era Context: A Case Study of Shanghai |
| doi |
https://doi.org/10.52842/conf.caadria.2022.1.515
|
| source |
Jeroen van Ameijde, Nicole Gardner, Kyung Hoon Hyun, Dan Luo, Urvi Sheth (eds.), POST-CARBON - Proceedings of the 27th CAADRIA Conference, Sydney, 9-15 April 2022, pp. 515-524 |
| summary |
In the post-carbon era, it has become a development and research trend on adjusting commercial locations to help achieve resource conservation by using big data. This paper uses multi-source urban data and machine learning to make reasonable evaluations and adjustments to commercial district planning. Many relevant factors are affecting urban commercial agglomeration, but how to select the appropriate ones among the many factors is a problem to be considered and studied, while there may be spatial differences in the strength of each influencing factor on commercial agglomeration. Therefore, this paper takes Shanghai, a city with a high economic and commercial development level in China, as an example and identifies the influencing factors through a literature review. Next, this paper uses the machine learning BORUTA algorithm of features selection to screen the influencing factors. It then uses multi-scale geographically weighted regression model (MGWR) to analyse the spatial heterogeneity of factors affecting retail spatial agglomeration. Finally, based on the background of the changing transportation modes and the unchanged social activities in the post-carbon era, the future spatial planning pattern of retail commercial space is discussed to provide particular suggestions for the future location adjustment of urban commerce. |
| keywords |
Business District Hierarchy, Agglomeration Effect, Spatial Variability, Multi-scale Geographically Weighted Regression Model, Machine Learning, Big Data Analysis, SDG 8, SDG 12 |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (3,627,640 bytes) |
| references |
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Chen, C., Lin, C., Xiu, C. (2013)
 Distribution of centrality of traffic network and its relationship with economic density of tertiary industry in Shenyang
, Progress in Geography, 32(11), 1612–1621
|
|
|
|
|
Fei, M. A., Fr, A., Kfy, B., Yg, A., Cz, A., Dan, G. A. (2019)
The spatial coupling effect between urban public transport and commercial complexes: A network centrality perspective - ScienceDirect
, Sustainable Cities and Society, 50, 101645-101645.Fotheringham, A. S., Yang, W., Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of the American Association of Geographers, 107(6), 1247-1265.Harris, P., Fotheringham, A. S., Crespo, R., Charlton, M. (2018). Inference in multi-scale geographically weighted regression. WILEY, 43(3), 399.Jia, J., Zhang, X. (2021). A human-scale investigation into economic benefits of urban green and blue infrastructure based on big data and machine learning: A case study of Wuhan. Journal of Cleaner Production, 316, 128321.Oshan, T. M., Li, Z., Kang, W., Wolf, L. J., Fotheringham, A. S. (2019). MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale. International Journal of Geo-Information, 8(6), 269.Rifkin, J. (2013). The Third Industrial Revolution. International Study Reference, 6(1), 8-11.Yu H, Fotheringham A S, Li Z, et al. (2019). Inference in multi-scale geographically weighted regression. Geographical Analysis, 52(1), 87-106
|
|
|
|
|
Zhang, S., Wang, L. & Lu, F.. (2019)
Exploring housing rent by mixed geographically weighted regression: a case study in Nanjing
, International Journal of Geo-Information, 8(10), 431
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| last changed |
2022/07/22 07:34 |
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