Academic Journal of Humanities & Social Sciences, 2024, 7(3); doi: 10.25236/AJHSS.2024.070301.
Chunyu Liu, Yang Wang, Yule Yan
Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, China
The data used in this study includes 100,305 Point of Interest (POI) collected in 2022. The spatial pattern of commercial vitality in the main urban area of Kunming City and its differentiation are analyzed by average nearest neighbor distance analysis and kernel density estimation. The results show that: (1) The overall spatial distribution is unbalanced, indicating that there may be discrepancies in the development of commercial activities throughout the city. (2) The spatial distribution of commercial vitality in the main urban area of Kunming City is not balanced, and the spatial distribution of commercial vitality in the core area of the urban area differs greatly from that outside the first ring road. (3) The hotspots of commercial vitality are mainly located in the first ring of the core area, as well as the southeast area of the new urban district and the second and third rings.
POI data, Commercial vitality, Kernel density estimation, Average nearest neighbor distance analysis, Kunming
Chunyu Liu, Yang Wang, Yule Yan. Spatial Differences of Commercial Vitality in Main Urban Area of Kunming City, China. Academic Journal of Humanities & Social Sciences (2024) Vol. 7, Issue 3: 1-7. https://doi.org/10.25236/AJHSS.2024.070301.
[1] Jacobs J. The Death and Life of Great American Cities. Vintage Books, 2012.
[2] Yu D, Wang X, Liang P, Sun X. Spatio-temporal convolutional residual network for regional commercial vitality prediction. Multimed Tools Appl, 2022;81(19):27923-27948.
[3] Gong Y, Ji X, Zhang Y, Cheng S. Spatial Vitality Evaluation and Coupling Regulation Mechanism of a Complex Ecosystem in Lixiahe Plain Based on Multi-Source Data. Sustainability, 2023;15(3):2141.
[4] Colaço R, de Abreu e Silva J. Commercial Classification and Location Modelling: Integrating Different Perspectives on Commercial Location and Structure. Land, 2021; 10(6):567.
[5] Clark, P.J, Evans, F.C. Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations. Ecology, 1954;35(4): 445-453.
[6] Baranyai D, Sipos T. Black-Spot Analysis in Hungary Based on Kernel Density Estimation. Sustainability, 2022 (14):8335.
[7] Gatrell, A.C. Autocorrelation in spaces. Environ.Plan. A: Econ. Space, 1979(11): 507-516.
[8] Anselin, L. Local indicators of spatial association-LISA. Geogr. Anal., 1995(27): 93-115.