| Citation: |
LU Binbin, TIAN Xiaoxi, QIN Sixian, SHI Yilin, LI Jiansong. Urban Land Carrying Capacity Evaluation of Wuhan City with Geographically Weighted Techniques[J]. Geomatics and Information Science of Wuhan University, 2025, 50(3): 430-438. DOI: 10.13203/j.whugis20220778
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With the rapid urbanization in China, urban land resource has become a key part in urban development, and attracted more and more attentions. However, global methods were traditionally adopted for its carrying capacity evaluation and analysis, which largely ignored the spatial heterogeneities or non-stationarities in the data relationships.
In this paper, we proposed a technical framework to evaluate and analyze the urban land carrying capacity with geographically weighted (GW) techniques from local perspective. We exemplified this framework with a case study in Wuhan City. In details, we adopted GW summary statistics, GW principal component analysis and GW regression to conduct the evaluation and analysis.
The comprehensive pressure of land carrying capacity shows a decaying trend from the city center to the outside areas, to which population density and floor area ratio contribute the most. Moreover, different factors present spatially varying influences on the comprehensive pressure of land carrying capacity.
The proposed framework with GW techniques works well on evaluating and analyzing urban land carrying capacity from local perspective, and its evolution is still ongoing with more local techniques involved, which makes it be applicable in more cases and scenarios.
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| [1] | LU Binbin, TIAN Xiaoxi, QIN Sixian, SHI Yilin, LI Jiansong. Urban Land Carrying Capacity Evaluation of Wuhan City with Geographically Weighted Techniques[J]. Geomatics and Information Science of Wuhan University, 2025, 50(3): 430-438. DOI: 10.13203/j.whugis20220778 |
| [2] | LU Binbin, GE Yong, QIN Kun, ZHENG Jianghua. A Review on Geographically Weighted Regression[J]. Geomatics and Information Science of Wuhan University, 2020, 45(9): 1356-1366. DOI: 10.13203/j.whugis20190346 |
| [3] | LUO Minghai, JIANG Zilong, CHENG Qi, QIN Sixian, YANG Nana, ZHANG Xinyue. Carrying Capacity Evaluation of Land Resources in Wuhan Based on the Geographical Condition Monitoring[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12): 2317-2324. DOI: 10.13203/j.whugis20180389 |
| [4] | NIE Lei, SHU Hong, LIU Yan. Interpolation of Monthly Average Temperature by Using (Mixed) Geographically Weighted Regression Kriging in the Complex Terrain Region[J]. Geomatics and Information Science of Wuhan University, 2018, 43(10): 1553-1559. DOI: 10.13203/j.whugis20160433 |
| [5] | LIU Xiaojing, GUI Zhipeng, CAO Jun, LI Rui, WU Huayi. Spatiotemporal-Aware Hybrid Prediction Model for Response Time of Web Map Services by Integrating GWR and STARMA[J]. Geomatics and Information Science of Wuhan University, 2018, 43(6): 951-958. DOI: 10.13203/j.whugis20160370 |
| [6] | LU Binbin, YANG Huan, SUN Huabo, YU Qingde. Algorithm of Approximating Road Network Distance Via the Minkowski Distance[J]. Geomatics and Information Science of Wuhan University, 2017, 42(10): 1373-1380. DOI: 10.13203/j.whugis20160225 |
| [7] | WANG Xu, LIN Zheng, ZHANG Zhi, LI Dan. Modelling the Spatial Distribution of Lake Surface Water Temperature of the Thaw Lakes in Arctic Coastal Plain Using Geographically Weighted Regression Model[J]. Geomatics and Information Science of Wuhan University, 2016, 41(7): 918-924. DOI: 10.13203/j.whugis20140411 |
| [8] | MA Ronghua, HE Zengyou, . Mining Complete and Correct Frequent Neighboring Class Sets from Spatial Databases[J]. Geomatics and Information Science of Wuhan University, 2007, 32(2): 115-119. |
| [9] | YU Leiyi. Study on the Architecture of GIS Grid[J]. Geomatics and Information Science of Wuhan University, 2004, 29(2): 153-156. |
| [10] | LIU Yanxiong, CHEN Yongqi, LIU Jingnan. Determination of Weighted Mean Tropospheric Temperature Using Ground Meteorological Measurement[J]. Geomatics and Information Science of Wuhan University, 2000, 25(5): 400-404. |
Supported by: Beijing Renhe Information Technology Co., Ltd. 
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