Spatially non-stationary relationships between urban residential land price and impact factors in Wuhan city,China |
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| Institution: | 1. Department of Land Resources Management, China University of Geosciences (CUG), Hubei, Wuhan 430074, China;2. Institute of Geographic Sciences and Natural Resources Research(CAS), Beijing 100101, China;3. Department of Geography, Center for Environmental Sciences and Engineering, University of Connecticut, 215 Glenbrook Rd, Unit 4148, Storrs, CT 06269, USA;1. Israel Tax Authority, Nazareth, Israel;2. University of Haifa, Israel;1. Institute of Geography and Spatial Planning, Belval, Luxembourg;2. Luxembourg Institute of Socio-Economic Research, Belval, Luxembourg;1. Department of Public Policy, City University of Hong Kong, Hong Kong, China;2. City University of Hong Kong Shenzhen Research Institute, Shenzhen, China;3. Department of Human Geography and Planning, Utrecht University, The Netherlands, The Netherlands;4. Department of Land Management, Zhejiang University, Hangzhou 310029, China;5. School of Civil Engineering and the Built Environment, Queensland University of Technology, Brisbane, Queensland, Australia;1. Department of Geography and Urban Planning, Golestan University, Gorgan, Iran;2. Department of Geography and Urban Planning, Ferdowsi University of Mashhad, Mashhad, Iran;3. Department of Geography and Urban Planning, University of Tehran, 1417854151, Tehran, Iran |
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| Abstract: | Land price plays an important role in guiding land resource allocation for urban planning and development, particularly in big cities of fast developing countries where infrastructures and populations change frequently. Therefore, detecting spatially implicit information in the spatial pattern of relationships between land price and related impact factors is critical. Geographically weighted regression (GWR) analysis was conducted in this study for the purpose in Wuhan, China, by using a 10-year panel data set of residential land price. Based on twelve factors in three aspects (land attributes, location factors and neighborhood attributes), an evaluation index system of resident land price was established. The spatial distributions of estimated coefficients and pseudo t-values of three major explanatory variables (floor area ratio, distance to nearest center business district (CBD) and distance to nearest lake), obtained from GWR analysis, indicated that their relationships of the impact factors with land price are spatially non-stationary. The positive impact of floor area ratio on land price is more significant in highly developed areas than in less developed areas. Conversely, the negative impact of distance to nearest CBD on land price is larger in highly developed areas than in less developed areas. Moreover, wealthier dwellers may be willing to pay a higher price for a good lake view (especially views of small lakes), but infrastructure barriers (near some large lakes) cause negative effect. The outputs of this study, which provide detailed information on the relationships between land price and impact factors in local areas, are promising for urban planners to scientifically evaluate land price and make area-specific strategies. |
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| Keywords: | Residential land price Impact factors Non-stationary Geographically weighted regression Wuhan China GWR"} {"#name":"keyword" "$":{"id":"kwrd0045"} "$$":[{"#name":"text" "_":"geographically weighted regression OLS"} {"#name":"keyword" "$":{"id":"kwrd0055"} "$$":[{"#name":"text" "_":"ordinary least squares regression coefficient of determination AICc"} {"#name":"keyword" "$":{"id":"kwrd0075"} "$$":[{"#name":"text" "_":"corrected Akaike Information Criteria EBK"} {"#name":"keyword" "$":{"id":"kwrd0085"} "$$":[{"#name":"text" "_":"Empirical Bayesian kriging VIF"} {"#name":"keyword" "$":{"id":"kwrd0095"} "$$":[{"#name":"text" "_":"Variance Inflation Factor CBD"} {"#name":"keyword" "$":{"id":"kwrd0105"} "$$":[{"#name":"text" "_":"central business district D_CBD"} {"#name":"keyword" "$":{"id":"kwrd0115"} "$$":[{"#name":"text" "_":"distance to nearest CBD D_lake"} {"#name":"keyword" "$":{"id":"kwrd0125"} "$$":[{"#name":"text" "_":"distance to nearest lake |
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