Title
Analyzing The Spatial Autocorrelation Of Regional Urban Datum Land Price
Abstract
This study focuses on spatial autocorrelation and the spatial distribution of urban land prices from a regional perspective. Taking Hubei province, China, as a case study area, spatial autocorrelation degree, spatial autocorrelation pattern, and the mechanism of its formation were discussed. The study employs Moran's I, local Moran's I, and Moran's I correlogram to analyze spatial autocorrelation degree and its change along with contiguity order. Some local clustering hot spots are found. This paper uses semi-variance statistic for land price based on route distance to find the spatial autocorrelation scale. We also adopt spatial clustering based on a kind of composite distance to probe into the clustering characteristic of land prices. By Moran's I and Moran's I correlogram, we find that datum price of the cities in Hubei province has faint spatial autocorrelation degree at the first and the second-order contiguity. Spatial variance hints that the scale of the autocorrelation is about 200 km in route distance. Spatial clustering result indicates that the spatial distribution of city land price is a kind of hierarchy structure similar to administrative regions. From principal factors analysis and stepwise linear regression, we find that the value added of city secondary and tertiary industry and the urban population are two of the most influential factors to urban datum land price. The value added of city secondary and tertiary industry has higher spatial autocorrelation than urban datum land price and has a bigger autocorrelation scale. But urban population has little spatial autocorrelation. It can be inferred that the spatial autocorrelation of urban land price is mainly caused by economic spatial autocorrelation. But its spatial autocorrelation degree is lower than economic factors because urban datum land price is also influenced by other special local factors, such as population, city infrastructure, land supply, etc.
Year
DOI
Venue
2012
10.1080/10095020.2012.714103
GEO-SPATIAL INFORMATION SCIENCE
Keywords
Field
DocType
spatial autocorrelation, spatial clustering, spatial variation, urban datum land price
Spatial analysis,Econometrics,Contiguity,Statistic,Spatial variability,Cluster analysis,Statistics,Correlogram,Mathematics,Spatial distribution,Autocorrelation
Journal
Volume
Issue
ISSN
15
4
1009-5020
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Limin Jiao1236.12
Yaolin Liu29725.42