Detecting spatially non-stationary and scale-dependent relationships between urban landscape fragmentation and related factors using Geographically Weighted Regression

• Published in: Applied geography (Sevenoaks) Vol. 31; no. 1; pp. 292 - 302
• Main Authors: Gao, Jiangbo, Li, Shuangcheng
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 2011
• Subjects: Autocorrelation; China; Fragmentation; Geographically Weighted Regression; Landscape fragmentation; Landscapes; Mathematical models; Reduction; Regression; Scale-dependence; Shenzhen City; Spatial non-stationarity; Urban planning; Urbanization; China, People's Rep., Guangdong Prov; China, People's Rep., Guangdong Prov., Shenzhen; Geographically Weighted Regression; Spatial non-stationarity; Shenzhen City; China; Landscape fragmentation; Scale-dependence
• ISSN: 0143-6228; 1873-7730
• DOI: 10.1016/j.apgeog.2010.06.003

Landscape fragmentation is usually caused by many different anthropogenic influences and landscape elements. Scientifically revealing the spatial relationships between landscape fragmentation and related factors is highly significant for land management and urban planning. The former studies on statistical relationships between landscape fragmentation and related factors were almost global and single-scaled. In fact, landscape fragmentations and their causal factors are usually location-dependent and scale-dependent. Therefore, we used geographically Weighted Regression (GWR), with a case study in Shenzhen City, Guangdong Province, China, to examine spatially varying and scale-dependent relationships between
effective mesh size, an indicator of landscape fragmentation, and related factors. We employed the distance to main roads as a direct influencing factor, and slope and the distance to district centers as indirect influencing factors, which affect landscape fragmentation through their impacts on land use and urbanization, respectively. The results show that these relationships are spatially non-stationary and scale-dependent, indicated by clear spatial patterns of parameter estimates obtained from GWR models, and the curves with a characteristic scale of 12 km for three explanatory variables, respectively. Moreover, GWR models have better model performance than OLS models with the same independent variable, as is indicated by lower AICc values, higher Adjusted
R
2 values from GWR and the reduction of the spatial autocorrelation of residuals. GWR models can reveal detailed site information on the different roles of related factors in different parts of the study area. Therefore, this finding can provide a scientific basis for policy-making to mitigate the negative effects of landscape fragmentation.