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

Abstract

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 R² 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.

Introduction

Landscape fragmentation due to road construction, urbanization, land use/land cover change (LUCC) and other anthropogenic factors leads to more and smaller habitat patches, increased isolation among habitat patches, decreased complexity of patch shape, and higher proportions of edge habitat (Saunders, Mislivets, Chen, & Cleland, 2002). It is considered as one of the most serious threats to the conservation of natural ecosystems as well as the health of agricultural and urbanized ecosystems (Zeng & Wu, 2005). Therefore, the phenomenon of landscape fragmentation has recently attracted more and more attention due to growing applications in biological conservation and ecosystem management (Velázquez et al., 2003, Velázquez et al., 2009). However, most studies on landscape fragmentation focus on the depiction of landscape pattern characteristics (Feranec et al., 2010, Long et al., 2010) and the indication of its impacts on wildlife habitats (Liu, Li, & Li, 2007), biodiversity (Trombulak & Frissell, 2000), ecological processes and functions (Givertz, Thorne, Berry, & Jaeger, 2008), but few of these studies indicate the causes of landscape fragmentation (Munroe, Croissant, & York, 2005), which is highly significant for land management and urban planning.

Landscape fragmentation is usually caused by many different human activities such as urbanization and LUCC, and landscape elements such as roads, railways and rivers, whereas a merely qualitative description of the causes is not convincing. Therefore, the quantitative indication of the relationships between landscape fragmentation and its impact factors should be strengthened.

In order to effectively characterize the process of landscape fragmentation and its impact factors, the measure of landscape fragmentation should be done firstly. Many landscape fragmentation metrics were proposed to quantify spatial segregation (Li, Chang, Peng, & Wang, 2009). One such metric used in this study is the effective mesh size (m_eff), which explicitly incorporates the ecological process of animal dispersal into its definition. Effective mesh size is an expression of the probability that any two randomly chosen locations in the landscape are connected, i.e. not separated by barriers such as transportation infrastructure or urban areas (Jaeger, 2000).

Spatial data exhibit two properties, i.e. spatial autocorrelation and non-stationarity, which makes it difficult to meet the assumptions and requirements of conventional regression techniques such as Ordinary Least Squares (OLS). Traditional statistical methods can only produce “average” and “global” parameter estimates (Bacha, 2003, Batisani and Yarnal, 2009, Geri et al., 2010), and thus they are unable to deal with spatial autocorrelation existing in the variables. In recent years, a relatively simple, but effective, new technique for exploring spatially varying relationships, called Geographically Weighted Regression (GWR), has been developed (Brunsdon et al., 2001, Fotheringham et al., 2001). GWR allows different relationships to exist at different points in the study area and improves the modeling performance by reducing spatial autocorrelations. In addition, these relationships also greatly depend on scale, which is inherent in natural and man-made processes and patterns (Lü & Fu, 2001). Therefore, local rather than global parameters can be estimated, and spatial non-stationarity can be detected at multi-scales by changing bandwidth of GWR.

The objective of this paper, with a case study in Shenzhen City, Guangdong Province, China, was to investigate the applicability of GWR in modeling the relationships between effective mesh size and related factors, and then examine their spatial non-stationarity and the scale-dependence. In this study, effective mesh size calculated using FRAGSTATS software is used as dependent variable, and three impact factors of urban landscape fragmentation are employed as explanatory variables in the regression.