Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Abstract

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

Assessing Urban Greenness Fragmentation and Analysis of Its Associated Factors: A Case Study in Wuhan Metropolitan Area, China

“…The spatio-temporal changes in urban greenness fragmentation is one of the key considerations for designing urban policies [68]. Understanding the associated factors of the changes in urban greenness fragmentation is of significance for urban management [27]. Numerous landscape indicators can be taken to map urban greenness fragmentation, varying from simple measures such as number of patches, mean size of the patches, simple edge metrics or patch density to more sophisticated ones in measuring specific fragmentation characteristics such as landscape division, adjacency-based metrics, cohesion, splitting index, Shannon's diversity, proximity, distance to a patch class, and connectivity [69].…”

“…Understanding the changes in urban greenness fragmentation and modeling the connection between the fragmentation changes and the associated factors are important for urban planning [27]. Multiple regression analysis provides a simple approach to understand their relationship.…”

Multiple Effects of Topographic Factors on Spatio-temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Tibet

“…GWR model can analyze the data, the characteristic of spatial nonstationary and explore between vegetation change and its driving factors in spatial heterogeneity [6; 58; 59]. The kernel and bandwidth of the GWR model, which are key parameters affecting the accuracy of the model, are now generally determined by Gaussian kernel function, and the optimal bandwidth is generally determined based on the estimated distance of the spatial autocorrelation of the dependent variable [60]. In order to avoid multicollinearity, Variance Inflation Factor (VIF) was used to eliminate highly correlated variables from the model.…”

Multiple Effects of Topographic Factors on Spatio-temporal Variations of Vegetation Patterns in the Three Parallel Rivers Region, Southeast Tibet

“…GWR model can analyze the data, the characteristic of spatial nonstationary and explore between vegetation change and its driving factors in spatial heterogeneity [6,61,62]. The kernel and bandwidth of the GWR model, which are key parameters affecting the accuracy of the model, are now generally determined by Gaussian kernel function, and the optimal bandwidth is generally determined based on the estimated distance of the spatial autocorrelation of the dependent variable [63]. In order to avoid multicollinearity, Variance Inflation Factor (VIF) was used to eliminate highly correlated variables from the model.…”