Abstract
Land development processes are driven by complex interactions between socio-economic and spatial factors. Acquiring an understanding of such processes and the underlying procedures helps urban and regional planners, environmental scientists, and policy makers to base their decisions on valid and profound information. In this work, remote-sensing-derived land-cover data were used to characterize the patterns of land development from the beginning of 1985 to the beginning of 2015, in the state of West Virginia (WV), US. We applied spatial pattern analysis, ridge regression, and Geographically Weighted Ridge Regression (GWRR) to examine the impact of population, energy resources, existing land developments dynamics, and economic status on land transformation. We showed that in presence of multicollinearity of explanatory variables, how penalizing regression models in both local and global levels lead to a better fit and decreases the model’s variance. We used geographical error analysis of regression models to visualize the difference between the model estimates and actual values. The findings of this research indicate that because of shifting geography of opportunities, the patterns and processes of land development in the studied region are unstable. This leads to fragmented land developments and prevents formation of large communities.
Highlights
Published: 30 March 2021In areas with an abundance of natural resources, landscape changes result in immense costs and irrevocable consequences [1,2]
We applied global and local regression models to investigate the drivers of land development and used data of West Virginia (WV), US (Figure 1) as a case study
The global regression models indicate that in 2005−2015 distance to mines do not demonstrate a significant impact on the land development
Summary
Published: 30 March 2021In areas with an abundance of natural resources, landscape changes result in immense costs and irrevocable consequences [1,2]. Investigation of drivers of landscape change is referred to as the study of the influential processes in the evolutionary trajectory of the landscape [3]. Study of land development and its different aspects is one of the applied methods for investigating the landscape change. Data fusion refers to the process of integrating data from multiple sources so that the constructed dataset is more synthetic, consistent, and informative [16]. The data collected from multiple sources is usually represented as contextually, conceptually, and typographically different. By fusing such data all the spatio-temporal information is unified and included in each geographic feature, i.e., point, line, polygon, or cell. It is important to consider fusing multisource geographic data, including data formatting, geo-referencing, and co-registering of the data [17,19]
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