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A route map for successful applications of geographically weighted regression
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of
social and environmental data. It allows spatial heterogeneities in processes and …
social and environmental data. It allows spatial heterogeneities in processes and …
Challenges in data-driven geospatial modeling for environmental research and practice
Abstract Machine learning-based geospatial applications offer unique opportunities for
environmental monitoring due to domains and scales adaptability and computational …
environmental monitoring due to domains and scales adaptability and computational …
Spatial heterogeneity analysis and driving forces exploring of built-up land development intensity in Chinese prefecture-level cities and implications for future Urban …
P Zhang, D Yang, M Qin, W **g - Land use policy, 2020 - Elsevier
Economic growth is inseparable from the input of land elements, and built-up land is the
most important category of land elements. Its spatial distribution and influencing factors play …
most important category of land elements. Its spatial distribution and influencing factors play …
Geographically weighted regression with parameter-specific distance metrics
Geographically weighted regression (GWR) is an important local technique to model
spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is …
spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) is …
The GWR route map: a guide to the informed application of Geographically Weighted Regression
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of
social and environmental data. It allows spatial heterogeneities in processes and …
social and environmental data. It allows spatial heterogeneities in processes and …
The importance of scale in spatially varying coefficient modeling
Although spatially varying coefficient (SVC) models have attracted considerable attention in
applied science, they have been criticized as being unstable. The objective of this study is to …
applied science, they have been criticized as being unstable. The objective of this study is to …
Geographically and temporally weighted co-location quotient: an analysis of spatiotemporal crime patterns in greater Manchester
Incident data, a form of big data frequently used in urban studies, are characterized by point
features with high spatial and temporal resolution and categorical values. In contrast to …
features with high spatial and temporal resolution and categorical values. In contrast to …
Akaike information criterion in choosing the optimal k-nearest neighbours of the spatial weight matrix
We use the Akaike information criterion (AIC) to assess the quality of non-nested spatial
econometric models with a different number of nearest neighbours (knn) included in the …
econometric models with a different number of nearest neighbours (knn) included in the …
Improvements to the calibration of a geographically weighted regression with parameter-specific distance metrics and bandwidths
In standard geographically weighted regression (GWR), the spatially-varying relationships
between the dependent and each independent variable are explored under a constant and …
between the dependent and each independent variable are explored under a constant and …
Exploring spatially varying and scale-dependent relationships between soil contamination and landscape patterns using geographically weighted regression
Landscape pattern is an important determinant of soil contamination at multiple scales, and
a proper understanding of their relationship is essential for alleviating soil contamination …
a proper understanding of their relationship is essential for alleviating soil contamination …