Population projections at a small area level are sensitive to random departures in temporal trends of population change components. In order to have robust trends for the county level projections in Costa Rica, the team in charge of forecasting decided to determine county clusters based on the past trends. In this paper, we compare three descriptive techniques used to build space-time clusters ...

Geographically Weighted Regression (GWR) is used to analyze the spatial variability of the relationship between the surface and the subsurface (b horizon) soil metal concentration. We used publiclyavailable soil samples from provincial government websites in Canada. The correlation between the log of concentration levels of the two layers are 0.51 for As, 0.40 for Cd, 0.33 for Cr, 0.52 for Co, ...

Multicollinearities among the coefficients obtained from the method of geographically weighted regression have been identified in recent research. This is a serious issue that poses a critical challenge for the utility of the method as a tool to investigate multivariate relationships. The evidence regarding the ability of GWR to retrieve spatially varying processes remains mixed due to partial ...

Airbnb has been increasingly gaining popularity since 2008 due to its low prices and direct interactions with the local community. This paper employed a general linear model (GLM) and a geographically weighted regression (GWR) model to identify the key factors affecting Airbnb listing prices using data sets of 794 samples of Airbnb listings of business units in Metro Nashville, Tennessee. The r...

While recent developments have extended geographically weighted regression (GWR) in many directions, it is usually assumed that the relationships between dependent and independent variables are linear. In practice, however, often case nonlinearly associated. To address this issue, we propose a artificial neural network (GWANN). GWANN combines geographical weighting with networks, which able to ...