Measuring the Contribution of Water and Green Space Amenities to Housing Values: An Application and Comparison of Spatially-weighted Hedonic Models

This study estimates the influence of proximity to water bodies and park amenities on residential housing values in Knox County, Tennessee, using the hedonic price approach. Values for proximity to water bodies and parks are first estimated globally with a standard ordinary least square (OLS) model. A locally weighted regression model is then employed to investigate spatial non-stationarity and generate local estimates for individual sources of each amenity. The local model is able to capture the variability in the quality of water bodies and parks across the county, something a conventional hedonic model using OLS cannot do.Land Economics/Use,

Defining a geographically weighted regression model of urban evolution. Application to the city of Volos, Greece

The main objective of this paper is the multivariate analysis of urban space and specifically with the use of data that refer to the level of city block. Part of the analysis has been the comparative assessment of multiple linear regression and geographically weighted regression (GWR) analysis as well as the application of the aforementioned methods in the study of the central district of the Volos metropolitan area. The city of Volos is an urban conglomeration of approximately 110.000 inhabitants, located at the middle-east of Greece and is considered to be in the upper extreme in the cities’ urban hierarchy in Greece. The results provide a response to a question raised by spatial scientists during the last decades: is there a way that regression analysis can reveal spatial variations of results and with respect to scale fluctuation? The use of classical multiple regression analysis provides a single result – equation for the entire area. On the other hand, geographically weighted regression analysis stems from the fact that the above result is inadequate to reflect the different relational levels among selected variables characterizing the entire area. New estimations with the use of GWR declare the existence of various sub-areas – divisions of the initial territory – formulating a set of equations that reveal the spatial variations of variable relations. The results of the application have well proved the dominance of the analysis in the local level towards the analysis in the global level, highlighting the existence of intense spatial differentiations of variables that “interpret” the rate of land values in the city. Moreover, the distinct spatial patterns that emerge throughout the entire area, establish an alternative approach of urban spatial phenomena interpretation and a new explanatory basis for the clarification of obscure relations.

RIDGE AND LASSO PERFORMANCE IN SPATIAL DATA WITH HETEROGENEITY AND MULTICOLLINEARITY

Spatial heterogeneity becomes a separate issue on the analysis of spatial data. GWR (Geographically Weighted Regression) is a statistical technique to explore spatial nonstationarity by form the differrent regression models at different point in observation space. Multicollinearity is a condition that the independent variables in model have linear relationship. It would be a problem for estimation parameters process, because that condition produces unstable model. This problem may be found in GWR models, which allow the linear relationship between independent variables at each location called local multicollinearity. GWRR (Geographically Weighted Ridge Regression) and GWL (Geographically Weighted Lasso) which use the concept of ridge and lasso is shrink the regression coefficient in GWR model. GWRR and GWL techniques are consider to be capable of overcoming local multicollinearity to produce more stable models with lower variance. In this study, GWRR and GWL is used to model Gross Regional Domestic Product (GRDP) in Java using kernel exponential weighted function. The results showed that GWL has better performance to predict GRDP with lower RMSE and higher value than GWRR.Keyword : Spatial Heterogeneity, GWR, Local Multicollinearity, Ridge, Lass

Extreme coefficients in Geographically Weighted Regression and their effects on mapping

This study deals with the issue of extreme coefficients in geographically weighted regression (GWR) and their effects on mapping coefficients using three datasets with different spatial resolutions. We found that although GWR yields extreme coefficients regardless of the resolution of the dataset or types of kernel function, 1) the GWR tends to generate extreme coefficients for less spatially dense datasets, 2) coefficient maps based on polygon data representing aggregated areal units are more sensitive to extreme coefficients, and 3) coefficient maps using bandwidths generated by a fixed calibration procedure are more vulnerable to the extreme coefficients than adaptive calibration.extreme coefficient, fixed and adaptive calibrations, geographically weighted regression, Mapping, Research Methods/ Statistical Methods,

The importance of scale in spatially varying coefficient modeling

While 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 show that capturing the "spatial scale" of each data relationship is crucially important to make SVC modeling more stable, and in doing so, adds flexibility. Here, the analytical properties of six SVC models are summarized in terms of their characterization of scale. Models are examined through a series of Monte Carlo simulation experiments to assess the extent to which spatial scale influences model stability and the accuracy of their SVC estimates. The following models are studied: (i) geographically weighted regression (GWR) with a fixed distance or (ii) an adaptive distance bandwidth (GWRa), (iii) flexible bandwidth GWR (FB-GWR) with fixed distance or (iv) adaptive distance bandwidths (FB-GWRa), (v) eigenvector spatial filtering (ESF), and (vi) random effects ESF (RE-ESF). Results reveal that the SVC models designed to capture scale dependencies in local relationships (FB-GWR, FB-GWRa and RE-ESF) most accurately estimate the simulated SVCs, where RE-ESF is the most computationally efficient. Conversely GWR and ESF, where SVC estimates are naively assumed to operate at the same spatial scale for each relationship, perform poorly. Results also confirm that the adaptive bandwidth GWR models (GWRa and FB-GWRa) are superior to their fixed bandwidth counterparts (GWR and FB-GWR)

A Mixed Geographically Weighted Approach to Decoupling and Rural Development in the EU-15

The CAP reform and the recent EC communication aimed at preparing its Health Check emphasise the need for interventions locally based where agricultural policy integrates with a broader policy for rural areas growth. In this context, the paper investigates the possible different sets policy indicators affecting agricultural productivity at the regional level considering spatial heterogeneity by means of a Mixed Geographically Weighted Regression approach. The analysis is based on a set of policy sensitive indicators selected according to the key component of the CAP reform and referred to a sample of 164 EU-15 regions at NUTS2 level. The methodology adopted, new for the empirical literature on the topic, allows for a more accurate understanding of spatial relationship of the agricultural and socio-economic factors affecting agricultural productivity at the local level providing useful information for policy making.CAP reform, agricultural productivity, spatial analysis, cluster analysis, Agricultural and Food Policy, Community/Rural/Urban Development, Research Methods/ Statistical Methods,