Geographical and temporal weighted regression (GTWR)

Both space and time are fundamental in human activities as well as in various physical processes. Spatiotemporal analysis and modeling has long been a major concern of geographical information science (GIScience), environmental science, hydrology, epidemiology, and other research areas. Although the importance of incorporating the temporal dimension into spatial analysis and modeling has been well recognized, challenges still exist given the complexity of spatiotemporal models. Of particular interest in this article is the spatiotemporal modeling of local nonstationary processes. Specifically, an extension of geographically weighted regression (GWR), geographical and temporal weighted regression (GTWR), is developed in order to account for local effects in both space and time. An efficient model calibration approach is proposed for this statistical technique. Using a 19-year set of house price data in London from 1980 to 1998, empirical results from the application of GTWR to hedonic house price modeling demonstrate the effectiveness of the proposed method and its superiority to the traditional GWR approach, highlighting the importance of temporally explicit spatial modeling

Geographical General Regression Neural Network (GGRNN) tool for geographically weighted regression analysis

This paper presents a new geographically weighted regression analysis tool, based upon a modified version of a General Regression Neural Network (GRNN). The new Geographic General Regression Neural Network (GGRNN) tool allows for local variations in the regression analysis. The algorithm of the GRNN has been extended to allow for both globally independent variables and local variables, restricted to a given spatial kernel. This mimics the results of Geographically Weighted Regression (GWR) analysis in a given geographical space. The GGRNN tool allows the user to load geographic data from the Shapefile into the underlying neural networks data structure. The spatial kernel can be either a fixed radius or adaptive, by using a given number of neighboring regions. The Holdout Method has been used to compare the fitness of a given model. An application of the tool has been presented using the benchmark working-age deaths in the Tokyo metropolitan area, Japan. Standardized residual maps produced by the GGRNN tool have been compared with those produced by the GWR4 tool for validation. The tool has been developed in the .Net C# programming language using the DotSpatial open source library. The tool is valuable because it allows the user to investigate the influence of spatially non-stationary processes in the regression analysis. The tool can also be used for prediction or interpolation purposes for a range of environmental, socioeconomic and public health applications

Model Regresi Binomial Negatif Terboboti Geografis Untuk Data Kematian Bayi

Negative binomial regression model is used to overcome the overdispersion in Poisson regression model. This model can be used to model therelationship of the infant mortality and the factors incidence. Geographical conditions, socio cultural and economic differ one of location another locationcauses the factors that influence infant mortality is different locally. Geographically Weighted Negative Binomial Regression (GWNBR) is one ofmethods for modeling that count data have spatial heterogeneity and overdispersion

Geography, environmental efficiency and Italian economic growth: a spatially-adapted Environmental Kuznets Curve

The present paper tests the hypothesis that environmental degradation and per capita income follow an inverted-U-shaped relationship (the so-called Environmental Kuznets Curve) at the Italian Nut3 level over the period 1990-2005. We adopt a spatial econometric approach to account for the localised nature of environmental damage. In this spatially-adapted EKC, we explicitly introduced the role of energy intensive sectors to control for local industrial structure. The experiment brought to light the existence of significant heterogeneity at the Italian Nut3 level and highlighted major differences between geographical clusters from the point of view of “ecological efficiency”.Environmental Kuznets curves; Spatial econometrics; global and local pollutants; Geographically Weighted Regression Model.

Does regional development explain international youth mobility? Spatial patterns and global/local determinants of the recent emigration of young Italians

In this essay, we tackle the issue of the international mobility of young Italians in relation to regional disparities. Our intention is to determine if and to what extent a relationship exists between regional development and the international mobility of young people. We analyze the international migration of Italian citizens aged 15-34 who left the country in the period 2010-2017 using several variables that reflect the varying conditions found in different NUTS 3-level regions in terms of economic dynamism, labor-market efficiency, social fragility, educational underdevelopment and spatial peripherality. Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models show that the international mobility of young Italians is very much dependent on local conditions and affected by spatial differences. It is greatest in the most economically dynamic areas of the country, in border regions and in metropolitan areas, with factors relating to spatial proximity and peripherality, imbalances in local labor markets, and paucity of human capital proving particularly significant

BayesX: Analysing Bayesian structured additive regression models

There has been much recent interest in Bayesian inference for generalized additive and related models. The increasing popularity of Bayesian methods for these and other model classes is mainly caused by the introduction of Markov chain Monte Carlo (MCMC) simulation techniques which allow the estimation of very complex and realistic models. This paper describes the capabilities of the public domain software BayesX for estimating complex regression models with structured additive predictor. The program extends the capabilities of existing software for semiparametric regression. Many model classes well known from the literature are special cases of the models supported by BayesX. Examples are Generalized Additive (Mixed) Models, Dynamic Models, Varying Coefficient Models, Geoadditive Models, Geographically Weighted Regression and models for space-time regression. BayesX supports the most common distributions for the response variable. For univariate responses these are Gaussian, Binomial, Poisson, Gamma and negative Binomial. For multicategorical responses, both multinomial logit and probit models for unordered categories of the response as well as cumulative threshold models for ordered categories may be estimated. Moreover, BayesX allows the estimation of complex continuous time survival and hazardrate models