Spatial Regression-Based Environmental Analysis in Infectious Disease Informatics
Studying relationships between environmental factors and infectious diseases is an important topic in public health research. The existing studies have been focused on temporal correlations among environmental risks and infectious disease outbreaks. In this paper, we advocate the importance of spatial data analysis in infectious disease-related environmental analysis. Using data from the Beijing CDC, we have conducted spatial regression analysis to study correlation between Measles occurrences and the following environmental factors: population density and proximities to railways, roads, and water systems. We report some preliminary findings concerning significant spatial autocorrelation identified from our analysis.
Abstract This paper describes the philosophy, algorithms, and implementation of a computer simulation model for land use forecasting, and the capability of generating maps of these forecasts. The model is applicable to SMSA's organized on a census tract basis by counties. The forecasts are macro to the census tract level for industrial, residential, commercial and public land uses. Modeling is based on an economic data base of the region, and is extremely flexible for the user. An example set of forecasts and maps is included for illustrative purposes.