No abstract

Visceral Leishmaniasis (VL) is endemic to the Attica region of Greece. The geographical distribution of VL cases was analyzed employing methods of spatial analysis in a GIS environment. A geographic database was constructed including data for the disease cases and environmental factors, such as land cover types, stray dog population, and socioeconomic factors. Classic and spatial regression models are presented that suggest the factors contributing most to the incidence of leishmaniasis are green urban areas and the population of stray dogs in the municipalities of Attica region. The results of the spatial regression model were more accurate, thus were used to produce a disease risk map. This map indicates the high-risk municipalities in which surveillance for the control of leishmaniasis is necessary.

In this article, geovisualization is used for the presentation and interpretation of spatial analysis results concerning several house attributes. For that purpose, point data for houses in the region of Attica, Greece are analyzed. The data concern houses for sale and comprise structural characteristics, such as size, age and floor, as well as locational attributes. Geovisualization of house characteristics is performed employing spatial interpolation techniques, kriging techniques, in particular. Spatial autocorrelation in the data is examined through the calculation of the Moran’s I coefficient, while spatial clusters of houses with similar characteristics are identified using the Getis-Ord Gi* local spatial autocorrelation coefficient. Finally, a model is developed in order to predict house prices according to several structural and locational characteristics. In that respect, a classic hedonic pricing model is constructed, which is consequently developed as a geographically weighted regression (GWR) model in a GIS environment. The results of this model indicate that two characteristics, i.e., size and age, account for most of the variability in house prices in the study region. Since GWR is a local model producing different regression parameters for each observation, it is possible to obtain the spatial distribution of the regression parameters, which indicate the significance of the house characteristics for price determination in different locations in the study area.

No abstract

No abstract