BACKGROUND Disease maps are used increasingly in the health sciences, with applications ranging from the diagnosis of individual cases to regional and global assessments of public health. However, data on the distributions of emerging infectious diseases are often available from only a limited number of samples. We compared several spatial modelling approaches for predicting the geographic dist...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatia...

Physical activity (PA) promotes healthy life and contributes to sustainable development. In this paper, we rely on the Utah Household Travel Survey data and analyze the determinants of PA in terms of neighborhood land use, accessibility to transportation, and socio-demographic status in Salt Lake County, Utah, United States using four-component walkability indices at various geographic scales. ...

In this paper, a method of analyzing the pattern of error when classification was done from remotely sensed data by using spatial autocorrelation analysis will be introduced. Various sites were picked (water, tree, grass, sand, and urban region) and corresponding reference data were supplied for comparison after classification. Classified images were compared to the reference data to assign whi...

Spatial data mining is the process of discovering, motivating and previously unknown, but potentially helpful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more tricky than extracting the parallel patterns from established numeric and definite data due to the complexity of spatial data types, spatial relationships, and spatial autocorr...

We present new instruments for solving the "best site selection" problem in a sequential setting, and we propose a comparison with classical non-sequential methods based on the Value of Information. The main motivation is selection of drilling sites in an oil and gas field, when there are several correlated prospects. We discuss the properties and the methods required for designing our optimal ...

The relationship between conditions in the local environment and individual-level behavior is of general interest to many fields of research. Unfortunately, researchers often encounter situations where individuals’ locations are reported at the region-level, and modeling individual-level outcomes as a function of region-level data introduces the potential for biased estimates. We show analytica...

This paper investigates single-family residential development for housing market equilibria using microeconomic theory and disaggregate spatial data. Mixed logit models and notions of price competition are used to simulate household location choices for three different household segments, assuming job sites of household members are known. Consistent with bid-rent theory, housing market equilibr...

Poverty mapping in developing countries has become an increasingly important tool in the search for ways to improve living standards in an economically and environmentally sustainable manner. Although the classical econometric methods provide information on the geographic distribution of poverty, they do not take into account the spatial dependence of the data and generally they do not consider...

Both habitat heterogeneity and species' life-history traits play important roles in driving population dynamics, yet there is little scientific consensus around the combined effect of these two factors on populations in complex landscapes. Using a spatially explicit agent-based model, we explored how interactions between habitat spatial structure (defined here as the scale of spatial autocorrel...