This paper presents methodology using dasymetric mapping from remotely sensed imagery, geographic information system (GIS), spatial analysis and spatial statistics to explore relationship between asthma and air pollution in the Pensacola metropolitan region of Florida. Health outcome indicators thought to be sensitive to increased exposure of airborne environmental hazards are mortality and morbidity rates for total population asthma patients. Environmental data for the time around the year 1999 include point source pollution sites and emissions, traffic count with emission estimates, and a Landsat ETM+ image. Standardized mortality/morbility ratios (SMRs) were used as dependent variables for the analysis. A centroid map was created from the zip code map with each centroid assigned the corresponding SMR values. Then spatial interpolation using the Kriging method was used to generate continuous SMR surfaces. An emission or point count based kernel density raster map was created from each of the air pollution maps. A raster layer 'greenness' was extracted using tasseled cap transformation from the Landsat ETM+ image. The dasymetric mapping technique was employed to limit the analysis and modeling to the area where human activities occur. The ETM+ image was classified into a thematic land use/cover map and the developed area extracted. A road network was combined with the developed area to generate a buffer (buffer distance=1.5 km). A random sample with enough number of points was generated across the study area and 505 points were found within the developed area and the buffer. Data values at these sample points were extracted and used for statistical modeling. Two spatial autoregressive models (spatial error and spatial lag) were fitted. Both models show relationship between the asthmas outcome indicators and air pollution (positive) and 'greenness' (negative).