The Problems

What sort of problems do we treat with the techniques and software of Project GeoMed? One way to answer that question is to list some of the projects our students undertook in the last year:

• Kriging Illinois County Breast Cancer Mortality Rates
• Dengue fever virus antibody in Santa Clara, Peru: Is there spatial autocorrelation?
• Spatial Clustering of Scleroderma in Three Michigan Counties
• Spatial analysis of Anopheles [mosquito] density in western Kenya
• Spatial analysis of Anopheles density in western Kenya (dry season)
• Using Data from a Landsat TM Image in Geospatial Analysis
• Exploratory Space-Time Analysis of Substance Use Among Adolescent Students in Puerto Rico
• Study of Perodontal Disease Symmetry
• Assessing Spatial Autocorrelation of Intraoral Loss of Periodontal Attachment
• Prevalence of antibody to an arenavirus (probably Whitewater Arroyo virus) and spatial and temporal patterns of infection in populations of the white-throated woodrat, Neotoma albigula, in southeastern Colorado
• Characterizing the Transmission Dynamics of Tuberculosis in Detroit, Michigan

Note some of the key words in the titles above:

• spatial autocorrelation,
• spatial clustering,
• exploratory analysis,
• space-time analysis,
• spatial and temporal patterns,
• kriging,
• transmission dynamics.

These words provide a fair coverage of the materials we study. We emphasize techniques which detect and characterize spatial autocorrelation, detect disease patterns, model disease patterns, and aid in the determination of the process behind particular disease patterns.