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1 The GeoMed Project 
In 1998 the University of Michigan and soft 
ware company BioMedware received a two- 
year grant funded by the Small business Tech 
nology Transfer Research (STTR) program of 
the National Cancer Institute. The objectives 
of this grant are 
• to produce software for surveillance and 
analysis of disease clusters, local spatial au 
tocorrelation statistics, and related tech 
niques (GeoMed), and 
• to prepare and evaluate educational mod 
ules for teaching spatial analytic theory and 
methods applied to human health data, and 
to use those modules in the classroom (in a 
course we call “Spatial Epidemiology”). 
We thus describe a work in progress: we have 
offered the course once, and will offer it again 
this winter term. In the meantime, we con 
tinue to develop and (we hope) improve the 
course modules and software. In the follow 
ing we present the preliminary products of 
our venture, and share with you the exciting 
prospects for collaboration and distance edu 
cation offered by our web site. 
2 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 Mor 
tality 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 Sub 
stance Use Among Adolescent Students in 
Puerto Rico 
• Study of Perodontal Disease Symmetry 
• Assessing Spatial Autocorrelation of Intrao 
ral Loss of Periodontal Attachment 
• Prevalence of antibody to an arenavirus 
(probably Whitewater Arroyo virus) and 
spatial and temporal patterns of infec 
tion 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 autocor 
relation, detect disease patterns, model dis 
ease patterns, and aid in the determination 
of the process behind particular disease pat 
terns. 
3 The Solutions 
In outline form our web site solutions include 
• Modules - self-contained units for instruc 
tion or self-learning, including the following 
components: 
• Lecture material - the basics of the mate 
rial discussed in class, in more or less de 
tail. Some of the lecture material is anno 
tated PowerPoint lectures; other lecture 
materials were developed on the web; still 
others are being converted to web mate 
rials by scanning documents. 
• Lab material - computer labs designed to 
test the student on the theoretical mate 
rial presented in lecture.