The GeoMed/Epi Project:

Here are some of the labs we've created to help you to work with spatial data.

1. Computing tools

   Introduction to the labs, the web component of the class, and computing tools we'll use during the course.

   • equipment in the labs
   • web.courses
     • threaded discussions
     • interaction with consultants, etc.
     • with profs, helpers
     • feedback
   • web stuff: browsers, html, http, etc.
   • ftp, telnet
   • gzip, zip, etc.
   • UNIX vs Windows
   • Finding spatial data on the web.
   • web software

2. Introduction to ArcView

   At the end of this lab you should have

   • tried out a simple web-based geocoding site.
   • loaded some of the proprietary ESRI data into ArcView
   • created and saved an ArcView "project"
   • loaded in some Cancer and nuclear site Data for Illinois
   • loaded in the data you downloaded from the census site last week
   • saved the assembled project to your course home page area
   • manipulated the data: zooming, selecting, map queries, overlays, buffering,....
   • printed a sensible plot of the cancer data, with your interpretations of the visual pattern of the cancer you've chosen.

3. Scientific Visualization/ESDA

   In this lab we will

   • create a BW plot from within ArcView (from which you could calculate BW statistics, if you were really industrious or had lots of time on your hands - the problem is writing the contiguity matrix, which describes which counties share boundaries. Vanilla ArcView doesn't do this.)
   • run XGobi, and explore some data on the United States (States) using the techniques of brushing, spinning, symbol size and color choice
   • use XGobi to explore the Illinois cancer data set.
   • if time allows, and you are interested, use S-PLUS on your Illinois data, generating some perspective plots, contour plots, and image plots.

4. Digitizing Maps

   We'll have a look at how to turn your map resources into electronic maps. For this, we may need additional equipment (i.e. a scanner) and resources which we hope you will find useful. We're be using old-fashioned mouse digitization within ArcView.

   Objectives:

   • be able to digitize point and polygon themes using the mouse and ArcView
   When you're done, you'll have a point theme and a polygon theme, either of which you could use to investigate spatial autocorrelation.

5. Spatial Statistics

   In Geoff's lecture, we saw a selection of techniques applied to some example data. We'll be trying to replicate his success today, as we use the same software he did - Stat! and SaTScan - to answer question concerning the example data that comes with the software.

   Objectives:

   1. become familiar with Stat! and SatScan;
   2. learn how to use Stat's Statistical Advisor;
   3. analyze a spatial cluster using Stat!;
   4. analyze a spatial cluster using SatScan;
   5. consider how to use these programs in the context of your projects; and
   6. give feedback about the software.

6. Designer Statistics Using Gamma

   Funded by NCI, Gamma is software for statistical inference from spatial data such as that in Geographic Information Systems. It uses location models and spatial Monte Carlo methods to fully specify the spatial sampling space. This provides three capabilities: first, it greatly increases our ability to make correct statistical inferences; second, it accounts for positional uncertainty in spatial data; and third, it allows confidentiality (e.g. place of residence) to be preserved without compromising health analysis (e.g. cluster investigations).

   Gamma software is currently under development at BioMedware (as of 2/99), and will soon be released commercially.

   Objectives:
   to work through exercises demonstrating several different proximity and distance metrics, as discussed in class. In the final exercise you will use Gamma to analyze a putative clustering of breast cancer cases.

7. Compartmental Modeling

   Compartmental models are at the process end of the "pattern and process" dichotomy. We may use these models to

   • model known disease processes;
   • serve as a playground, where we can experiment with a variety of effects in attempting to capture the behaviors of a system;
   • simulate a process, creating data to study.
   As Dr. Jacquez says in his lecture material, spatial processes can be modelled in at least two different ways:
   • one way is to create a grid of compartments (representing space), with each cell in the grid connected to its neighbors to create diffusion;
   • another way is to represent each "county" as a compartment, and allow for flows between all compartments (providing for dispersal behavior, as he mentioned).
   In this lab, you'll be doing some exercises suggested by Dr. John Jacquez. We will be using STELLA to do the exercises. STELLA is a piece of software designed for creating and solving (approximately) the types of compartmental models discussed in the module.

   Objectives:
   You will use compartmental models and the software STELLA to

   • represent an SIS (Susceptible Infected Susceptible) model of an epidemic in a closed system.
   • represent an SIR (Susceptible Infected Resistant) model of an epidemic in an open system.