Spatial Epidemiology

Spatial Epidemiology: An Interactive Introduction to Using Advanced GIS Methods, Spatial Analysis, Spatial Statistics, GPS, and Remote Sensing for the Investigation, Prevention, and Control of Health Threats

Course Outline

 

 1. Spatial Epidemiology

 Why Spatial Epidemiology

 GIS and Public Health

 Epidemiology

 The Spatial Component of Epidemiology

2. The Spatial Analysis of Health Data Using ArcView®  GIS

 Location

 Distance

 Overlay

 Spatial Query

 Spatial Selection

 Trend (Change)

 Etc.

 3. Visual and Statistical Analysis of Health Data

 Thematic mapping

 Natural Breaks

 Equal Ranges

 Equal Counts

 Quantile

 Standard Deviation

 4. Visualizing Epidemiologic Data Geographically

 Mapping Rates

 Mapping Statistical Significance

 Mapping Confidence Interval (CI)

 Standard Morbidity/Mortality Ratio and CI

Adjusting for Age, Race/Ethnicity, etc.

5. Potential Problems When Analyzing Spatially Enabled Epidemiologic Data

 Scale Effect

 Modifiable Areal Unit Problem (MAUP)

 Problems with Small Numbers

 Areal Interpolation

 Spatial Autocorrelation

6. Spatial Autocorrelation

 Moran Coefficient

 Geary Ratio

 Binary Connectivity

 Adjacency Matrix

 Joint Count Statistic

 Nearest Neighbor Tests

 Mean Nearest Neighbor

7. Bayesian Methods

 Bayesian Smoothed Rate

 Bayesian Nearest Neighbor

 8. Descriptive Spatial Statistics

 Exploratory Spatial Statistics

 Mean Center and Standard Distance Deviation

 Standard Distance Deviation

 Standard Deviational Ellipse

 Center of Minimum Distance – Median Center

 Hot Spots

 Quadrat Analysis

 K – S Test

 Spatial T-Test

 Spatial ANOVA

9. Spatial, Temporal, and Spatial and Temporal Cluster Detection

 Spatial Scan Statistic

 Poisson

 Bernoulli

 Space Time Permutation

 Focused Test

10. Spatial Modeling

 Trend Surface

 Stochastic Process Predicting (Kriging)

                Disjunctive Kriging

                Categorical Kriging

 Weights of Evidence

11. Multivariate Analysis of Spatial Health Data

 Liner Regressing

 Spatial Linear Regression

12. Multivariate Analysis and Remotely Sensed Data

                Tree Models

13. Spatial Analysis of Syndromic Surveillance Data

Additional Information

The training will consist of a one-week course. The students will be taught to apply GIS, spatial statistics, and spatial analysis for the prevention and control of disease.

Laboratory time is reserved for students to apply the skills, software, and methodology to the health data brought from their respective organizations.

The software that will be provided for this course was selected on the bases of functionality and ease of use. Minimal student learning time will be lost due to problems with transitioning from statistical package to GIS package. All of the software products are fully integrated.

Questions about software:

www.phrl.org/REGS/