This document provides instructions for two tasks using Geoda software and geospatial data on HIV prevalence in Kenya. Task 1 involves using cluster-level data to identify the best regression model to predict HIV prevalence based on demographic variables. Task 2 involves using district-level HIV and demographic data to identify the best spatial regression model, then mapping the results while considering ecological fallacy issues.

1. INTRODUCTION TO GEODA
Richard W Wamalwa1 - MSc., MBA(Finance), BSc.
1Directorate of Academic Quality Assurance,
JKUAT
RM 610-Environmental, Spatial, GIS, 2011

2. TASK 1
Using the shape file ‘cluster.shp’, and the logit
‘prophiv’ as the response variable, identify a
model that best predicts this variable
The predictions are;
Proportion of population aged between 15 and 19
Proportion of population aged between 15 and 24
Proportion of population that is circumcised
Proportion with primary or higher education
Proportion tested for HIV
Proportion that had sex at age less than 15 years

3. TASK 2
District level HIV prevalence data
Use the 6 variables in slide 2 to explain the HIV
prevalence at cluster level in Kenya based on the
data in the shapefile data1.shp provided. A
spatial polygon shapefile
ke_district_boundaries.shp is also provided.
Identify the best possible regression model (OLS,
spatial error or spatial lag) that best suits this
data.
Produce maps of slide 2 prevalence, and each of
the variable given in Table 1 [Taking ecological
fallacy problem into consideration]