water sources, infrastructure, space and the dynamics of ...

Matthew Biniyam KursahTRITA LWR Master Thesisrecords from the hospital, clinics and health posts. This is because, andsadly of course, the more the data goes up the health administrativeladder, the more space become unimportant variables and omitted fromthe data. I will like to caution readers that when it comes to the spatialanalysis in chapter 3, the totals may not add-up to the sampled size sincesome villages are not on the map, hence their respective healthincidences are not shown as well.Statistical analysisTrends, changes, relationships and variations of variables from the datacollected were done using Statistical Package for Social Sciences (SPSS).SPSS was used to present the various views from the questionnairesadministered to respondents. Simple statistical methods such as tablesand graphs are used to show distribution and trends, while percentagesand ratios are used to depict rates of health indices.Spatial AnalysisGeographic Information System (GIS – ArcGIS) was used to identifyspatial dimensions of water related diseases in the district. The incidencesof the three leading diseases (Malaria, Typhoid and RTI) in the districtand distances to major towns, roads and watercourses, and the type ofland cover and elevation were extracted using ArcGIS tool: Spatial Analyst– Distance – Straight line. The results was extracted into DBF 4 – MSExcel and then to SPSS where the significance was tested usingcorrelation. For the purpose of this study, 0.05 level is consideredsignificant, and any variables with a correlation higher than 0.05 isconsidered insignificant. The dependent variables includedenvironmental diseases, geographically defined, while the independentvariables are the distances to the urban area (the main town in thedistrict), watercourses, roads, the type of land cover and elevation.Figure 3: Data collection process14