The use of spatial statistics for analyzing health and demographic data in Tanzania

In most of African countries, the demographic Health data is obtained by conducting surveys. The interviewers and field facilitators are always responsible for collecting data at field level. The papers work (questionnaires) or Person Digital Assistant (PDA) are always used as a tools during the data collection stage. After that, the data is submitted to the cleaning department before the analysis performed. Generally, different organizations have different ways of cleaning and organizing their data. In this research, the data used was officially obtained from The Demographic and Health Surveys Program of ICF International in Rockville (USA) and National institute for Medical Research (Tanzania). The GIS software (Arc GIS 10.2 and Map Info Version 11.5) were used to analyze and visualize a spatial pattern relationship and autocorrelation between the data. Among of the statistical methods for analyzing spatial Demographic Health Survey Data (DHSD), is the use of the GIS spatial statistics tools. In this tools, the data can be captured and processed so as to visualize their distribution pattern and its spatial autocorrelation. These tools include, the high or low clustering (Getis-Ord General G), cluster and outlier analysis, spatial autocorrelation (Moran I) and Hot Spots analysis. Before running these tools, the data should be cleaned and projected into an appropriate coordinate system. These data were extracted based on questions froma research questionnaire. MapInfo version 11.5 was used as a tool toextract these data by using query commands and recorded on a Microsoft excel spread sheet. The sheet will be attached at the end of this research for reference purpose. The spatial and cartographic capabilities of the Arc GIS helped in generating different maps which used as the basis of the discussion in the analysis stage. Since the research is based on analysis of Health data, only malaria, anemia and HIV/AIDS diseases were detailed analyzed and other data were also analyzed so as to build additional recommendation based on findings.