GIS and correlation analysis of geo-environmental variables influencing malaria prevalence in the Saboba district of Northern Ghana
Analysing the significance of geo-environmental variables influencing malaria incidence will help decision makers design area-specific interventions for tackling the menace, particularly in high-risk areas. This study applied geocoding and raster extraction functionalities in GIS (ArcMap) and Pearson correlation in SPSS to identify the relationship between five geo-environmental variables and malaria incidence. It first geocoded malaria incidence data and extracted the corresponding values for five geo-environmental variables in ArcMap 10.1. The five geo-environmental variables are: distance to marshy areas, distance to watercourses (rivers and streams), soil water retention capacity, elevation and population. Pearson correlation was then used to find the relationship between the variables and malaria incidence. The study also applied spline interpolation technique to map malaria prevalence in the district using standardised malaria incidence. The result indicates that distance to marshy areas is inversely and significantly (at 1% level) related to malaria incidence. This means that malaria incidence decreases as distance to marshy areas increases. The distance to watercourses and elevation are also inversely related to malaria incidence in the study area. This means that as distance to watercourses increases and elevation rises, malaria incidence decreases. However, these relationships are not statistically significant at any of the conventional levels of significance (p<0.01 and p<0.05). The result also indicates that water retention capacity of different soils and population are positively related to malaria incidence. This means that malaria incidence rises with increases in the two variables, but the relationships are not statistically significant at any of the conventional levels. The study concluded that the null hypothesis (H0) that there is no significant relationship between distance to marshy areas and malaria incidence may be rejected at 1% (P<0.01) level of significance. However, there is not enough evidence to reject the null hypothesis (H0) that there is no significant relationship between distance to watercourses, different soil retention capacity, elevation and population, on the one hand, and malaria incidence on the other. It is, therefore, recommended that much broader settlement planning policies be adopted to curb building in those areas that are malaria prone.