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Across case analysis or qualitative comparative analysis (QCA) aims to identify similarities and differences between configurations of explanatory variables.
Our study uniquely goes beyond HIV "hotspot" analysis to map the spatial variation of likely drivers allowing identification of configurations of explanatory variables for particular clusters of HIV hotspot districts for more effective intervention targeting.
Most notably, this study maps the spatial distribution and clustering of the identified factors in order to begin matching configurations of explanatory variables with particular clusters of HIV hotspots at district level for potential spatial targeting of HIV interventions in Malawi.
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The importance of explanatory variables differed considerably among response groups.
Several environmental datasets were used as sources of explanatory variables.
It deals with imprecise measurement of response variable and crisp measurement of explanatory variables.
We used Hellinger transformed OTU abundance data as the response variable and two sets of explanatory variables which included environmental variables (pH, Ele, TOC, AP, GSW, SX, TN, and ST) and spatial variables (geographical co-ordinates for sampling sites), respectively.
Comparative analysis of data was followed by regression analysis of explanatory variables.
Table 2 Descriptive statistics of explanatory variables.
β n = Coefficients of explanatory variables.
We consider different groups of explanatory variables.
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