TITLE:
GIS-Based Local Spatial Statistical Model of Cholera Occurrence: Using Geographically Weighted Regression
AUTHORS:
Felix Ndidi Nkeki, Animam Beecroft Osirike
KEYWORDS:
Local Statistics; Global Statistics; Geographically Weighted Regression; Cholera; Ordinary Least Square
JOURNAL NAME:
Journal of Geographic Information System,
Vol.5 No.6,
December
11,
2013
ABSTRACT:
Global statistical techniques
often assume homogeneity of relationships between dependent variable and predictors
across space. This assumption has been criticized by statistical geographers as
a fundamental weakness that may yield misleading result when it is applied to
dataset with spatial context. To strengthen this weakness, a new method that accounts
for heterogeneity in relationships across geographic space has been presented.
This is one of the family of local spatial statistical techniques referred to
as geographically weighted regression (GWR). The method captures non-stationarity
of relationship in spatial data that the ordinary least square (OLS) regression
fails to account for. Thus, the paper is designed to explore and analyze the
spatial relationships between cholera occurrence and household sources of water
supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset
(spatial) of the study region by state levels and statistical data
(non-spatial) on cholera cases, household sources of water supply and
population data were used in this exploratory analysis. The result shows that
GWR is a significant improvement on the global model. Comparing both models
with the AICc value and the R2 value revealed that for the former, the
value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the
latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model’s fitness
is higher than global model. In addition, the empirical analysis revealed that
cholera occurrence in the study region is significantly associated with
household sources of water supply. This relationship, as detected by GWR,
largely varies across the region.