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Open defecation (OD) poses a serious public health risk by exposing communities to diseases and compromising the well-being of people. One potential approach to mitigating its impact is the application of spatial planning techniques. However, spatial data paucity is one of the major problems in addressing open defecation issues in Nigeria. This study employed spatial analysis to identify areas particularly susceptible to OD using Geographical Information System (GIS) including Merginmap applications on Android and QGIS software, which were used with field observations to georeferenced residential areas, public toilets, and instances of open defecation. Proximity analysis and local indicators of spatial autocorrelation, based on Moran’s I, were employed to assess vulnerability. Buffer analysis was conducted to identify residential areas within 100 m of an OD site, and the findings indicated residential areas at high risk of experiencing negative impacts from OD. The LISA analysis revealed a high-to-high clustering pattern (p < 0.05) in the southern region of the study area, which aligns with the results of the buffer analysis and further suggests consistent spatial patterning. Furthermore, OD was more prevalent near water bodies and inner residential areas, increasing the risk of water and air pollution. This pollution could facilitate the spread of harmful bacteria, such as E. coli and Salmonella, from OD sites to nearby households, increasing the risk of disease. This study stresses the potential of GIS in evaluating areas vulnerable to OD and highlights the need to expand awareness campaigns regarding proper hygiene and sanitation practices in rural communities.
Open defecation (OD) poses a serious public health risk by exposing communities to diseases and compromising the well-being of people. One potential approach to mitigating its impact is the application of spatial planning techniques. However, spatial data paucity is one of the major problems in addressing open defecation issues in Nigeria. This study employed spatial analysis to identify areas particularly susceptible to OD using Geographical Information System (GIS) including Merginmap applications on Android and QGIS software, which were used with field observations to georeferenced residential areas, public toilets, and instances of open defecation. Proximity analysis and local indicators of spatial autocorrelation, based on Moran’s I, were employed to assess vulnerability. Buffer analysis was conducted to identify residential areas within 100 m of an OD site, and the findings indicated residential areas at high risk of experiencing negative impacts from OD. The LISA analysis revealed a high-to-high clustering pattern (p < 0.05) in the southern region of the study area, which aligns with the results of the buffer analysis and further suggests consistent spatial patterning. Furthermore, OD was more prevalent near water bodies and inner residential areas, increasing the risk of water and air pollution. This pollution could facilitate the spread of harmful bacteria, such as E. coli and Salmonella, from OD sites to nearby households, increasing the risk of disease. This study stresses the potential of GIS in evaluating areas vulnerable to OD and highlights the need to expand awareness campaigns regarding proper hygiene and sanitation practices in rural communities.
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