Spatial Analysis
Cross-source consensus on Spatial Analysis from 2 sources and 11 claims.
2 sources · 11 claims
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Highlighted claims
- Spatial analysis was conducted at the regional administrative level using weighted proportions of healthcare access barriers. — Spatial distribution and multilevel analysis of factors associated with healthcare access barriers among women of reproductive age in Somalia: insights from the 2020 Somalia Demographic and Health Survey
- Healthcare access barriers showed major regional heterogeneity rather than a uniform national pattern. — Spatial distribution and multilevel analysis of factors associated with healthcare access barriers among women of reproductive age in Somalia: insights from the 2020 Somalia Demographic and Health Survey
- The highest prevalence of barriers was found in Togdheer and a south-central cluster including Bay, Bakool, and Hiiraan. — Spatial distribution and multilevel analysis of factors associated with healthcare access barriers among women of reproductive age in Somalia: insights from the 2020 Somalia Demographic and Health Survey
- Getis-Ord Gi* analysis identified significant hotspots in the central interior and north-central regions. — Spatial distribution and multilevel analysis of factors associated with healthcare access barriers among women of reproductive age in Somalia: insights from the 2020 Somalia Demographic and Health Survey
- Coldspots were identified in Bari, Nugaal, and Lower Juba, indicating lower spatial clustering of barriers relative to neighboring areas. — Spatial distribution and multilevel analysis of factors associated with healthcare access barriers among women of reproductive age in Somalia: insights from the 2020 Somalia Demographic and Health Survey
- Spatial analysis included prefecture prevalence, interpolation, Moran’s I, and Kulldorff cluster detection. — Spatial distribution of HIV prevalence and associated factors in Guinea: retrospective cross-sectional study using Demographic and Health Surveys (DHS) data from 2012 and 2018
- Raw HIV prevalence was calculated for each of Guinea’s 33 prefectures using HIV positives divided by people tested. — Spatial distribution of HIV prevalence and associated factors in Guinea: retrospective cross-sectional study using Demographic and Health Surveys (DHS) data from 2012 and 2018
- Continuous prevalence surfaces were generated with Gaussian kernel density estimation and adaptive bandwidths. — Spatial distribution of HIV prevalence and associated factors in Guinea: retrospective cross-sectional study using Demographic and Health Surveys (DHS) data from 2012 and 2018
- Moran’s I assessed spatial autocorrelation using a 10-nearest-neighbour geographic-distance matrix. — Spatial distribution of HIV prevalence and associated factors in Guinea: retrospective cross-sectional study using Demographic and Health Surveys (DHS) data from 2012 and 2018
- HIV prevalence was significantly positively autocorrelated in both survey years. — Spatial distribution of HIV prevalence and associated factors in Guinea: retrospective cross-sectional study using Demographic and Health Surveys (DHS) data from 2012 and 2018