Quantitating Non-Zero Autocorrelation to Determine Moran's I Coefficients for Mapping Clustering Tendencies of Fast Food Restaurants in Lower SES Neighborhoods in Hillsborough County, Florida

Stanley, Nathanael B.; Alao, Samuel; Jacob, Benjamin

doi:10.4172/2469-4134.1000179

Cited by 2 publications

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References 18 publications

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“…The estimate of Local Moran's Index (LMI) can also contribute to further analysis of urban building structures in addition to the GMI and Gini coefficient values (Stanley et al 2016;Yeh and Li 2006). The spatial scenario of building structure form can be better illustrated on a thematic map of structure classes (Fig.…”

Section: Spatial Pattern Of Local Moran's I Indexmentioning

confidence: 99%

Geospatial Analysis of Building Structures in Megacity Dhaka: the Use of Spatial Statistics for Promoting Data-driven Decision-making

et al. 2019

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Information on spatial building structures is limited, but it can support efficient planning and management in the context of fastgrowing big cities in many developing countries. In this paper, we present a spatial analysis approach that includes an estimate of building intensity in the megacity of Dhaka and a spatial analysis using spatial statistics. The entire city was divided into regular grids and the building intensity (both horizontal and vertical) was extracted using vector type building information; the spatial statistics were calculated on the basis of Moran's I and Gini indices. The variability of the estimated spatial statistics is interpreted according to co-relationship or clustering patterns with the location of the central business district (CBD) area as well as the public bus transit infrastructure (routes and stops). The results show that the residential building structure intensity is prominent and the concentrations are distributed all over the city. The mixed-use type building structures show highest clustering, with fewer outliers in the old part of the city. The vertical-use intensities indicate extreme clustering within highly intensified building activity in the nearby CBD area. The higher presence of low-low clustering of horizontal intensity indicated low development at the suburban area. However, the strongly clustered grid cells within residential sector as well as horizontal development classes are less accessible by bus transit within a defined catchment area, whereas the service sector and vertical development type seem to be more accessible. This type of geographic approach, visualization, and statistical information can help in making data-driven planning decisions with the advantage of monitoring urban development; however, the modeling sensitivity and uncertainties in the building data set remain open for further investigation.

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Section: Spatial Pattern Of Local Moran's I Indexmentioning

confidence: 99%

Geospatial Analysis of Building Structures in Megacity Dhaka: the Use of Spatial Statistics for Promoting Data-driven Decision-making

et al. 2019

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Predictive and spatial analysis for estimating the impact of sociodemographic factors on contraceptive use among women living with HIV/AIDS (WLWHA) in Kenya: Implications for policies and practice

et al. 2019

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BackgroundDespite the availability and knowledge of various contraceptive methods, consistent utilisation in women living with HIV/AIDS (WLWHA) within the reproductive age group remains below the Sustainable Development Goals (SDGs) and Family Planning 2020 goals. This study examines the association between sociodemographic factors and contraceptive use including the effect of clustering tendencies of these factors on contraceptive usage among WLWHA in Kenya.MethodsWeighted multivariate logistic regression models were conducted to determine the association of sociodemographic factors on contraception use among WLWHA using the 2008–2009 Kenya Demographic Health Survey. Spatial autocorrelation techniques were used to explore clustering tendencies of these factors on contraception utilisation. Our study population included 304 HIV positive women, aged 15–49 years.ResultsAmong 304 HIV-positive women in our study population, 92 (30.3%) reported using one method of contraception. Contraceptive use was significantly associated with wealth and education after adjustment for other sociodemographic variables. Women classified as having low and middle wealth index were less likely to use contraceptives (OR=0.17, 95% CI 0.07 to 0.43; OR=0.33, 95% CI 0.11 to 0.98, respectively) compared with women classified as having high wealth index. Similarly, women with primary education only were less likely to use contraceptives compared with women with secondary or higher education (OR=0.42, 95% CI 0.18 to 0.98). Spatial autocorrelation revealed significant positive clusters with weak clustering tendencies of non-contraceptive use among different levels of wealth index and education within different regions of Kenya.ConclusionThese findings underscores the need for intervention programmes to further target socially disadvantaged WLWHA, which is necessary for achieving the SDGs.

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Quantitating Non-Zero Autocorrelation to Determine Moran's I Coefficients for Mapping Clustering Tendencies of Fast Food Restaurants in Lower SES Neighborhoods in Hillsborough County, Florida

Cited by 2 publications

References 18 publications

Geospatial Analysis of Building Structures in Megacity Dhaka: the Use of Spatial Statistics for Promoting Data-driven Decision-making

Geospatial Analysis of Building Structures in Megacity Dhaka: the Use of Spatial Statistics for Promoting Data-driven Decision-making

Predictive and spatial analysis for estimating the impact of sociodemographic factors on contraceptive use among women living with HIV/AIDS (WLWHA) in Kenya: Implications for policies and practice

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