Social and spatial characteristics of a population often interact to influence health outcomes, suggesting a need to jointly analyze both to offer useful insights in community health. However, researchers have used either social or spatial analyses to examine community-based health issues and inform intervention programs. We propose a combined socio-spatial analytic approach to develop a social network with spatial weights and a spatial statistic with social weights, and apply them to an ongoing study of mental and physical well-being of rural Latino immigrants in North Florida, USA. We demonstrate how this approach can be used to calculate measures, such as social network centrality, support contact dyads, and spatial kernel density based on a health survey data. Findings reveal that the integrated approach accurately reflected interactions between social and spatial elements, and identified community members (who) and locations (where) that should be prioritized for community-based health interventions.
Who and Where: A Socio-Spatial integrated Approach for Community-Based Health Research / J.R. Stacciarini, R. Vacca, L. Mao. - In: INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH. - ISSN 1660-4601. - 15:7(2018 Jul), pp. 1375.1-1375.17. [10.3390/ijerph15071375]
Who and Where: A Socio-Spatial integrated Approach for Community-Based Health Research
R. VaccaSecondo
;
2018
Abstract
Social and spatial characteristics of a population often interact to influence health outcomes, suggesting a need to jointly analyze both to offer useful insights in community health. However, researchers have used either social or spatial analyses to examine community-based health issues and inform intervention programs. We propose a combined socio-spatial analytic approach to develop a social network with spatial weights and a spatial statistic with social weights, and apply them to an ongoing study of mental and physical well-being of rural Latino immigrants in North Florida, USA. We demonstrate how this approach can be used to calculate measures, such as social network centrality, support contact dyads, and spatial kernel density based on a health survey data. Findings reveal that the integrated approach accurately reflected interactions between social and spatial elements, and identified community members (who) and locations (where) that should be prioritized for community-based health interventions.File | Dimensione | Formato | |
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