Browsing by Author "Kandris, Sharon"

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    Community Health Information Resource Guide: Volume 1 - Data
    (The Polis Center at IUPUI, 2011-06) Comer, Karen F; Derr, Michelle; Seyffarth, Chris; Thomaskutty, Champ; Kandris, Sharon; Ritchey, Matthew
    This resource guide contains useful information for those who would like to use data to assess the health status of an Indiana community. Targeted users include local organizations such as county health departments and community health coalitions. Being able to access and use relevant data and information resources is a common hurdle for those interested in assessing and advancing community health. As a result of this need and at the request of the Community Advisory Council of the Community Health Engagement Program, we developed this resource guide to assist individuals, organizations, and coalitions in Indiana in identifying appropriate resources that guide their community health research and evaluation activities. The term “data” is used in this volume in reference to both data and information sources. While data consist of raw facts and figures, information is formed by analyzing the data and applying knowledge to it so that the findings are more meaningful and valuable to the community. The benefit of using data is that you can often manipulate it for your specific purposes. The benefit of using information sources is that the work of generating meaning from the data might already have been done, while a potential downside is that the available sources might not answer your specific questions. There are diverse sources of data that can be used as a basis for community health evaluation and decision making. Those looking to use data must consider multiple factors before determining the appropriate data to seek and use.
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    Digital Atlas of American Religion
    (2013-04-05) Bodenhamer, David; Kandris, Sharon; Devadasan, Neil; Colbert, Jay; Dowling, Jim; Danielson, Laura
    Our poster presentation will introduce DAAR, the Digital Atlas of American Religion (http://www.religionatlas.org). DAAR is a web-based research platform with innovative data exploration and visualization tools to support research in the humanities. Time and location are essential components of humanities exploratory research; however, GIS technology, especially in its web form, does not support the easy exploration and visualization of the complex spatio-temporal data manipulated by humanists. DAAR presents researchers with an integrated solution stemming from several fields including GIS, visualization, and classification theory. Researchers using DAAR are provided with the following exploration/visualization techniques: maps, cartograms, tree maps, pie charts, and motion charts. Using these tools and methods, researchers can explore patterns, trends, and relationships in the data that otherwise are not apparent with traditional GIS or statistical software. DAAR allows researchers to understand the multiple dimensions and diversity of religion across geographies, or within geographies. Paired with historic census data, it allows them to explore relationships to give better context and meaning to the patterns and trends. Maps provide the spatial patterns and relationships, tree maps show relative strength and relationships, charts show trends, cartograms reveal relative numbers of adherence, and motion charts animate trends over time.
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    Improved Analysis and Visualization of Community Indicators and Indices
    (Office of the Vice Chancellor for Research, 2013-04-05) Farah, Christopher; Kandris, Sharon; Frederickson, Karen Comer
    The analysis and interpretation of community indicators has been widely conducted to understand community trends, and subsequently, to support program planning, public policy initiatives, and target geographic regions for research. Given the importance of the outcomes, selecting good indicators is key, and usually a balance of stakeholder input and analytical evaluation. The most common analysis method used to evaluate a set of indicators is principal component analysis (PCA), a linear multivariate analysis method. However, the assumptions of PCA may be too restrictive and consequently, the analysis may fail to provide a sound evaluation of the set of indicators. In response to this shortcoming, we paired PCA with an unsupervised, non-linear multivariate method, known as self-organizing maps (SOMs), to analyze a set of indicators focused on population trends in education, income, employment, among others, at both the county level and the census tract level. The joint results were used to: exclude or include indicators from the indicator set, determine the latent primary dimensions of the dataset, identify peer counties and census tracts (relative to Indiana counties / census tracts), identify associations among different indicators at different geographic scales, identify temporal changes in the value of indicators, and develop one or more indices to describe socio-economic conditions of communities. Outcomes are presented geographically, topologically, tabularly, and graphically, offering different mechanisms of understanding and interpreting the analysis results. A goal of this project is to provide a web-based interface for researchers and community stakeholders to identify and evaluate candidate sets of community indicators, potentially accelerating sound public policy decisions and public health research.
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    SAVI Public Health Needs Assessment: Final Report and Recommendations
    (The Polis Center, IUPUI, 2007-07) Comer, Karen F; Kandris, Sharon; Colbert, Jay; Devadasan, Neil; Bodenhamer, David J
    This report summaries the 2007 assessment of current and projected health sector uses of the SAVI Community Information System (SAVI) and recommends SAVI enhancements to meet the information needs of decision makers, practitioners, and researchers. Based on focus groups and key informant interviews, it was discovered that local decisionmakers and practitioners in Central Indiana currently used SAVI, or would like to use SAVI, to assess the relative spatial demand and supply of health and human services, select sites for new health and human service facilities, assess patient access to health and human service facilities, select locations for services and programs, and track characteristics of facility catchment areas. Health practitioners and public health professionals were interested in using geospatially-enabled indicators for more effective planning and interventions, including to track public health outcomes, understand the socio-economic and physical environment of individual patients and communities, locate target populations for existing and potential health programs and services, support grant applications, and inform the public about environmental risk factors and disease prevalence in their communities. Clinical translational and public health researchers are using, or would like to use, geospatially-enabled measures for the study of social and environmental determinants of health, health disparities, environmental exposure and health risk, predictors of health knowledge, ecological models of health behavior, health service access, quality, and cost, and efficacy of health interventions. Detailed recommendations are provided for both short- and long-term enhancements to SAVI based on the existing and potential SAVI users and uses identified via this study and toward assisting the local health sector improve health knowledge and ultimately the health and wellbeing of Central Indiana communities.