Abstract
Population health is influenced by many socioeconomic and demographic factors that may include levels of employment, income, education, ethnicity and age. For health planning and service delivery, it is important to take into account demographic trends over time. This temporal component is usually incorporated into analyses by comparing multiple maps of variables at different points in time. In this study demographic variables with spatial and temporal components are used in a multi-criteria analysis within an interactive spatial decision support tool. We illustrate how the exploration of an area-based composite index over time can help analysts with identifying trends of increasing social deprivation and health-care needs. The paper focuses on the conceptual challenges of spatio-temporal multi-criteria analysis due to changing geographic boundaries, the standardization of variables across time, comparability of variables, and comparability of index scores.
Highlights
The health of a population is highly influenced by social and economic factors [5]
In this paper we address the challenge of visualizing multiple variables and multiple time periods using multi-criteria analysis (MCA) methods, such as the comparability of variables, changes in census tract (CT) geometry, and the comparability of indices through different standardization techniques
We propose to extend the latter two studies by combining multiple socio-demographic indicators into a composite deprivation index. By implementing this index in a geovisual research tool our objective is to address the conceptual challenges associated with spatio-temporal decision-making problems
Summary
The health of a population is highly influenced by social and economic factors [5]. These may include levels of employment, income, education, ethnicity and age [2]. Area-based composite indices ( called “area indices” or in the context of this paper “deprivation indices”) are an effective way of incorporating multiple variables into an analysis by aggregating weighted indicators into an index Such indices use GIS-based multi-criteria analysis (MCA) methods [9] and can be used for ranking and prioritizing of areas for the delivery of health and social services [10, 12, 13]. Sinton [cited by 8] and others [20, 1] identify space/location, time, and theme/attribute as the three dimensions of geospatial data They further observe that frequently, space and/or time are considered the independent, or controlled, variable(s) while theme is the dependent, or measured, variable. Walks [19] examined socioeconomic trends in data for the City of Toronto between 1971 and 1991 He used static maps to show variables for different years and to analyze the change in spatial pattern of occupation, immigration and income variables. Hulchanski [6] and the United Way [18] reported on spatio-temporal trends for the City of Toronto
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