Interactive Data Visualizations of Community Assets and Community Issues

Asset-based community development (ABCD) is an approach for sustainable community-development. In this approach, communities create an inventory of assets that they possess in order to leverage them for community development purposes. Assets are of many types, including (a) the physical environment, such as parks, (b) social associations and groups, such as service groups, (c) institutions, such as schools and hospitals, (d) economic, such as businesses, and (e) individuals who live and/or work in the community. This inventory of assets is typically presented in either a tabular or descriptive format. Furthermore, surveys of community residents that study the importance of issues to community members usually present results in a descriptive format that summarize findings. This research discusses an effort by one Jesuit University in Eastern Washington to build an inventory of assets for its immediate neighborhood and identify issues that residents of the community felt were important to be addressed. It builds upon existing practices of information dissemination by using open-source statistical and software technologies to make two key contributions. First, it presents an example of an interactive data visualization that displays 1321 assets of one community in Eastern Washington. These include 78 associations, 19 individuals, 175 institutions, 780 economic assets, and 269 physical spaces, which include 7 proposed or existing bike routes. This visualization is interactive in nature and makes assets searchable. The display integrates the presentation of information in both, tabular and spatial formats and there is interactivity between both these formats Second, it presents interactive spatial visualizations that show how survey-based spatial data from communities could help community members identify and issues in a better manner than what might be possible using summary findings alone. These visualizations are available on the Web and created using the open source R statistical programming environment and multiple R packages, such as shiny, dataTable, and leaflet, among others.