Home // HUSO 2017, The Third International Conference on Human and Social Analytics // View article

Community Works: Predicting Changes in Community Resilience

Authors:
Alexandra Trani
Clayton J. Hutto
Dennis Folds
Tom McDermott

Keywords: human capital modeling, social capital modeling, prediction, human capital investments, social capital investments, disability, self-driving cars, mass transit

Abstract:
Community resilience is a multidimensional concept that would be difficult if not impossible to measure with a single assessment. To capture this system-of-systems nature of community resilience, we argue that considerations of human and social capital must be included because humans are both the source of community resilience and the beneficiaries of it. We build on a data transformation method proposed by Hutto and colleagues [7] allowing researchers to create comprehensive measures of community resilience and its underlying social constructs (i.e., subjective well-being and objective standard of living). Using a combination of data simulation via probability sampling and confirmatory factor analysis, we demonstrate the impact of some future (conjectured) proposed legislation—e.g., governmentally provided self-driving cars as a public transportation alternative—on community resilience for three demographically defined communities: the elderly, the disabled, and all Americans of legal driving age (i.e., 16+) for each of the geographically bounded communities consisting of the 50 United States and the District of Columbia.

Pages: 39 to 44

Copyright: Copyright (c) IARIA, 2017

Publication date: July 23, 2017

Published in: conference

ISSN: 2519-8351

ISBN: 978-1-61208-578-4

Location: Nice, France

Dates: from July 23, 2017 to July 27, 2017