Information Fusion to Estimate Resilience of Dense Urban Neighborhoods

TL;DR

This paper introduces a novel data fusion approach combining social science theories and structured ontologies to estimate the resilience of dense urban neighborhoods at fine spatiotemporal scales, aiding civil decision-making.

Contribution

It presents a new method that integrates multi-modal data and structured ontologies to improve resilience estimation and explainability in dense urban areas.

Findings

Identifies destabilizing areas in urban neighborhoods.

Enhances decision-making for civil governments.

Provides finer spatiotemporal resilience estimates.

Abstract

Diverse sociocultural influences in rapidly growing dense urban areas may induce strain on civil services and reduce the resilience of those areas to exogenous and endogenous shocks. We present a novel approach with foundations in computer and social sciences, to estimate the resilience of dense urban areas at finer spatiotemporal scales compared to the state-of-the-art. We fuse multi-modal data sources to estimate resilience indicators from social science theory and leverage a structured ontology for factor combinations to enhance explainability. Estimates of destabilizing areas can improve the decision-making capabilities of civil governments by identifying critical areas needing increased social services.