# 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.

## Key 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.

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Source: https://tomesphere.com/paper/1903.11478