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作者

Carlos A. Villegas;Matthew J. Martinez

来源

CITIES,Vol.121,Issue1,Article 103500

语言

英文

关键字

Big data;Smart City;Social media;Disaster maps;Text analysis;Flood;Damage assessments

作者单位

Pardee RAND Graduate School, Santa Monica, CA, United States;The University of Texas at San Antonio, San Antonio, TX, United States;Pardee RAND Graduate School, Santa Monica, CA, United States;The University of Texas at San Antonio, San Antonio, TX, United States

摘要

Social media systems and crowdsourced data sites were incredibly active during disasters. Residents, first responders, and officials all turn to these systems to impart information and make calls for assistance. These systems will likely continue to hold a central informational and communication role in future disasters. Analyzing the trends and information that come from these sources in real-time aids the recovery process and help public agencies, first responders and researchers more quickly assess damages during and immediately after a disaster. Traditional sources, such as the initial FEMA damage estimates can miss areas of heavy impact and are often time delayed by several weeks. Using Harris County, Texas in the Houston region and the 2017 Hurricane Harvey flooding, the study provides a novel use-case in crisis informatics. The study leverages calls for help during the flooding event to mine address-level information to proxy damage estimates at the parcel-level. The study finds 36- to 53% of Twitter-sourced damage estimates are not captured in the FEMA estimates, significantly augmenting initial estimates with new data – feasibly within hours after the information is first tweeted. Empirically, the study evaluates how parcel-level FEMA damage estimates and Twitter-sourced damage estimates complement each other to proxy damaged structures.