Integrating Earth Observation Data with Socio-Economic Data to Understand the Relationship Between Urban Heat Islands and Social Vulnerability
Abstract
Urban growth is a contributing factor for cities to becoming significantly warmer than their surrounding rural areas. This phenomenon is referred to as the Urban Heat Island (UHI) effect. UHI results from increased impervious surfaces, reduced vegetation, heat released from human activities, changing radiation and wind dynamics due to urban morphology. As heatwaves increase in frequency and magnitude, especially in the Sun Belt region of the United States, the public health impacts of UHI, specifically, heat-related illnesses are exacerbated. In this study, we investigated the relationships between UHI and socio-economic factors across four cities in the Sun Belt. We selected the cities based on their population density and growth between 2010 and 2017. We used UHI and following socio-economic (SE) variables - age, employment status, income, and housing tenure - in 2017 to explore the relationship in each city. Because SE variables were available at block group level, we implemented a dasymetric mapping approach to create SE layers at census block level. We then computed percentage distribution for each SE variable in each block, and combined the layers using an equal weighting approach to determine SE vulnerability distribution across two pilot cities (Pheonix, AZ and Dallas-Fort Worth, TX). Using Landsat imagery, we derived land surface temperature (LST), a metric for UHI. We implemented hot-spot analysis, Pearson correlation, local Moran's I (for spatial autocorrelation) and linear regression to explore relationships between UHI and SE variables. preliminary results indicate that increase in LST is associated with increase in percent rented homes, low-income, unemployed, and population. This trend was apparent even in Phoenix, which is cooler than the surrounding desert, further revealing that the vulnerable populations are at a higher risk of experiencing UHI effect. Future analyses will incorporate impervious surface, vegetation, precipitation and city attributes to model UHI effect.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFMNH13C0830K
- Keywords:
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- 4313 Extreme events;
- NATURAL HAZARDS;
- 4332 Disaster resilience;
- NATURAL HAZARDS;
- 4337 Remote sensing and disasters;
- NATURAL HAZARDS;
- 4342 Emergency management;
- NATURAL HAZARDS