Urban Heat and Climate Justice: A Landscape of Thermal Inequity in Pinellas County, Florida

Abstract

ABSTRACT. The combined effects of two global trends, urbanization and climate change, have generated considerable concern regarding their adverse and disproportionate impacts on the health of urban populations. This study contributes to climate-justice research by determining whether elevated levels of urban heat, indicated by land surface temperature (LST), are distributed inequitably with respect to race/ethnicity, age, and socioeconomic status in Pinellas County, Florida. Our study utilizes 2010 MODIS and Landsat mediumresolution, remotely sensed thermal data, census socio-demographic information, and both conventional and spatial statistical methods. Results indicate that LST is significantly greater in census tracts characterized by higher percentages of certain racial/ethnic minorities and higher poverty rates, even after controlling for contextual factors and the effects of spatial autocorrelation. This reveals the presence of a landscape of thermal inequity: uneven distribution of heat within the built urban environment and a community structure with varying vulnerability.

Examining Vulnerability Factors to Natural Disasters with a Spatial Autoregressive Model: The Case of South Korea

“…For the reduction of spatial autocorrelation and better model performance, the SAM was employed, and the SEM was identified as the most suitable model, based on an LM diagnostic test. The results of this study are in line with earlier studies in other fields [15][16][17], and confirm that the SAM can be used to identify statistically significant relationships among variables, using the spatially autocorrelated model residuals. Through a spatial error model, this study showed that vulnerable people (less-educated people, minorities) and places (high precipitation, fewer manufacturers, fewer open spaces, lower densities, and high slope) are more likely to experience disaster damage.…”

“…A geographically weighted regression (GWR) was applied to find the spatially-varying relationship between water quality and land use, showing the decrease of spatial autocorrelation in the model residuals [11]. A spatial autoregressive model (SAM) has been used in disaster-related studies, including environmental justice, flood mitigation, and social vulnerability, to account for the spatial autocorrelation in the model residuals or independent variables [14,15,17,40]. Myers et al identified the relationship between social vulnerability and migration measures using a spatial lag model (SLM) after two hurricanes-Katrina and Rita [40].…”

“…This study made a comparison between the results of the OLS regression model and SAM, through the value of the log-likelihood, Schwarz criterion (SC), and Akaike information criterion (AIC). The SAM showed improvements over the OLS model, as the log-likelihood increased, and the value of AIC and SC decreased [16,17]. The procedure of examining factors determining vulnerability to a natural disaster with SAM is indicated in Figure 2.…”

Could urban greening mitigate suburban thermal inequity?: the role of residents’ dispositions and household practices

“…This research sought to determine whether thermal inequity might exist in an Australian suburb, extending environmental justice research from North America Chakraborty 2014, Mitchell andChakraborty 2015) based primarily upon higher density and inner-city locales (Jesdale et al 2013). We tested statistical associations between indicators of social disadvantage and measures of climate change awareness, concern and perceived efficacy in adapting to impacts, as well as residents' energy expenditure, perceived thermal comfort, and disposition towards use of green infrastructure as a policy intervention to lessen heat in built environments.…”

“…This urban heat island (UHI) effect results in substantially higher temperatures in the urban core than in suburbs and hinterlands (Harlan et al 2007, Mccarthy et al 2010, Maller and Strengers 2011, Xiang et al 2014. As a result of uneven social geographies, urban heating disproportionately impacts lowerincome and ethno-racially marginalised populations -a phenomenon termed 'thermal inequity' Chakraborty 2014, Mitchell andChakraborty 2015). Such populations can become spatially concentrated in hotter urban environments (Harlan et al 2007), and may not be able to afford to cool their homes due to lower-incomes, an energy security concern (Byrne and Portanger 2014).…”