Census Tract Level Research Articles

Abstract 4134079: Neighborhood-Level Indices of Social Disadvantage and Cardiovascular-Kidney-Metabolic Health

Background: Several area-level indices exist that integrate a variety of social drivers of health. However, whether certain indices better capture neighborhood-level variability in cardiovascular-kidney-metabolic (CKM) outcomes is not well known. Aims: We sought to examine the associations of census tract-level indices of social disadvantage with CKM outcomes. Methods: Exposures for the analysis included seven indices of social disadvantage (ADI, COI, EJI, NDI, SDI, SREI, SVI) based on census tract-level data from the 2010-2019 American Community Survey (ACS) and census tract-level median household income from the 2015-2019 ACS data. Outcomes included the census tract-level prevalence of CKM outcomes (obesity, hypertension, diabetes, hyperlipidemia, chronic kidney disease, coronary heart disease [CHD], and stroke) from the 2019 Behavioral Risk Factor Surveillance System. All indices and median income were standardized, and we used linear regression to examine the associations between each census tract-level social measure and CKM outcomes, adjusted for population size and median age. Next, we calculated the △r 2 in adjusted models for the association of median income with each CKM outcome when each index was added. Results: Among 65,476 US census tracts, median (IQR) population size was 4068 (2969, 5374), age was 39 years (35, 44), and household income was $58,870 (44276, 79802). Median income as well as each index was significantly associated with census tract-level prevalence of each CKM outcome assessed (p < 0.05). The r 2 (Panel A) and △r 2 values (Panel B) varied for each of the social measures and CKM outcomes, with higher r 2 values when each index was individually added to median income compared with income alone. For example, for CHD, the r 2 ranged between 0.39 to 0.67 and △r 2 ranged from 0.01 to 0.10. Conclusions: Neighborhood-level measures of social disadvantage are differentially associated with CKM outcomes, with a large proportion of the variability explained by median income. Identifying the advantages and disadvantages of each index and comparison with median income can inform the prioritization of measures for specific outcomes.

Gun Free Zones in Alcohol-Serving Establishments and Risk for Firearm Violence: A Cross-Sectional, Geospatial Study in Texas.

To date, there have been no peer-reviewed studies in the United Statesestimating the impact of gun-free zonepolicies in alcohol-serving establishments on rates of firearm violencein and aroundsuch establishments. In this study, we utilized a cross-sectional design to estimate the impact of Texas's 51% alcohol law, which prohibits the carrying of firearms in establishments that generate over half of their revenue from alcohol sales. The analysis focused on the difference in shooting incidents in and around establishments with and without firearm carrying prohibitions in 2021 and 2022. After adjusting for establishment type (bar/restaurant), alcohol sales volume, census tract level demographic factors, and the number of nearby restaurants and bars, results indicated that gun-prohibiting bars experienced significantly fewer shootings compared to those that allowed guns. Specifically, establishments that were gun-prohibited had 37% fewer shootings within 50m than those that were gun-allowing, with a 95% confidence interval ranging from 60% fewer to 0.2% fewer. This association was more prominent in bars than in restaurants. The protective association with gun-prohibited status diminished with increased distance from the establishments; results were not significant at 100m. Our study findings align with research suggesting that gun-free zones can reduce firearm violence. However, future studies using quasi-experimental designs that can better support causal inference are needed to support such a conclusion, as are studies exploring the efficacy of such policies in various settings and over longer periods.

The association between composite measures of social vulnerability and PICU admission for status asthmaticus.

Current knowledge of the impact of socioeconomic factors on the risk of admission to the pediatric intensive care unit (PICU) for asthma is limited. Using composite measures of social vulnerability-Social Vulnerability Index (SVI) and Child Opportunity Index (COI) 2.0-we compared patients admitted for status asthmaticus to the PICU and pediatric ward at Children's Hospital Los Angeles (CHLA). We hypothesized patients with a high SVI and low COI are at higher risk for PICU admission. Patients were identified using ICD-10 codes for asthma. Primary outcome was admission to PICU versus ward for status asthmaticus. Patient-registered residential street addresses were geocoded and spatially joined to SVI and COI 2.0 data at the census tract level. Univariate and regression analyses using the patient's SVI, COI 2.0, and admission location were conducted. From January 2017 to March 2022, there were 2458 admissions matched to addresses from 1983 distinct patients. The overall median SVI for all patients was 0.86 (IQR 0.6, 0.9). Overall median COI was 25.0 (IQR 10, 50). There was no difference in SVI or COI for admission to the PICU versus the ward. However, children requiring multiple hospital admissions for asthma were associated with higher SVI and lower COI. Children admitted to CHLA for asthma had an elevated SVI and low COI. There was no difference between admission locations based on SVI or COI scores. This indicates we care for children at increased socioeconomic risk, but this did not increase PICU use for asthma.

Neighborhood Ecologies, Cases, and Deaths during the Beginning of the COVID-19 Pandemic: Lessons for Current and Future Epidemics?

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected Black and Latinx communities. Ecologic analyses have shown that counties with a higher percentage of Latinx and Black people have worse COVID-19 outcome rates. Few ecologic analyses have been published at the neighborhood (census tract) level. We sought to determine whether certain sociodemographic neighborhood ecologies were associated with COVID-19 case and death rates in metropolitan Atlanta, Georgia. We used census data and principal-component analysis to identify unique neighborhood ecologies. We then estimated correlation coefficients to determine whether the neighborhood profiles produced by a principal-component analysis were correlated with COVID-19 case and death rates. We conducted geographically weighted regression models to assess how correlation coefficients varied spatially for neighborhood ecologies and COVID-19 outcomes. We identified two unique neighborhood profiles: (1) high percentage of residents, Hispanic ethnicity, without a high school diploma, without health insurance, living in crowded households, and lower percentage older than 65 years; and (2) high percentage of residents, Black race, living in poverty, unemployed, and households receiving Supplemental Nutrition Assistance Program benefits. Profile 1 was associated with COVID-19 case rate (Pearson r = 0.462, P < 0.001) and profile 2 was associated with COVID-19 death rate (Spearman r = 0.279, P < 0.001). Correlations between neighborhood profiles and COVID-19 outcomes varied spatially. Neighborhoods were differentially at risk of COVID-19 cases or deaths depending on their sociodemographic ecology at the beginning of the COVID-19 pandemic. Prevention methods and interventions may need to consider different social determinants of health when addressing potential cases and deaths during future emergent epidemics.

Structural Influences on Methamphetamine Use Among Black Sexual Minority Men (HISTORY Study): Protocol for a Longitudinal Cohort Study.

Sexual minority men are disproportionately affected by methamphetamine use, with recent studies suggesting an increase in use specifically among Black sexual minority men. Black sexual minority men face unique structural barriers to achieving optimal health. Given its harmful effects, and in light of existing health disparities, an increase in methamphetamine use among Black sexual minority men poses a significant public health concern. The Health Impacts and Struggles to Overcome the Racial Discrimination of Yesterday (HISTORY) study is investigating the potential impacts of exposure to the census tract-level structural racism and discrimination (SRD) on methamphetamine use among Black sexual minority men in Atlanta, Georgia, and will identify intervention targets to improve prevention and treatment of methamphetamine use in this population. This study uses a mixed methods and multilevel design over a 5-year period and incorporates participatory approaches. Individual-level quantitative data will be collected from a community-based cohort of Black sexual minority men (N=300) via periodic assessment surveys, ecological momentary assessments, and medical record abstractions. Census tract-level measures of SRD will be constructed using publicly available administrative data. Qualitative data collection will include longitudinal, repeated in-depth interviews with a subset (n=40) of study participants. Finally, using a participatory group model-building process, we will build on our qualitative and quantitative data to generate causal maps of SRD and methamphetamine use among Black sexual minority men, which in turn will be translated into actionable recommendations for structural intervention. Enrollment in the HISTORY study commenced in March 2023 and is anticipated to be completed by November 2024. The HISTORY study will serve as a crucial background upon which future structural interventions can be built, to mitigate the effects of methamphetamine use and SRD among Black sexual minority men. DERR1-10.2196/63761.

The role of ethnic enclaves and neighborhood socioeconomic status in invasive breast cancer incidence rates among Asian American, Native Hawaiian, and Pacific Islander females in California.

Few studies have examined whether the incidence rates of invasive breast cancer among Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations differ by the neighborhood social environment. Thus, we examined associations of ethnic enclave and neighborhood socioeconomic status (nSES) with breast cancer incidence rates among AANHPI females in California. A total of 14,738 AANHPI females diagnosed with invasive breast cancer in 2008-2012 were identified from the California Cancer Registry. AANHPI ethnic enclaves (culturally distinct neighborhoods) and nSES were assessed at the census tract level using 2007-2011 American Community Survey data. Breast cancer age-adjusted incidence rates and incidence rate ratios (IRRs) were estimated for AANHPI ethnic enclave, nSES, and their joint effects. Subgroup analyses were conducted by stage of disease. The incidence rate of breast cancer among AANHPI females living in lowest ethnic enclave neighborhoods (quintile (Q)1) were 1.21 times (95% Confidence Interval (CI) 1.11, 1.32) that of AANHPI females living highest ethnic enclave neighborhoods (Q5). In addition, AANHPI females living in highest vs. lowest SES neighborhoods had higher incidence rates of breast cancer (Q5 vs. Q1 IRR = 1.30, 95% CI 1.22 to 1.40). The incidence rate of breast cancer among AANHPI females living in low ethnic enclave + high SES neighborhoods was 1.32 times (95% CI 1.25, 1.39) that of AANHPI females living in high ethnic enclave + low SES neighborhoods. Similar patterns of associations were observed for localized and advanced stage disease. For AANHPI females in California, incidence rates of breast cancer differed by nSES, ethnic enclave, when considered independently and jointly. Future studies should examine whether the impact of these neighborhood-level factors on breast cancer incidence rates differ across specific AANHPI ethnic groups and investigate the pathways through which they contribute to breast cancer incidence.

Spatial and Racial/Ethnic Variation in the Prevalence of Cesarean Delivery in a South Carolina Medical Center.

While racial/ethnic disparities in cesarean delivery have been noted in the literature, less is known about the intersection between individual-level race/ethnicity and community-level social vulnerability as factors in cesarean delivery. The goal was to use medical record data from a large medical center combined with census tract-level data to examine patterns of social vulnerability, racial population distribution, and prevalence of cesarean delivery. Data were obtained from electronic medical records of patients from a large medical center in South Carolina from 2019 to 2020. The outcome variable was cesarean delivery (yes/no), and covariates included the year of delivery; age of patient; race/ethnicity; spoken language; BMI categories; clinical indications of anemia, hypertension, preeclampsia, and diabetes; and census tract Social Vulnerability Index (SVI). Generalized linear mixed models for multilevel binary logistic regression were used to test the main hypothesis that the census tract level Social Vulnerability Index is positively associated with cesarean delivery. Among a total of 5011 patients, we found that non-Hispanic Black mothers were more likely to have cesarean deliveries compared with non-Hispanic White mothers. After controlling for census tract-level SVI, the individual-level race/ethnicity association was no longer significant. Significant spatial autocorrelation across census tracts was evident for cesarean delivery prevalence, non-Hispanic Black population, and SVI. A high prevalence of cesarean delivery tended to cluster with high SVI and a high non-Hispanic Black population. We found that non-Hispanic Black mothers were more likely to have cesarean deliveries, which was explained by census tract differences in the SVI.

A Disparate Disaster: Spatial Patterns of Building Damage Caused by Hurricane Ian and Associated Socio-Economic Factors

The literature shows that communities under different socio-economic conditions suffer different levels of damage in disasters. In addition to the physical intensity of hazards, such differences are also related to the varying abilities of communities to prepare for and respond to disasters. This study analyzes the spatial patterns of building damage in Hurricane Ian in 2022 and investigates the socio-economic disparities related to the damage. Specifically, this study employs NASA’s Damage Proxy Map (DPM2) to analyze spatial patterns of building damage caused by the hurricane. Then, it uses statistical analysis to assess the relationships between building damage and hurricane intensity, building conditions, and socio-economic variables at the building and census tract levels. Furthermore, the study applies geographically weighted regression (GWR) to examine the spatial variation of the damage factors. The results provide valuable insights into the potential factors related to building damage and the spatial variation in the factors. The results also reveal the uneven distribution of building damage among different population groups, implying socio-economic inequalities in disaster adaptation and resilience. Moreover, the study provides actionable information for policymakers, emergency responders, and community leaders in formulating strategies to mitigate the impact of future hurricanes by identifying vulnerable communities and population groups.

A novel approach to assessing food insecurity for small geographic areas using household living budgets

AbstractThe USDA Economic Research Service has monitored food insecurity at national and state levels since 1995 using the Current Population Survey Food Security Supplement. But if a food insecurity measure is to inform action and target interventions, it must be constructed for smaller geographic levels that consider geographic price differences. This article constructs a novel measure of food insecurity using an alternative approach based on financial needs of households, known as the household living budget (HLB). The HLB is defined as the income required to satisfy a household's essential needs, enabling it to maintain a modest yet sufficient standard of living while covering federal and state income taxes. The HLB is constructed at the census tract level and incorporates three key determinants of food insecurity: household size and composition, household income, and food costs. We demonstrate how the HLB along with publicly available data can be used to construct a food insecurity measure using a residual income approach to assess if households are able to afford paying for food expenditures and assess the qualification thresholds of the Supplemental Nutrition Assistance Program (SNAP). Food insecurity estimates are obtained for households in Washington, D.C. and benchmarked to regional results provided by a survey sponsored by the Capital Area Foodbank.

Energy burden and mental health: A national study in the United States

The prevalence of mental health issues in the US has significantly risen over the past decade, and it is presumably linked to an energy burden issue that has recently gained attention as a critical social determinant of mental health. Utilizing extensive nationwide datasets at the census tract, we found that the census tract level energy burden is positively associated with two key mental health indicators even after accounting for living, housing, and sociodemographic characteristics: the prevalence of frequent mental distress and physician-diagnosed depression, across all US urban areas. We also observe that these associations are consistent across various climate regions. The findings highlight that energy burden has a detrimental impact on mental health, and that it should be e considered a significant social determinant of health in future studies. Lastly, our study advocates for national policies to achieve energy justice and address disparities in mental health.

Association between outdoor artificial light at night and low birth weight in California.

Artificial light at night (ALAN) is a growing environmental issue associated with adverse health consequences, but the association with low birth weight remains unknown. We aim to explore associations between outdoor ALAN and low birth weight across 7,808 census tracts in California. We use data from the California Communities Environmental Health Screening Tool (CalEnviroScreen) at the census tract level. VIIRS Nighttime Day/Night Band Composites Version 1 dataset within the Google Earth Engine platform was used to assess outdoor ALAN. Multivariable linear regression was used to analyze the data. Our findings showed that an interquartile range increase in outdoor ALAN resulted in an increase of 0.42% (95% CI: 0.37, 0.46) in low birth weight rate on average, after adjusting for all potential covariates. The results demonstrate a significant association between outdoor ALAN and low birth weight. These associations remained consistent in sensitivity analyses.

Racial and Ethnic Disparities in HIV Diagnosis Rates by Social Determinants of Health at the Census Tract Level among Adults in the United States and Puerto Rico, 2021.

To compare racial and ethnic disparities in HIV diagnosis rates among adults in census tracts with most disadvantaged vs advantaged levels of social determinants of health (SDOH). In this ecological analysis, we used the National HIV Surveillance System data in 2021 and SDOH data from 2017-2021 American Community Survey. We measured racial and ethnic disparities stratified by sex in the most disadvantaged quartiles and advantaged quartiles for: 1) Poverty 2) Education level 3) Median household income and 4) Insurance coverage. We calculated 8 relative disparity measures (Black-to-White rate ratio [RR], Hispanic/Latino-to-White RR, Index of Disparity [ID], population-weighted ID, Mean Log Deviation, Theil Index, Population Attributable Proportion, Gini coefficient) and 4 absolute disparity measures (Black-to-White rate difference [RD], Hispanic/Latino-to-White RD, absolute ID, and population-weighted absolute ID). Comparing the most disadvantaged quartiles to the most advantaged quartiles, all four absolute disparity measures decreased, but 7 of the 8 relative disparity measures increased: the median percentage decrease in the absolute measures for males and females respectively was 38.1% and 47.6% for poverty, 12.4% and 42.6% for education level, 43.6% and 44.0% for median household income, and 44.2% and 45.4% for insurance coverage. The median percentage increases for the relative measures for males and females respectively were 44.3% and 61.3% for poverty, 54.9% and 95.3% for education level, 19.6% and 90.0% for median household income, and 32.8% and 46.4% for insurance coverage. Racial and ethnic disparities in the most disadvantaged and advantaged quartiles highlight the need for strategies addressing the root causes of disparities.

Are new pediatric trauma centers located close to the high-risk populations? A geolocation study.

Higher rates of injury occur in children who live in low socioeconomic areas. Since 2010, the number of verified Level I and Level II pediatric trauma centers (PTCs) has doubled. The purpose of this study is to look at the location of new verified PTC in relation to children living in high-risk areas. Historical and recent data about verified Level I and Level II PTCs were obtained from the American College of Surgeons (ACS), the American Trauma Society, and State Data where available. Census data were obtained from the US Census Bureau's American Community Survey for 2010 and 2020 for children younger than 18 years. The pediatric population was stratified by (1) poverty threshold and (2) living within 30 miles of PTC. The census data and trauma center locations were geocoded using the ArcGIS Pro program. Data analysis was performed at the census tract level. A total of 55 Level I or Level II ACS-verified PTCs were in the United States in 2010 and 116 in 2020. In 2010, there were 14.5 million children (19.6%) below the poverty threshold and 12.9 million (17.8%) in 2020. In 2010, 23 states did not have an ACS-verified PTC either in the state or in a neighboring state within 30 miles. By 2020, only nine states did not have an ACS-verified PTC or a center in a neighboring state within 30 miles. When state level data were included, this dropped to four. The 19 states who had new verified PTCs covered between 14% and 21% of children below the poverty level. The percentage of children below the poverty threshold and less than 30-mile access to a Level I or Level II PTC in 2010 was 74.56% compared with children above poverty and less than 30-mile access, which was 70.34% (p < 0.0001). In 2020, children below poverty without 30-mile access had been reduced to 52% and 45.54% for children above poverty level (p < 0.0001). Thus, there was a greater increase in 30-mile access for children above the poverty level than for those below the poverty level (24.8% vs. 22.6%, p < 0.001). On a national level, despite doubling the number of PTC, it has not improved 30-mile access for high-risk children. Alternatively, 19 states now have better access. Retrospective Cohort Study, Level III.

Evaluating Statewide Wastewater Affordability for Users of Sewer Systems and Onsite Wastewater Treatment Systems Based on Household Incomes at the Census Tract Level

Evaluating Statewide Wastewater Affordability for Users of Sewer Systems and Onsite Wastewater Treatment Systems Based on Household Incomes at the Census Tract Level

Understanding the zero-emission vehicle market spatial diffusion and its determinants from 2019 to 2022 using spatial econometric models

Due to the rapid rise in the acceptance of zero-emission vehicles (ZEVs), much research has been conducted on the market evolution of alternative fuel technology. The research reported in this paper builds on prior studies by incorporating several new directions. Firstly, the spatial distribution of ZEV purchases in California is explored, then seven models (two nonspatial models and five spatially explicit models) are developed to predict regional ZEV sales. The predictions are based on fundamental and widely accessible characteristics such as infrastructure and demographic statistics. Next, a spatial econometric model known as the “spatial error model” is chosen to distribute ZEV sales from their original postal code (ZIP) level to the more often utilized US census tract level. This allows for carrying out a comprehensive analysis that includes studying the correlation of ZEV sales with local incentives and disadvantaged communities. Results reveal that ZEV sales in California increased significantly between 2019 and 2022, as spatial autocorrelation decreased. Vulnerable groups were less likely to have access to ZEVs, particularly during the initial stage of the ZEV market penetration in 2019. As expected, regions with considerable ZEV sales tended to also experience substantial purchase rebates and lower disadvantaged population, especially in 2019.

Higher levels of neighborhood deprivation and risk for incident colorectal cancer and mortality outcomes: A systematic review and meta-analysis.

131 Background: Significant geographic and racial disparities exist for colorectal cancer (CRC) incidence and mortality in the United States (US), which may be caused by neighborhood-level deprivation and limited access to preventative cancer screening. We completed the first systematic review and meta-analysis of neighborhood-level deprivation in relation to CRC incidence, mortality, and screening among individuals from the US. Methods: A systematic literature review was completed to identify original research articles from PubMed, Scopus, and Web of Science. Eligible studies reported associations between deprivation at the census block or tract level using a validated deprivation index (e.g. the Area Deprivation Index or Yost Index) and CRC incidence, mortality, or screening outcomes in a US-based sample. A fixed-effects meta-analysis was completed using inverse-variance weighting to estimated hazard ratios (HRs) or odds ratios (ORs) with 95% confidence intervals (CIs) for CRC incidence, mortality, and screening outcomes, comparing the highest vs. lowest quantiles of neighborhood-level deprivation. All analyses were completed using the meta package in R. Results: In total, 19 studies were included in the meta-analysis. Living in a neighborhood in the highest quantile of deprivation was associated with significantly higher risk for incident CRC (HR 1.37, 95% CI 1.34-1.41, seven studies). In stratified analysis, positive associations with incident CRC were observed for White (HR 1.43, 95% CI 1.39-1.47, three studies), Black (HR 1.18, 95% CI 0.96-1.44, three studies), and Asian/Pacific Islander individuals (HR 1.18, 95% CI 1.08-1.28, two studies). For individuals diagnosed with CRC, higher deprivation was associated with elevated risk for overall mortality (HR 1.11, 95% CI 1.10-1.13, four studies) and CRC-specific mortality (HR 1.17, 95% CI 1.15-1.19, seven studies). Higher deprivation was associated with lower completion of recommended screening for CRC, including colonoscopy, sigmoidoscopy, or fecal occult blood testing (OR 0.76, 95% CI 0.74-0.79, four studies). Conclusions: Individuals who reside in neighborhoods with high levels of deprivation have significantly elevated risk for CRC incidence and mortality compared to individuals from low deprivation neighborhoods. Targeting healthcare resources to highly deprived neighborhoods to increase uptake of preventative screening may help to address racial and geographic disparities for CRC.

Monitoring Changes in Walkability Over Time: An Environmental Exposure Change Detection Framework with Implications for Equity and Social Justice

Evidence suggests that walkability and greenspace impact travel related activity patterns and vehicle emissions which affect sustainability, public health, and equity. Resulting levels of physical activity, active, or sedentary travel time impact obesity, diabetes, and heart disease which impact COVID-19 mortality. It is now possible to track changes in locally controlled land use characteristics known to impact sustainability and health. This information can provide decisionmakers with feedback required to spatially prioritize and better link state and nationally funded transportation investments with locally sanctioned land use actions. Linking the achievement of established benchmarks of health equity-based indicators with funding establishes a more performance-based approach connecting land use with transportation investment. This study longitudinally tracks neighborhood-level walkability features at the census tract level for 2013 and 2020 for the entire USA. Longitudinal volatility-based change detection models are developed to examine how changes in walkability over time correlate with racialization and social justice. Walkability tends to increase over time with significant variations across metro regions and the urban-rural continuum. Largest and smallest increases in walkability were observed in Western Pacific and Northwest states, respectively. Increased racial and social justice disparities were observed in access to more walkable infrastructure by marginalized populations (such as less-educated, older, unemployed, and black individuals). Significant heterogeneity in the spatial distribution of walkability was observed, over the variation captured by observed sociodemographic, regional, and urban/rural factors. The findings highlight the potential for an “environmental surveillance” system to support a “performance-based” approach to transportation funding that prioritizes resource allocation consistent with Justice40 and United Nation's Sustainable Development Goals.

Examination of Water Use and Social Vulnerability: A Comparison of Four Communities in Alachua County, Florida

It is projected that about 30% of the world’s major cities will face severe water stress and urban drought from 2050 if climate change continues and has the anticipated effects (Florke et al, 2018; www.wrcwebsite.azurewebsites.net). Socio-economic drought, meaning water shortage in urban life, can have significant effects on a city’s inhabitants, including health and quality of life. Social and economic factors play an important role in planning and decision making of a society (Zhang et al, 2020). The analysis investigated the relationship between socio-economic factors and the use of potable water, its associated impact on aquifer recharge, and potentially identifying major factors influencing water demand and resource sustainability. The Centers for Disease Control and Prevention’s Social Vulnerability Index (SVI) and other measures were used for evaluating socio-economic factors. However, since SVI showed weak relationship with water use in the county and the neighborhoods, other socio-economic measures were assessed. Past research had found a correlation between water use and population, GDP, per capita income, electricity usage and irrigated land areas (Alacoma et al., 2007, www.usf.uni-kassel.de/watclim). Therefore, socio economic measures such as population density, household size, per capita income, and poverty rate and irrigation (with and without) potable water were analyzed to evaluate the relationship between water use patterns and these factors across the county and in the communities. The western communities had newer development and less parcels compared to the eastern with much older construction, which impacted their water use. Oakmont with separate reclaimed irrigation meter had lesser usage on potable water. Tioga on the other hand irrigated with potable water. Yearly SVI on a census tract level for the specific years of the study and other socio-economic measures such as population, per capita income, poverty, and household size were used in the analysis. The analysis was performed on four communities in Alachua County, Florida; two at the west and two at the east of the county. Socio-economic measures and physical features were evaluated for communities in the four locations to evaluate whether there is a relationship between water use patterns, spatial characteristics of development such as percent impervious and runoff, and measures of SVI. Spatial and basic statistics was used for this analysis. The western part of the county has had more intensive development in recent years compared to the eastern part of the county. The eastern communities have a high SVI and low potable water use per capita compared to the western communities. The number of households and population were the primary drivers of potable water use per census block. Percent impervious surface and runoff volume did not show any significant. relationship with a community’s SVI. At the parcel level, statistically significant differences were found between communities. For example, potable water use per parcel was lower in communities with high SVI. Keywords: water sustainability, urban water management, social vulnerability index, socio-economic factors, potable water use, aquifer recharge, reclaimed water use, runoff, imperviousness

The intersection of socioeconomic status and wildfire risk: Insights from California

Wildfires in California have increased over the past 20 years, raising serious concerns. Due to the significant damage these fires cause to the environment, infrastructure, and communities, it is essential for policymakers and researchers to find effective ways to reduce their impact. This study aims to improve our understanding of wildfires by examining the relationship between population density, wildfire severity, and socioeconomic factors like average household income. The research uses socioeconomic data from the California State Geoportal (CA.gov) and fire severity zones from ArcGIS Hub. Statistical methods, such as ordinary least squares (OLS) regression and correlation analysis, were applied using ArcGIS Pro software from the Environmental Systems Research Institute. This study confirms earlier findings that there is no correlation between population density and wildfire severity. This conclusion is based on a more detailed analysis that moves from the county level to the census tract level. Additionally, we found no connection between socioeconomic status and fire severity, though we did observe a clear link between socioeconomic status and the likelihood of fires starting. Both OLS and correlation analysis supported these results. However, a hot-spot analysis showed that areas with the lowest-income households are also the regions with the most severe wildfires. This suggests a concerning relationship between lower-income communities and increased vulnerability to wildfires. The findings emphasize the need for targeted interventions and a fair distribution of resources to address the socioeconomic inequalities that contribute to wildfire risk.

Revealing spatial and temporal patterns of residential cooling in Southern California through combined estimates of AC ownership and use

Air conditioning (AC) is an important tool for combatting the adverse health effects of heat, but its use can also drive surges of high demand for electricity. To better understand these effects, there is a need for non-intrusive methods of estimating AC access and operation. In this study, we use a novel methodology to identify residential AC ownership rates using smart meter data from 200,000 customers in Southern California, and find that 79 % of all customers in the region have AC. In contrast to previous methods, we classify AC ownership using hourly, rather than daily, electricity consumption records and directly account for the potential presence of electric heating. We then adapt and apply an algorithm to determine in which hours these households operate their AC. We estimate that the average customer runs their AC during 8.3 % of all hours in the two-year study period, but census-tract level averages range from 1 to 23 % of all hours. Lastly, we combine our estimates of AC ownership and use to analyze cooling behavior spatially and temporally, and are able to identify pockets of high cooling demand, areas lacking in access or the ability to use their AC, and potential targets for cooling-related DR programs.