Census Block Group Level Research Articles

Health care visits during the COVID-19 pandemic: A spatial and temporal analysis of mobile device data.

Transportation disruptions caused by COVID-19 have exacerbated difficulties in health care delivery and access, which may lead to changes in health care use. This study uses mobile device data from SafeGraph to explore the temporal patterns of visits to health care points of interest (POIs) during 2020 and examines how these patterns are associated with socio-demographic and spatial characteristics at the Census Block Group level in North Carolina. Specifically, using the K-medoid time-series clustering method, we identify three distinct types of temporal patterns of visits to health care facilities. Furthermore, by estimating multinomial logit models, we find that Census Block Groups with higher percentages of elderly persons, minorities, low-income individuals, and people without vehicle access are areas most at-risk for decreased health care access during the pandemic and exhibit lower health care access prior to the pandemic. The results suggest that the ability to conduct in-person medical visits during the pandemic has been unequally distributed, which highlights the importance of tailoring policy strategies for specific socio-demographic groups to ensure equitable health care access and delivery.

Displacement of Racially and Ethnically Minoritized Groups after the Installation of Stormwater Control Measures (i.e., Green Infrastructure): A Case Study of Washington, DC.

Stormwater control measures (SCMs) (i.e., green infrastructure) are advantageous methods of stormwater management. However, studies suggest that urban greening may be associated with gentrification, displacing racially/ethnically minoritized groups due to increased housing costs and loss of feelings of belonging. We studied displacement of racially/ethnically minoritized groups after SCM installation in Washington, DC. We compared the change in percentage of persons in racial/ethnic groups at the Census block group level with varying levels of SCM installation (i.e., area-weighted SCM count at 300 m buffer). We stratified findings by SCM type, pre-installation income, and SCM size. DC installed a higher density of SCMs in areas with a higher percentage of Black and/or Hispanic/Latino residents. Nonetheless, findings suggest SCM installation is associated with displacement of Black residents. The percentage of residents who are Black decreased by 2.2% [95% Confidence Interval: 1.7, 2.7] and 4.1% [95% Confidence Interval: 3.4, 4.8] after low and high levels of SCM installation, respectively. In turn, the change in percentage of residents who are White increased with increasing levels of SCM installation. Compared to ecological studies on SCMs, studies about social impacts are scarce. This research intends to help optimize SCM installations so more residents can enjoy their health, economic, and ecological benefits.

Changing employment subcenters and opportunity in the Pittsburgh metropolitan area

ABSTRACT Urban structure reflects land use processes and affects the livability of metropolitan areas. Structure and process can be illuminated by analysis of employment subcenters whose delineation often utilizes density and threshold criteria modified by local knowledge. This study addresses two questions: (1) are employment subcenters thus defined differently from the metropolitan area as a whole and (2) are employment subcenters different from each other? Employment density thresholds and totals are analyzed at census block group level of geography. Location quotients for subcenters and subcenter components (census block groups) are analyzed using Mann-Whitney rank order tests. The results indicate that the answer is “yes” to both questions. Affirmation of the first supports the credibility of density/threshold delineations, and of the second indicates an under-studied aspect of urban structure. Subcenter differences may contribute to problems of worker accessibility to employment opportunities. The results suggest that unstable urban structure may be an impediment to socioeconomic mobility and that, on a theoretical level, it may be desirable to revisit the question of whether influence fields are discrete or probabilistic.

The impact of COVID-19 on trips to urban amenities: Examining travel behavior changes in Somerville, MA.

While there has been much speculation on how the pandemic has affected work location patterns and home location choices, there is sparse evidence regarding the impacts that COVID-19 has had on amenity visits in American cities, which typically constitute over half of all urban trips. Using aggregate app-based GPS positioning data from smartphone users, this study traces the changes in amenity visits in Somerville, MA from January 2019 to December 2020, describing how visits to particular types of amenities have changed as a result of business closures during the public health emergency. Has the pandemic fundamentally shifted amenity-oriented travel behavior or is consumer behavior returning to pre-pandemic trends? To address this question, we calibrate discrete choice models that are suited to Census block-group level analysis for each of the 24 months in a two-year period, and use them to analyze how visitors' behavioral responses to various attributes of amenity clusters have shifted during different phases of the pandemic. Our findings suggest that in the first few months of the pandemic, amenity-visiting preferences significantly diverged from expected patterns. Even though overall trip volumes remained far below normal levels throughout the remainder of the year, preferences towards specific cluster attributes mostly returned to expected levels by September 2020. We also construct two scenarios to explore the implications of another shutdown and a full reopening, based on November 2020 consumer behavior. While government restrictions have played an important role in reducing visits to amenity clusters, our results imply that cautionary consumer behavior has played an important role as well, suggesting a likely long and slow path to economic recovery. By drawing on mobile phone location data and behavioral modeling, this paper offers timely insights to help decision-makers understand how this unprecedented health emergency is affecting amenity-related trips and where the greatest needs for intervention and support may exist.

Is the installation of stormwater control measures (i.e., green infrastructure) associated with racial and ethnic minority displacement?

BACKGROUND AND AIM: Stormwater Control Measures (SCMs) (i.e., green infrastructure) are becoming common approaches to tackle urban stormwater. The efficient use of space and health/ecological benefits make SCMs an attractive alternative or addition to traditional stormwater management. However, studies suggest that the installation of greening methods, such as parks, may lead to gentrification or displacement of racial/ethnic minorities due to factors such as increased housing costs and property values. We studied whether the installation of SCMs is associated with displacement of racial/ethnic minorities and how this association varies by SCM type, pre-installation income, and SCM size. Washington DC, our study site, installed a higher density of SCMs in areas with a higher percentage of racial/ethnic minorities, adding to the importance of studying how these installations may impact minority groups. METHODS: We compared the change in percentage of racial/ethnic groups at the Census block group level between the 5-year periods before and after SCM installation (2011-2014) among four levels of SCM Installation Exposure Density. We stratified our findings by SCM type, pre-installation median household income, and SCM size. RESULTS:Preliminary results reveal that SCM installation is associated with racial and ethnic minority displacement (i.e., a decrease in the percentage of racial/ethnic minority residents). Census block groups with a higher density of surrounding SCM installations have more racial/ethnic minority displacement. This association was observed for most types of SCMs and pre-installation median household incomes. Preliminary results also revealed that the installation of multiple small green roofs, a common type of SCM, is associated with less racial minority displacement than fewer medium and large sized green roofs. CONCLUSIONS:In conclusion, careful attention is needed to address racial/ethnic minority displacement in relation to SCM installation and strive to ensure that SCMs can benefit all the surrounding residents. KEYWORDS: Environmental Justice, Minority Displacement, Stormwater Control Measures, Green Infrastructure, Racial/Ethnic Minorities, Built Environment

Use of a model-based gradient boosting framework to assess spatial and non-linear effects of variables on pedestrian crash frequency at macro-level

This paper presents a study that evaluates the nature of the associations (i.e., linear or non-linear) between built environment variables and pedestrian crash frequency at the census block group level. A machine learning approach, called the componentwise model-based gradient boosting algorithm, was implemented to estimate the nature and effects of sociodemographic, land use, road network, and traffic attributes on pedestrian crashes from Broward and Miami-Dade Counties in Florida. The algorithm provides the flexibility to use different types of base-learners, including but not limited to decision tree (DT), generalized additive model (GAM), and Markov Random Field (MRF). While gradient boosting with DT base-learner has widely been used in safety studies, other base-learners and their performances in crash frequency predictions are yet to be explored. This study compared the performance of DT and GAM base-learners, with an MRF base-learner to account for spatial correlation among analysis units. Models fitted with GAM base-learner were found to perform better than the models fitted with DT base-learner, with several variables showing non-linear and several showing linear or approximately linear correlations with pedestrian crash frequency. The study provides useful insights on how the results can help urban planners and policy makers to optimize pedestrian safety measures.

Social disorganization and police arrest trajectories

Prior police decision-making research is limited by (1) its emphasis on individual and organizational predictors and (2) cross-sectional designs, which fail to account for the time-varying aspects of police activities and the factors explaining them. Using group-based trajectory modeling, this study tested the ability of social disorganization theory to explain arrest activity at the Census block group level in Dallas, Texas. Social disorganization variables helped predict certain arrest trajectories, but not all of them. Specifically, socio-economic status was significant in low and medium arrest trajectory groups. An interaction between racial heterogeneity and family disruption was also significant in the medium arrest trajectory group. Theoretical implications are discussed.

Ignored but not Forgotten: The Broken Windows Tipping Point and the Question of Functional Form

AbstractWilson and Kelling provide a simple instruction to address disorder: direct limited police resources to neighbourhoods at the tipping point. In doing so, they suggest that the disorder–crime relationship reflects a threshold effect. A proper test of the validity of broken windows theory should, therefore, accommodate nonlinearity. To this end, this study empirically examines the functional form of the relationship between physical disorder and violent crime rate in Detroit, MI, utilizing a dose–response propensity score methodology. This examination is aided by open-sourced data collected at the census block-group level. Findings support a nonlinear relationship, favouring a threshold effect with two caveats. Several directions for future research are explored, as well as implications for theory, practice and policy.

Unequal welfare costs of staying at home across socioeconomic and demographic groups

ABSTRACT Using daily census block group level data from the U.S., this paper investigates the welfare costs of staying at home due to COVID-19 across socioeconomic and demographic groups. The investigation is based on an economic model of which implications suggest that the welfare costs of staying at home increase with the stay-at-home probabilities of individuals. The empirical results provide evidence for significant heterogeneity across census block groups regarding the welfare effects of staying at home. This heterogeneity is further used to obtain measures of welfare changes for different socioeconomic and demographic groups at the national level.

The contingency of neighbourhood diversity: Variation of social context using mobile phone application data

This research uses high-density anonymised mobile phone application (MPA) global-positioning system (GPS) data to describe exposure to racial diversity in different social contexts with an aim to clarify the mechanism linking residential diversity to opportunities for diverse social interactions. In particular, it explores the hypothesis that a diverse residential context does not lead to diverse social contact by comparing three exposure measures – residential, observed and interaction – on the census block group level in Chicago. In doing so, it also explores the contribution of activity spaces to opportunities for diverse social contact. The findings show that the exposure to opportunities for diverse social contact measured by MPA data is generally higher than what is implied by residential census data, especially in areas of high residential segregation in the city. Further, measures using MPA data reveal more spatiotemporal heterogeneity of exposure than that implied by the residential context.

Neighborhood area-level social determinants and receipt of mammography screening in an area with high deprivation: A retrospective cohort study.

e18550 Background: Early detection through screening mammography has been shown to decrease breast cancer mortality. Screening mammography rates remains low among racial/ethnic minorities and patients with socioeconomic deprivation (SED). Most studies evaluating the role of area-level social determinants of health and breast cancer screening have included only a small number of variables; in this study, a comprehensive and granular measure of socioeconomic deprivation (SED) which included 17 variables was used to determine an association with screening mammogram completion. Methods: A retrospective cohort study was conducted at an academic hospital system between 2014-2020 to identify asymptomatic female patients who received screening mammogram referrals in their primary care clinic after they were deemed eligible per screening guidelines. Patients were assessed for mammogram completion at their annual visits. SED was evaluated using the area deprivation index (ADI), a measure of 17 variables including education, housing, and income at the census block group level. Other covariates analyzed were insurance status, age, and race. Chi-square test, Kruskal-Wallis test and a multivariate logistic regression model were used for statistical analysis. Results: 856 women were referred for screening mammography. 324 (38%) underwent mammogram. Patients with high, moderate, and low SED comprised 69 (8%), 287 (34%) and 500 (58%) of the cohort, respectively. In multivariable analysis, SED and race were not associated with higher screening rates. Uninsured and self-pay patients had the lowest odds of screening mammography completion (AOR 0.22; 95% 0.08, 0.60) and Medicare patients had decreased odds of mammogram completion relative to privately insured patients (AOR 0.64; 95% CI 0.43, 0.97). Older age was associated with a slightly higher odds of mammography completion (AOR 1.02; 95% CI 1.00, 1.04). Conclusions: The receipt of screening mammography was low among all patients relative to previously published rates. Uninsured/self-pay status was the strongest indicator for completion of mammography. Additional research is needed to understand the barriers that may influence mammography completion in this population with high socioeconomic deprivation. Multivariate Logistic Regression Estimates for Associations Between Mammogram Completion and SED category.[Table: see text]

318 Area Deprivation and Sleep Health among White, Black, and Hispanic/Latina Women

Abstract Introduction Although neighborhood environments have been shown to affect sleep health, few studies have directly measured multiple indicators of both neighborhood deprivation and sleep while considering modification by race/ethnicity. Methods Among 49,833 eligible U.S. women enrolled in the Sister Study from 2003 to 2009, we investigated associations between neighborhood deprivation (e.g., percentage of residents unemployed, household crowding) and multiple sleep dimensions. Participants’ addresses were linked to U.S. census block group level Area Deprivation Index rankings (range: 1–100) for the year 2000, and participant rankings were divided into quintiles where the highest quintile represented the highest deprivation level. Participants self-reported habitual sleep duration, sleep debt, frequent napping, and insomnia symptoms. Adjusting for sociodemographic and clinical characteristics, we used Poisson regression with robust variance to estimate prevalence ratios (PRs) and 95% confidence intervals (CIs) for sleep dimensions among participants within quintiles (Qs) 2–5 vs. Q1. Interaction terms were used to assess modification by race/ethnicity. Results Mean age ± standard deviation was 55 ± 9 years. Women with higher neighborhood deprivation were more likely to self-identify as a racial/ethnic minority and had higher unadjusted prevalence of poor sleep dimensions. After adjustment, higher ADI was positively associated with very short sleep (≤5 hours), and race/ethnicity was a modifier (e.g., race-stratified results for Q5 vs. Q1:PRWhite=1.31 [95% CI: 1.14–1.51], PRBlack=0.91 [0.71–1.18], PRHispanic/Latina= 1.17 [0.68–2.04], p-interaction <0.05). Although race/ethnicity did not modify remaining associations, women with higher neighborhood deprivation also had a higher prevalence of sleep debt, frequent napping, and insomnia symptoms. When compared to White women with the lowest neighborhood deprivation, Black women across all deprivation levels and Hispanic/Latina women in Q2-Q5 were substantially more likely to report each poor sleep dimension (PR range: 1.21 to 5.01). Conclusion A multidimensional measure of neighborhood deprivation was associated with poor sleep and sleep disparities among a diverse cohort of U.S. women. Support (if any):

Type 1 Diabetes incidence among youth in Utah: A geographical analysis

Type 1 Diabetes (T1D) poses an increasing threat to public health, as incidence rates continue to rise globally. However, the etiology of T1D is still poorly understood, especially from the perspective of geography. The objective of this research is to examine the incidence of T1D among youth and to identify high-risk clusters and their association with socio-demographic and geographic variables. The study area was the entire state of Utah and included youth with T1D from birth to 19 years of age from 1998 to 2015 (n = 4161). Spatial clustering was measured both globally and locally using the Moran's I statistic and spatial scan statistic. Ordinary least squares (OLS) regression was used to measure the association of high-risk clusters with certain risk factors at the Census Block Group (CBG) level. The mean age at diagnosis was 9.3 years old. The mean incidence rate was 25.67 per 100,000 person-years (95% CI, 24.57–26.75). The incidence rate increased by 14%, from 23.94 per100,000 person-years in 1998 to 27.98 per 100,000 person-years in 2015, with an annual increase of 0.80%. The results of the spatial scan statistic found 42 high-risk clusters throughout the state. OLS regression analysis found a significant association with median household income, population density, and latitude. This study provides evidence that incidence rates of T1D are increasing annually in the state of Utah and that significant geographic high-risk clusters are associated with socio-demographic and geographic factors.

Staying at Home Is a Privilege: Evidence from Fine-Grained Mobile Phone Location Data in the United States during the COVID-19 Pandemic

The coronavirus disease 2019 (COVID-19) has exposed and, to some degree, exacerbated social inequity in the United States. This study reveals the correlation between demographic and socioeconomic variables and home-dwelling time records derived from large-scale mobile phone location tracking data at the U.S. census block group (CBG) level in the twelve most populated Metropolitan Statistical Areas (MSAs) and further investigates the contribution of these variables to the disparity in home-dwelling time that reflects the compliance with stay-at-home orders via machine learning approaches. We find statistically significant correlations between the increase in home-dwelling time () and variables that describe economic status in all MSAs, which is further confirmed by the optimized random forest models, because median household income and percentage of high income are the two most important variables in predicting The partial dependence between median household income and reveals that the contribution of income to is place dependent, nonlinear, and different given varying income intervals. Our study reveals the luxury nature of stay-at-home orders with which lower income groups cannot afford to comply. Such disparity in responses under stay-at-home orders reflects the long-standing social inequity issues in the United States, potentially causing unequal exposure to COVID-19 that disproportionately affects vulnerable populations. We must confront systemic social inequity issues and call for a high-priority assessment of the long-term impact of COVID-19 on geographically and socially disadvantaged groups.

Measuring neighborhood deprivation for childhood health and development - scale implications in rural and urban context.

Neighborhood deprivation plays an important role in childhood health and development, but defining the appropriate neighborhood definition presents theoretical as well as practical challenges. Few studies have compared neighborhood definitions outside of highly urbanized settings. The purpose of the current study was to evaluate how various administrative and ego-centric neighborhood definitions may impact measured exposure to deprivation across the urban-rural continuum. We do so using the Family Life Project, a prospective longitudinal population-based sample of families living in North Carolina and Pennsylvania (USA), which also sets the stage for future investigations of neighborhood impacts on childhood health and development. To measure neighborhood deprivation, a standardized index of socioeconomic deprivation was calculated using data from the 2007-2011 American Community Survey. Families' residential addresses when children were 2 months of age (n=1036) were geocoded and overlaid onto a deprivation index layer created at the census block group level to construct multiple administrative and ego-centric neighborhood definitions. Friedman tests were used to compare distributions of neighborhood deprivation across these neighborhood definitions within urbanized areas, urban clusters, and rural areas. Results indicated differences in urbanized areas (Chisquare= 897.75, P<0.001) and urban clusters (Chi-square=687.83, P<0.001), but not in rural areas (Chi-square=13.52, P=0.332). Findings imply that in urban areas, choice of neighborhood definition impacts measured exposure to neighborhood deprivation. Although exposure to neighborhood deprivation appears to be less sensitive to neighborhood definition in rural areas, researchers should apply theoretical reasoning to choose appropriate definitions of children's neighborhood.

Assessment of urban flood vulnerability using the social-ecological-technological systems framework in six US cities

As urban populations continue to grow through the 21st century, more people are projected to be at risk of exposure to climate change-induced extreme events. To investigate the complexity of urban floods, this study applied an interlinked social-ecological-technological systems (SETS) vulnerability framework by developing an urban flood vulnerability index for six US cities. Indicators were selected to reflect and illustrate exposure, sensitivity, and adaptive capacity to flooding for each of the three domains of SETS. We quantified 18 indicators and normalized them by the cities’ 500-yr floodplain area at the census block group level. Clusters of flood vulnerable areas were identified differently by each SETS domain, and some areas were vulnerable to floods in more than one domain. Results are provided to support decision-making for reducing risks to flooding, by considering social, ecological, and technological vulnerability as well as hotspots where multiple sources of vulnerability coexist. The spatially explicit urban SETS flood vulnerability framework can be transferred to other regions facing challenging urban floods and other types of environmental hazards. Mapping SETS flood vulnerability helps to reveal intersections of complex SETS interactions and inform policy-making for building more resilient cities in the face of extreme events and climate change impacts.

Unequal Welfare Costs of Staying at Home across Socioeconomic and Demographic Groups

Using daily census block group level data from the U.S., this paper investigates the welfare costs of staying at home due to COVID-19 across socioeconomic and demographic groups. The investigation is based on an economic model of which implications suggest that the welfare costs of staying at home increase with the stay-at-home probabilities of individuals. The empirical results provide evidence for significant heterogeneity across census block groups regarding the welfare effects of staying at home. This heterogeneity is further used to obtain measures of welfare changes for different socioeconomic and demographic groups at the national level.

Effects of family and neighborhood risks on glycemic control among young black adolescents with type 1 diabetes: Findings from a multi-center study.

While individual and family risk factors that contribute to health disparities in children with type 1 diabetes have been identified, studies on the effects of neighborhood risk factors on glycemic control are limited, particularly in minority samples. This cross-sectional study tested associations between family conflict, neighborhood adversity and glycemic outcomes (HbA1c) in a sample of urban, young Black adolescents with type 1 diabetes(mean age = 13.4 ± 1.7), as well as whether neighborhood adversity moderated the relationship between family conflict and HbA1c. Participants (N = 128) were recruited from five pediatric diabetes clinics in two major metropolitan US cities. Diabetes-related family conflict was measured via self-report questionnaire (Diabetes Family Conflict Scale; DFCS). Neighborhood adversity was calculated at the census block group level based on US census data. Indictors of adversity were used to calculate a neighborhood adversity index (NAI) for each participant. Median family income was $25,000, suggesting a low SES sample. In multiple regression analyses, DFCS and NAI both had significant, independent effects on glycemic control (β = 0.174, P = 0.034 and β = 0.226 P = 0.013, respectively) after controlling for child age, family socioeconomic status and insulin management regimen. Tests of effects of the NAI and DFCS interaction on HbA1c found no significant moderating effects of neighborhood adversity. Even within contexts of significant socioeconomic disadvantage, variability in degree of neighborhood adversity predicts diabetes-related health outcomes in young Black adolescents with type 1 diabetes. Providers should assess social determinants of health such as neighborhood resources that may impact adolescents' ability to maintain optimal glycemic control.

Neighborhood-level socioeconomic status and outcomes in patients with localized pancreatic adenocarcinoma treated with first-line FOLFIRINOX

Presenter: Douglas S Swords MD, MS | The University of Texas MD Anderson Cancer Center Background: National data demonstrate that neighborhood-level low socioeconomic status (SES) is associated with shorter overall survival (OS) in patients with localized pancreatic ductal adenocarcinoma (PDAC). No previous studies have focused on the impact of SES in patients initially treated with chemotherapy at high-volume institutions. We aimed to assess the associations between SES and receipt of surgery, pathologic/postoperative outcomes, and OS in this population. Methods: We performed a single-center retrospective cohort study of patients who completed ≥ 1 cycle of FOLFIRINOX for non-metastatic PDAC from 2012-2019. Among 519 eligible patients, we excluded those living outside the US (n = 6), residing in areas with suppressed area deprivation index (ADI) values (n = 5), variant histology (n = 5), and unknown surgical status (n = 4). The exposure was ADI, a SES composite constructed using 17 variables measured at the census block group-level. SES was dichotomized as high (ADI values of 1-40th percentile) and low (41-100th percentile). We assessed the associations of SES with resection, pathologic/postoperative outcomes, and OS. Results: Among 499 patients, median age was 64 years (IQR 58, 69), 42.9% were female, and 80.8% were White. The population was skewed towards high SES patients (median ADI 40, scale 0-100[IQR 23, 64]). The resection rate was 31.2% in low SES vs. 39.3% in high SES patients (p = 0.06). When stratified by stage, low SES was associated with lower resection rates in potentially resectable (PR) patients (48.2% vs. 71.8%, p = 0.002) but not in borderline resectable (BR) (38.9% vs. 36.4%, p = 0.75) and locally advanced (LA) (9.8% vs. 15.5%, p = 0.24) patients. Among low vs. high SES resected patients, rates of R1 margins (10.5% vs. 10.5%, p = 1.0), hospital stay (mean 7.9 vs. 6.9 days, p = 0.14), 90-day readmissions (29.0% vs. 19.0%, p = 0.13), major complications (21.3% vs. 14.1%, p = 0.22), and postoperative chemotherapy (64.9% vs. 72.5%, p = 0.29) did not differ significantly. In the overall cohort, median OS in low SES patients was 19 months (95% CI 17, 22) vs. 29 months (95% CI 23, 34) in high SES patients (Figure A, p<0.001). In a multivariable model in the overall cohort, low SES was independently associated with shorter OS (adjusted HR 1.46, 95% CI 1.14, 1.89). Low SES was associated with shorter OS in PR patients (Figure B) but OS did not differ significantly in BR (not shown, p = 0.09) and LA (not shown, p = 0.12) patients. Low SES was associated with similar OS in resected patients (Figure C) but was associated with a modest survival decrement in unresected patients (Figure D). Conclusion: In this cohort of 499 patients with newly diagnosed non-metastatic PDAC who were selected to receive first-line FOLFIRINOX, low SES patients had similar postoperative outcomes and OS to high SES counterparts, reinforcing the value of multimodal therapy at specialized centers. The finding of shorter OS in low SES patients appears to be partially driven by lower rates of being oncologically and medically appropriate for surgery at the conclusion of neoadjuvant therapy. If these findings are confirmed in multi-center studies then it would be appropriate to view low SES patients as a high-risk group deserving of additional supportive resources during first-line therapy.

The Relationship between Land Cover and Sociodemographic Factors

Multiple social and environmental justice concerns are linked to the urban form such as the distribution of socioeconomic class populations, healthcare spending, air pollution exposure, and human mobility. Because of this, the implications of the relationships between built urban form, sociodemographic factors, and air quality warrant analysis at a high spatial resolution. This study used 1m resolved LiDAR data to characterize land use in Salt Lake County, Utah, and associate it with sociodemographic and air quality data at the census block group and zip code levels. We found that increasing tree cover was associated with higher per capita income and lower minority populations while increasing built cover was linked to lower per capita income and higher minority populations. Air quality showed less strong correlations, however, decreased non-irrigated cover, increased built cover, and higher amounts of households living under poverty were related to higher long-term PM2.5 exposure. Due to regional air pollution concerns, several policy efforts have been undertaken to improve air quality and reduce negative health outcomes in Utah which are being informed by regulatory and research-grade air quality sensors.