Speaker & Symposium Schedule

Decades of racialized development policies, including redlining, have disproportionately exposed residents to toxic metals that drive antimicrobial resistance (AMR) gene evolution in soil microbiomes. We hypothesize that metal-driven AMR represents an unrecognized consequence of soil heavy metal contamination in urban areas, potentially magnifying health disparities in redlined neighborhoods. We aim to assess the impact of policies, such as redlining, on soil health, metal contamination, and AMR gene evolution, and the implications for disease risk in marginalized communities. We will construct geostatistical models using existing soil grid survey data to examine the relationship between social determinants of health, metal contamination, and antibiotic resistance genes throughout Chicago. We'll then investigate whether residents in areas with high AMR levels are more vulnerable to bacterial infections, potentially influenced by redlined ZIP codes. Our multidisciplinary approach, which incorporates both microbial and human ecology, will uncover overlooked healthcare disparities that are not apparent when focusing solely on molecular-scale pathogen virulence mechanisms, thereby reshaping the role of soils in urban health.

This spatial analysis project explored the relationship between neighborhood demographic characteristics and the distribution of priority snowplow routes in Duluth, Minnesota—a city known for its heavy and persistent winter snowfall. I investigated whether certain populations, particularly non-white and foreign-born residents, are disproportionately underserved in municipal snowplow services. Using spatial regression analysis tools in ArcGIS Pro, I evaluated the impact of variables such as population density, median household income, reliance on public transit, and foreign-born population on the density of Priority snowplow routes across census tracts. Findings indicated that higher densities of priority snowplow routes are associated with more densely populated, lower-income areas where public transit reliance is high. These areas are located primarily in the city center. Contrary to initial hypotheses, the percentage of foreign-born residents showed no significant effect. These results suggest that Duluth’s snowplow routing aligns more with socioeconomic and infrastructural need than with demographic bias, offering implications for urban planners and municipalities in similar climates.

The Miyawaki method of dense, native tree planting is a place and nature-based tool to build healthy communities by bringing the benefits of forests and green space to communities. Miyawaki forests improve neighborhood and built environments by revitalizing urban places that were previously designed to not have accessible green space, such as formerly redlined communities. Communities with less green space have increased rates of chronic disease, more identified social determinants of health (SDOH), and often have higher documented racial health disparities. By creating tiny native forests, the Miyawaki method increases green space, builds tree equity and increases the ability of community members to benefit from the health, community, and social benefits that greenspaces provide. Miyawaki forests grow quickly and are self-sustaining in two to three years and mature in twenty to thirty years (versus centuries). They increase biodiversity in the local area and reduce heat island effects in a community. These forests are low maintenance and can help historically disadvantaged communities mitigate the effects of climate change through remediation of damaged urban soil, increasing the water absorption capacity and reducing storm water run-off. As these green spaces mature, they also mitigate air pollution. When developed with community engagement, these forests can foster multi-generational social interaction through planting and maintenance activities, by providing natural spaces for exercise and socialization, and by providing place-based nature spaces that improve mental and physical wellbeing. Nature and greenspace have a direct impact on a community’s overall health and wellness that directly impacts health equity

In geographies with considerable numbers of immigrants, immigrant-run ethnic food retailers provide healthy, affordable, and culturally appropriate foods to newcomer communities. They also provide economic opportunities for immigrants that are not otherwise available due to language, educational, and social barriers. Drawing from my master's thesis research on South Asian immigrant-run food retailers in Scarborough and Markham (Greater Toronto Area) from 1996 to 2016, I posit that frameworks in food environment research rooted in assimilationist narratives fail to account for immigrant agency in the food environment and flatten nuanced immigrant interactions within foodscapes. For example, analyzing the built food environment in broad ethnic categories flatten the diverse ways immigrant communities shape and are shaped by the built environment. Thus, this talk will explore how disaggregating ethnocultural populations in space-time hot spot analysis and spatial panel models that measure the relationship between immigration and the built environment reveal varying spatial and economic dynamics that reflect varied migration histories, political identities, and modes of economic integration.  In doing so, it will demonstrate the need for a nuanced view of immigration as a social determinant of health that is otherwise flattened when diverse populations are grouped together. It will offer an alternative framework to conceptualize immigrant interactions with their host country beyond acculturation, acknowledging and quantifying immigrants’ power in the food environment over space and time.

Despite the increasing attention to research on the food environment as a social determinant of health, much work remains in establishing standardized, valid, and reliable measures. One limitation in many studies on the food environment is the use of census tracts as the unit of analysis. Because individual health data are often protected or restricted, census tracts are typically the lowest level at which geographic identifiers are available. However, because of the variation in tract size across rural and urban regions and the distribution of households within tract, existing methods used to calculate food environment measures are limited by the modifiable areal unit problem (MAUP). Publicly available parcel data offer one way to attenuate these limitations. Specifically, they allow for measures of the food environment that (1) reflect real locations of households in a census tract and (2) capture intra-tract differences (within-tract inequality). In this presentation, we explore these implications by (1) utilizing parcel-level data to calculate FE measures, (2) comparing these measures to traditional FE measures in the context of Los Angeles, and (3) evaluating the new measures' predictiveness of relevant health outcomes. Additionally, we present a reproducible workflow for using open-source software (RStudio and Github), revealing how these measures can be calculated for any geographic area and datas

While a vast body of literature conceptualizes and measures stigma as a set of individual attitudes about groups of people, more place-based theory posits that one or several individuals' attitudes towards a group of people do not constitute stigma. This theory conceptualizes stigma as a social norm. In other words, a setting-level conceptualization of stigma reflects the degree to which it is normative or acceptable in a given setting for people to hold negative attitudes towards a group of people. If we are interested in understanding this setting-level, normative phenomenon, how do we measure it? Dr. Williams will discuss the pitfalls of trying to measure setting-level or place-based stigma using only validated measures of individual attitudes about stigmatized groups, and will illustrate ways to measure stigma (both with individual-level data and setting-level data) that capture variation at the setting level.

Hepatitis C virus (HCV) infection is the most prevalent blood-borne infection in the United States (U.S.), with approximately 2.4 million people living with chronic HCV. The infection intersects with the opioid epidemic primarily due to unsafe injection drug practices disproportionately impacting individuals who inject drugs, are underserved, experience poverty, and are unstably housed. The goal of HCV elimination by 2030 has been firmly established, with initiatives aimed at enhancing testing, facilitating linkage to care and treatment, and improving preventative measures. However, there have been challenges in linking people to treatment and preventing new infections. Testing and immediately linking people with an active HCV infection to treatment is essential for reducing the prevalence and ongoing transmission of the disease, as it decreases the number of people with active infection in the community. To improve testing, treatment, and prevention of HCV within the community, it is essential to understand the geographic accessibility of these services to enhance comprehension of disparities in access to HCV screening and treatment services within local health jurisdictions in the U.S. Given the current guidelines from the U.S. CDC advocating for universal HCV screening and prompt linkage of infected individuals to care, it is critical to identify and map HCV services to address barriers to access to HCV care. Aim: This project aims to create an interactive visualization prototype, HepResourceVu, to effectively map and visualize Hepatitis C care services— including testing, treatment, and prevention— to improve accessibility and inform decision-making for healthcare providers and communities.

In 2024, Cardea partnered with The Raven Collective to explore barriers to pregnancy care for Indigenous communities nationwide, with a particular goal of mapping the social and structural factors contributing to a drastic increase in congenital syphilis in Indian Country. As the project continues to progress, the mapping scope has surpassed my existing skills in mapping data visualizations. I am currently using Story Maps but could use guidance to inform whether that is the right application for the goals of this work. Through the SDOH and Place Fellowship, I hope to build on this work by developing a map that highlights systemic barriers to care access for pregnant people in Indian Country, ensuring the findings are both visually compelling and actionable.Pregnant people diagnosed with syphilis face serious neonatal consequences if not treated before delivery. Since 2018, primary and secondary syphilis diagnoses among women and subsequent congenital syphilis (CS) cases have drastically increased among American Indian and Alaska Native (AI/AN) people, surpassing all other race/ethnicities by 2021. These disparities stem from inequities in experiences with historical trauma, medical trauma and resulting mistrust, limited care access, structural racism, and the disenfranchisement of Indigenous communities. The intersection of substance use, barriers to accessing care, and other social determinants of health are key factors when understanding the rise in syphilis and CS rates among AI/AN people and neonates. AI/AN data is often excluded from maternal and child health data surveillance due to small numbers suppression which only contributes to a lack of attention to the inequities which make data visualizations an even more compelling tool to uplift Indigenous perspectives and contextualize health outcomes.The goal of this place-based equity project is to focus on mapping social determinants of health data available through nationwide publicly available datasets to overlay syphilis rates, reproductive health care provider locations, harm reduction resources, and any additional key SDOH indicators that shape access to prenatal care for pregnant people in Indian Country. By compiling the visualization of these data in one place, we aim to illuminate the structural barriers that contribute to disparities in syphilis and CS rates among AI/AN communities.This project also seeks to underscore the urgent need for policy change by visually depicting the complex landscape of care access, particularly for those impacted by substance use disorder and syphilis. By creating an accessible and compelling Story Map, we hope to equip policymakers, healthcare providers, and community advocates with the insights needed to identify gaps in services and create interventions where they are most needed.

Gaa-waabaabiganikaag, or White Earth Indian Reservation, stretches across 1,300 square miles of rolling prairies, many lakes, and conifer forests in rural northwestern Minnesota. As the state’s largest Indian reservation by land mass, it encompasses all of Mahnomen County and portions of Becker and Clearwater counties. White Earth residents face stark health disparities, with Mahnomen County consistently reporting dramatically higher rates of chronic diseases compared to Minnesota averages. These persistent disparities stem from centuries of maltreatment, exclusion, inadequate funding for Native healthcare infrastructure, and much more. To combat a sliver of these challenges, the Minnesota Department of Health’s (MDH) Diabetes Unit has partnered with the White Earth Nation Tribal Health Department (White Earth Nation THD) to prevent type 2 diabetes and other chronic diseases among White Earth youth and their families. Our collaboration centers on implementing Smart Moves, a CDC-recognized evidence-based program. This comprehensive healthy lifestyle intervention is designed for children ages 7 to 16 through four integrated components: nutrition education, behavior modification, physical activity sessions, and supportive parent/caregiver classes.With implementation set for early Fall 2025, this is an opportune time to develop an asset map, which will aid in the social determinants of health (SDOH) screening and referral process that we are planning to integrate into the program. While there are many resource locators available online, the unique location of rural Minnesota and target population including people at or below poverty level, renders these locators inaccessible and ineffective for many. Using existing resource locators as a guide, the asset map will contain resources related to affordable or free nutritious foods, vetted by White Earth Nation THD, that align with the needs of Smart Moves participants.The asset map will be functional for both Smart Moves implementation staff and participants. Using a ‘search nearby’ feature, the functionality would be similar to other services like GPS, where you can enter an address or location and find nearby services in a selected radius. When a family participating in the Smart Moves program screens positively for food insecurity, this asset map will be a great resource, enabling program staff to refer the family to relevant and appropriate food access services nearby.

Kānaka Maoli (Native Hawaiian) in Hawai’i are disproportionately impacted by multiple structural health inequities. Militourism, which is the reciprocal relationship between military and tourism industries, is a formation of settler colonial and racial violence. Militourism is an underexplored structural determinant of Kānaka Maoli health. Kānaka Maoli are underrepresented in health equity research, largely because of harmful colonial data aggregation practices. In part of my dissertation, the purpose of this prototype is to explore how militourism impacts neighborhood health. I will co-create GIS-based deficit maps of military and tourism sites and the distribution of community assets that advance Kānaka Maoli health equity from both a spatial-structural lens.This project will involve three stages. First, I will meet with my dissertation research hui (group) to set up a protocol for this project. This will encompass an overview of geospatial public health research, asset/deficit mapping fundamentals, GIS methods (i.e., dashboards, storymaps), and the community research protocol. Second, I will conduct a manual extraction of point data through a triangulation of Google Earth searches, online community resource pages from the government, and additional resources from the research hui. Then, my research hui will meet again to discuss the mapped data. Asset and deficit maps will be created using ArcGIS Dashboards. I will use hot spot analyses with the Getis-Ord Gi statistic to identify asset and deficit clustering and spatial autocorrelations with Moran’s I to measure patterns. All analyses will be conducted in R or ArcGIS Pro.Proposed asset data points (addresses) will encompass the following: 1) medical services, 2) sexual health (HIV/STI services), 3) grocery stores and food banks, 4) housing shelters, 5) education services (e.g., schools, libraries), 6) recreational amenities (e.g., parks, gyms), and 7) churches. Deficit data points will contain 1) military bases, 2) tourist infrastructures (e.g., hotels, resorts, short-term rentals), and 3) landfills. Each map will also be overlayed by existing Census block group data sources of the 2023 American Community Survey (i.e., percentages of age, race, income, employment, and crime) from the Census. I will also use the 2022 Neighborhood Deprivation Index scores (1=least disadvantaged and 10=most disadvantaged) by the Center for Health Disparities Research at the University of Wisconsin School of Medicine and Public Health.To the best of my knowledge, there are no existing asset/deficit mapping applications in Hawai’i. Expressed by members of my research hui, members of Papa Ola Lōkahi, Hawai’i Health & Harm Reduction Center, and Kumukahi, that building an asset/deficit mapping infrastructure would help mobilize health equity projects for community organizers, and policymakers in Hawai’i. This infrastructure will complement my future photomapping findings of qualitative interviews on neighborhood health in Hawai’i and provide a rigorous opportunity to inform local policies to curb distinct health inequities. I plan to disseminate this resource and the findings with the help of my research hui to other community coalitions and civil activists. The SDOH & Place Fellowship will be integral to strengthening my GIS technology proficiencies as a geospatial researcher and growth as a creative interdisciplinary storyteller.

Tobacco use is a leading preventable death in the United States, with even greater risks for sexual and gender minority (SGM) populations (National Institute of Health, 2024). SGM individuals are 1.5-2 times more likely to smoke compared to those who are cis-gender, heteronormative (Caceres et al., 2021). SGMs face unique challenges, including targeted tobacco marketing and a lack of access to culturally competent cessation resources. With a smoking rate of 35%, Cleveland, Ohio, is a key city for studying disparities in tobacco cessation (Prevention Research Center for Healthy Neighborhoods, 2023). However, little is known about how tobacco cessation services are distributed across major cities and whether they adequately serve SGM populations.This project aims to map the availability of tobacco cessation resources in Cleveland, Ohio—the city with the highest smoking rates in the United States—focusing on accessibility for SGMs. The findings will help identify disparities in access and inform policy recommendations to ensure equitable distribution of cessation support. An asset mapping approach will identify and visualize tobacco cessation resources for SGMs in Cleveland. Public health datasets, including the Behavioral Risk Factor Surveillance System and the National Survey on Drug Use and Health, will confirm smoking prevalence. A systematic search will identify LGBTQ+-affirming cessation programs, health centers, and harm reduction services. These data will be geocoded and integrated into ArcGIS or QGIS. A buffer analysis will measure proximity to SGM-dense areas, highlighting underserved neighborhoods.

This project will examine how diverse communities interact with and value the lower Clackamas River Basin watershed to achieve health equity goals through the development of an interactive story map visualization tool that integrates narratives, imagery, and spatial data. The Clackamas River Basin watershed provides high-quality drinking water to over 300,000 people while sustaining significant biodiversity and offering recreational opportunities and other crucial health benefits. As the lower Clackamas region develops within Portland, Oregon’s urban growth boundary, its streams, parks, and natural areas become spaces where ecological values merge with emerging recreational and cultural interests. Future development and population growth require strategies to ensure that watershed resources can effectively serve community needs and identities, including understanding how watershed spaces are used, what barriers limit engagement and awareness, and how cultural perspectives influence environmental values and practices. The proposed visualization approach will identify barriers to watershed access and support relationship-building among diverse stakeholders. This will inform targeted outreach programs that promote health equity by expanding access to green spaces, promoting environmental education, and supporting collaborative decision-making. Using a mixed-methods approach, this research combines an ongoing community survey with photovoice and emotional mapping focus groups conducted in partnership with the Clackamas River Basin Council, a locally organized, non-governmental citizen group focused on collaborative ecological restoration, conservation, and community development. The research uses quantitative data on community demographics and spatial access, as well as qualitative insights into community members’ perceptions, knowledge, and experiences of the watershed. Photovoice narratives and emotional mapping data serve to visually and spatially document watershed strengths and concerns through collective environmental health knowledge building that promotes green space access and social connection opportunities. The story map visualization tool will serve multiple purposes, including mapping community perceptions and place-based connections, identifying barriers to watershed access, and supporting relationship-building among diverse stakeholders to inform targeted outreach programs. The project outcomes will provide watershed agencies, community leaders, and users with valuable and accessible insights for promoting equitable access to watershed spaces that serve as vital public health assets while fostering greater environmental stewardship and appreciation of place. By connecting social determinants of health (SDOH) to the accessibility of watershed resources, this project will support more equitable health and community outcomes while empowering diverse communities to engage meaningfully with their natural environments. Through interactive visualization, the fellowship project will illustrate community attitudes, perceptions, and barriers to accessing watershed health opportunities, revealing factors that influence community engagement with watershed resources and spaces. By highlighting how diverse communities utilize and value the watershed, the story map platform will facilitate a meaningful dialogue between agencies and communities about watershed protection and access, informing culturally responsive programs that promote health equity by ensuring that traditionally underserved communities have equal access to natural spaces and educational opportunities that enhance mental, physical, and social well-being.

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While I’ve learned basic asset mapping through alcohol retailer density maps, my passions lie in environmental justice and creating platforms to amplify the stories of those disproportionately affected by unsafe environments. There is a real need to shed light on the growing issue of climate change, especially here in San Diego. One of the projects at my organization, Elevate Youth, and their youth coalition, the Clean Coast Collaborative (CCC), provides volunteer and leadership opportunities to students interested in climate advocacy in their communities in the South Bay of San Diego. This region is located at the southern tip of California, just north of Tijuana, Mexico, and is primarily populated by Spanish-speaking Latinx. This community is heavily impacted by the pollution caused by the Tijuana River Estuary sewage problem. Community members lose access to valuable outdoor green spaces and beaches and are exposed to toxic water and air daily, putting residents at risk for numerous health issues. The CCC helps raise awareness and advocate for a cleaner and healthier environment for their community. After discussing goals and receiving input from the youth volunteers and the Elevate Youth team, I have developed a project proposal to bring attention to and spread awareness on this environmental justice issue. I will map water and air quality (particulate matter, PM 2.5, concentration, and drinking water contaminants) and overlay that with socioeconomic information (race/ethnicity and income) and health rates of pulmonary-related chronic disease (asthma, COPD) across San Diego County to show the health inequities South Bay residents face. I will create a story map using data from the California Office of Environmental Health Hazard Assessment, the San Diego Association of Governments, and the County of San Diego websites. I will collect personal statements and photos of health outcomes, outdoor space closures, and visible contamination from coalition members. All quantitative data has been identified and downloaded, and the Elevate Youth team will collect the qualitative data through a survey over two weeks. My previous experience creating asset maps will provide ample time for me to learn and add qualitative content to the story map and finish the project on time. A story map will be an engaging way for different audiences to view and understand the data and information to create more attention and concern for this issue across the county and local government. Working closely with the CCC and South Bay community members will help guide my project in a way that best captures and shares their stories. Achieving health equity begins with empathy and trust between communities and public health workers. My position in my organization and the San Diego community provides a valuable pathway to sharing authentic experiences and enacting positive change. The power of data presentation is immense and necessary to amplify the voices of overshadowed populations. Through acceptance into this fellowship, I want to learn more ways to share health data for policy change.

Since 2023, my advisor and I have worked in Illinois neighborhoods lacking resources for socioemotional well-being and mental health. Through our community collaborations, we have also seen how environmental disparities impact these neighborhoods. In Danville, African American neighborhoods express concerns over poor air quality and its impact on asthma. Schools in Bloomington share worries about nearby highway traffic on the health of low-income and immigrant communities. A decommissioned Air Force Base in Rantoul concerns African American and low-income residents about environmental contamination leaving them exposed to unsafe contaminants in drinking water and residential homes and increased cancer rates. These communities face intersecting challenges related to environmental disparities and socioeconomic and health inequities. I propose developing an Illinois data dashboard highlighting this information, which would be valuable for communities. Residents could access the dashboard to understand their own neighborhood conditions. Community organizations would use the dashboard to demonstrate environmental and health disparities to important stakeholders in local planning boards and foundations involved in neighborhood development. The dashboard would help support them in pursuing new programs and obtaining funding to address environmental concerns.The dashboard I envision includes 3 categories: socioeconomic, environmental, and health. The dashboard will display a map of Illinois census tracts, in which users can select category variables to display the thematic distribution (e.g. color-coded standard deviation/box plot maps). Environmental variables are values for environmental quality measures, while socioeconomic and health variables are population percentages. The data is from the 2022 CDC’s Environmental Justice and Social Vulnerability Index, which I have previously attained/backed up. Initially, I will focus the dashboard on key variables of interest for the mentioned communities. Socioeconomic data will have 5 variables: poverty, population under 18, population over 65, civilians with a disability, and limited English speakers. Environmental data will have 7 variables: PM 2.5, diesel pollution, national priority list site proximity, parks, pre-1980 housing, high-volume roads, and water pollution. There will be 3 health variables: asthma, cancer, and poor mental health. Additionally, clicking on the map can show census tract population percentages by different races and ethnicities. My project will be achievable within the fellowship timeline. The modules will improve my wrangling skills and teach me an appropriate dashboard application. I am skilled and experienced in coding and analysis in R. I am excited about creating a dashboard like the ones showcased by the 2024 cohort. I look forward to developing skills in dashboard applications, and a strength of mine is learning new applications quickly from materials and practice. Outside the sessions, I’ll have 5 hours/week to practice module material and apply it to my data. I will have additional time to develop and complete the dashboard, especially in May-July. The program will help me incorporate a user-centered design that is friendly, relevant, and usable for underserved Illinois communities. Additionally, I have community partners with whom to share prototypes and receive feedback to enhance dashboard usability. This program and community would nicely build upon my knowledge of spatial data and collaborations in health equity research.

Wildfires now account for up to 25 percent of PM2.5 concentrations across the US and up to 50 percent in Western states, and the number of premature deaths attributable to fire-related PM2.5 is estimated to double over the next 80 years. This increase in the volume and burden of smoke exposure has led to calls to re-frame wildfire as a public health disaster; as a recent American Journal of Public Health editorial states, “It’s the smoke, not the fire” (Eisenman, 2023).  However, poor health outcomes related to wildfire smoke are unevenly distributed across subpopulations, with racial and ethnic minorities, children and adolescents, older adults, urban residents, and those living in low socioeconomic areas at greatest risk. Communities not directly affected by the flames themselves may also bear the brunt of aerosolized particulate matter; between 2011-2021, US census tracts with the greatest ‘social vulnerability’ experienced a 358 percent increase in the average annual number of heavy smoke days. In this way, wildfire smoke reflects the “slow violence” of climate change, an exposure that accumulates and becomes disproportionately embodied among those with higher socio-structural vulnerability.While current data visualizations exist for mapping discrete wildfire events (i.e., to map containment or plan evacuation routes), for my proposed place-based data project, I aim to create a dashboard which maps the spatial variation in cumulative wildfire *smoke* exposure in Northern California, and its association with tract-level demographic, respiratory, and mental health outcomes. For my exposure data, I will use a novel Wildfire Smoke Footprint dataset, developed by colleagues at the National Center for Atmospheric Research (NCAR). The ‘Smoke Footprint’ is a dynamic, near real-time dataset which uses historical data from the NCAR Fire Inventory (FINNv2) to simulate the formation and evolution of carbon monoxide (CO) and primary PM2.5 and PM10 aerosols emitted by fires in California since 2005. By having access to persistent PM2.5 surface concentrations over time, in conjunction with a record of major wildfire events over the past two decades, this novel database provides researchers with the unique opportunity to disaggregate acute periods of heightened exposure from ambient air pollution.I will also use American Community Survey (ACS) five-year estimates to characterize demographics within high, moderate, and low exposure tracts of the Wildfire Smoke Footprint, mapping variations in age distribution, racial/ethnic composition, and household income. I will use CDC Population Level Analysis and Community Estimates (PLACES) public use dataset as my source of health data. I will visualize concentrations of high, moderate, and low wildfire smoke and their spatial association with physical and mental health outcomes in Northern California, using Kriging Density Functions to identify hot spots (high values) and cold spots (low values) for prevalence of health outcomes of interest. This dashboard aims to highlight areas of social vulnerability for public health risk communication, mitigation, and resource allocation during wildfire smoke events. I plan to do these analyses either in ArcGIS ArcMAP or in R, to be completed between April-July 2025.

Health and wellbeing are shaped not only by individual behaviors but also by the built environment, including access to social infrastructure—shared sites that support social connection, community engagement, and care. These places play a critical role in mental, emotional, and physical health across the lifespan by reducing loneliness, strengthening social networks, and fostering resilience. For emerging adults (ages 18-29), social infrastructure is particularly vital, as this life stage is marked by transitions in education, work, and relationships that shape long-term health. However, disparities in access may limit their benefits, especially for young adults in lower-income or historically marginalized neighborhoods. This project will create a place-based, equity-focused, interactive dashboard visualizing social infrastructure accessibility for emerging adults in the Denver-Boulder area. Drawing on preliminary qualitative research and literature, I will identify social infrastructure sites commonly used by this age group—such as recreational facilities, bars, and restaurants—and analyze their accessibility. Using spatial analysis and GIS-based clustering, I will determine where social infrastructure is concentrated, where gaps exist, and how accessibility (a multi-dimensional concept including availability, proximity, and affordability) varies by neighborhood demographics. This project will address: Which neighborhoods have limited access to social infrastructure for emerging adults? How do spatial patterns of social infrastructure align with neighborhood-level social determinants of health, such as socioeconomic status, racial segregation, and housing stability? How can interactive data visualization support public health, urban planning, and community-led efforts to improve equitable access? Approach Spatial Analysis: Identify and map social infrastructure sites, conduct cluster analysis, and overlay demographic data to highlight disparities. Dashboard Development: Build a public-facing, user-friendly platform integrating thematic asset maps and accessibility metrics. Community Feedback Features: Enable users to rate affordability, ambiance, and inclusivity; add descriptive tags; and filter locations based on personal priorities. Health and Policy Linkages: Examine how spatial patterns relate to social isolation and mental wellbeing, highlighting implications for policy. This project aligns with the SDOH & Place Fellowship’s mission to use spatial tools to advance health equity. It builds on my expertise in GIS, spatial analysis, and health geography while strengthening my skills in interactive dashboard development, user-centered design, and participatory mapping. Working with the Healthy Regions and Policies Lab, I will refine this tool into a practical resource for urban planning, public health, and community-led initiatives to support emerging adults’ social health and wellbeing. By incorporating community-driven data and feedback, this project will not only visualize access to social infrastructure but also amplify lived experiences—ensuring that the voices of emerging adults are centered in conversations about health equity, urban planning, and community wellbeing.

Addressing Social Determinants of Health (SDOH) requires more than data collection—it demand a deep understanding of the lived experiences behind the numbers. This session introduces Human-Centered Design (HCD) as a powerful methodology for bridging the gap between systems and the communities they serve. Through an interactive exercise, participants will explore how HCD surfaces the knowledge, values, and emotional needs of individuals impacted by social and structural inequities. Attendees will gain practical insight into how co-creation with patients and community members can shift both the questions we ask and the solutions we build. Rather than relying solely on expert-driven models, HCD invites multidisciplinary collaboration and elevates nontraditional voices in the design process. This approach emphasizes empathy, storytelling, and iterative learning. These are essential components when navigating the complexity and sensitivity of SDOH-related data. Participants will leave equipped with a framework for facilitating inclusive dialogue and reframing challenges in ways that foster trust, agency, and relevance. By integrating HCD into SDOH strategies, we move toward more responsive, equitable interventions that not only reflect but are shaped by the communities they aim to serve. This session is ideal for public health professionals, healthcare leaders, data strategists, and anyone committed to advancing equity through participatory practices.

SDOH risk scores generated by Socially Determined can infer and explain disease outcomes at the ZCTA resolution, demonstrating how place-based SDOH factors shape community health for equitable policy decisions. Socially Determined measures community SDOH risk for the entire United States with seven risk scores, each quantifying a distinct, community-level social determinant of health: financial strain, food insecurity, housing instability, transportation barriers, health literacy challenges, digital landscape, and social connectedness. Applying the risk scores and supplementary measures, we enriched our data with the CDC PLACES dataset and built interpretable machine-learning models for predicting high-cost health conditions at the ZCTA resolution. Our analysis focused on predicting three health outcomes: stroke, obesity, and diabetes. The study's results revealed that the best model captured more than 75% of the variance in each of the three outcomes. The models indicated the main drivers of the disease prevalences were attributed to the neighborhoods' median housing cost, poor health literacy, the level of education attained, and lack of opportunities for economic advancement. The findings from the study demonstrate the importance of community-level SDOH risk scores in data-driven health equity interventions and decision pipelines.

Children are particularly vulnerable to climate-related disasters due to their biologic and developmental age-related tolerance. Yet, healthcare systems often lack the geospatial framework necessary to proactively identify and support patients most at risk. We introduce a single-center, retrospective study that applies spatial science methodologies to identify pediatric subpopulations most susceptible to the health impacts of climate-driven events like the January 2025 Southern California wildfires. Integrating address data from the electronic health records (EHR) of pediatric patients from Children’s Hospital Los Angeles with evacuation zone data and PM## estimates during the time Reporting provided by Web Services at Public Affairs of the fires enabled us to examine how social determinants of health, including race/ ethnicity, insurance status, and health-related social needs intersect with clinical comorbidities and disaster exposure. Our hypothesis generating study aimed to uncover place-based disparities and inform more equitable disaster response planning. There is a need for geospatial informatics to support emergency decision making when disasters impact health delivery and outcomes. Identifying displaced patients can help health systems coordinate continuity of care like telehealth outreach for those with disrupted access. Identifying displaced staff can help health systems optimize scheduling so resources are allocated accordingly. Measuring patient exposure to environmental hazards like poor air quality can help health systems anticipate populations most vulnerable to respiratory conditions or other chronic illnesses. This work contributes to the broader effort to ground clinical and public health decision-making in place-based data to advance health equity in a changing climate.