Daniel Johnson
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Interactions Between Social and Environmental Vulnerability
Social vulnerability refers to the limited ability of people or communities to respond to an external stressor such as a heat wave or epidemic. Environmental vulnerability in this context implies the environmental stressors that a location is likely to experience. When social and environmental vulnerabilities overlap, there is a high likelihood of significant negative impacts in the local community .Professor Daniel Johnson's research focuses on social and environmental vulnerability and how they inter-relate in space and time. His focus is on modeling these interactions and working toward predictive models that can help increase resilience, prevent disaster or mitigate the effects. The forces of both social and environmental vulnerability are very pronounced in urban areas where disparities between populations are highly evident.
His models have been deployed in several cities to guide the opening of cooling centers during extreme heat events. Some of his earlier work found that cooling centers were not located in areas or neighborhoods where vulnerable populations lived. Using models he and his research team developed, cities were able to open cooling centers or use buses, as mobile cooling centers, in neighborhoods where they were needed the most.
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Recent Submissions
Item A foundation-model GeoAI framework for continuous heat and health risk mapping(Frontiers Media, 2026) Johnson, Daniel P.; Geography, School of Liberal ArtsIntroduction: Urban heat represents one of the most critical and inequitable manifestations of climate change, with mounting impacts on human health, energy systems, and urban sustainability. Bridging the gap between observation and inference requires scalable approaches that enable all-weather, continuous urban heat mapping at decision-relevant resolutions. Methods: This study introduces a multimodal Geospatial Artificial Intelligence (GeoAI) pipeline that fuses atmospheric reanalysis and Earth observation to generate hourly, super-resolved land-surface temperature (LST) estimates for urban heat and health-risk assessment. The pipeline integrates three complementary foundation models—Prithvi-WxC, Prithvi-EO, and Granite-LST—to capture interactions between atmospheric dynamics and surface morphology. The system is implemented over Indianapolis, Indiana. Results: The pipeline produces continuous temperature fields at 10–30 m resolution with sub-2 °C error, reproducing realistic diurnal heat-island dynamics across the Indianapolis study area. The fused model captures fine-scale thermal heterogeneity driven by impervious surface fraction, vegetation cover, and building morphology, resolving intra-urban temperature gradients that single-source products miss. Hourly temporal continuity enables characterization of heat exposure timing and duration, including nocturnal heat retention in historically underserved neighborhoods. Discussion: Beyond technical performance, the framework demonstrates how foundation-model fusion can bridge environmental monitoring and health analytics, offering a scalable tool for exposure mapping, early-warning systems, and equitable climate adaptation. This work establishes a reproducible blueprint for AI-enabled urban climate twins, advancing the integration of environmental intelligence into public health resilience planning.Item Introduction to Daniel Johnson & His Work(Center for Translating Research Into Practice, IU Indianapolis, 2022-04) Johnson, DanielProfessor Daniel Johnson briefly discusses his translational research that focuses on social and environmental vulnerability and how they inter-relate. When they coincide, there is an increased risk for a disaster. Professor Johnson models these interactions and is working toward creating predictive models that can help increase resilience, prevent disaster or mitigate the effects.Item Interactions Between Social and Environmental Vulnerability(Center for Translating Research Into Practice, IU Indianapolis, 2022-04-22) Johnson, DanielIn this presentation, Professor Daniel Johnson discusses how he uses predictive analytics to create models that can forecast the effects of extreme events. These models are useful as preparation tools for disaster or as aids to plan for mitigation of potential hazards.Item Landsat zooms in on cities’ hottest neighborhoods to help combat the urban heat island effect(Indiana University, 2022) Johnson, Daniel P.; Geography, School of Liberal ArtsItem Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network(Frontiers, 2022) Johnson, Daniel P.; Lulla, Vijay; Geography, School of Liberal ArtsAs COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability—including environmental determinants of COVID-19 infection—into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for “what-if” analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications.Item Population-Based Disparities in U.S. Urban Heat Exposure from 2003 to 2018(MDPI, 2022) Johnson, Daniel P.; Geography, School of Liberal ArtsPrevious studies have shown, in the United States (U.S.), that communities of color are exposed to significantly higher temperatures in urban environments than complementary White populations. Studies highlighting this disparity have generally been cross-sectional and are therefore “snapshots” in time. Using surface urban heat island (SUHI) intensity data, U.S. Census 2020 population counts, and a measure of residential segregation, this study performs a comparative analysis between census tracts identified as prevalent for White, Black, Hispanic and Asian populations and their thermal exposure from 2003 to 2018. The analysis concentrates on the top 200 most populous U.S. cities. SUHI intensity is shown to be increasing on average through time for the examined tracts. However, based on raw observations the increase is only statistically significant for White and Black prevalent census tracts. There is a 1.25 K to ~2.00 K higher degree of thermal exposure on average for communities of color relative to White prevalent areas. When examined on an inter-city basis, White and Black prevalent tracts had the largest disparity, as measured by SUHI intensity, in New Orleans, LA, by <6.00 K. Hispanic (>7.00 K) and Asian (<6.75 K) prevalent tracts were greatest in intensity in San Jose, CA. To further explore temporal patterns, two models were developed using a Bayesian hierarchical spatial temporal framework. One models the effect of varying the percentages of each population group relative to SUHI intensity within all examined tracts. Increases in percentages of Black, Hispanic, and Asian populations contributed to statistically significant increases in SUHI intensity. White increases in population percentage witnessed a lowering of SUHI intensity. Throughout all modeled tracts, there is a statistically significant 0.01 K per year average increase in SUHI intensity. A second model tests the effect of residential segregation on thermal inequity across all examined cities. Residential segregation, indeed, has a statistically significant positive association with SUHI intensity based on this portion of the analysis. Similarly, there is a statistically significant 0.01 K increase in average SUHI intensity per year for all cities. Results from this study can be used to guide and prioritize intervention strategies and further urgency related to social, climatic, and environmental justice concerns.Item Hoosiers’ Health in a Changing Climate: A Report from the Indiana Climate Change Impacts Assessment(Purdue University, 2018-01-01) Filippelli, Gabriel; Widhalm, Melissa; Filley, Rose; Comer, Karen; Ejeta, Gebisa; Field, William; Freeman, Jennifer; Gibson, Joe; Jay, Stephen; Johnson, Daniel P.; Moreno-Madriñán, Max; Mattes, Richard; Ogashawara, Igor; Prather, Jeremy; Rosenthal, Frank; Smirat, Jeries; Wang, Yi; Wells, Ellen; Dukes, JeffreyItem Method of modeling the socio-spatial dynamics of extreme urban heat events(United States Patent Office, 2013-10-22) Johnson, Daniel P.; Wilson, Jeffrey S.A method of coupling surface urban heat island measures with socio-economic indicators of vulnerability to create improved spatially specific models to assist public health professionals in predicting extreme heat events mortality in urban environments. The method includes utilizing landsat TM imagery for the measuring of the urban heat island intensity levels and a spatial analysis of the variables in question.Item Spatiotemporal Associations Between Social Vulnerability, Environmental Measurements, and COVID‐19 in the Conterminous United States(AGU, 2021-07-21) Johnson, Daniel P.; Ravi, Niranjan; Braneon, Christian V.; Geography, School of Liberal ArtsThis study summarizes the results from fitting a Bayesian hierarchical spatiotemporal model to coronavirus disease 2019 (COVID-19) cases and deaths at the county level in the United States for the year 2020. Two models were created, one for cases and one for deaths, utilizing a scaled Besag, York, Mollié model with Type I spatial-temporal interaction. Each model accounts for 16 social vulnerability and 7 environmental variables as fixed effects. The spatial pattern between COVID-19 cases and deaths is significantly different in many ways. The spatiotemporal trend of the pandemic in the United States illustrates a shift out of many of the major metropolitan areas into the United States Southeast and Southwest during the summer months and into the upper Midwest beginning in autumn. Analysis of the major social vulnerability predictors of COVID-19 infection and death found that counties with higher percentages of those not having a high school diploma, having non-White status and being Age 65 and over to be significant. Among the environmental variables, above ground level temperature had the strongest effect on relative risk to both cases and deaths. Hot and cold spots, areas of statistically significant high and low COVID-19 cases and deaths respectively, derived from the convolutional spatial effect show that areas with a high probability of above average relative risk have significantly higher Social Vulnerability Index composite scores. The same analysis utilizing the spatiotemporal interaction term exemplifies a more complex relationship between social vulnerability, environmental measurements, COVID-19 cases, and COVID-19 deaths.Item The Primary Advantage in Utilizing Remote Sensing Assets for Extreme Heat Vulnerability Studies(Earthzine, 2014-07-10) Johnson, Daniel P.Remotely sensed imagery provides an alternate view of spatial characteristics that in situ measurements typically lack. This is an advantage to utilizing such datasets for the analysis of environmental health vulnerabilities.