Publications

( * Corresponding Author )

Working Papers

1. Seong, K., Mandalapu, A, & Jiao, J*. The Legacy of Redlining: Unveiling the Impact on Natural Hazard Risk and Community Resilience. (Under Review: Applied Geography)
2. Seong, K., Mandalapu, A., & Jiao, J*. Uneven Heat Vulnerability across Urban and Rural Texas: Emergency Care Burdens, Built Environment, and Planning Implications. (Under Review: Cities)
3. Rouhi Rad, M.*, Abella-Gutiérrez, J., Banner, J., Nielsen-Gammon, J., Seong, K., Dessler, A. E., Bahrami, S., Kang, N., Mace, R. E., Medina, N., Wyatt, B. M., Mieno, T., Cook, M., Fares, A., & Schlichting, L. Economic costs and mechanism of the summer 2023 heat wave and drought on Texas. (Under Review: Texas Water Journal)
4. Choi, S.*, Lee, H., & Seong, K. Assessing the Flood Resilience of Electric Vehicle Charging Stations in Seoul: Evidence from Utilization Data and Accessibility Analysis. (Under Review: Transportation Research Interdisciplinary Perspectives)
5. Toscan, P. C., Mai, G., Liu Z., Seong, K., Jiao, J. Oliveira, M., Pereira, E. Automated Identification of Nature-Based Solutions in Urban Environments Using Remote Sensing and GeoAI: A Systematic Review. (Under Review: Geoscience Frontiers)
6. Seong, K., Planning for Urban Power Resilience after Winter Storm Uri: Nighttime Light-Based Recovery Patterns and Spatial Strategies in Harris County, Texas. (Plan to submit in Sep 2026: International Journal of Disaster Risk Reduction)
7. Seong, K. & Jiao, J.*. Predicting Heat-Related Emergency Department Visits: A GeoAI Approach to Assessing Extreme Heat Vulnerability in Texas. (Plan to submit in February 2027: Sustainable Cities and Society)
8. Jiao, J. & Seong, K. Smart Cities with AI. Edited Book contracted with Taylor & Francis/CRC Press. (Planned publication: December 2026)

Peer-reviewed Journal Articles

2026

20. Choi, S., Seong, K.*, & Jiao, J. (2026). Investigating data center site planning of major ICT companies in the US: A spatial and methodological framework. Cities, 107509. [ abstract | official link ]

As AI becomes more embedded in urban systems, the demand for data centers continues to rise. This study analyzes 212 data centers in the U.S., focusing on those operated by major ICT enterprises. We examined key site-selection factors using clustering, suitability scoring, graph-based network analysis, and conditional logistic regression. K-means was selected for clustering. Variables were grouped into environmental and locational site-level factors to generate separate suitability indices. To identify siting determinants, we then estimated conditional logistic regression models, comparing each data center against spatially comparable control locations. The results highlight the complexity of selecting a site for a data center. In general, data centers exhibit spatial agglomeration and gain operational advantages from economies of scale. Clustering analysis grouped sites into six categories, each reflecting distinct locational and environmental patterns. The graph-based network analysis further illustrates the trade-offs companies must make between environmental suitability and operational site-level advantages. Regression results revealed that land use structure, particularly land use diversity and was the most powerful determinant of data center siting. Infrastructural factors, including substation density and proximity to primary roads, as well as the concentration of IT workers, also exhibited strong positive effects. Among environmental factors, lower wind speeds and PM2.5 concentrations positively influenced data center siting, while state-level policy incentives remained statistically insignificant. Our findings highlight the distinctive infrastructural characteristics of these facilities. Acknowledging their widespread operational footprint across diverse urban landscapes, planners must proactively engage with the physical and spatial dimensions of digital infrastructure.

19. Seong, K.*, Van Zandt, S., Peacock, W. G., & Newman, G. (2026). Racial and Ethnic Change in Floodplain Buyout Neighborhoods: Twenty-five Years of Evidence from Houston. Journal of Planning Education and Research. 0739456X251397310. [ abstract | official link ]

In the face of growing impacts from climate change, particularly in flood-prone areas, buyouts have become critical for mitigating disaster risks. While the environmental and financial benefits of buyouts are well-documented, their broader sociocultural implications—especially their effects on the racial and ethnic composition of communities—have received less attention. Understanding the influence of floodplain buyouts on these demographic shifts is important not only for comprehending urban dynamics but also for crafting equitable planning policies. This study examines long-term racial and ethnic shifts following floodplain buyouts over twenty-five years in Houston, Texas, USA. Using smaller neighborhood units in analysis and a mixed-method approach, the study enables a nuanced understanding of the interplay between buyout policies and racial dynamics, filling a significant gap both in urban planning literature and practice. By uncovering the patterns and consequences of racial turnover following floodplain buyouts, this research provides planners and policymakers with insights necessary for developing strategies that mitigate environmental risks while enhancing the social fabric of urban communities.

2025

18. Seong, K.*, Jiao, J., Lewis, R. H., Farahi, A., Navratil, P., Casebeer, N., Jones, J., & Niyogi, D. (2025). Towards a Digital Twin for Smart Resilient Cities: Real-time fire and smoke tracking and prediction platform for community awareness (FireCom). Computational Urban Science, 5, 49. [ abstract | official link ]

This paper discusses the development and application of a digital twin (DT) for urban resilience, focusing on an integrated platform for real-time fire and smoke. The proposed platform, FireCom, adapts DT concepts for the unique challenges of urban fire management, which differ significantly from regional wildfire systems. Through an exploratory case study in Austin, Texas, in the United States, this research bridges the theoretical foundations of 3D DT with their practical application in fire and smoke management. By fusing diverse data sources, ranging from air quality sensors and meteorological data to 3D urban infrastructure, FireCom supports both emergency response and public awareness through a publicly accessible dashboard. Unlike platforms developed primarily for wildland fire applications, FireCom is specifically designed to account for urban complexities such as building canyon effects on smoke dispersion and the heightened exposure risks associated with dense populations. This study contributes a scalable, replicable framework for municipalities seeking data-driven tools for proactive disaster management, with implications for broader climate resilience planning in urban areas.

17. Toscan, P. C.*, Seong, K., Jiao, J., Ribeiro, C. A. L. R., Carvalho, F. A. C., Oliveira, M. L., & Pereira, E. B. (2025). Impact of Nature-Based Solutions (NBS) on urban surface temperatures and land cover changes using remote sensing and machine learning. Remote Sensing Applications: Society and Environment, 101721. [ abstract | official link ]

Urban areas are increasingly vulnerable to climate change, with rising urban heat driven by the replacement of vegetation with impervious surfaces. Nature-Based Solutions (NBS) provide promising strategies to mitigate urban heat while promoting environmental sustainability. This study analyzes the spatiotemporal dynamics of Land Cover (LC) and Land Surface Temperature (LST) in Guimarães, Portugal, from 2013 to 2023, and forecasts scenarios for 2028 using advanced machine learning techniques. Key methodologies included supervised LC classification via Random Forest (RF), LC prediction using the MOLUSCE plugin, and LST prediction using ensemble models such as XGBoost, Bagging, and AdaBoost, with XGBoost demonstrating the highest performance (R2 = 0.9543). The results highlight significant transitions from barren and built-up areas to vegetation, reflecting localized environmental recovery. NBS interventions, such as green roofs and urban gardens, achieved measurable cooling effects, reducing temperatures by up to 2.49 °C in their surroundings. Projections for 2028 indicate a slight decline in vegetation (−0.35 %), underscoring the urgent need for strengthened conservation efforts. Identified thermal hotspots, particularly in urban and industrial zones, further emphasize the importance of expanding NBS strategies. This research advances the integration of remote sensing and machine learning for urban climate analysis, offering practical insights for urban planning and climate mitigation policies. Future studies should incorporate additional variables to refine prediction models, assess large-scale impacts of distributed NBS, and leverage high-resolution data for broader applications. These findings provide a scalable framework for sustainable urban development worldwide.

16. Seong, K.*, Choi, S., and Jiao, J. (2024). IoT sensors as a tool for assessing spatiotemporal risk to extreme heat. Journal of Environmental Planning and Management. 1-23. [ abstract | official link ]

The safety of urban populations sensitive to extreme heat is under increasing threat. Few studies examine the potential benefits of deploying IoT environmental sensors in the urban context and their integration with large-scale human activity data. This paper examines the deployment of IoT sensors in high-resolution extreme heat risk assessment in the case of Seoul, South Korea. This study conducted spatiotemporal analysis on heat exposure with IoT sensors, compared it with an existing land surface temperature map for validation, combined it with human activity data for risk assessment, and finally discussed the benefits of IoTs in detecting abnormal weather events. The results show that extreme heat risks and characteristics vary by age group, and socio-demographic nature overlaps with contextual factors concerning climate risk. This paper discussed possible policy implications to better deal with recurring climate hazards using IoT sensors.

15. Jiao, J., Seong, K.*, Sammer, M., Lewis, R. H., Reese, A., Olvera, N., Gronseth, SL., Anderson-Fletcher, E. & Kakadiaris, I. (2025). Developing a healthy food access index (HFAI): Web-based mapping and future directions for AI integrations. Cities, 161, 105908. [ abstract | official link ]

Food insecurity remains a pressing issue in urban communities, particularly in underserved areas with limited access to healthy and affordable food. This study introduces a comprehensive approach to assessing and addressing food access challenges in the Greater Houston region by developing a Healthy Food Access Index (HFAI). The HFAI incorporates a wide range of data sources, including economic and sociocultural deprivation indices, retail food environment measures, physical access indicators, and accommodation access data, that offer a multidimensional understanding of food access. Using GIS-based mapping and hot spot analysis, we identify areas of critical need, highlighting significant disparities in food access and health across the region. We also present the web-based platform, “AI-FEED,” designed to support community leaders and urban planners by visualizing food access challenges and providing AI-driven suggestions for optimizing food pantry placements, incentivizing healthy options at dollar stores, and improving public transit routes. This platform integrates real-time data, offering dynamic and proactive solutions to enhance food security and improve public health outcomes. Our study introduces the importance of targeted, data-driven interventions in addressing food insecurity and demonstrates the potential of AI and digital platforms in supporting sustainable urban food systems.

2024

14. Seong, K.*, Jiao, J., Mandalapu, A., and Niyogi, D. (2024). Spatio-Temporal Patterns of Heat Index and Heat-Related Emergency Medical Services (EMS). Sustainable Cities and Society, 111, 105562. [ abstract | official link]

Despite growing concerns about heat waves due to climate change and their health impacts, there has been limited research on patterns of extreme heat during summertime and their association with heat-related Emergency Medical Services (EMS) incidents. This study examines spatiotemporal patterns of the heat index (HI) and its relationship to heat-related EMS incidents in Austin-Travis County, Texas, focusing on the summers of 2020 and 2021. Collecting 47,838 heat-related EMS incidence cases and aggregating them at the tract level (N = 290), the research employs spatiotemporal analysis, spatial autocorrelation, K-means clustering, and geographically weighted Poisson regression to identify disparities in heat-related health outcomes. Key findings indicate a significant correlation between high HI frequency and intensity and increased EMS incidents, particularly in East Austin, underscoring the area’s heightened vulnerability to heat. The study also reveals that heat vulnerability and urban growth patterns are closely linked to the incidence of heat-related illnesses, and its impact varies by region. These results emphasize the critical need for targeted heat resilience strategies in urban planning and emergency response. This research merges socio-economic and environmental data to offer insights into heat-related health risks, informing targeted public health policies and urban planning for more equitable and effective interventions.

13. Mandalapu, A., Seong, K.*, & Jiao, J. (2024). Evaluating urban fire vulnerability and accessibility to fire stations and hospitals in Austin, Texas. PLOS Climate, 3(7), e0000448. [ abstract | official link]

Anthropogenic climate change has increased the frequency and intensity of fires. Despite their widespread consequences, current research has largely overlooked urban fires and their associated vulnerability. This study seeks to identify patterns of fire vulnerability, map out areas with high fire vulnerability and limited access to fire stations and hospitals, and ultimately determine the factors contributing to increased fire incidents. Principal Component Analysis was used to develop a fire vulnerability index comprising variables capturing health status and socio-environmental factors. Enhanced 2-step floating catchment area (E2SFCA) analysis was conducted to determine relative accessibility to resources such as hospitals and fire stations. Ordinary least squares (OLS) regression and geographically weighted regression (GWR) were utilized to determine factors associated with higher fire incident counts. The results of the fire vulnerability analysis highlight areas of high fire vulnerability in the eastern periphery and the north-central parts of Austin. Moreover, the eastern periphery experiences decreased accessibility to fire stations and hospitals. Finally, the results of the GWR analysis highlight a varied negative relationship between health vulnerability and fire incidents and a positive relationship with socio-environmental vulnerability. The GWR model (R2: 0.332) was able to predict a greater extent of the variance compared to OLS (R2: 0.056). Results of this study underscore that areas with socio-environmental vulnerabilities are likely to face a higher number of fire incidents and have reduced access to hospitals and fire stations. These findings can inform public health officials, city planners, and emergency services departments in developing targeted strategies to mitigate the harm caused by fire incidents.

12. Sammer, M., Seong, K., Olvera N., Gronseth S. L., Anderson-Fletcher, E., Jiao, J., Reese, A., & Kakadiaris, I. A. (2024). AI-FEED: Prototyping an AI-Powered Platform for the Food Charity Ecosystem. International Journal of Computational Intelligence Systems, 17(259). [ abstract | official link ]

This paper presents the development and functionalities of the AI-FEED web-based platform (ai-feed.ai), designed to address food and nutrition insecurity challenges within the food charity ecosystem. AI-FEED leverages advancements in artificial intelligence (AI) and blockchain technology to facilitate improved access to nutritious food and efficient resource allocation, aiming to reduce food waste and bolster community health. The initial phase involved comprehensive interviews with various stakeholders to gather insights into the ecosystem’s unique challenges and requirements. This informed the design of four distinct modules in the AI-FEED platform, each targeting the needs of one of four stakeholder groups (food charities, donors, clients, and community leaders). Prototyping and iterative feedback processes were integral to refining these modules. The food charity module assists charities in generating educational content and predicting client needs through AI-driven tools. Based on blockchain technology, the food donor module streamlines donation processes, enhances donor engagement, and provides donor recognition. The client module provides real-time information on food charity services and offers a centralized repository for nutritional information. The platform includes a comprehensive mapping and proposal system for community leaders to strategically address local food insecurity issues. AI-FEED’s integrated platform approach allows data sharing across modules, enhancing overall functionality and impact. The paper also discusses ethical considerations, potential biases in AI systems, and the transformation of AI-FEED from a research project to a sustainable entity. The AI-FEED platform exemplifies the potential of interdisciplinary collaboration and technological innovation in addressing societal challenges, particularly in improving food security and community health.

11. Lewis, R. H.*, Jiao, J., Seong, K., Farahi, A., Navrátil, P., Casebeer, N., and Niyogi, D. (2024). Fire and smoke digital twin – A computational framework for modeling fire incident outcomes. Computers, Environment and Urban Systems, 110, 102093. [ abstract | official link ]

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens.

10. Seong, K.* and Jiao, J. (2024). Is a Smart City Framework the Key to Disaster Resilience? A Systematic Review. Journal of Planning Literature. [ abstract | official link ]

Despite a growing body of research on the smart city framework for disaster resilience, a comprehensive systematic literature review from urban planning perspectives has never been attempted. In this review of smart and resilient cities, we distill vital learning and shared concepts, identify research trends and limitations, and suggest avenues for future research. The results reveal that reviewed articles primarily focused on methodological approaches addressing how to adapt technologies for disaster resilience, yet rarely discussed the sociological approaches to environment, economy, and governance. This study will provide a reference to trace existing research and suggest equitable smart resilience.

2023

9. Seong, K.*, Jiao, J., and Mandalapu, A. (2023). Hourly Associations between Heat Index and Heat-Related Emergency Medical Service (EMS) Calls in Austin-Travis County, Texas. International Journal of Environmental Research and Public Health. 20(19), 6853. [ abstract | official link ]

This paper aims to investigate the following research questions: (1) what are the hourly patterns of heat index and heat-related emergency medical service (EMS) incidents during summertime?; and (2) how do the lagged effects of heat intensity and hourly excess heat (HEH) vary by heat-related symptoms? Using the hourly weather and heat-related EMS call data in Austin-Travis County, Texas, this paper reveals the relationship between heat index patterns on an hourly basis and heat-related health issues and evaluates the immediate health effects of extreme heat events by utilizing a distributed lag non-linear model (DLNM). Delving into the heat index intensity and HEH, our findings suggest that higher heat intensity has immediate, short-term lagged effects on all causes of heat-related EMS incidents, including in cardiovascular, respiratory, neurological, and non-severe cases, while its relative risk (RR) varies by time. HEH also shows a short-term cumulative lagged effect within 5 h in all-cause, cardiovascular, and non-severe symptoms, while there are no statistically significant RRs found for respiratory and neurological cases in the short term. Our findings could be a reference for policymakers when devoting resources, developing extreme heat warning standards, and optimizing local EMS services, providing data-driven evidence for the effective deployment of ambulances.

8. Jiao, J., Lewis, R. H.*, Seong, K., Farahi, A., Navratil, P., Casebeer, N., and Niyogi, D. (2023). Fire and Smoke Digital Twin--A computational framework for modeling fire incident outcomes. arXiv preprint. [ abstract | official link ]

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens.

7. Seong, K.*, Jiao, J., and Mandalapu, A. (2023). Effects of Urban Environmental Factors on Heat-related Emergency Medical Services (EMS) Response Time. Applied Geography. 155, 102956. [ abstract | official link ]

Due to the time-sensitive nature of heat-related illnesses, disparities in access to heat-related emergency medical services (EMS) services may contribute to urban health disparities. This paper is an empirical study utilizing Austin-Travis County EMS data to estimate the delays in response time due to traffic congestion through spatiotemporal analysis and to conduct the Ordinary Least Square (OLS) and Geographically Weighted Regres­ sion (GWR) models to examine the underlying factors affecting delays in peak traffic rush hours. Our results reveal that heat-related EMS is most delayed in the morning and the evening; there are higher clustering patterns of EMS travel time difference in Austin’s metropolitan outskirts, notably in the east and west Austin. OLS and GWR analyses suggest that larger EMS counts, longer distances from an EMS station to the scene and from the scene to a hospital, and neighborhoods with a greater black and Hispanic population exacerbate heat-related EMS delays. Road density, average speed limit, and open space growth rate are statistically significant in the OLS model, although GWR findings suggest coefficient signs vary locally, requiring more investigation. Our findings provided additional insights through the spatial patterns of EMS delays to practitioners for their reference to reduce local response times.

2022

6. Seong, K.*, Jiao, J., and Mandalapu, A. (2022). Evaluating the effects of heat vulnerability on heat-related emergency medical service incidents: Lessons from Austin, Texas. Environment and Planning B: Urban Analytics and City Science. 50(3), 776–795. [ abstract | official link ]

Extreme heat exposure and sensitivity have been a growing concern in urban regions as the effects of extreme heat pose a threat to public health, the water supply, and the infrastructure. Heatrelated illnesses demand an immediate Emergency Medical Service (EMS) response since they might result in death or serious disability if not treated quickly. Despite increased concerns about urban heat waves and relevant health issues, a limited amount of research has investigated the effects of heat vulnerability on heat-related illnesses. This study explores the geographical distribution of heat vulnerability in the city of Austin and Travis County areas of Texas and identifies neighborhoods with a high degree of heat vulnerability and restricted EMS accessibility. We conducted negative binomial regressions to investigate the effects of heat vulnerability on heat-related EMS incidents. Heat-related EMS calls have increased in neighborhoods with more impervious surfaces, Hispanics, those receiving social benefits, people living alone, and the elderly. Higher urban capacity, including efficient road networks, water areas, and green spaces, is likely to reduce heat-related EMS incidents. This study provides data-driven evidence to help planners prioritize vulnerable locations and concentrate local efforts on addressing heat-related health concerns.

5. Seong, K.*, Losey, C., and Gu, D. (2022). Naturally Resilient to Natural Hazards? Urban-Rural Disparities in Hazard Mitigation Grant Program Assistance. Housing Policy Debate. 32(1), 190-210. [ abstract | official link ]

The American public generally sees its rural communities as autonomous and self-sufficient—inherently resilient. Accordingly, research on federally funded hazard mitigation has disproportionately focused on urban areas, as rural communities rebuild largely by themselves. Our exploratory research challenges this overarching narrative on rural communities by examining disparities in the mitigation process—specifically, the amount of Hazard Mitigation Grant Program (HMGP) assistance awarded per recipient and the duration of HMGP projects—between urban and rural counties from 1989 to 2018. Our analysis reveals vast inequities in the distribution and duration of HMGP assistance between urban and rural counties. Controlling for characteristics of the mitigated properties and corresponding counties, social and physical vulnerability, and climate change factors, we find (a) the amount of HMGP assistance awarded per recipient is higher in urban counties, and (b) projects are completed more quickly in rural counties. Ultimately, our findings indicate that the current structure of the HMGP leaves rural counties in the dust.

2021

4. Seong, K.*, Losey, C., and Van Zandt, S. (2021). To Rebuild or Relocate? Long-term Mobility Decisions of the Hazard Mitigation Grant Program (HMGP) Recipients. Sustainability. 2021; 13(16), 8754. [ abstract | official link ]

Limited funds and the demand for disaster assistance call for a broader understanding of how homeowners decide to either rebuild or relocate from their disaster-affected homes. This study examines the long-term mobility decisions of homeowners in Lumberton, North Carolina, USA, who received federal assistance from the Hazard Mitigation Grant Program (HMGP) for property acquisition, elevation, or reconstruction following Hurricane Matthew in 2016. The authors situate homeowners’ decisions to rebuild or relocate in the context of property attributes and neighborhood characteristics. Logit and probit regressions reveal that homeowners with lower-value properties are less likely to relocate, and those subjected to higher flood and inundation risks are more likely to relocate. Additionally, homeowners in neighborhoods of higher social vulnerability—those with a higher proportion of minorities and mortgaged properties—are more likely to rebuild their disasteraffected homes. The authors discuss homeowners’ mobility decisions in the context of the social vulnerability of neighborhoods. Our results contribute to an ongoing policy discussion that seeks to articulate the housing and neighborhood attributes that affect the long-term mobility decisions of recipients of HMGP assistance. The authors suggest that local governments prioritize the mitigation of properties of homeowners of higher physical and social vulnerability to reduce socioeconomic disparities in hazard mitigation and build equitable community resilience.

3. Park, Y.*, Kim, M., and Seong, K. (2021). Happy neighborhoods: Investigating neighborhood conditions and sentiments of a shrinking city with Twitter data. Growth and Change, 52(1), 539-566. [ abstract | official link ]

Planning interventions have been applied to improve the well‐being, hereafter happiness, of residents. The happiness in shrinking cities, in particular, becomes more critical since urban decline tends to induce an unequal and uneven distribution of care under a limited budget and human resources. Using geo‐tagged Twitter, census, and geospatial data on Detroit, Michigan, which is one of the well‐known shrinking cities in the U.S., the spatial distribution of sentiments, topics of tweets appeared, and the association between neighborhood conditions and the level of happiness were examined. The outcomes indicate that people in Detroit are posting happy tweets more than negative tweets. The downtown area holds both positive and negative hotspots, which are clustered around sports arenas and bars, respectively. Neighborhoods with young and well‐educated residents, situated close to amenities (i.e., recreation facilities, colleges, and commercial areas), and less crime tend to be happier. The use of SNS data could serve as a meaningful social listening tool to reconcile the declining urban vitality of neighborhoods since people interact with those spaces. Negative sentiments are attached to specific neighborhoods with certain conditions so that regeneration efforts should take place in neighborhoods with a higher priority.

2020

2. Losey, C.*, Seong, K., and Van Zandt, S. (2020). Redressing Racial and Environmental Injustice Through a Voluntary Acquisition and Relocation Program. Environmental Justice, 13(4), 120-126. [ abstract | official link ]

Tucked in the northeast corner of Corpus Christi, adjacent to one of the nation’s largest ports, lies Hillcrest, a low-income and minority neighborhood clouded by a history of segregation, discrimination, and environmental injustice. The siting of heavy industry—refineries and petrochemical and energy companies—along its northern and western borders and infrastructure—an expansive highway—along its southern border have depressed property values, reducing the mobility of homeowners, and disproportionately subjected residents to noise, air pollution, and noxious odors for decades. Upon the completion of the new Harbor Bridge, which will skirt Hillcrest’s eastern border, the neighborhood will be enclosed by industry or infrastructure on all four sides, cementing the isolation of its residents from the rest of the city and perpetuating the longestablished practice of unduly inflicting environmental burdens on marginalized and disadvantaged communities. In 2015, civil rights lawyers filed a Title VI complaint with the Federal Highway Administration on behalf of two Hillcrest residents. The complaint, which alleged that the adverse health and economic impacts of the proposed route for the bridge would be disparately shouldered by the neighborhood’s large and already overburdened African American population, prompted the creation of the 3-year Voluntary Acquisition and Relocation Program, intended to provide restitution for decades of segregation, discrimination, and environmental injustice. The landmark program offers financial assistance and relocation counseling to participating residents who chose to relocate or financial compensation for homeowners who elected to remain. This program provides a model for civil rights, fair housing, and environmental justice advocates to procure more equitable outcomes for communities beset with racial and environmental injustice.

2019

1. Sutley, E. J.*, Hamideh, S., Dillard, M. K., Gu, D., Seong, K., and van de Lindt, J. W. (2019). Integrative Modeling of Housing Recovery as a Physical, Economic, and Social Process. Proceedings of the 13th International Conference on Applications of Statistics and Probability in Civil Engineering (ICASP13). [ abstract | official link ]

In early October 2016 Hurricane Matthew crossed North Carolina as a category 1 storm with some areas receiving 15-18 inches of rainfall on already saturated soil. The NIST-funded Center for Risk-Based Community Resilience Planning (Center) teamed with researchers from NIST's Community Resilience, Disaster Failure Studies, and Applied Economics programs to conduct a quick response field study focused on the small city of Lumberton, NC and the flooding experienced from the Lumber River. Approximately one year later, the Center and NIST team returned to Lumberton to document and better understand Lumberton's recovery progress with an emphasis on housing, businesses, schools, community and state-level decisions, and the intersection of these sectors in community recovery. This type of investigation is critical for the study of community resilience as it will ultimately provide longitudinal recovery data and analyses to support guidance and recommendations on what is needed to enable communities the ability to recover more quickly and equitably, and more generally, what attributes make most communities more resilient to natural hazards. This second in a series of community resilience-focused field studies is presented herein as Wave 2 of the on-going Lumberton, North Carolina Flood of 2016 report series. Wave 2 had two major objectives: First, to document community interdependencies ; and secondly, to document the progress of Lumberton's recovery. Both of these objectives support data needs for understanding resilience and recovery. Wave 2 dealt primarily with the recovery process of the most heavily affected households and businesses through systematic surveys. Analysis revealed that even after 14 months, Lumberton is only in the early stages of recovery.

Peer-reviewed Professional Reports

2022

3. Seong, K. and Choi, S. (2022). Spatiotemporal Climate Risk Vulnerability Assessment to Extreme Heat and Particulate Matter: Combining Realtime Concentration of De Facto Population. Small Research Project Competition Final Report. Seoul, South Korea: Seoul Institute. [ official link ]

2020

2. Seong, K. and Losey, C. (2020). To Remain or Relocate? Mobility Decisions of Homeowners Exposed to Recurrent Hurricanes. Natural Hazards Center Quick Response Grant Report Series, 303. Boulder, CO: Natural Hazards Center, University of Colorado Boulder. [ official link ]

2010

1. Seong, K., Myung, J., and Jang, E. (2010). Collaborative Governance Model for Solving the Conflicts among Stakeholders: A Study of Historic City Gyeongju. Graduate Thesis Competition Reports of Historic City Policy, Korean Cultural Heritage Administration.

Published Data

2021

3. Seong, K. and Van Zandt, S. (2021) "Interview Instruments for Buyout Participants, Remaining Residents, and Project Managers in Harris County, Texas", in Longitudinal Impact of Floodplain Buyouts on Neighborhood Change in Harris County, Texas. [ DesignSafe-CI ]

2. Rosenheim, N., Day, W., and Seong, K. (2021). “Automated Neighborhood Characteristics for Community Resilience Planning.”. [ DesignSafe-CI ]

2020

1. Sutley, E., Dillard, M., Hamideh, S., Peacock, W., Tobin, J., Peek, L., Seong, K., Barbosa, A., Tomiczek, T., van de Lindt, J., and Gu, D. (2020)."Household Survey Instrument, January 19, 2018: Wave 2", in A Longitudinal Community Resilience Focused Technical Investigation of the Lumberton, North Carolina Flood of 2016. [ DesignSafe-CI ]