Theo Lim

tclim@vt.edu

Arch Annex RM 212

140 Otey St NW

Blacksburg, VA 24061

Welcome! I am an assistant professor of Urban Affairs and Planning in the School of Public and International Affairs at Virginia Tech. My research focuses on decision-making around the linkages between land, water, infrastructure, and people. I currently have projects in two major areas:

Climate adaptation in cities, and building community resilience to the effects of climate change. This work is supported by NSF and NOAA grants, in which we use community-engaged approaches, especially working with youth and families, to build physical and social infrastructures necessary for communities facing challenges of a changing climate.
How data and models are used within environmental management and planning contexts. In 2023, I was awarded an NSF CAREER grant to explore the development of computer models of the environment as a socio-political process. I will be looking at international case studies of environmental management in which computer models are used to guide decision-making and as “boundary objects” to facilitate communication between diverse knowledges and stakeholders. Better understanding of these processes will help reduce considerable institutional uncertainty around how computer models are developed and used in our most consequential socio-environmental decisions.

I am interested in studying these topics to help communities become more sustainable given limited natural resources and more resilient in the face of social and environmental change. In my work, I center the importance of inclusive and representative decision-making processes. At Virginia Tech, I teach classes related to sustainable urban infrastructure, smart cities, environmental policy/planning, and urban analytics.

In addition to my academic research and teaching, I have also worked in the following roles:

Environmental data scientist in the agricultural technology industry
Civil engineer focused on sustainable water infrastructure planning and design
Public health and urbanization researcher as a Fulbright Scholar in Northeast China

I use both quantitative and qualitative methods in my research, including: statistical analysis and machine/deep learning, environmental simulation, focus groups/interviews, content/document analysis, and participant observation. I received my PhD in City and Regional Planning from the University of Pennsylvania, MS in Environmental Science and Engineering from Tsinghua University, and my BA from Swarthmore College.

I am also currently one of two co-chairs of the Asian Pacific Islander Desi American (APIDA) Caucus for faculty, staff, students, and community members at Virginia Tech.

This is my most recent CV.

news

Sep 21, 2023	Theo Lim and a collaborative team of researchers, artists, faith leaders, civic organizations, and local government receive a $1M Stage 2 NSF CIVIC Innovation award to pilot and evaluate a trauma-informed, healing-centered approach to building heat resilience in Roanoke, VA!
May 2, 2023	PhD student Ayda Kianmehr publishes a paper in Sustainable Cities & Society compariing spatial temperature data sources and resolutions for use in understanding intra-urban heat variation. Congrats Ayda!!
Apr 22, 2023	Theo is interviewed in a short segment by WSLS on risks of increasing temperatures in cities
Apr 21, 2023	Undergraduate researcher Jack Carroll presents research on spatial bias of ChatGPT
Feb 6, 2023	Theo Lim awarded an NSF CAREER grant "Governance of Computer Models in Sustainable Water Quality Management"

selected publications

Recognizing political influences in participatory social-ecological systems modeling

Lim, Theodore C., Glynn, Pierre D., Shenk, Gary W., Bitterman, Patrick, Guillaume, Joseph H.W., Little, John C, and D.G., Webster

Socio-Environmental Systems Modelling 2023

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Stakeholder participation in social-ecological systems (SES) modeling is increasingly considered a desirable way to elicit diverse sources of knowledge about SES behavior and to promote inclusive decision-making in SES. Understanding how participatory modeling processes function in the context of long-term adaptive management of SES may allow for better design of participatory processes to achieve the intended outcomes of inclusionary knowledge, representativeness, and social learning, while avoiding unintended outcomes. Long-term adaptive management contexts often include political influences – attempts to shift or preserve power structures and authority, and efforts to represent the political and economic interests of stakeholders – in the computer models that are used to shape policy making and implementation. In this research, we examine a period that included a major transition in the watershed model used for management of the Chesapeake Bay in the United States. The Chesapeake Bay watershed model has been in development since the 1980s, and is considered by many to be an exemplary case of participatory modeling. We use documentary analysis and interviews with participants involved in the model application and development transition to reveal a variety of ways in which participatory modeling may be subject to different kinds of political influences, some of which resulted in unintended outcomes, including: perceptions of difficulty updating the model in substantive ways, “gaming” of the model/participatory process by stakeholders, and increasing resistance against considering uncertainty in the system not captured by the model. This research suggests unintended or negative outcomes may be associated with both participatory decision-making and stakeholder learning even though they are so often touted as the benefits of participatory modeling. We end with a hypothesis that further development of a theory of computer model governance to bridge model impact and broader theories of environmental governance at the science-policy interface may result in improved SES modeling outcomes.
Community-engaged heat resilience planning: Lessons from a youth smart city STEM program

Lim, Theodore C., Wilson, Bev, Grohs, Jacob R., and J., Pingel Thomas

Landscape and Urban Planning 2022

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While recognition of the dangers of extreme heat in cities continues to grow, heat resilience remains a relatively new area of urban planning. One barrier to the creation and successful implementation of neighborhood-scale heat resilience plans has been a lack of reliable strategies for resident engagement. In this research, the authors designed a two-week summer STEM module for youth ages 12 to 14 in Roanoke, Virginia in the Southeastern United States. Participants collected and analyzed temperature and thermal comfort data of varying types, including from infrared thermal cameras and point sensors, handheld weather sensors, drones, and satellites, vehicle traverses, and student peer interviews. Based on primary data gathered during the program, we offer insights that may assist planners seeking to engage residents in neighborhood-scale heat resilience planning efforts. These lessons include recognizing: (1) the problem of heat in neighborhoods and the social justice aspects of heat distribution may not be immediately apparent to residents; (2) a need to shift perceived responsibility of heat exposure from the personal and home-based to include the social and landscape-based; (3) the inextricability of solutions for thermal comfort from general issues of safety and comfort in neighborhoods; and (4) that smart city technologies and high resolution data are helpful “hooks” to engagement, but may be insufficient for shifting perception of heat as something that can be mitigated through decisions about the built environment.
Patterns in environmental priorities revealed through government open data portals

Lim, Theodore C

Telematics and Informatics 2021

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The ways in which environmental priorities are framed are varied and influenced by political forces. One technological advance–the proliferation of government open data portals (ODPs)–has the potential to improve governance through facilitating access to data. Yet it is also known that the data hosted on ODPs may simply reflect the goals and interests of multiple levels of political power. In this article, I use traditional statistical correlation and regression techniques along with newer natural language processing and machine learning algorithms to analyze the corpus of datasets hosted on government ODPs (total: 49,066) to extract patterns that relate scales of governance and political liberalism/conservatism to the priorities and meaning attached to environmental issues. I find that state-level and municipal-level ODPs host different categories of environmental datasets, with municipal-level ODPs generally hosting more datasets pertaining to services and amenities and state-level ODPs hosting more datasets pertaining to resource protection and extraction. Stronger trends were observed for the influences of political conservatism/liberalism among state-level ODPs than for municipal-level ODPs.
Model emulators and complexity management at the environmental science-action interface

Lim, Theodore C.

Environmental Modelling & Software 2021

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As our understanding of the interactions present in socio-ecological systems advance, emulation modeling can help reduce the complexity and required computational resources of the models used to represent these systems. While emulation is commonly used in model meta-analyses and parameterization, it has been less explored in the context of environmental management. In this research, I analyze the reflections of a group of watershed modelers on environmental model emulation. I find that decreased simulation run-times are an important motivation because emulators enable stakeholders to interact directly with the model. However, participants also reported that criteria for an emulator in an environmental management context should also assess its capability to act as a platform for learning and to manage stakeholder perceptions of the modeling process. Further, at the science-action interface, stakeholder perceptions play a significant role in the approach to model emulation through determining acceptable levels complexity in model processes and inputs.