About

Thomas W. Crawford is associated with the Center for Geospatial Information Technology (CGIT) at Virginia Tech, which collaborates across research, education, and outreach with a transdisciplinary approach, addressing complex problems with geospatial science. The center focuses on applying geospatial science to improve quality of life, environment, and community through smart decision making. CGIT utilizes extensive knowledge in Geographic Information Systems to provide powerful geospatial tools with an easy-to-use interface, transforming spatial data into secure, intuitive decision-making tools that empower agencies, researchers, and communities across the Commonwealth. The research at CGIT, where Thomas W. Crawford is involved, fuses geospatial science, software engineering, and user experience design to develop applications that translate complex datasets into practical insights. These tools support decision-makers in mapping risk, tracking infrastructure, forecasting change, and enhancing safety, efficiency, and strategic planning. Notable projects include redesigning the DMV Geocoding Tool for address accuracy and safety analytics, developing the Virginia State Police Crash Analysis Platform to visualize crash data, and creating the Commonwealth Connection & Opportunity Dashboards to connect data across sectors for smarter policy and community development. CGIT's work advances the science of data-driven decision-making to foster a more informed, resilient, and secure future.

Research topics

• Social Science
• Oceanography
• Sociology
• Geology
• Environmental science
• Political Science
• Geography
• Finance
• Environmental resource management
• Geotechnical engineering
• Economic growth
• Ecology
• Economics
• Physical geography
• Geomorphology
• Business

Selected publications

• Comparing the Spatial Querying Capacity of Large Language Models: OpenAI’s ChatGPT and Google’s Gemini Pro

  The Professional Geographer · 2025-01-06 · 13 citations

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  PublisherDOI

• Climatic Drivers of Malaria Incidence: A Study in Naikhongchhari Subdistrict, Bandarban, Bangladesh

  Weather Climate and Society · 2025-10-01

  article

  Abstract In the fight to eliminate malaria from its borders, Bangladesh continues to have the disease entrenched in 13 of its 64 districts, and the mechanisms that explain the persistence of malaria particularly in the Chittagong Hill Tracts (CHTs) are poorly understood. Previous work in other study areas has noted the importance of specific climate and weather conditions that support malaria outbreaks, and an understanding of those relationships in Bangladesh’s endemic area will aid in efforts to control and eventually eliminate the disease. To identify the climatic factors associated with malaria in these areas, this study investigates the relationship between climatic variables and malaria incidence in the Naikhongchhari subdistrict of Bandarban, in the CHTs of Bangladesh, over a 10-yr period from 2013 to 2022. Malaria case data were obtained from nongovernmental organization registry books, while meteorological data on rainfall and temperature were sourced from the Bangladesh Meteorological Department. The cross-correlation analysis and multiple linear regression models were employed to understand temporal associations between climatic variables and malaria cases. The results reveal significant correlations between rainfall, temperature, and malaria incidence, with spikes in temperature and rainfall preceding increases in malaria cases. In particular, lags of 3 and 1 months are important for temperature and precipitation, respectively. These findings underscore the importance of considering climatic factors in malaria surveillance, control, and education efforts with respect to how malaria prevention can be focused during times in which outbreaks are most likely. Specifically, our findings will support Bangladesh’s efforts to eliminate malaria by supporting an early warning system during which public health support teams can concentrate malaria control and education campaigns in the weeks and months that are likely leading up to an outbreak, given certain weather conditions.

  PublisherDOI

• Impacts of point of interest (POI) data selection on 15-Minute City (15-MC) accessibility scores and inequality assessments

  Transportation Research Part A Policy and Practice · 2025-03-14 · 10 citations

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• Implications for spatial non-stationarity and the neighborhood effect averaging problem (NEAP) in green inequality research: evidence from three states in the USA

  Journal of Geographical Systems · 2024-09-04 · 8 citations

  articleOpen access

  Abstract Recent studies on green space exposure have argued that overlooking human mobility could lead to erroneous exposure estimates and their associated inequality. However, these studies are limited as they focused on single cities and did not investigate multiple cities, which could exhibit variations in people’s mobility patterns and the spatial distribution of green spaces. Moreover, previous studies focused mainly on large-sized cities while overlooking other areas, such as small-sized cities and rural neighborhoods. In other words, it remains unclear the potential spatial non-stationarity issues in estimating green space exposure inequality. To fill these significant research gaps, we utilized commute data of 31,862 people from Virginia, West Virginia, and Kentucky. The deep learning technique was used to extract green spaces from street-view images to estimate people’s home-based and mobility-based green exposure levels. The results showed that the overall inequality in exposure levels reduced when people’s mobility was considered compared to the inequality based on home-based exposure levels, implying the neighborhood effect averaging problem (NEAP). Correlation coefficients between individual exposure levels and their social vulnerability indices demonstrated mixed and complex patterns regarding neighborhood type and size, demonstrating the presence of spatial non-stationarity. Our results underscore the crucial role of mobility in exposure assessments and the spatial non-stationarity issue when evaluating exposure inequalities. The results imply that local-specific studies are urgently needed to develop local policies to alleviate inequality in exposure precisely.

  PublisherOA PDFDOI

• Evaluation of predicted loss of different land use and land cover (LULC) due to coastal erosion in Bangladesh

  Frontiers in Environmental Science · 2023-04-19 · 20 citations

  articleOpen access

  Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988–2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments.

  PublisherOA PDFDOI

• Predicted and Actual Household Loss: Insights into Geophysical Vulnerability and Community Perceptions of Coastal Erosion Risks in Bangladesh

  SSRN Electronic Journal · 2023-01-01

  preprintOpen access
  PublisherDOI

• PALMETTO GRANITE-BREVARD ZONE-KATY CREEK FAULT RELATIONSHIPS AS ILLUSTRATED ON THE PALMETTO, CAMPBELLTON, RICO, AND WINSTON, 7.5-MINUTE QUADRANGLES: GEORGIA PIEDMONT/BLUE RIDGE

  Abstracts with programs - Geological Society of America · 2023-01-01

  articleSenior author
  PublisherDOI

• Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions

  Remote Sensing · 2022-12-16 · 28 citations

  articleOpen accessSenior author

  Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally.

  PublisherOA PDFDOI

• Changing Pattern of Coastline and its Impact on Land Use and Land Cover (LULC) in the Lower Meghna River Region of Bangladesh

  2022-06-20 · 1 citations

  preprintOpen accessSenior author

  <p>Coastal erosion is one of the major natural hazards issue throughout the world. Due to erosion, people living in the coast lose their houses, land, and livelihood. Due to anthropogenic and climatic influences, it is predicted that the erosion will be increased in the future. With an aim to assess the impacts of changing coastline, this study investigates the spatio-temporal changes in coastline movement and its impact on coastal land use and land cover in the lower Meghna river region of Bangladesh. Multi-temporal Landsat imagery from 1988 to 2021 (34 years) were used to quantify the rate of annual shoreline movement. The End Point Rate (EPR), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR) were used to quantify the erosion rates. To assess the impacts of coastline movement on different LULC, twelve different images were classified using Random Forest and Support Vector Machine supervised algorithm. Our results revealed that this region is experiencing dominant erosion over the last three decades. The south region experienced extreme erosion while central region had dominant accretion over the studied period. Our results also found that the agricultural lands are the dominant form of land cover that has been eroded most over time. While empirical results are specific to the project’s study area, results can inform this region’s mitigation and adaptation strategies. We believe that the findings of this study will be helpful for policy makers in managing and developing associated mitigation and adaptation measures for this part of the coast in Bangladesh.</p>

  PublisherDOI

• Resident perceptions of riverbank erosion and shoreline protection: a mixed-methods case study from Bangladesh

  Natural Hazards · 2022-07-28 · 13 citations

  articleOpen accessSenior author

  Abstract Riverbank erosion is a common hazard in Bangladesh, posing a significant threat to homes, properties, and livelihoods. In recent years, the government of Bangladesh has intensified efforts to mitigate riverbank erosion by hardening shorelines, including the building of concrete revetments, but the local dynamics of these interventions are not well documented. To address this, we present results from a study of community-level response to a 2-mile long concrete revetment recently constructed in the administrative district of Ramgati, in the lower Meghna River basin of Bangladesh. Our study combines quantitative analysis of data from a household survey with qualitative data from semi-structured interviews to assess resident perceptions of the new revetment and its effect on the landscape of riverbank erosion hazard. The study finds, firstly, that hazard awareness is widespread but may be influenced by livelihood factors related to the dynamics of displacement and resettlement. Second, we find that that the negative impacts of riverbank erosion vary spatially. Hazard perception in Ramgati is significantly influenced by the physical location of the household, with those residing closer to the river and in unprotected zones north and south of the revetment expressing much greater worry that they will lose their homes, and believing that this will happen much sooner than residents further away or in the zone now protected by the embankment. As an empirically grounded case study, our findings add to the literature on riverbank erosion in Bangladesh and perception studies focused on water-based hazards in similar settings globally.

  PublisherOA PDFDOI

Recent grants

• Coastal Erosion Vulnerabilities, Monsoon Dynamics, and Human Adaptive Response

  NSF · $406k · 2017–2022

Frequent coauthors

• Munshi Khaledur Rahman

  Georgia Southern University

  10 shared

• Md Sariful Islam

  10 shared

• Bimal Kanti Paul

  9 shared

• Scott Curtis

  Citadel

  8 shared

• Jared T. McGuirt

  University of North Carolina at Greensboro

  6 shared

• Thomas C. Keyserling

  University of North Carolina at Chapel Hill

  6 shared

• MR Islam

  6 shared

• Alice S. Ammerman

  University of North Carolina at Chapel Hill

  6 shared

Labs

• Center for Geospatial Information TechnologyPI

  1-2 sentence research focus

Awards & honors

• Geography and Spatial Sciences Program (GSS) Coastal erosion…
• COCA – Supporting Resilient Coastal Communities and Ecosyste…