Guangqing Chi's website    
Overview of My Research Program: My research is focused on socio-environmental systems, aiming to understand the interactions between human populations and built and natural environments and to identify important assets (social, environmental, infrastructural, institutional) to help vulnerable populations adapt and become resilient to environmental changes.

Environmental Change, Land Use, and Rural Development: My research in this area is focused on rural development and community resilience in response to climatic and natural environmental change, land use and land cover change, and natural amenities in four themes. First, my work in natural amenities and rural development has examined their relationship in their social, economic, and political settings (Landscape and Urban Planning, 2013; Regional Studies, 2011) and investigated the spatial variations of their relationships along the urban-rural continuum (Annals of Regional Science, 2013). Second, I developed a land developability measure and co-developed the population stress concept to integrate and analyze land use and population dynamics (Land Use Policy, 2018). Third, my collaborative work involves the use of machine learning methods to identify human-environment hotspot areas such as rural residential land vulnerable to be abandoned (Habitat International, 2019), critical riparian zones (Ecological Indicators, 2018), and urban areas with high heat risks (Applied Geography, 2018). Fourth, lately my research program has focused on environmental migration. I am currently studying climate-driven migration and left-behind children in Central Asia (funded by NASA), permafrost erosion impacts on coastal communities in Alaska (funded by NSF), and ecological migration in China. I lead a three-million-dollar NSF project, titled POLARIS (Pursuing Opportunities for Long-term Artic Resilience for Infrastructure and Society), to investigate environmental migration and food security in response to climate change in Arctic indigenous communities.

Built Environment and Population Dynamics: My research in the population-infrastructure realm is focused on the impacts of transportation and community infrastructure on population change and health within the smart cities framework, at multiple scales (from regional to local grid levels), over time (long term and in hourly intervals) and across space. I have three research themes in this area. First, my work has examined the impacts of transportation infrastructure and accessibility on population change along the urban-rural continuum over time and on urban racial redistribution (Population Research and Policy Review, 2018; Social Science Quarterly, 2018), neighborhood built environment and population health (Urban Design International, 2015), and accessibility impacts on the food environment (Cities, 2018). Second, I led investigations of the role that gasoline prices play in reducing traffic crashes (American Journal of Public Health, 2015) and in affecting residential mobility (Journal of Planning Education and Research, 2017). This research has been highlighted more than 2,000 times by various news media outlets, such as National Public Radio, Huffington Post, and Money. Third, my current focus is on how people and populations respond to disruptions to critical infrastructure before, during, and after disasters. I have also co-produced population estimates in near-real time at fine geographic scales using big data analytics (IEEE Transactions on Intelligent Transportation Systems, 2019) in collaboration with civil engineers and computer scientists.

Computational and Spatial Analysis: I have also contributed to methodological advances in areas of population estimation and forecasting, spatial demography, and big data social science. First, I led the development of a spatio-temporal regression approach for population forecasting at subcounty levels by considering demographic and socioeconomic characteristics, land use, transportation accessibility, legal constraints, and geophysical and environmental factors (Demography, 2008; Population, Space, and Place, 2011, 2018). This line of research has been awarded two E. Walter Terrie Awards for the best paper in State and Local Demography by the Southern Demographic Association. Second, I extended existing spatio-temporal regression approaches and spatial filtering methods in collaboration with others to decompose spatio-temporal population process effects into seven components (SAGE, 2019; Regional Studies, 2019). I have also applied spatial methods to a variety of population research topics including fertility, migration, and poverty (Demographic Research, 2017, 2019). Third, I led the establishment of an infrastructure for collecting and managing Twitter data as well as a capacity in processing and analyzing the data. So far we have collected 45 TB of data, covering more than 95% of geotagged tweets for the entire world since June 2013. I lead a half-million-dollar NSF project to study the (mis)representativeness of Twitter data and develop weights to generalize the data. This endeavor will create opportunities for social scientists to take advantage of the rich social media data.

I have a book with my co-author Jun Zhu published by SAGE in Spring 2019. The book is entitled “Spatial Regression Models for the Social Sciences”. The past few decades have seen rapid development in spatial regression methods, which have been introduced in a great number of books and journal articles. However, when teaching spatial regression models and methods to social scientists, we had difficulty recommending a suitable textbook. Most of the existing textbooks are written for natural scientists or regional scientists and require that readers have a good understanding of advanced statistics and probability theory. These textbooks are either too technical for social scientists or are focused on only a few methods and exclude others. A textbook that provides a relatively comprehensive coverage of spatial regression methods for social scientists and introduces the methods in an easy-to-follow approach is much needed. Therefore, we have written a primer type of textbook for social scientists who would like a quick start to learning spatial regression methods. While the methods are many and the number keeps increasing, we have decided to focus on the methods that are commonly used by social scientists and are probably most useful to them. These methods include exploratory spatial data analysis, methods dealing with spatial dependence, methods dealing with spatial heterogeneity, advanced spatial regression models, and other methods that are used in other fields and can be useful for social science research. To get a copy of the book, go to SAGE or Amazon. Supplemental materials including full-color versions of all the maps in the book can be found here.

  Active Research Projects

1. NNA Track 1: Pursuing Opportunities for Long-term Arctic Resilience for Infrastructure and Society (POLARIS), funded by the National Science Foundation (Award # 1927827). Role: PI (Co-PIs: Davin Holen, Ann Tickamyer, Lance Howe, and Chris Maio). $3,000,000.


Alaskan coastal Indigenous communities face severe, urgent, and complex social and infrastructural challenges resulting from environmental changes. Coastlines are degrading and this impacts infrastructure that communities use on a daily basis, changing how people access and hunt for food and other natural resources and conduct their lives. The magnitude and significance of impacts are unclear as is how local communities will respond to resulting disruptions and disasters. A major problem facing researchers, stakeholders, and policymakers in addressing these issues is that existing research is piecemeal. The whole picture of coastal communities is not well understood, and ways to address problems they face are not as effective as they could be. A changing environment drives changes to the populations of Alaskan coastal Indigenous communities due to families and individuals relocating either seasonally or permanently, which complicates efforts understand the relationship between environmental changes and society. These challenges demand a robust, integrated, and convergent research platform to identify the complexities of the issues and the ways communities can respond. The POLARIS (Pursuing Opportunities for Long-term Arctic Resilience for Infrastructure and Society) project supplies just that kind of research platform for analyzing current and future needs in order to create resilient communities in the face of a changing environment.

The POLARIS project has identified three convergent research pillars to help communities adapt: environmental hotspots of disruption to communities and infrastructure, food in complex adaptive systems, and migration and community relocation. These pillars are interwoven with five component processes: education, outreach, local community engagement, international comparison and collaboration, and evaluation. Research integrates the pillars where system responses and uncertainties are predicted under several socio-environmental scenarios. Researchers from a variety of fields are coming together with local community members to conduct the research. The data and analysis created through surveying local community members, modeling environmental changes, and conducting economic research inform local, state, and national decision makers and leaders about how to address infrastructure and social needs in the face of environmental changes. In addition to the research and community focus of the project, POLARIS is training junior researchers, graduate students, and undergraduate students in interdisciplinary research as they participate in work across the pillars and five components. This helps ensure that the rising generation of researchers is well prepared to continue the crucial work to address the issues that the project identifies well past its conclusion. In addition, local educators are working with local communities to develop classroom tools to engage students in K-12 settings. This integrated research project will enable communities to become more resilient with both stronger societies, civic culture, and improved infrastructure needed as the new Arctic continues to emerge.

2. RR: The Generalizability and Replicability of Twitter Data for Population Research, funded by the National Science Foundation (Award # 1823633). Role: PI (Co-PIs: Heng Xu, Jennifer Van Hook, Eric Plutzer, and Junjun Yin). $500,000.


Social media data have the potential to track phenomena in real time, such as percentage of the population fearful in the minutes after a disaster or terrorist event, or the degree of anger immediately after the announcement of a jury verdict in a highly publicized case. In each of these examples, it would be difficult to conduct a field survey in real time, and respondents may not be able to reconstruct how they felt or behaved at the time of the event, even if interviewed just a few days later. Social media data have the potential to overcome these limitations. This project will analyze how the application of survey weighting can rebalance samples of Twitter data, and assesses how well this rebalancing will allow valid generalizations about population behaviors. The project will provide a foundation for future advances in the use of social media data for scientific, health, and applied research, thus permitting a wide variety of inferences useful in social policy formulation. A key aspect of the project will provide new evidence regarding the accuracy of migration flows in real time, thus assisting social policy relevant to providing assistance in response to natural disasters.

This project will evaluate the extent to which Twitter users represent or misrepresent the population across different demographic groups and test the feasibility of developing weights that, when applied to Twitter data, make the results more representative of the underlying population. The project conducts the research at the county level in the United States from January 2014-December 2017, using 96% geotagged tweets in the study period and 100% tweets in one month. The project will: (1) extend and refine existing methods for imputing the gender, age, race/ethnicity, and county of residence of each Twitter user; (2) use these values to assess the representativeness of Twitter samples at the county level and explain the determinants of biases; (3) adapt five methods developed for probability or non-probability surveys to reweight Twitter samples and compare their performance in producing model estimates that can be used to infer characteristics of the general population; and (4) test the feasibility of using Twitter data to estimate migration at the county level by comparing to the Internal Revenue Service migration data, as well as estimate Puerto Rico migrants to the continent after Hurricane Maria. Analysis of these migration data will provide a new source of information with which to estimate migration flows in real time and at unprecedentedly detailed geographic scales.

3. Convergence NNA: Coordinate a Transdisciplinary Research Network to Identify Challenges and Solutions of Permafrost Coastal Erosion and Its Socioecological Impact in the Arctic, funded by the National Science Foundation (Award # 1745369). Role: Co-PI (PI: Ming Xiao. Other co-PIs: Kathleen Halvorsen, Benjamin Jones, and Vladimir Romanovsky). $500,000.


The Permafrost Coastal Erosion-RCN (PCE-RCN) will bring together leaders in fields of natural and social science and engineering to address the challenges faced by coastal communities in the Arctic due to rapid coastal erosion. Rapid coastal erosion can force communities to consider moving inland and limit access to resources. One goal of the proposed PCE-RCN will be to better understand the challenges associated with coastal erosion, which is driven by permafrost thaw and changing sea ice conditions. Another goal is to identify potential solutions and their socio-ecological impacts. These goals will be addressed through a series of international workshops, publications and direct interaction with local media. Engagement with regional and local resource managers and communities will be incorporated throughout many of the activities of the PCE-RCN. This project promotes convergence by focusing on a topic of high societal concern, coastal erosion in the Arctic, and by approaching this topic in a manner that will integrate diverse fields, including social science and natural science disciplines (coastal geophysics, soil physics, climate modelling, and atmospheric science) and disciplines in civil and environmental engineering. 

The proposed Permafrost Coastal Erosion-RCN (PCE-RCN) will bring together national and international leaders in the diverse scientific and engineering disciplines needed to address the pressing societal issue of rapid coastal erosion. Rapid coastal erosion is underway throughout the Arctic, and is impacting coastal communities in profound ways, including displacement and loss of livelihood. The goal of the proposed PCE-RCN will be to further resolve through synthesis activities how coastal erosion is driven by permafrost thaw and changing sea ice conditions and to identify potential solutions and their socio-ecological impacts. These goals will be addressed through a series of international workshops, white papers and other outlets. Engagement with regional and local resource managers and communities will be incorporated throughout many of the activities of the PCE-RCN.

  Recently Completed Research Projects

1. Collaborative Research: Population–Infrastructure Nexus: A Heterogeneous Flow–based Approach for Responding to Disruptions in Interdependent Infrastructure Systems, funded by the National Science Foundation Critical Resilient Interdependent Infrastructure Systems and Processes program (Award # 1541136). Role: PI (with PI Xiaopeng Li at USF and co-PI Mengqi Hu at UIC). $150,000.


Reducing the instability and vulnerability of the critical and complex population–infrastructure system is essential for a more efficient, resilient, and vital society. Recent catastrophic events, such as the Northeast Blackout of 2003 and Hurricane Sandy in 2012, shut down or interrupted essential and interdependent components of our national infrastructure, such as electric networks, fuel supplies, and transportation systems. This vulnerability is heightened by changing population dynamics that impose serious challenges to our infrastructure system in efficiently responding to both moderate disturbances and extreme events.

The primary goal of this interdisciplinary research project, says Chi, is to increase the resilience of our interdependent population–infrastructure system during disturbances of various magnitudes, ranging from operational uncertainties to major disruptions. He adds that the research will contribute to the development of "smart communities/cities" where multiple stakeholders can work together to achieve common goals. Another goal of this research is to develop innovative educational and training modules to provide a vision of efficient, resilient, and socially vital communities and built environments as well as the means to achieve them.

For the project, researchers plan to develop a framework to assess the critical and complex interdependence of various infrastructure systems and population groups. The framework will also assist city planners in analyzing short-term mobility behaviors as well as the long-term social and demographic evolution of the interconnection of population and infrastructure. Chi says that the model developed in the research will be integrated with a cyber-communications system based on self-organized “swarm intelligence” to create a realistic system in which individuals and groups, by communicating their available information, behave in a unified, cohesive manner.

2. How Environmental Change in Central Asian Highlands Impacts High Elevation Communities, funded by National Aeronautics and Space Administration Land Cover/Land Use Change Program (Award # NNX15AP81G). Role: co-I (with PI Geoffrey Henebry of South Dakota State University and co-I Pavel Groisman). $969,551.

Project Summary:

Highlanders are different.  People gestated, born, and raised at high elevation (>2500 m) exhibit distinct physiological characteristics, including increased blood viscosity due to higher hemoglobin content. Chronic physiological stress and lower reproductive success coupled with the short growing season, long cold season, and harsh climatic extremes associated with the montane agro-pastoralism, make high elevation communities particularly vulnerable to additional stressors.

Prior to the Soviet era, highlanders in Central Asia practiced vertical transhumance to raising livestock—sheep and goats—for wool, meat, milk, and hides. Collectivization disrupted this practice with multiple external subsidies. Since 1991 montane agro-pastoralism has been disrupted by withdrawal of external subsides and introduction of a market economy.
Our project evaluates four aspects of environmental change in human settlements and associated summer and winter pasturelands in representative areas of Kyrgyzstan (KG) and Uzbekistan (UZ) since the 1970s and projected changes into the middle of the 21st century to assess impacts on these highland communities and the pastures upon which they depend. Our areas of interest are located in the Central and Southwestern Tien-Shan in the highlands of Osh, Naryn, and Issyk-Kul oblasts in southern KG, and Qashqadayro and Surxondaryo in southern UZ.

The four aspects of environmental change are (1) changes in the thermal regime including growing season timing and extremes, (2) changes in the moisture regime including peak precipitation timing and snow cover duration, (3) changes in socio-economic conditions including income, education, agricultural production and practices, and institutions, and (4) changes in land cover, land use, and land condition including alterations in terrain from landslides and earthquakes.
Key response variables at the scale of human settlements in high elevation regions are the demographic profile (especially aging and gender), population outflow, fertility, and infant mortality, as these indicate the aggregate well-being of the communities. Key response variables for pasture condition are the temporal and spatial patterns of spectral indices based on remote sensing data Landsat and MODIS.

Initial synthesis leads us to pose the following linkages:
[I]    Increasing temperatures reduce snow cover duration and change the growing season in highland pastures, but more warmth may also reduce forage production;
[II]   Increased remittances mean more livestock and more grazing pressure on nearby pastures, but not in remote highland pastures, which led to the declined status of lower pastures nearby human settlements and improved status of higher and more remote pastures; and
[III]  Differential changes in pasture condition and increased remittances led to changes in community well-being, characterized by population decline, population aging, lower fertility rates, higher infant mortality rates, and higher international out-migration and internal migration.

Our fundamental question is whether change in pasture condition can be detected through remote sensing and linked to community well-being through econometric and structural equation modeling. The ancillary question of how climate change drives the change of pasture condition can be addressed through remote sensing of land surface seasonality (snow cover metrics) and land surface phenology (vegetation indices) and careful analysis of precipitation station data complemented by remote sensing of precipitation and soil moisture. The linkage from remittances to community well-being will be tested through econometric and structural equation modeling. Impacts of climate change, changes in pasture condition, and increased remittances on community well-being will be used along with forecasted demographic changes to recommend policy strategies for building resilient communities.