Guangqing Chi's website    
Overview of My Research Program

Climate Change, Land Use, and Community Resilience

My research interest in environmental demography is focused on community vulnerability and resilience as a consequence of climate change, land use and land cover change, natural resources, and demographic changes. My current research exmaines left-behind children in Kyrgyzstan in the context of climate change, international migration, and remittance. My previous research has revisited the migration effects of natural amenities by 1) placing rural development and population change in their social, economic, and political settings, and 2) investigating the spatial variations of their relationships along the urban-rural continuum (for a summary of the research, click here).

I have also been developing a land developability index in relation to population dynamics in collaboration with Derrick Ho. Land developability is a measure of land available and suitable for future conversion and development in a geographic entity. It is a large-scale data-driven webGIS project ( By utilizing remote sensing imagery and spatial analysis methods, weI identified developable lands for each 90 by 90 meter pixel in the continental U.S. and developed a land developability index at the state, county, and census tract levels.

Critical Infrastructure/Transportation, Population Change, and Population Health

My research in this area is focused on the impacts of transportation and neighborhood infrastructure on population change and public health under the framework of smart cities. My research has examined the causality between highway construction and population change, highway and airport impacts on population change along the urban-rural continuum, highway construction effects on urban racial redistribution, neighborhood built environment and population health, transportation accessibility, and food environment and accessibility.

My research in transportation and health demography has investigated the role that gasoline prices play in reducing traffic crashes (for a summary of the research, click here; for a YouTube video, click here). The research has also been highlighted over 2,000 times by numerous domestic and international news agencies, websites, and blogs as of December 2011. These news media includes National Public Radio, Huffington Post, The Weather Channel, South Dakota Public Broadcasting, VOAnews, KSFY ABC, Money, and others.

Computational and Spatial Analysis

Population Estimation and Forecasting: I have also developed an expertise in population estimation and forecasting. In particular, I proposed a knowledge-based spatio-temporal econometric approach to population forecasting at subcounty levels. This approach considers the spatio-temporal interactions between population change and demographic and socioeconomic characteristics, land use, transportation accessibility, legal constraints, and geophysical and environmental factors by employing spatial statistics, GIS, and remote sensing software. This endeavor attempts to meet policy demands (such as the “Smart Growth” law and comprehensive planning) for better small-area population estimation and forecasting techniques; help communities understand demographic challenges of deteriorating physical infrastructure, increasing income gap, threats to environmental quality and natural amenities, housing shortages, and traffic congestion; look at the variety of results in the “what-if” scenarios; and suggest strategies to solve potential development problems. This line of research has been awarded two E. Walter Terrie Awards for best paper in State and Local Demography by the Southern Demographic Association.

Spatial Demography: The above four areas of research build upon my methodological strengths in spatial statistics, geographic information techniques, and spatial demography. I have accumulated knowledge and experience in applying spatial statistics and geographic information techniques to demographic analysis. My expertise in spatial statistics includes spatial econometric modeling, hierarchical linear modeling, and geostatistics. I am proficient in these methods with R, GeoDa, SpaceStat, and HLM. GIS acts as the platform and the principal tool in my research. My strength in GIS is in its applications to spatially referenced data analysis. I currently serve as Director of the Computation and Spatial Analysis Core of the Social Science Research Institute and the Population Research Institute, which promotes and enhances social science and population research at Penn State by assisting researchers in spatial statistics and analysis, data support, programming and statistical analysis, Federal Statistical Research Data Center microdata access and analysis, and Big Data for population research.

I am also working on a book manuscript entitled “Spatial Regression Models for Social Scientists” with my co-author Jun Zhu. 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, the authors 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.

  Active 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).


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).

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.

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).


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.


4. 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).


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.