My research has evolved around the broad theme of spatially-integrated social sciences, public policy and planning in Geographic Information Systems (GIS). 

One cluster of my research focuses on developing spatial analytical methods

  1. Measures of spatial accessibility. Spatial accessibility refers to the relative ease by which the locations of activities or services can be reached and is thus a classic issue in location analysis well suited for GIS to address. A method termed “2-step floating catchment area (2SFCA)” has drawn considerable interests from the research community and public policy practitioners, particularly in public health. More recently, the inverted 2SFCA (i2SFCA) method is proposed to capture the potential crowdedness for facilities for providing a service. Residents-based accessibility and facility crowdedness are two sides of the same coin in examining the geographic disparity of resource allocation. Most recently, the 2-step virtual catchment area (2SVCA) method is developed to measure telehealth accessibility.
  2. Regionalization approach to the small population problem. The problem arises when rates are used as estimates for an underlying risk of a rare event (e.g., crime or cancer), and those with a small base population are very sensitive to data errors and are thus less reliable. Following the long tradition of regionalization, we have been involved in developing GIS-automated geographic approaches to mitigate the problem by merging small, adjacent, and homogeneous areas to form large areas that are comparable in population size. One example is to refine the REDCAP method by incorporating a minimum base population (e.g., 20,000) and/or a threshold for cancer cases (e.g., 15), adaptable to cancer data analysis, termed “REDCAPc.” Another is the mixed-level regionalization (MLR) method that accounts for spatial connectivity and compactness, attributive homogeneity, and exogenous criteria such as minimum (and approximately equal) population or disease counts.
  3. The Maximal Accessibility Equality Problem (MAEP). Traditional optimization scenarios for resource/service planning are designed to maximize coverage, minimize travel need of clients, limit the number of facilities, or maximize outcome. A new formulated optimization problem is to maximize equality (or minimize inequality) in accessibility, termed the Maximal Accessibility Equality Problem (MAEP). The problem has great potentials of applications in a wide range of public policy issues (job market access, urban green space planning, healthcare facility site selection, etc.). 
  4. Monte Carlo simulation in spatial modeling. Monte Carlo simulation provides a powerful computational framework for spatial analysis. The technique has been used on (1) mitigating the zonal effect in modeling urban population density functions, (2) decomposing contributors to the wasteful commuting problem, (3) designing the local indicator of colocation quotient (LICQ) to detect local patterns of colocation of two types of point data with a statistical significance test, (4) proposing a spatio-temporal kernel density estimation (STKDE) method for predictive hotspot mapping and evaluation, and most recently (5) developing an agent-based crime simulation model for testing criminology theories and assessing impacts of policing strategy.
  5. GIS-automated service area delineation. Hospital Service Areas (HSAs) are more meaningful analysis units for studies of health care than geopolitical, administrative or census units because they represent health care markets. The popular Dartmouth method needs to be refined in order to be automated and replicated on a consistent and efficient way. Some promising network community detection methods can be adapted to account for spatial constraints required for HSA delineation. Our 2022 book sythesizes such an effort toward data-driven, evidence-based, and automated delineation of HSAs in GIS.

On substantive issues, most of my work can be grouped under five sub-fields:

  1. On intraurban studies, the themes include urban density patterns, suburbanization and polycentricity, commuting, social areas, street centrality and land use patterns, transit ridership, etc. Study areas include cities in the U.S., China, Latin America (Haiti) and Europe (Italy and Spain).  
  2. On interurban studies, the empirical work focuses on China such as uneven population distribution divided by the Hu Line, interdependence between transportation networks (e.g., railway, air transport) and regional development over time, urbanization sink-source areas, etc. 
  3. On historical GIS, our NSF funded project examined the historical distribution of Tai toponyms in southern China and Southeast Asia, including Zhuang toponyms in Guangxi and toponyms of multi-ethnic origins in Yunnan in China. 
  4. On crime geography, GIS is widely used in academic research and law enforcement practice. One line of the work focuses on how the intraurban variation of crime rates can be explained by job accessibilitylocalized income inequality, and concentrated disadvantages.
  5. On health geography, my work focuses on health disparities. One cluster of studies examines the spectrum of disparities from health care access, to utilization, and to outcome (e.g., primary care, cancer care, pharmacy), and then proposes planning and policy scenarios to reduce the disparities. Another cluster analyzes how built environments influence physical activities and obesity. Overall, GIS-based spatial analytics has advanced public health research and policy analysis on various fronts, especially in methodological breakthroughs.  

The integration of computational methods and GIS in applications in social science and public policy is exemplified in a newly released book: Computational Methods and GIS Applications in Social Sciences (3rd ed.).

Note: Click a link embedded on the page will lead you to a representative paper that is free for download (e.g., open-access, or via PMC) or downloadable via its publisher