Wei Kang

Board membership

Mon, 01 Jan 0001 00:00:00 +0000

Board member 2019-2022

Spatial Analysis and Modeling (SAM) Specialty Group - American Association of Geographers

Editorial Board

Mon, 01 Jan 0001 00:00:00 +0000

2023-, Computers, Environment and Urban Systems

Journal Referee

Mon, 01 Jan 0001 00:00:00 +0000

Annals of the American Association of Geographers, Annals of Regional Science, Applied Geography, Applied Spatial Analysis and Policy, Arabian Journal of Geosciences, Cartography and Geographic Information Science, Computational Urban Science, Computers, Environment and Urban Systems, Environment and Planning A: Economy and Space, Environment and Planning B: Urban Analytics and City Science, Forests, Geographical analysis, Global Environmental Change, Growth and Change, Housing Policy Debate, Human Geography, Humanities and Social Sciences Communications, International Journal of Geographical Information Science, International Regional Science Review, Journal of Geographical Systems, Journal of Maps, Journal of Occupational and Environmental Medicine, Journal of Planning Education and Research, Journal of Spatial Science, Papers in Regional Science, Population, Space and Place, Socio-Economic Planning Sciences, The Annals of Regional Science, The Journal of Open Source Software

COVID-19 Emergency Rental Assistance Improved Mental Health Care And Psychotherapy Use Among Low-Income Renters

Tue, 06 Jan 2026 00:00:00 +0000

Rising temperatures, rising disparities: The future of heat exposure in the U.S. by 2050

Wed, 01 Oct 2025 00:00:00 +0000

PBPL 177 - Housing Policy

Thu, 14 Aug 2025 08:00:00 +0000

Course Description

This course offers students an introduction to affordable housing policies in the United States. After briefly tracing the history of public housing in the United States, the course will familiarize students with the key policy tools available to build, maintain, and preserve affordable housing. Many of these policies also endeavor to deconcentrate poverty, create mixed-income communities, and ensure economic opportunities for low-income households. Students will draw on current social science research to evaluate the effectiveness of these programs and identify challenges to current policy. Students will gain working knowledge about core housing programs and innovative policy interventions designed to ease the crisis of housing affordability.

PBPL 236 - U.S. Census Data and Policy Studies

Thu, 14 Aug 2025 08:00:00 +0000

Course Description

This graduate-level course addresses in depth the census data as the primary resource for analyzing local and regional changes. The course emphasizes the application of census data to various areas of public policy, including health, immigration, economy, education, and housing. Students will learn about survey design, census geography, and advanced techniques for accessing and analyzing census data. Practical sessions will involve working with the U.S. Census API for mass data downloads, data merging, (spatial) analysis, and visualization.

Scale and correlation in multiscale geographically weighted regression (MGWR)

Sun, 01 Jun 2025 00:00:00 +0000

Theil-Sen Estimation for Moran's I: A Robust Representation and Estimation Approach

Tue, 01 Apr 2025 00:00:00 +0000

The OLS-derived local Moran estimator is extensively studied. Yet, few have focused on whether it may be possible to estimate local Moran statistics in a statistically-robust fashion. Indeed, robustness is important when trying to distinguish between spatial outliers, which are unusual observations relative to other observations within a geographic locale, and distributional outliers, which are unusual observations no matter where they are or who they’re around. We show how robust Moran estimation actually involves two problems: (I) how do we robustly represent the surroundings of each site? and, (II) how do we robustly estimate the relationship between sites and their surroundings? We show that an optimally-robust estimator, the Trimmed Least Squares estimator, and the Theil-Sen estimator solve both representation and estimation issues fairly well. We posit that the Theil-Sen estimator should be explored as a default estimator for local and global Moran’s I statistics.

Do inclusionary zoning policies affect local housing markets? An empirical study in the United States

Sat, 15 Mar 2025 00:00:00 +0000

Exploring spatiotemporal dynamics, seasonality, and time-of-day trends of PM2.5 pollution with a low-cost sensor network Insights from classic and spatially explicit Markov chains

Fri, 04 Oct 2024 00:00:00 +0000

Race/Ethnicity and Employment Insecurity: Impacts of COVID-19 on the US Labor Force and Beyond

Thu, 15 Aug 2024 00:00:00 +0000

NSF 2024-2026: POSE: Phase II: An Open Source Ecosystem for Spatial Data Science

Fri, 12 Jul 2024 11:00:31 -0700

This project is funded by Pathways to Enable Open-Source Ecosystems (POSE) Program which seeks to harness the power of open-source development for the creation of new technology solutions to problems of national and societal importance. In today’s rapidly evolving world, it is important to understand location data as related to complex societal issues. From urban planning and environmental management to public health and economic development, spatial data science stands at the forefront of decision-making processes, enabling the visualization and analyzis of data in ways that reveal relationships, patterns, and trends across various geographies. This project, spearheaded by an interdisciplinary team at the San Diego State University in collaboration with the University of Chicago, the University of Maryland, and the University of California Riverside, aims to improve the field of spatial data science through the development and enhancement of the Python Spatial Analysis Library (PySAL) open-source ecosystem. With a focus on expanding accessibility, functionality, and collaborative potential, the initiative is poised to democratize spatial data analysis, making powerful tools available to researchers, policymakers, and the public. The project’s dedication to open-source principles fosters innovation and ensures that advancements in spatial data science are shared freely, promoting transparency and inclusivity in research and application.

The technical core of this project revolves around the strategic expansion of PySAL and the cultivation of a supportive ecosystem that bridges the gap between scientific inquiry and practical application. By integrating advancements in spatial analysis with the latest computational techniques, the project aims to refine and extend PySAL?s capabilities to meet the growing demands of diverse data-intensive environments. The initiative seeks to build a robust network of users, developers, and educators through participatory learning and targeted outreach. This multi-faceted approach includes enhancing educational resources to train the next generation of spatial data scientists, fostering cross-domain collaborations, and developing user-friendly tools that cater to the specific needs of industry and government sectors. Through these efforts, the project aspires to advance the science of spatial analysis and equip stakeholders with the means to address real-world challenges more effectively and equitably.

Maternal residential exposure to solvents from industrial sources during pregnancy and childhood cancer risk in California

Sat, 04 May 2024 00:00:00 +0000

Small businesses and government assistance during COVID-19: Evidence from the paycheck protection program in the U.S.

Thu, 31 Aug 2023 01:13:22 +0000

California Collaborative for Public Health Research (CPR3) 2023-2024: Identifying the Effect of Housing policy on Mental Health Outcomes Among Low-Income Renters and their Children During the COVID-19 Pandemic

Wed, 30 Aug 2023 11:00:31 -0700

UNT 2023-2024: Fair Housing Reforms in Low Income Housing Tax Credits: An investigation of implications for neighborhoods and communities in the DFW region

Sat, 25 Feb 2023 11:00:31 -0700

UNT research seed grant

Cross-regional analysis of the association between human mobility and COVID-19 infection in Southeast Asia during the transitional period of ``living with COVID-19''

Sun, 01 Jan 2023 00:00:00 +0000

Fair Chance Act failures? Employers' hiring of people with criminal records

Sun, 01 Jan 2023 00:00:00 +0000

Add the full text or supplementary notes for the publication here using Markdown formatting.

Geographies of Opportunity for Youth Across the Contiguous United States

Sun, 01 Jan 2023 00:00:00 +0000

Add the full text or supplementary notes for the publication here using Markdown formatting.

GEOG 4560 - Introduction to Python Programming

Wed, 14 Dec 2022 08:00:00 +0000

Course Description

Computational skills of practitioners are in increasing demand in contemporary research in analytical geography. Advances in spatial data analysis have also largely outpaced the capabilities of standard statistical software. At the same time, the multidisciplinary nature of the spatial sciences often translates into the need to deal with disparate data sources, formats, and programming languages. As such, students undertaking research are often confronted with a daunting set of tasks that are seldom covered in an integrated fashion in course work. This course is designed to address this situation. It introduces geography students to basic computational concepts using Python, an object-oriented scripting language, for data processing, analysis, and application development in geographic research. It is aimed at providing students with skill sets that are in high demand within academic GIScience and commercial GIS development.

Course Objectives

Upon successful completion of this course, students will be able to:

Master the fundamentals of writing Python scripts.
Write Python functions to facilitate code reuse.
Make their code robust by handling errors and exceptions properly.
Develop python programs for data manipulation.

Software

Python 3, Anaconda 3, Jupyter Notebook, Git/GitHub, Visual Studio Code

GEOG 5550 - Advanced Geographic Information System

Wed, 14 Dec 2022 08:00:00 +0000

Course Description

This course aims to improve students’ spatial thinking skills through advanced GIS topics in spatial analysis, three-dimensional (3D) analysis, and network analysis.

Spatial dynamics

Tue, 15 Nov 2022 00:00:00 +0000

The Impact of COVID-19 on Small Businesses in the US: A Longitudinal Study from a Regional Perspective

Mon, 17 Oct 2022 00:00:00 +0000

GEOG 5560 - Application Development with Python Programming

Mon, 29 Aug 2022 08:00:00 +0000

Course Description

Course Objectives

Upon successful completion of this course, students will be able to:

Master the fundamentals of writing Python scripts.
Write Python functions to facilitate code reuse.
Make their code robust by handling errors and exceptions properly.
Develop python programs for data manipulation.
Create python programs for solving research problems.

Software

Python 3, Anaconda 3, Jupyter Notebook, PyCharm, Git/GitHub

NSF 2022-2025: Forced Displacement and Community Resilience: Housing Insecurity under COVID-19 in Inland Southern California

Sat, 18 Jun 2022 11:00:31 -0700

The unique nature of the COVID-19 pandemic created a disaster situation that highlights the importance of stable housing, particularly as recent evidence suggests that eviction increased the risk of COVID-19 infection and mortality. This study will improve knowledge of processes and consequences of evictions before and after the COVID-19 pandemic in Inland Southern California. The first goal is to analyze the demographic and socioeconomic profile of renters who recently experienced an eviction, as well as the relocation process and outcome. The second goal is to analyze how community resilience and neighborhood change, such as neighborhoods that are gentrifying or becoming more impoverished, are tied to outcomes for renters. The third goal is to evaluate whether and how these outcomes are changed by the pandemic. This study will advance our understanding of involuntary residential choices under an external shock like a pandemic. It contributes to resilience scholarship and helps us understand why the root cause of socioeconomic disadvantage is the primary source of vulnerability under disastrous events, and how housing security interacts with community resilience. As eviction is linked to social, economic, and health disparities, and urban poverty, effective eviction-prevention initiatives could go a long way toward addressing these enduring problems. This study provides evidence for policy interventions designed to address eviction and stem its consequences. It will also provide significant implications for practice and policy in housing markets and social welfare to alleviate social and spatial divides by race, ethnicity, and class that have been exacerbated by the pandemic disruption.

This study investigates formal and informal eviction and neighborhood change before and after the COVID-19 pandemic in Inland Southern California using a multiscalar, comparative, and mixed-methods framework. Using both public and restrictive datasets, this study will model the prevalence of eviction and the threat of it at both the household and neighborhood levels. As residential mobility shapes the future life course of evicted households and neighborhood dynamics, the team will model the residential choice of evicted renters and neighborhood dynamics. Further, the project conducts in-depth interviews with tenants, landlords, real estate agents and housing developers, non-profit organizations, and government officials to examine the pathways through which individual characteristics, neighborhood environment, and institutional forces contribute to eviction. The multiscalar, mixed-methods and comparative framework will advance knowledge on the process of eviction at the household level, as well as neighborhood dynamics, policy interventions, power relations, and the coping process of local communities during a pandemic-like disruption. Findings from this study will not only directly benefit policymaking and practice in this region, but also contribute to knowledge in the field for national audiences.

Changes in the economic status of neighbourhoods in US metropolitan areas from 1980 to 2010: Stability, growth and polarisation

Sat, 01 Jan 2022 00:00:00 +0000

The PySAL Ecosystem: Philosophy and Implementation

Sat, 01 Jan 2022 00:00:00 +0000

Robust empirical Bayes approach for Markov chain modeling of air pollution index

Fri, 01 Jan 2021 00:00:00 +0000

Smoothed Estimators for Markov Chains with Sparse Spatial Observations

Fri, 01 Jan 2021 00:00:00 +0000

Spatiotemporal patterns of alcohol outlets and violence: A Spatial Heterogeneous Markov Chain Analysis

Fri, 01 Jan 2021 00:00:00 +0000

What Are the Impacts of COVID-19 on Small Businesses in the U.S.? Early Evidence based on the Largest 50 MSAs

Fri, 01 Jan 2021 00:00:00 +0000

PySAL and Spatial Statistics Libraries

Thu, 16 Jul 2020 00:00:00 +0000

When Spatial Analytics Meets Cyberinfrastructure: an Interoperable and Replicable Platform for Online Spatial-Statistical-Visual Analytics

Tue, 30 Jun 2020 00:00:00 +0000

Sensitivity of sequence methods in the study of neighborhood change in the United States

Fri, 13 Mar 2020 00:00:00 +0000

`splot` - visual analytics for spatial statistics

Wed, 01 Jan 2020 00:00:00 +0000

A Visual Analytics System for Space--Time Dynamics of Regional Income Distributions Utilizing Animated Flow Maps and Rank-based Markov Chains

Wed, 01 Jan 2020 00:00:00 +0000

Inference for Income Mobility Measures in the Presence of Spatial Dependence

Wed, 01 Jan 2020 00:00:00 +0000

Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

Wed, 01 Jan 2020 00:00:00 +0000

A New Optimal Matching Approach to Uncovering Neighborhood Sequencing Structure

Sat, 16 Nov 2019 10:00:00 +0000

Markov chain modeling for air pollution index based on maximum a posteriori method

Fri, 01 Nov 2019 00:00:00 +0000

A roundtable discussion: Defining urban data science

Tue, 01 Oct 2019 00:00:00 +0000

Smoothed Estimators for Markov Chains with Sparse Spatial Observations

Tue, 01 Oct 2019 00:00:00 +0000

PySAL

Sat, 20 Jul 2019 08:00:00 +0000

PySAL is the Python Spatial Analysis Library for open source, cross-platform geospatial data science. I have been actively participating in its development and maintenance since 2014.

MGWR: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale

Sat, 01 Jun 2019 00:00:00 +0000

Urban income mobility as a multifaceted concept in the United States

Thu, 04 Apr 2019 18:20:00 +0000

Space-Time Statistics for Economic Inequality Dynamics

Tue, 05 Mar 2019 12:00:00 +0000

A comment on geographically weighted regression with parameter-specific distance metrics

Fri, 01 Feb 2019 00:00:00 +0000

Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents

Tue, 01 Jan 2019 00:00:00 +0000

Inference in Multiscale Geographically Weighted Regression

Tue, 01 Jan 2019 00:00:00 +0000

Regional inequality dynamics, stochastic dominance, and spatial dependence

Tue, 01 Jan 2019 00:00:00 +0000

Fishing for neighborhood trajectory patterns - a comparison of sequence analysis methods

Sat, 10 Nov 2018 10:00:00 +0000

Workshop on Spatial Data Analysis with PySAL

Wed, 07 Nov 2018 08:00:00 +0000

Workshop Description

A unique feature of this tutorial is the use of Python based software tools for spatial data analysis. Python is an object oriented scripting language that is gaining rapid adoption in the computational sciences. Since its initial release in July 2010, PySAL has been downloaded over 500,000 times. This two-part tutorial will first provide participants with an introduction to Python and related tools for spatial and regional analysis. In the second part of the tutorial participants will learn version 1.14 of PySAL applied to spatial and regional analysis. Part I is thus designed for participants with no prior Python experience, while the second part assumes knowledge of materials covered in Part I.

Please find the workshop material on github.

Conditional and joint tests for spatial effects in discrete Markov chain models of regional income distribution dynamics

Mon, 01 Jan 2018 00:00:00 +0000

Spatio-temporal analysis of socioeconomic neighborhoods: The Open Source Longitudinal Neighborhood Analysis Package OSLNAP

Mon, 01 Jan 2018 00:00:00 +0000

Multiscale Geographically Weighted Regression (MGWR)

Tue, 01 Aug 2017 00:00:00 +0000

The properties of tests for spatial effects in discrete Markov chain models of regional income distribution dynamics

Fri, 01 Jan 2016 00:00:00 +0000