Hui(Henry) Luan 栾辉

Hui(Henry) Luan 栾辉

Assistant Professor
Peter O'Donnell Jr. School of Public Health
UT Southwestern Medical Center
Email:hui.luan@utsouthwestern.edu
Phone:TBD

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Pages

Posts

Future Blog Post

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Blog Post number 4

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Blog Post number 3

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Blog Post number 2

less than 1 minute read

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Blog Post number 1

less than 1 minute read

Published:

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portfolio

publications

talks

Invited

  • [4] Luan H. “Bayesian spatial modeling and its applications in health and crime geography”, School of Resource and Environmental Sciences, Wuhan University, Dec 27, 2019, Wuhan, China.

  • [3] Luan H. “Harnessing the power of probabilistic modeling in spatial data science: The application of Bayesian spatial and spatiotemporal analysis in addressing societal challenges”, Department of Geography, University of Oregon, Feb 15, 2018, Eugene, USA.

  • [2] Luan H. “Advanced spatial analysis: Bayesian spatial statistical modeling”, School of Geodesy and Geomatics, Wuhan University, June 1, 2017, Wuhan, China.

  • [1] Luan H. “The Healthy City movement in Canada: What can Artificial Intelligence-enabled high-definition mapping do?”, the company of Ecopia – AI Enabled Feature Extraction (https://www.ecopiatech.com/), Dec 8, 2017, Toronto, Canada.

Conference/Symposium Presentations

(Advisees are underlined)

  • [15] Luan H. “HIV infection prevalence significantly intersects with COVID-19 infection at the area-level: a USA county-level spatial analysis”. Intersectionality and Public Health Symposium, May 7, Chicago. (Online, peer-reviewed)

  • [14] Luan H. “Estimating county-level mortality rates using left-censored data: a Bayesian spatiotemporal approach”. SPATIAL ACCURACY 2020, July 7-10, Buffalo, USA. [Cancelled due to COVID-19]

  • [13] Song I and Luan H. “Does missing data mechanism matter?: Exploring its effect on spatiotemporal data imputation accuracy”. SPATIAL ACCURACY 2020, July 7-10, Buffalo, USA. [Cancelled due to COVID-19]

  • [12] Luan H, Ransome Y, Nassau T, and Brady K. “Bayesian spatiotemporal models for zero-inflated count data: Exploring the trends of late HIV diagnosis rates among Black/African American population in Philadelphia, 2010-2016”, American Association of Geographers Annual Meeting, April 6-10, 2020, Denver, USA. [Cancelled due to COVID-19]

  • [11] Song I and Luan H. “Local explanations of individual characteristics and district-level air pollution on low birth weight in South Korea: a Bayesian network approach”. American Association of Geographers Annual Meeting, April 6-10, 2020, Denver, USA. [Cancelled due to COVID-19]

  • [10] Fuller D, Luan H, Alfosool A, and Chen Y. “Time weighted approaches for combining GPS and area level data to create individual measures”. American Association of Geographers Annual Meeting, April 6-10, 2020, Denver, USA. [Cancelled due to COVID-19]

  • [9] Luan H. “Imputing censored health data at small-area levels: A Bayesian spatiotemporal modelling approach”, American Association of Geographers Annual Meeting, April 2-7, 2019, Washington DC, USA.

  • [8] Luan H, Fuller D, and Dorani F. “Urban sprawl in Canada at the Census Tract level: a nationwide spatial analysis”, Small and Adaptive Cities 2017: Sustainable futures in the urban periphery, September 29 to October 1, 2017, St. John’s, Newfoundland, Canada.

  • [7] Minaker L, Law J, Luan H, and Quick M. “Obesity, its associations with the food environment, and spatio-temporal indicators of the food environment in the Region of Waterloo, Ontario”, Canadian Obesity Summit, April 26-28, 2017, Banff, Alberta, Canada.

  • [6] Luan H, Quick M, and Law J. “A spatial zero-inflated Poisson model for analyzing violent crime data with zero-inflation”, American Association of Geographers Annual Meeting, April 5-9, 2017, Boston, Massachusetts, USA.

  • [5] Luan H, Law J, and Quick M. “Local spatio-temporal patterns of relative healthy food access in the Region of Waterloo, Ontario, 2011-2014”, The Ontario Public Health Convention (TOPHC), April 4-6, 2016, Toronto, Canada.

  • [4] Quick M, Luan H, and Law J. “The influence of alcohol outlet density on violent crime calls-to-police in Waterloo Region: A Bayesian spatial analysis”, Canadian Association of Geographers Annual Meeting, June 1-5, 2015, Vancouver, Canada.

  • [3] Luan H, Law J, and Minaker L. “Diving into the consumer nutrition environment: a retail food environment index based on spatial Bayesian factor analysis at a small-area level”, American Association of Geographers Annual Meeting, April 21-25, 2015, Chicago, Illinois, USA.

  • [2] Luan H and Quick M. “Is neighborhood socioeconomic status associated with fast food accessibility? A spatial analysis of the Region of Waterloo”, Canadian Association of Geographers – Ontario Division (CAGONT), Critical Human and Physical Geographies, October 24-25, 2014, York University, Toronto, Canada.

  • [1] Luan H and Law J. “Web GIS in Public Health Surveillance and Planning”, Canadian Association of Geographers Annual Meeting, May 28 – June 2, 2012, Waterloo, Canada.

Conference posters

(Advisees are underlined)

  • [5] Nzeyimana A and Luan H. “Representation learning from geospatial data”, American Association of Geographers Annual Meeting, April 2-7, 2019, Washington DC, USA.

  • [4] Dean L, Luan H, Brady K, and Ransome Y. “Does bankruptcy risk predict HIV new cases? A spatio-temporal analysis of the local credit economy and HIV in Philadelphia, PA”, Annual Meeting of Society for Epidemiologic Research, June 19-22, 2018, Baltimore, USA.

  • [3] Luan H, Fuller D, Buote R, and Stanley. “User guide for big data in population and public health”, Public Health 2018, May 28-31, 2018, Montreal, Canada.

  • [2] Luan H and Ransome Y. “A Bayesian spatial versus tradition approach to characterize socioeconomic deprivation: what is the impact on public health ecological studies?”, GeoMed 2017: International conference on spatial statistics, spatial epidemiology & spatial aspects of public health – deeper insight from big data and small areas, September 7-9, 2017, Porto, Portugal.

  • [1] Perlman CM, Law J, Rios S, Luan H, Seitz D, and Stolee P. “ ‘Hotspots’ of Psychiatric Hospitalizations among Older Adults with Delirium, Dementia, and Cognitive Disorders in Ontario, 2011-2014”, 2016 CAHSPR Conference: A Learning Healthcare System: Let the Patient Revolution Begin!, May 10-12, 2016, Toronto, Canada.

Workshops

  • [5] Landscape Carbon for Atmospheric Recovery (LCSAR) Workshop, Oct 25-27, 2019, Portland.

  • [4] Applications of Geospatial Information and Spatial Analytical Techniques in Public Health, July 17-19, 2018, Shanghai, China

  • [3] Food Environment Measures for Canadian Cities, April 26-27, 2018, Toronto, Ontario, Canada.

  • [2] Healthy Cities Research Think Tank meeting, November 30 to December 1, 2017, Toronto, Ontario, Canada

  • [1] The Future of Walkability Measures, November 16-17, 2017, St. John’s, Newfoundland, Canada.

teaching

GEOG281: The World & Big Data

Lecture&Lab, Department of Geography, University of Oregon, 2024

This course introduces relevant concepts (e.g., 5 V’s) and techniques (e.g., cloud computing) of big (spatial) data as well as its applications in the real word, such as delineating communities using social media data and responding to disasters with volunteered geographic information (VGI). The students will have hands-on experiences such as contributing VGI (e.g., creating and uploading geographic information to Open Street Map), retrieving social media data associated with location (e.g., “geo-tagged” Tweets), operating relational database management system (RDMS) and NoSQL databases, and visualizing big spatial datasets.

GEOG4/594: Spatial Analysis

Lecture&Lab, Department of Geography, University of Oregon, 2024

This course introduces the theory, methods, and tools used to conduct spatial analysis and understand geographical phenomena. Topics covered in this course include descriptive spatial analysis, probability theory, spatial sampling, inferential spatial analysis, spatial interpolation, spatial correlation, and spatial regression. Common issues in spatial analysis will also be introduced, such as the Modifiable Areal Unit Problem (MAUP) and the Uncertain Geographic Context Problem (UGCoP). Lectures focus on the geographic theory and associated (mathematical) equations behind each method, and assignments provide an opportunity for students to implement a variety of methods to address questions that are geographic in nature.

GEOG607: Selected Topics in Spatial and Spatiotemporal Analysis

Graduate seminar, Department of Geography, University of Oregon, 2024

This seminar discusses selected topics in spatial and spatiotemporal analyses, including long-standing and newly proposed fundamental issues such as the Modifiable Areal/Temporal Unit Problem and the Uncertain Geographic Context Problem. Opportunities and challenges that spatial and spatiotemporal analyses face in the big data era are also covered, for example, the ethical and geoprivacy issues. For each topic, there will be readings from journal articles and/or book chapters. Students will lead presentations of readings and participate in discussions. Depending on the enrollment size of the class, each student will be a discussion leader at least once during the term. Students will also conduct independent research on a topic covered in the course that they are interested in and/or relevant to their own research. An oral presentation and a 15-page report of student research are required. This report could take the form of an article, a dissertation chapter, or a term paper.

GEOG4/590: GIS and Public Health

Lecture&Lab, Department of Geography, University of Oregon, 2024

This course introduces the applications of GIS and spatial statistics in exploring and analyzing spatial health and medical datasets. Three major themes will be explored: 1) spatial and spatiotemporal patterns of health phenomena, 2) risk factors associated with health outcomes, and 3) health service access. The course focuses on preparing and organizing health datasets, detecting disease clusters, statistically modeling the association between risk factors and health outcomes, interpolating point- and area-based health data, and measuring and optimizing health services. Weekly assignments will provide hands on experience with example health data. Students who successfully complete this course will develop a skill set of spatial analytical methods to solve contemporary health and medical geographical problems.