Qiang Pu

Qiang Pu

Ph.D. Candidate

University at Buffalo, SUNY

Bio

Hello! This is Qiang Pu. I’m a Ph.D. candidate in the Department of Geography at the University at Buffalo, SUNY. My research interests are broadly in the areas of GIScience and environmental health. Specifically, by leveraging advanced geospatial technologies, such as geostatistics/machine learning models, satellite remote sensing, GPS, and GIS, I study the spatiotemporal distributions and uncertainties of ambient PM2.5 exposures. I’m also interested in evaluating the healthcare accessibility in low and low-middle income countries using open geospatial data.

I received a M.S. in Cartography and GIS and a B.S. in Geomatics Engineering both from School of Geosciences and Info-physics at Central South University. I am currently a member of GEOEH Lab at UB.

Here is my CV.

Interests
  • Air pollution modeling
  • Environmental health
  • GIScience
  • Satellite air quality remote sensing
Education
  • Ph.D. in Geography, 2017-Now

    University at Buffalo, SUNY

  • M.S. in Cartography and GIS, 2017

    Central South University, China

  • B.S. in Geomatics Engineering, 2014

    Central South University, China

Work Experience

 
 
 
 
 
University at Buffalo
Research Assistant
University at Buffalo
Jun 2022 – Present Buffalo

Supervisor: Dr. Jessie Poon

Responsibilities include:

  • Survey data cleaning and anlysis
  • Web development
  • Assist research on socioeconomic issues in Asia and around the globe
 
 
 
 
 
University at Buffalo
Research Assistant
University at Buffalo
Jun 2018 – Aug 2018 Buffalo

Supervisor: Dr. Enki Yoo

Funded through Community for Global Health Equity Seed Funding - “Pediatric Surgery Infrastructure Development in Eastern Democratic Republic of Congo”.

Responsibilities include:

  • Data collection
  • Data analysis & modelling
  • Manuscript preparation
 
 
 
 
 
University at Buffalo
Teaching Assistant
University at Buffalo
Aug 2017 – Jun 2022 Buffalo
Worked as laboratory instructor (major role), grader, and guest lecturer for various courses in the department of geography

Recent Publications

Journal Articles

(2021). The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5. International Journal of Environmental Research and Public Health, 18(4), 2194.

Cite Source Document

(2021). Ground PM2.5 prediction using imputed MAIAC AOD with uncertainty quantification. Environmental Pollution, 274, 116574.

Cite Source Document

(2020). Improving the spatial accessibility of healthcare in North Kivu, Democratic Republic of Congo. Applied Geography, 121, 102262.

Cite Source Document

(2020). Geospatial Mapping of Pediatric Surgical Capacity in North Kivu, Democratic Republic of Congo. World journal of surgery, 44(11), 3620-3628.

Cite Source Document

(2020). Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing, China. International Journal of Geographical Information Science, 34(3), 423-447.

Cite Code Source Document

(2016). High-resolution satellite mapping of fine particulates based on geographically weighted regression. IEEE Geoscience and Remote Sensing Letters, 13(4), 495-499.

Cite Source Document

(2014). Spatial pattern evolution and casual analysis of county level economy in Changsha-Zhuzhou-Xiangtan urban agglomeration, China. hinese Geographical Science, 24(5), 620-630.

Cite Source Document

Conference Presentations

2022

  • Yoo, E. H., Roberts, J., Pu, Q. & Palermo, T. Geospatial modeling of national health survey delivery data: A case study of Tanzania. International Conference on Geostatistics for Environmental Applications, Parma, Italy, June 22-24, 2022.

  • Eum, Y., Pu, Q. & Yoo, E. H. Spatio-temporal exposure assessment of urban cyclists: Using bike-sharing data and highly-resolved PM2.5 estimates. UCGIS Symposium 2022 GIScience Forward: Meeting the Challenge, Syracuse, U.S., June 7-9, 2022.

  • Pu, Q. & Yoo, E. H., A hybrid Approach to estimate spatially and temporally resolved PM2.5 distributions from multi-satellite AOD data. AAG Annual Conference, John Odland student paper competition through the Spatial Analysis and Modeling specialty group. New York City, U.S., Feb 25 - Mar 1, 2022.
    (Finalist, top 10 out of 25)

2020

  • Pu, Q. & Yoo, E. H., Modeling spatial variation of hourly PM2.5 concentrations using both CMAQ model and satellite aerosol optimal depth. Exposome Symposium: Measuring the Exposome Using Novel Methods and Big Data to Improve Human Health, New York City, U.S., Mar 5-6, 2020.

2019

  • Pu, Q. & Yoo, E. H., Spatio-temporal modeling of PM2.5 concentrations with missing data problem. AAG Annual Conference, Symposium on Frontiers in Geospatial Data Science, Washington DC, U.S., Apr 3-7, 2019.

  • Niu, Z., Mu, L., Wen, X., & Pu, Q. Leukocyte telomere length and cardiovascular disease mortality among US adults: effect modification by race. Annals of Epidemiology.

2018

  • Pu, Q. & Yoo, E. H., Perdition of Urban PM2.5 Concentrations Using a Bayesian Spatio-temporal Modelling Approach. The 13th International Symposium of Spatial Accuracy: Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Beijing, China, May 21-24, 2018.
    (Best Paper Presentation, First Place)

Teaching

As Laboratory Instructor

Geographical Information Systems (Laboratory) | UB
Cross-listed Undergrads/Grads. 40 students in Fall 2019, 47 students in Fall 2021 (InPerson/Remote), 26 students in Spring 2022. Introduced fundamental concepts in GIScience and provided hands-on exercises using ArcGIS Pro. Students are expected to build skills for elementary GIS internships.
GIS for Environmental Modeling (Laboratory) | UB
Cross-listed Undergrads/Grads. 21 students in Spring 2019, 33 students in Spring 2020, 29 students in Spring 2021 (Remote), 27 students in Spring 2022. Introduced advanced GIS methods and techniques for environmental modeling. Presented a series of laboratory exercises on environmental modeling/analysis using ArcGIS Pro&Online. Provided guidance on research project design and implementation.
Remote Sensing (Laboratory) | UB
Cross-listed Undergrads/Grads. 22 students in Fall 2018; 29 students in Fall 2020 (Remote). Introduced principles of remote sensing and guided a series of digital image processing tasks with the software of ENVI (primary) and Google Earth Engine.

Skills

R

Data analytics and modeling

Statistics

(Geo)Statistical modeling

Machine Learning

Modeling for tabular data

Python

Data analytics and modeling

Software Tools

ArcGIS Suite, ENVI, Google Earth Engine, LaTex, SAS

Contact