Center for Spatial-Temporal Modeling for Applications in Population Sciences

Pioneering the use of spatial-temporal data science to uncover hidden patterns in public health data, transcending disciplinary boundaries, and facilitating the translation of insights into impactful public health interventions

About the Center for Spatial-Temporal Modeling for Applications in Population Sciences (CSMAPS)

Welcome. CSMAPS aims to uncover hidden patterns and dynamics within public health data, providing crucial insights across a myriad of public health fields - including infectious disease control, cancer research, mental health research, environmental health, health disparities, chronic disease management, and implementation sciences. Through this multidisciplinary lens, we are able to expose intersections and interdependencies typically overlooked by traditional analyses, offering a comprehensive understanding of population health as a complex, multifaceted phenomenon. The incorporation of implementation science further bridges the gap between research findings and practical application, facilitating the translation of these insights into tangible, impactful public health interventions and preventions.

Mission and Vision 

Our mission and vision are founded upon three fundamental principles: 

  • Innovation and Interdisciplinary Research: We are committed to pioneering the development and application of cutting-edge spatial-temporal data science, enriching our understanding of population health dynamics. By embracing an interdisciplinary approach, we integrate expertise to drive impactful solutions. 
  • Real-World Impact and Improvement of Health Outcomes: Our ultimate goal is to significantly enhance health outcomes for Texas. We aim to achieve this by transforming our innovative research findings into actionable policies and practices, and by forging strategic collaborations with community organizations, healthcare providers, policymakers, and other stakeholders. 
  • Dissemination and Support for Public Health Policy-making: We prioritize the broad dissemination of our research insights and the development of user-friendly software tools and visualizations. This approach not only makes complex data and analyses accessible to a wider audience but also supports informed public health policy-making. Furthermore, we are dedicated to bolstering the understanding and use of spatial-temporal modeling techniques among researchers, practitioners, and decision-makers through targeted capacity-building initiatives.

Current Projects

Artificial Light at Night (ALAN):

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This project is funded by NASA’s Health and Air Quality Applied Science Team Program (PI: Qian Xiao). The aim of the project is to apply satellite data to improve mapping of ALAN levels in the US for public health surveillance. The project dashboard can be found here.

Geospatial Approaches to Melanoma Early Detection (GAMED):

Project link: https://www.cprit.texas.gov/grants-funded/grants/rp230036

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This project is funded by CPRIT RP230036 (MPI: Cici Bauer/Kelly Nelson). This project aims to use a data-driven approach to create statistical models that predict which areas may have the most late-stage melanomas in the future, and to identify the multi-level factors associated with late-stage diagnosis in Texas. We plan to identify primary care physicians (PCPs) in high melanoma burden areas who are interested in learning about skin cancer and deploy our multimodal educational intervention program – tailored to the social and community patient context – to clinical sites in those communities.

Predict to Prevent: Dynamic Spatiotemporal Analyses of Opioid Overdose to Guide Pre-emptive Public Health Responses (P2P):

Project link: https://reporter.nih.gov/search/3N0RMTkLh0iceaQeZ4jHfQ/project-details/10444263

Dashboard link: https://spatiotemporal-data-science.shinyapps.io/PredictToPrevent/

Publication link: https://publichealth.jmir.org/2023/1/e41450

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This project is funded by NIH R01DA054267-01A1 (MPI: Cici Bauer/Thomas Stopka). This project proposes to use a statewide Public Health Data Warehouse with a large number of linked administrative datasets to identify current opioid overdose patterns, predict future opioid overdose epidemics, and evaluate the effectiveness of public health and clinical interventions. Guided by the social ecological model, we plan to develop a comprehensive approach to identify individual, interpersonal, community and societal factors that contribute to opioid overdose, efficiently detect overdose hotspots, and develop dynamic forecasting models for timely prediction and prevention of future opioid overdose epidemics. We will disseminate blueprints, visualization tools, and code to other jurisdictions to facilitate replication of our approach.

TEPHI Wastewater-Based Epidemiology Project:

Publication link: https://www.nature.com/articles/s41467-023-42064-1


This project is funded by Texas Epidemic Public Health Institute (TEPHI) https://tephi.texas.gov/. This research area delves into the epidemiology of various infectious diseases, emphasizing their prevalence, risk factors, and associated health outcomes. It particularly highlights the innovative use of wastewater surveillance as a potent tool for monitoring and understanding the dynamics of infectious diseases within communities. This approach allows for the timely detection of disease prevalence and the identification of emergent health threats, ultimately aiding in the formulation of effective and responsive public health strategies and interventions.

Spatial Homogeneity Learning Models with Applications to Socioeconomic Problems:


This project is funded by NSF-SBE 2243058 (PI: Guanyu Hu). This project aims to develop a geographically adaptive concave fusion penalized (GACP) learning method to estimate model parameters and recover latent memberships simultaneously. This project will advance the frontiers of spatial heterogeneity learning, benefitting regional economic policy and other complex socioeconomic problems.

Bayesian Learning for Spatial Point Processes: Theory, Methods, Computations, and Applications:


This project is funded by NSF-DMS 2210371 (PI: Guanyu Hu). The primary aim of this project is to develop theories, methods, and computing algorithms for spatial point pattern data from various applications through a nonparametric Bayesian framework. We will fill the gap between nonparametric Bayesian methods and spatial point process, including intensity estimation and heterogeneity learning for univariate and multivariate processes. The applications of the proposed methods vary from environmental science, social science, and sports analytics.


Cici Bauer, PhD
Associate Professor, Center Director
Department of Biostatistics and Data Science
[email protected]

Qian Xiao, PhD, MPH
Associate Professor
Department of Epidemiology, Human Genetics and Environmental Sciences
[email protected]

Guanyu Hu, PhD
Assistant Professor
Department of Biostatistics and Data Science
[email protected]

Yiping Dou, PhD
Assistant Professor Non-Tenure Research
Department of Biostatistics and Data Science
[email protected]


Porsha V. Day, MPA
Center Administrator
[email protected]

Kehe Zhang, MS
[email protected]

Jocelyn Hunyadi, MPH
[email protected]

Thuy Nguyen, MS
[email protected]

Nico Reger, MS
[email protected]

Ghada Hassan, PhD
Postdoctoral Research Fellow
[email protected]

"Alone we can do so little, together we can do so much." - Helen Keller

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Past Events

2024 Symposium on Spatial-Temporal Data Science for Population Health

Date: March 1, 2024

Time: 9:00am - 4:00pm

Location: Cooley Center, 7440 Cambridge St, Houston, TX 77054

Review the full agenda and list of speakers.


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We would like to extend a very special thank you to everyone who were able to attend our center's first symposium! As well as, all of the speakers and vendors who contributed to it's success!

Interested in collaboration opportunities, please contact [email protected].

Post-Event Anonymous Quotes

What were your key take aways from this event?

“Geospatial analyses are used in a wide array of PH fields and have many applications, which is very encouraging.”

“Spatial-Temporal can be applied to many research field like cancer.”

What was your biggest takeaway from the event?

“CSMAPS is awesome.”

Is there anything else you would like us to know?

It was great overall and very helpful. I liked the way it was organized and topics.”



Porsha V. Day, MPA
Center Administrator