This course will introduce researchers and practitioners to artificial intelligence (AI), focused on deep learning, and use cases for urban health practice and research. The course will provide the historical context and development of some of the deep learning models currently in use, the key concepts and design of these models, what resources are needed to implement them in your research or practice, and collaborating with AI experts.

It will also provide some case examples of use for both research and practice, such as collecting built environment data and prompting generative AI GPT models to provide text, images, videos or other outputs for your practice or research use. No programming knowledge is needed or required for this course, though suggested resources will be provided to those interested in that aspect.

The learning objectives of the course are:

• Describe some of the primary AI algorithmic approaches in plain language.
• Assessing the quality and reliability of AI models and output.
• Identify use cases of AI for your area of research or practice.
• Become familiar with some of the available AI tools and platforms through practice

Prerequisite Knowledge: None

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

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The course is an introduction to the basic concepts and techniques of Geographic Information Systems (GIS). This course contains lectures and exercises in ArcGIS, which introduce the basic concepts of GIS, data models, coordinate system and map projections, data management and processing, spatial analysis, spatial estimation, and GIS data visualization.

Students will also learn how to use the fundamental knowledge and techniques of GIS to solve real-world problems. Through the exercises, students will get familiar with the interfaces and analysis tools in the ESRI software package ArcGIS. Students will also practice applying GIS as a tool and a methodological approach to spatially analyze environment and public health data.

Prerequisite Knowledge: None

Technical Requirements: A temporary license for Esri's ArcGIS Pro will be provided for participants to install on their computers, if needed. ArcGIS Pro system requirements are available here. Students planning on using a Mac computer with Intel processors will need Boot Camp with Microsoft Windows installed on the partition or a virtual environment (e.g., VMWare or Parallels 15 or greater). Students with a MacBook with the Apple Silicon (i.e., M-processors), can complete the course assignments with an alternative software to ArcGIS or via virtual desktop. QGIS is open software that can be installed on PC or Mac computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH

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This course will discuss the rationale for multilevel studies and multilevel analysis in public health, as well as differences with other study designs and analytical approaches. Although the course will not be heavily mathematical, the basics of fitting multilevel models for different types of outcomes, as well as the interpretation of estimates obtained from multilevel models, will be reviewed and practiced.

Emphasis will be on the conceptual understanding, application, and interpretation of multilevel analysis in the context of urban health research. The course will also review and critique empirical applications in urban health research and discuss conceptual and methodological challenges in using multilevel analysis.

Prerequisite Knowledge: Knowledge of regression analysis (linear, logistic, Poisson) is required.

Technical Requirements: Participants will need access to SAS, Stata, or R (students can choose which; R is publicly available for free).

Continuing Education Credits*: 1.5 CEU or 15 CPH

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This course provides an introduction to qualitative data and analysis, as well as the integration of quantitative and qualitative data in mixed methods research designs. Students will be introduced to a range of qualitative primary data collection approaches and will learn how to analyze qualitative data through both hand coding and with analytic software.

The course will also include an introduction to the integration of quantitative and qualitative data and methods. Students will learn about the pragmatic approach to study design, in which the research/evaluation question at hand drives the selection of methods. The advantages and challenges to mixed methods research will be covered, as well as an overview of the major mixed methods study designs.

Prerequisite Knowledge: None

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

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This course will provide an overview of:

1. The public health impacts of a global climate change in urban areas.
2. Urban policies for climate change mitigation and adaptation, and their resulting benefits and harms to public health.
3. Fundamental concepts and research methods from exposure science and epidemiology to assess exposures and quantify the impacts of climate change on urban health.

The course will have a specific focus on marginalized and/or vulnerable subpopulations, as well as the role of climate hazards and climate policies in exacerbating or ameliorating health disparities.

Prerequisite Knowledge: None

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH
*Please note that in-person attendance is required for local participants in this course. 

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A comprehensive introduction to the statistical methods used in the analysis of geo-referenced spatial data. The course covers the topics of disease mapping (relative risk estimation), disease clustering, and ecological analysis. The methods covered are mainly in the area of generalized linear models and mixed models. The course addresses the use of appropriate software packages for the analysis of disease incidence data.

The progression of methods begins with simple Poisson regression (log-linear models) and logistic linear models, moves to Bayesian hierarchical modeling for mapped data, and finally models with spatially correlated prior distributions only available in advanced software. If time permits, we also examine space-time modeling, multivariate analysis, and survival modeling. Knowledge of intermediate statistics and basic proficiency in R is expected.

Prerequisite Knowledge: Basic knowledge of statistics and R 

Technical Requirements: Participants will need access to R

Continuing Education Credits*: 1.5 CEU or 15 CPH

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This course explores how artificial intelligence (AI) can transform health communication, fostering stronger multisectoral collaboration to address complex public health challenges, including systemic issues like the social determinants of health (SDOH). Multisectoral approaches bring together public health professionals and stakeholders from diverse sectors—such as healthcare, education, housing, transportation, and community organizations—to collaboratively design sustainable and impactful solutions.

When developing a health communication strategy, graphical and culturally sensitive health communication plays a pivotal role in enhancing collaboration by making interactions more inclusive, accessible, and effective. By incorporating cultural relevance and visual elements, these communications improve understanding and engagement among diverse stakeholders. This public health-focused course highlights the use of AI tools to create personalized, visually compelling, and culturally respectful messages. These tools empower stakeholders by tailoring communication to their unique needs and contexts, ultimately strengthening collaboration and driving meaningful change.

Learning objectives:

• Understand the Role of Multisectoral Approaches in Urban Health.
• Develop Strategies for Cross-Cultural Collaboration.
• Understand how to leverage AI tools to enhance multi-sector collaboration in public health initiatives.
• Develop practical skills in using AI to communicate program successes to diverse stakeholders through hands-on action labs.

Prerequisite Knowledge: None

Technical Requirements: None  

Continuing Education Credits*: 1.5 CEU or 15 CPH

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Effective communication in public health is critical to building trust, collaboration, and ensuring equitable access to health information. This interactive course provides participants with a foundational understanding of people-centered and nonviolent communication principles and how to apply them in professional public health settings.

Through hands-on exercises, participants will learn to craft messages that prioritize empathy, inclusivity, and clarity while avoiding language that may alienate or retraumatize communities. Participants are encouraged to bring materials from their own organizations for real-world application and feedback.

Learning objectives:

• Explain the role of people-centered, nonviolent communication in public health.
• Identify and revise language that may contribute to stigma, bias, or harm.
• Critically analyze and refine public health materials to align with people-centered and nonviolent communication principles.

Prerequisite Knowledge: None

Technical Requirements: Any word processing software 

Continuing Education Credits*: 1.5 CEU or 15 CPH

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Traditional organization policies often prioritize compliance over care, unintentionally reinforcing punitive structures that can retraumatize staff and clients. This interactive workshop explores how to apply trauma-informed principles to policy development and writing, ensuring policies promote safety, trust, and empowerment while maintaining accountability.

Participants will learn practical strategies to revise existing policies and develop new ones that reflect a more inclusive, supportive, and effective workplace culture.

Learning objectives:

• Define the core principles of trauma-informed policy development.
• Identify language and structures that may unintentionally cause harm in policies.
• Apply strategies to revise and create policies that balance accountability with support.
• Develop a plan for integrating trauma-informed policies into their organization’s practices.

Prerequisite Knowledge: None

Technical Requirements: Any word processing software 

Continuing Education Credits*: 1.5 CEU or 15 CPH

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This course will give participants the internal tools they need to provide healing spaces and encounters within their public health practice that will motivate their clients and patients to make the changes they want in their lives. Furthermore, participants will gain a larger perspective about the collective trauma that can amplify, as well as worsen, trauma in communities and families. 

Participants will then be given tools to counteract this and begin to create healing policies and culture at their organizations. This training is the beginning of a journey to be trauma-informed and healing-centered.

Learning objectives:

• Identify individual and collective trauma in your clients.
• Implement stress reducing techniques to regulate yours and your clients' nervous system.
• Utilize trauma-informed, healing-centered practices and policies in their workplace.

Prerequisite Knowledge: None

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

Please note: this course will include sensitive information about trauma and invites participants to reflect on their own emotional experiences.

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This course introduces quantitative tools to evaluate programs and public health policies in the context of urban health. The course provides hands-on training in the theoretical approach and practical application of evaluation methodology, ranging from logic models and process evaluation to quasi-experimental designs (including novel applications of the 'Differences-in-Differences' design).

We will explore different publicly available datasets, discuss various study design and analytical approaches, and critically assess key examples from the literature. The course is designed to prepare practitioners and academics to identify appropriate data sources, approaches, and quantitative methods for evaluating programs and policies, and to determine when, how, and under what circumstances an evaluation should be conducted.

Course activities will include discussions about the strengths and weaknesses of various methods, hands on labs to explore datasets and practice analytic methods, and presentations that critically review published evaluation studies.

Learning objectives:

• Understand the fundamentals and applications of policy and program evaluation.
• Gain familiarity with common publicly available public health datasets.
• Understand the most common methodological approaches used to evaluate policies and programs in public health, and strengths and limitations of each approach.
• Critically assess the appropriate study design and analysis method, based on the policy context, data availability, stakeholder buy-in, and timeline.

Prerequisite Knowledge: Basic understanding of statistics, probability, and regression techniques

Technical Requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

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