The course is an introduction to the basic concepts and techniques of Geographic Information Systems (GIS). This course contains lectures and exercises in ArcGIS. The lectures 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.

After completing this course, participants will be able to:

• Understand basic concepts and terms of GIS, and know how to spatially analyze data using ArcGIS or similar software.
• Acquire spatial data that is relevant to urban health, e.g., census data, land use data, greenspace, road networks, satellite images, etc.
• Build knowledge of data structure, data management, and coordination and projection systems.
• Practice featuring symbols and making elegant maps in ArcGIS or similar software.
• Apply the knowledge of GIS to solve research questions in urban health research.

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 here https://pro.arcgis.com/en/pro-app/latest/get-started/arcgis-pro-system-requirements.htm. 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

This course provides an introduction to spatial statistics. The course will cover various types of spatial data including discrete space and continuous space data and will compare/contrast the challenges encountered when analyzing these types of data. The course will cover standard and state-of-the-art methods from the statistical literature for modeling spatial data and delve into their motivations. Models will be fitted using a combination of code written from scratch and existing/freely-available software tools e.g., R, STAN etc.

Learning objectives:

• Familiarity with different types of spatial data
• Knowledge of spatial and spatiotemporal modeling approaches
• Familiarity with software (BUGS, NIMBLE, CARBayes, INLA, spX etc.).
• Building open-source user friendly software that focuses on spatial data ad applications.

Prerequisite knowledge: None

Technical requirements: R/R-Studio (Posit)

Continuing Education Credits*: 1.5 CEU or 15 CPH

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 the resulting benefits and harms to public health, and 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 and the role of climate hazards and climate policies in exacerbating or ameliorating health disparities.

Learning objectives:

• Explain the scientific basis of observed and projected climate changes.
• Describe the broad spectrum of public health impacts of climate hazards in urban areas (heat waves, floods, air pollution, sea level rise, population displacement, etc.) and distinguish the ways in which climate exposures and health impacts are distributed differentially within and across urban populations.
• Identify urban policies to mitigate and adapt to climate change and characterize the public health benefits and harms of these policies, with a focus on marginalized and/or vulnerable populations.
• Define key concepts and research methods to quantify climate exposures and health impacts in urban populations.

Prerequisite knowledge: Understanding of basic epidemiological concepts and methods, if possible.

Technical requirements: No software will be required. Students will use publicly available, online interactive apps/websites.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Built environment exposure data are key for epidemiological studies focused on walkability, road safety, physical activity, nutrition, violence, mental health and others, yet there are many challenges to obtaining these data for researchers. Built environment data from geographic information systems (GIS) may be out of date, low quality or missing features and can vary substantially between the administrative entities that create them. Collecting built environment data manually or virtually via audits can be time consuming, expensive and may have limited geographic coverage. Artificial intelligence approaches via computer vision and deep learning offer researchers the potential to consistently collect built environment data at a much greater scale within and across geographies from publicly available street-level and satellite imagery. This workshop will provide attendees a broad and basic overview of the potential of AI for collecting built environment data from street-level imagery and provide examples of how they are being used for road safety research. The workshop will provide fundamentals about deep learning algorithms and models, how they compare to traditional data collection methods, what resources are needed to implement them, recommendations for collaboration with AI experts, finding and creating training data, and methodological aspects analysis and interpretation from the epidemiologic perspective. A brief tutorial will also be provided on how to set up, create and process training data for AI models.

As a result of this workshop, attendees will be able to:

• identify the best tools and costs for their built environment assessment needs;
• define and prioritize built environment items to collect using AI;
• prepare data for training AI models;
• describe how to conduct quality assessments of collected data; and
• how to find and collaborate with AI expert colleagues.

Prerequisite knowledge: None.

Technical requirements: None required, this course will cover conceptual, design and interpretation of artificial intelligence for public health research and not the programming and setting up a deep learning computing environment.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Community-based participatory research (CBPR) is an orientation to research that begins with community informational and research interests and seeks to use the knowledge gained to inform action for community health improvements and social change. Although this approach to research is widely adopted in urban health and generally familiar to both community-based organizations and researchers, many still struggle with the lack of knowledge or experience with applying practical CBPR approaches to a pressing community health issue or potential research partnership. This course will review the history and principles of CBPR, and introduce participants to practical approaches and tools for equitable and authentic community-researcher collaboration in all phases of research: from the creation of research questions to study design, data collection, data analysis, and dissemination. Participants will also learn strategies for participatory grant-writing, budgeting, and opportunities for workforce development and capacity building for community residents and leaders in research. This course is open to researchers and community-based organizations.

After completing this course, participants will be able to:

1. Learn about the history and principles of CBPR, and what distinguishes it from community-placed research.
2. Understand the importance of and practical approaches for community-researcher collaboration in all phases of a research process.
3. Apply CBPR principles to research partnership development and grant-seeking processes.

Prerequisite knowledge: None

Technical requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

Multilevel studies and multilevel analysis are widely used in the public health field. This course will discuss the rationale for multilevel studies and multilevel analysis in public health as well as differences with other study designs and other 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 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.

After completing this course, participants will be able to:

• Understand the fundamentals of multilevel studies and multilevel analysis and their differences with other study designs and analytical approaches.
• Fit multilevel models and interpret estimates derived from them.
• Describe applications of multilevel analysis in urban health research.
• Understand the strengths and limitations of multilevel analysis for urban health research.

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

This course is designed to equip students with a comprehensive understanding of the aging population, a global phenomenon that significantly influences various aspects of our society, such as the economy, health/healthcare, intergenerational relations, and the environment. By exploring the biopsychosocial framework, this course will enhance students’ understanding of the complex process of adult development and aging, a crucial area of study for those interested in careers in healthcare, social work, or policymaking. This course will especially delve into the effects of social marginalization (e.g., classism, racism, and ageism) on the psychosocial and biological processes of adult development and aging.

Learning objectives:

• Provide an opportunity for students to think critically about the societal implications of an aging society.
• Provide a deeper understanding of adult development and aging, especially by utilizing the biopsychosocial framework.
• Gain an appreciation of the complexity of the conceptual and methodological issues related to studying the aging process.
• Consider the implications of this course for one’s own aging and aging of family. 

Prerequisite knowledge: None

Technical requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course provides an introduction and to qualitative data and analysis and 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.

Learning objectives:

• Explain the role and describe applications of quantitative and qualitative methods for assessing population health and health equity.
• Identify the strengths, limitations, and underlying philosophical assumptions and interpretive framework of qualitative methods, data, and analysis.
• Assess the advantages, disadvantages, and complementary nature of qualitative and quantitative data.
• Describe ethical considerations in qualitative and mixed methods research.
• Analyze and interpret qualitative and mixed methods data.

Prerequisite knowledge: None

Technical requirements: None

Continuing Education Credits*: 1.5 CEU or 15 CPH