Bayesian methods combine information from various sources and are increasingly used in biomedical and public health settings to accommodate complex data and produce readily interpretable output. This course will introduce students to Bayesian methods, emphasizing the basic methodological framework, real-world applications, and practical computing. Special consideration will be given to methods for spatial data analysis.

After completing this course, participants will be able to:

• Understand the fundamentals of Bayesian inference and the differences between Bayesian and frequentist (classical) methods.
• Be able to formulate research questions and develop Bayesian approaches to address these questions.
• Become familiar with the available software for implementing Bayesian methods.
• Understand advanced Bayesian methods used in the scientific literature.

Improving the quality of data visualizations can help in communicating results more clearly and transparently to other scientists, stakeholders and the general public. This course will use a workshop format to help students build their own data visualization projects using R, with Urban Health examples. The five sessions will include: (1) a general introduction to basic data visualization concepts; (2) best practices to build static figures + a workshop on different types of static figures (bar, line-points, etc.); (3) basic concepts regarding maps and GIS + a workshop on how to map your geographic data in R; (4) interactive web-plots and their utility + a workshop on interactive web plotting in R using Shiny; (5) a plotting project, where students will bring their own plotting needs, present a basic sketch to other students and then work in the code needed to get a production-quality figure with help from the code from previous sessions. 

This course will provide all of the required code, so participants do not need to have much prior knowledge of R to get something out of it. However, this is a programming course, so students with some knowledge of R will benefit. For example, some of the code may be useful to apply to your own visualizations with some adjustments. Thus participants with a stronger R background coming into the course will be able to create more complex visualizations, but again, you do not need to have advanced pre-knowledge of R to take this course. If you are interested in an introduction to the material that will be covered in this course, please check out one of the Coursera courses in R.

After completing this course, participants will be able to:

• Understand the basic principles of data visualization
• Perform basic data management to get data ready for visualization using R
  
• Use the ggplot2 R package to plot high-quality figures
• Create interactive web-plots using R and the shiny R package
• Plot spatial data using R

Community-based participatory research (CBPR) is an orientation to research that begins with community 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 engagement 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 welcomes researchers and community-based organizations as participants.

After completing this course, participants will:

• Know the history and principles of CBPR, and what distinguishes it from community-placed research.
• Understand the importance of and practical approaches for community-researcher engagement in all phases of a research process.
• Be able to apply CBPR principles to research partnership development and grant-seeking processes.

The way information is communicated can make the difference between a successful or failed response in a public health disaster. Combining theory and practice, this course places an emphasis on the core principles of crisis and risk communication, examines how various audiences perceive and react to risk, and emphasizes the important role of effective communication before, during, and following a disaster. Through a series of readings, interactive lectures, case studies, and activities, students will learn how to better understand and communicate with communities affected by a crisis.

The five days will focus on: 1. Overview of public health preparedness, risk communication, and threats facing urban environments; 2. Audience analysis, formative research, and behavior change; 3. Developing messages for disasters using the Message Mapping technique; 4. Evaluating message content using CDC’s Clear Communication Index; 5. The role of social media and crisis mapping in disasters.

After completing this course, participants will be able to:

• Understand how people perceive risk and process information during crisis situations.
• Apply principles of risk and crisis communication to each phase of disaster preparedness, response, and recovery.
• Develop and evaluate messages for diverse audiences and stakeholders. 
• Leverage social media as a two-way communication channel during disasters.

Health Impact Assessment has been proposed by the World Health Organization as a key tool for health in all policies and has been widely used in the urban context. The course will describe the methods and utility of Health Impact Assessment (HIA) in urban areas. The steps for completing HIA will be learned through practical exercises. 

After completing this course, participants will be able to:

• Understand the various types of HIA.
• Complete all of the steps to conduct a HIA.
• Identify how HIA could be used as a tool for policy translation.

The goal of this course is to familiarize students with the applications of Geographic Information Systems (GIS) to assess or evaluate urban health challenges, and potential solutions, with a focus on the Philadelphia context. Students are expected to have some prior experience with GIS and be familiar with acquiring spatial data, data management, symbolizing features and making maps, and data coordinate and projection systems. Through hands-on exercises based on local case studies, students will gain practice in assessing the relationship between aspects of our environments (e.g. housing, transportation, food/nutrition, crime, chemical pollution, parks, and vegetation) and health in Philadelphia. Students will gain practice in using tools such as spatial joins, buffer analyses, spatial overlays and interpolation with both vector and raster data, in combination with basic statistics to answer urban health research questions.

After completing this course, participants will be able to:

• Identify, acquire and use spatial data relevant to urban health, such as from the census, satellite imagery, administrative records, or street address geocoding.
• Work with various spatial object representations, such as raster and vector data. 
• Answer spatial research questions using data produced from spatial operations in combination with basic statistics.
• Develop maps that effectively communicate spatial data and analysis results.