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.
• 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.
• Apply the knowledge of GIS to solve research questions in urban health research.

The course is co-sponsored by the Center for Public Health Practice (CPHP) at the Drexel University Dornsife School of Public Health. The CPHP is the home of the federally funded Mid-Atlantic Regional Public Health Training Center, a collaborative project with the University of Pittsburgh Graduate School of Public Health to increase the technical skills of the public health and health care workforce.

Prerequisite knowledge: None

Technical requirements: A temporary license for Esri's ArcGIS Pro will be provided for participants to install on their computers. Students planning on using a Mac computer with Intel processors will need Boot Camp with Microsoft Windows installed in the partition or a virtual environment (e.g., VMWare or Parallels 15 or greater). Students with a MacBook with the new Apple Silicon (i.e., M1 or M2 processors), can complete the course assignments with an alternative software to ArcGIS. QGIS is open software that can be installed on PC or Mac computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH

The goal of this intermediate 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 using GIS. 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, trees and parks, food/nutrition) and health in Philadelphia. Class sessions will consist of brief lectures and extensive hands-on computer lab exercises that students can tailor based on geographic location or health outcome interests.

Prerequisite knowledge: Knowledge of basic concepts and techniques of GIS and some basic knowledge of ESRI's ArcGIS Pro software is required for this course.

Technical requirements: A temporary license for Esri's ArcGIS Pro will be provided for participants to install on their computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Urban health requires the interconnection of knowledges and practices from different disciplines to depict the complexity of urban systems. This poses several challenges in the study of urban health such as the need for a common vocabulary (i.e., how we define urban areas, informal settlements); the use of data from different sources (i.e., place based and spatial data, health registries); the creation of data that could be comparable across urban areas and over time (i.e. accounting for differences between cities and within cities over time), among others. All these challenges are closely related to the principles of FAIR (Findable, Accessible, Interoperable, Reusable) and CARE (Collective benefit, Authority to control, Responsibility, and Ethics) data policies and practices. The implementation of these principles contributes to a more transparent, efficient, participatory, technology enabled, and interdisciplinary science to tackle current and future global societal challenges Beginning in 2023, all NIH-funded grant applications or renewals that generate Scientific Data must include a robust and detailed plan for how researchers will manage and share data during the entire funded period (data management and sharing plan, DMSP). This includes information on data storage, access policies/procedures, preservation, metadata standards, and distribution approaches, which are closely related to the knowledge and implementation of FAIR and CARE principles. The common understanding of the FAIR and CARE principles is fragmented and uneven among researchers and practitioners in urban health. Therefore, the overall purpose of this course is to provide participants with a broad understanding of FAIR and CARE principles that could help them to design and develop data management plans for research and community -based projects according to NIH data management requirements.

Prerequisite knowledge: None

Technical requirements: None

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

This three-hour course is split into two parts: content overview and skills-based learning. In the first part of the course, participants will learn what public deliberation is, its history, and utility. They will learn how public deliberation, in both approach and framework, is distinct from other approaches to community engagement and understand the processes and core elements of a PD. The skills-based learning will focus on facilitation for a public deliberation, including the goals, and responsibilities of the facilitators, best practices, and time for participants to practice their newly acquired skills.

Learning objectives:

• Describe the purpose of public deliberation and its utility for meaningful community engagement
• Explain the differences between public deliberation and other approaches to community engagement
• Identify the components that make up a public deliberation Demonstrate public deliberation facilitation best practices

Prerequisite knowledge: None

Technical requirements: None

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.
• 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.

Prerequisite knowledge: Basic understanding of linear models (e.g., regression) and “generalized linear models” (e.g., logistic regression) is required. Basic familiarity with R programming language is recommended for this course.

Technical requirements: R and WinBUGS are required for the course. The free software is available for participants to install on their computers. A Windows computer is preferred for use of this software.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Multilevel studies and multilevel analysis have been increasingly 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.
• Be familiar with 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. Familiarity conducting regression analysis in SAS, Stata, or R software is recommended for this course.

Technical requirements: Participants will need access to SAS, Stata, or R software for the course. R free software is available for participants to install on their computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH