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.

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

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 will need Boot Camp with Microsoft Windows installed in the partition or a virtual environment (Parallels 15 or greater).

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 briefs lectures and extensive hands-on computer lab exercises that students can tailor based on geographic location or health outcome interests.

After completing this course, participants will be able to:

• Use GIS tools such as spatial overlays, interpolation and zonal statistics with both vector and raster data.
• Utilize GIS tools in combination with basic statistics and graphing to answer urban health research questions.

Prerequisite knowledge: Knowledge of basic concepts and techniques of GIS or an introductory course in GIS is required. Prior basic knowledge of ESRI's ArcGIS Pro software is recommended 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*: 1.7 CEU or 17 CPH

This course is designed to provide students with a hands-on introduction to programming skills and analytic concepts in R. Participants will address analytical challenges that a public health data analyst might encounter. The focus of the course is not comprehensive knowledge of R, but rather a foundation of knowledge that can be expanded on after the course concludes. The interactive classes will feature a lecture component and a laboratory component. The RStudio environment will be the interface used for all classroom discussion.

After completing this course, participants will be able to:

• Comfortably operate in the R environment.
• Perform basic data management in R.
• Understand generic programming concepts in R.
• Analyze data in R.

Prerequisite knowledge:Prior experience with a statistical programming language (e.g., SAS, Stata) is recommended but not required.

Technical requirements: R and RStudio free software is available for participants to install on their computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Course participants will gain hands-on programming skills for data management and data presentation using Stata, one of the commonly used packages in the public health field. The course will use examples from survey, mortality, and longitudinal data. Please note that this course will not cover statistical methods but focus on how to prepare data for analysis and how to manage result data.

After completing this course, participants will be able to:

• Conduct data management to prepare datasets for data analyses and construction of tables, graphs, and maps.
• Prepare tables, graphs, and maps for reports and publications.
• Implement these tasks in a reproducible manner using programs.

Prerequisite knowledge: Prior basic knowledge of Stata (e.g. how to create simple analyses, frequency tables and summary statistics) is recommended.

Technical requirements: Participants will need access to Stata for the course. The course does not provide the software package and participants are expected to have it installed on their computer.

Continuing Education Credits*: 1.5 CEU or 15 CPH

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.
• Be familiar with the available software for implementing Bayesian methods.
• Understand advanced Bayesian methods used in the scientific literature.

Prerequisite knowledge: Basic understanding of linear regression and "generalized linear models" (e.g., logistic and Poisson regression) is required for this course. Statistical programming experience is recommended but not required.

Technical requirements: R and WinBUGS are required for the course. The free software is available for participants to install on their computers.

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 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

Data is the most powerful tool in decision making, however sometimes it is difficult to show your audience significant or relevant findings. This course will focus on how to transform a raw dataset into a compelling interactive data dashboard that will guide an audience to see the insights within.

After completing this course, participants will be able to:

• Use Tableau’s point and click software.
• Utilize best practices in data visualization.
• Incorporate interactive features that allow your audience to explore the data on their own.

Prerequisite knowledge: Prior experience in SAS, SPSS, STATA, R, or other statistical coding software is required for this course.

Technical requirements: Participants will need access to Microsoft Excel, a statistical software package (SAS preferred) and Tableau. Free teaching licenses for Tableau will be provided for this course.

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course introduces 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 methodologically rigorous quasi-experimental designs to small tests of change using a Plan-Do-Study-Act (PDSA) approach. The course will overview how to conduct various study design and analytical approaches, and the strengths and weaknesses of these methods. The course is designed to prepare practitioners and academics to identify appropriate approaches and methods for evaluating programs and policies, and to determine when, how, and under what circumstances an evaluation should be conducted. Course work will include critical assessments of strengths and weaknesses of previous urban health evaluations, problem sets for practicing methods of analysis, and short proposals of prospective evaluations.

After completing this course, participants will be able to:

• Understand the fundamentals and applications of policy and program evaluation.
• 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: Some prior experience with regression models is required.

Technical requirements: Instructors will use R and Stata software to demonstrate analytical approaches used in program and policy evaluation. Access to R or Stata is recommended if participants want to follow along but not required. R free software is available for participants to install on their computers. Code will be provided in the course material.

Continuing Education Credits*: 1.5 CEU or 15 CPH

This course will provide an overview of the fundamentals of human health risk assessment of chemical pollution in the urban environment. Students will develop a critical understanding of the steps of risk assessment (hazard identification, dose-response assessment, exposure assessment, and risk characterization) through a series of examples and applied problems. Examples will include health risks for residential communities surrounding point-source polluters (e.g., industrial facilities) or contaminated sites, as well as risks from non-point source exposures, such as ambient air pollution and drinking water contamination. Quantitative methods for assessing risk will be taught, following guidance from the US EPA and utilizing software tools. The course will emphasize identification of data sources for use in risk calculations and interpretation of risk analysis results, including characterization of risk variability and robust discussion of the uncertainties. We will also examine how quantitative assessment of risk can inform regulation for protection of human health from urban pollution.

After completing this course, participants will be able to:

• Specify quantitative methods for assessing risk from environmental chemical pollution; accurately explain results, both verbally and in writing.
• Estimate dose-response and exposure based on published data, for use in risk calculations.
• Identify key areas of uncertainty in risk predictions and understand methods to address such uncertainty.
• Describe factors that affect susceptibility to health effects from environmental pollution and approaches to characterize variability as part of a risk analysis.

Prerequisite knowledge: Basic knowledge of statistics or biostatistics is required; participants are expected to have completed at least one university-level course.

Technical requirements: Participants will need access to Microsoft Excel, R, and US EPA Benchmark Dose Software (BMDS) for the course. R and BMDS free software will be available for participants to install on their computers.

Continuing Education Credits*: 1.5 CEU or 15 CPH

Increasingly data mined from the electronic health record (EHR) are being used in epidemiological research. But more data does not equate to better quality research. This workshop will cover the basics of working with EHRs and designing valid epidemiological analyses. The workshop will include a mix of didactic lecture and interactive group exercises. Participants are requested to provide planned or active research questions in advance, as these will form the basis of the group exercises.

After completing this course, participants will be able to:

• Identify appropriate epidemiological study designs using EHR data for both inpatient and outpatient settings, with particular focus on urban locations.
• Understand the architecture of the EHR along with terminology and data standards.
• Discuss data requirements in the EHR, including data export, linkage, and variable manipulation (e.g., parsing data from free text).
• Identify common pitfalls in working with EHR data and where to go for additional resources.

Prerequisite knowledge: Intermediate knowledge of epidemiology is required.

Technical requirements: There is no required software for the course.

Continuing Education Credits*: .4 CEU or 4 CPH

This self-paced, online course will cover an introduction to climate change’s impact on urban health. The course will provide an overview of different climate change effects in cities (environment, health, inequalities) as well as projected changes in the climate system. This course will also review some potential solutions and research areas that can be applied to urban areas. Online course material will include video-based lectures, self-assessments, reading material and real-world examples.

After completing this course, participants will be able to:

• Explain the scientific basis of observed climate change effects and projected changes in the climate system.
• Identify possible actions and strategies to reduce climate change impacts in urban settings.
• Identify research gaps and methodologies that can be applied in future research to address effects on climate change and urban health

Prerequisite knowledge: No prerequisite knowledge or skills are required for this course.

Technical requirements: There are no technical requirements for this course

Continuing Education Credits*: .4 CEU or 4 CPH