The Master of Applied Science in Spatial Analysis for Public Health is an interdisciplinary online degree. Faculty at the Johns Hopkins Bloomberg School of Public Health contribute to the program via course development, teaching, and advising students. The topics and concepts allow graduates to effectively design and conduct public health-related spatial analysis by applying knowledge and tools learned in the program.
Students will complete 50 credits to graduate. The program is designed to be completed in 8 academic terms - two academic years (Sept-May). In addition to the coursework, students must complete an Integrative Activity, where newly acquired knowledge and skills are used to create an applicable activity (e.g., design a study, plot the map and analyze the data) – with a final paper that describes the methodology used and the final assessment.
By the end of the program, students should be able to:
- Interpret and critique epidemiologic studies addressing public health problems
- Apply measures of morbidity and mortality to the evaluation and comparison of the health of populations
- Synthesize how geography affects public health
- Obtain and transfer information from spatial data technologies into a database appropriate for mapping
- Utilize a geographic information system to map and spatially integrate public health related databases
- Analyze and interpret maps using tools from the field of spatial statistics to describe and interpret distributions of health outcomes in a population
- Design and implement a spatial analysis protocol for addressing a public health problem
|Academic Year||Academic Term||Curriculum|
|Year 1 Curriculum|
|Introduction to Online Learning (0 credits, required)|
Academic and Research Ethics (0 credits, required)
|2||Spatial Data Technologies for Mapping (4 credits)|
Seminars in Public Health (2 credits)
|3||Introduction to Epidemiology (4 credits)|
Professional Development Workshop:
Writing for Professionals (2 credits)
|4||Public Health Statistics 1 (4 credits)|
Spatial Analysis Lab 1 (2 credits)
|Year 2 Curriculum|
|1||Public Health Statistics 2 (4 credits)|
Professional Development Workshop:
Effective Online Searching (2 credits)
|2||Intermediate Epidemiology (4 credits)|
Spatial Analysis Lab 2 (2 credits)
|3||Applied Spatial Statistics (4 credits)|
Spatial Analysis Journal Club (2 credits)
|4||Spatial Applications (4 credits)|
Integrative Activity (4 credits)
Spatial Analysis for Public Health (4 credits)
Introduces the field of spatial analysis for public health. Concepts are examined through the use of ArcGIS Geographic Information System (GIS) mapping software as a tool for integrating, manipulating, and displaying public health related spatial data. GIS topics covered include mapping, geocoding, and manipulations related to data structures and topology. Introduces the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics. Selected case studies are used to demonstrate concepts along the paradigm. Focus is on using GIS to generate and refine hypotheses about public health related spatial data in preparation for follow up analyses.
Spatial Data Technologies for Mapping (4 credits)
Examines technologies for collecting, obtaining and creating spatial data. Technologies considered include, but are not limited to: GPS, tablets, tracking devices, cell phones, mHealth, Google Earth, remote sensing applications, and the internet. Integrates spatial data from the aforementioned technologies into ArcGIS for spatial analysis. Other GIS related software applications such as QGIS, ERDAS, and Rare introduced. Covers relevant properties of spatial data such as metadata, confidentiality/disclosure and spatial data accuracy. Covers additional topics and concepts that reinforce the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics.
Introduction to Epidemiology (4 credits)
Introduces principles and methods of epidemiologic investigation of diseases. Illustrates methods by which studies of the distribution and transmission of diseases in populations (including disease outbreaks and epidemics) can contribute to an understanding of etiologic factors and modes of transmission. Covers various study designs, including randomized trials, case-control and cohort studies, as well as risk estimation and causal inference. The course also discusses applications of Epidemiology to solving public health problems, such as identifying sources and strategies for control of disease outbreaks, applying research findings to policy and practice, and program evaluation. Quantitative and analytic methods covered during the course include life tables, disease surveillance, measures of morbidity and mortality, and measures of diagnostic test accuracy.
Public Health Statistics I (4 credits)
Provides students with a broad overview of Biostatistical methods and concepts used in the public health sciences. Emphasizes the interpretation and conceptual foundations of statistical estimation and inference. Covers summary measures, measures of association, confidence intervals, p-values, and statistical power. The statistical software package R will be introduced in the class and utilized to demonstrate the concepts and methods with data.
Public Health Statistics II (4 credits)
Employs a conceptual framework to highlight the similarities and differences between linear, logistic, Poisson and Cox Proportional Hazards methods, in terms of usage and the interpretations of results from such models. Provides details for these regression approaches in the “simple” scenario, involving relating an outcome to single predictor. Following this overview of simple regression, explores the use of multiple regression models to compare and contrast confounding and effect modification, produce adjusted and stratum-specific estimates, and allow for better prediction of an outcome via the use of multiple predictors. Students will learn to use the statistical software package R to fit linear, logistic and Poisson regression models.
Intermediate Epidemiology (4 credits)
Expands knowledge beyond introductory level epidemiologic concepts and methods material, using examples from the published literature. Emphasizes interpretation and the ability to critically evaluate issues related to populations/study design, measurement, population comparisons and inference, including: modern cohort study designs; advanced nested designs; novel techniques for exposure assessment; interpretation and utility of measures of impact; sources of bias and methods for their prevention; descriptive and analytical goals for observational study inference; the counterfactual model for defining exchangeability, cause, and confounding; and synthesis of inferences from observational studies as compared with randomized clinical trials.
Applied Spatial Statistics (4 credits)
Introduces statistical techniques used to describe, analyze and interpret public health related spatial data. Included will be methods for characterizing clustering, cluster detection and spatial variation in health related outcomes and events. Three well known types of spatial data, geostatistical data, point event data, and area-level data, will be defined and used to motivate presented material. Regression methods previously learned will be adapted to the spatial setting. The statistical software package R will be used for analysis. Covers additional topics and concepts reinforcing the spatial science paradigm: Spatial Data, GIS, and Spatial Statistics.
Spatial Applications (4 credits)
Provides an opportunity for students to gain a working knowledge of resources for conducting spatial analysis (i.e., literature, software, and data). Introduces new and relevant topics in GIS, spatial data technologies and spatial statistics not previously covered in the OPAL spatial analysis series. Expands students’ abilities to design and conduct spatial analysis by applying knowledge and tools learned from the previous three OPAL spatial analysis courses by providing data for reproduction, and in some cases extension, of analyses from existing studies.. Focuses on further developing and integrating components of the spatial science paradigm: Spatial Data, GIS and Spatial Statistics.
Seminars in Public Health (2 terms required, 2 credits each)
Senior faculty present public health topics of current interest, such as those related to global health, health promotion and disease prevention, health care delivery systems, environmental issues and the spectrum of factors influencing the health status of populations and communities.
Spatial Analysis Labs (2 terms, 2 credits each)
The spatial analysis labs will extend GIS concepts and skills previously learned with more hands-on practice with public health applications. Spatial Analysis Lab I will focus on translating an epidemiological problem or setting into a set of spatial objectives that align with our spatial science paradigm and to perform the GIS analysis portion of those objectives. Spatial Analysis Lab II will continue and extend this practice to include a focus on biostatistical concepts such as estimation, variation and correlation.
Spatial Analysis Journal Club
This course will involve reading and critically evaluating the application and interpretation of spatial statistical methodology in published public health literature. Focus will be on understanding how the epidemiological/public health objectives translate into spatial statistical analyses. Literature reviews will also include outlines detailing spatial statistical methods and analyses that can be applied as an extended and/or alternative analysis.
Professional Development Workshops (2 terms, 2 credits each)
Each 2-credit workshop will focus on a specific professional development topic. Students are expected to take several sections of the course, in order to obtain training in a variety of areas. Workshop topics include, but may not be limited to: Effective Online Searching, Writing for Professionals, and Optimal use of Free Online Resources.
Integrative Activity (4 credits)
This course will involve the research, analysis and writing of a complete and independent spatial analysis project. Intermediate outlines, hypotheses and objectives produced in previous classes will be finalized. No new material will be covered. The finalized project will follow journal article format including an abstract, and introduction/background, methods, results and conclusion sections. The final project will represent an integrated and synthesized assessment of the spatial science paradigm (Spatial Data, GIS, Spatial Statistics) applied to a relevant public health problem.
PREPARING STUDENTS FOR ONLINE LEARNING
Introduction to Online Learning is a free, mandatory prerequisite for all online courses offered. It’s open to prospective students and designed to give a thorough view into what the online experience on CoursePlus will be like. It also prepares students and faculty for success on the first day of class since everyone is already acclimated to the technology. Introduction to Online Learning will allow students to:
- Ensure all tools and applications are properly installed
- Troubleshoot any component that may not work properly
- Experience the online tools for peer-to-peer and instructor communication
- Become familiar with the coursework structure and learning management system
The course takes between 1-4 hours to complete, depending on your skills working online. With the notable exception of one LiveTalk, you can do the class work on your own schedule. For details on the course, including assignments and requirements for completion, please see the Syllabus page.
Ready to test-drive online courses at JHSPH? Register for this class. You can take this free course at any point before or during the application process!
Get In Touch
Request more information or call us at 844-379-1319 to speak with an admissions officer.
State-specific Information for Online Students
Students should be aware of additional state-specific information for online programs.