PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

 

 PHW272C: Applied Spatial Data Analysis for Public Health 

Course Syllabus  Draft Subject to Change 

   

Table of Contents Course Description 2 

Course Goals 2 

Instructor Information 5 

Course Format 6 

Required Course Materials 7 

Course Schedule 8 

Course Grading 12 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

Course Requirements 13 

Completion of Course Modules 13 

Participation in Course Activities and Discussions 13 

Final Exam 14 

Course Communication 14 

Announcements 14 

Course mail 14 

Office hours 14 

Policies 14 

Due Dates 14 

Late Assignments 15 

Policy on Sharing, Copying, or Reusing SPH Online Materials15 

Disability support services 15 

Accommodation of religious creed 15 

Course evaluations 15 

Netiquette 16 

Expectations of Student Conduct 16 

Academic honesty 17  

 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

Course Description   Spatial analysis is a powerful set of techniques that can be used to describe and explain patterns of health and disease data through the statistical analysis of locational data. As location information becomes routinely collected alongside health data, including through the application of mobile and other technologies, public health researchers and practitioners are increasingly harnessing the power of geography to increase the impact of their public health work. This course will cover the theory and methods behind the analysis of patterns of health and disease in space. Students will increase their proficiency in the application of spatial analysis of public health data, and will learn how to perform a wide variety of space and space-time analyses. The course will introduce statistical techniques useful for describing, analyzing and interpreting layers of mapped data, including the acquisition and classification of remote sensing data. Exercises will guide students to a stronger understanding of the role of spatial data science in public health, and will provide a framework for applying spatial analysis to common questions that arise in public health practice and research. Students will learn how to pose substantive questions regarding spatial analysis of health data (including in the domain of spatial epidemiology), identify appropriate methods and data necessary to address spatial questions, apply appropriate spatial statistics to diverse locational data, and report results of analyses in a clear and interpretable manner to both public health and ion-public health audiences. This course will make heavy use of R and will touch on different forms of spatial regression analysis. As such, students are expected to be able to use R to a basic level and be familiar with regression analyses.    

Course Goals 

On successful completion of the course you should possess the following skills and knowledge:   

1. Understanding of the power of geographical analysis and spatial data science for characterizing health and 

health-related processes in space and time. 

2. Working knowledge of the theory and methods behind the analysis of patterns of health and disease in space. 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

3. Knowledge of statistical techniques useful for describing, analyzing and interpreting layers of mapped data in public 

health applications. 

4. Ability to apply appropriate spatial statistics to diverse locational data, and report results of analyses in a clear and 

interpretable manner to both public health and non-public health audiences. 

   

Instructor Information 

 

 

Hugh Sturrock, PhD  Email: hugh.sturrock@ucsf.edu Office Hours: Fridays 3-4pm PST 

Hugh Sturrock, MSc, PhD, is a Spatial Epidemiologist at the UCSF Global Health Group's Malaria Elimination Initiative (MEI) and an Assistant Professor of Epidemiology and Biostatistics at UCSF. With the MEI, Hugh is focusing on the use and optimization of active case detection to find and target asymptomatic infections as well as using routine surveillance data to generate risk maps to guide interventions.  Hugh has a broad interest in optimizing surveillance methods for tropical infectious diseases, with his research focused on a combination of field work, spatial analyses, geostatistics, and computerized simulations. As a doctoral student, he investigated optimal survey methods for neglected tropical diseases and malaria.  Hugh has a PhD from the London School of Hygiene and Tropical Medicine and an MSc in Zoology from the University of Otago.  

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

 

Ricardo Andrade, PhD  Email: Ricardo.AndradePacheco@ucsf.edu Office Hours: Fridays 3-4pm PST 

Ricardo Andrade holds a BS in Actuarial Science from the Technological Autonomous Institute of Mexico, a MS in Statistics and Operational Research from the University of Essex and a PhD in Computer Science from the University of Sheffield. Ricardo is currently a Researcher at Institute of Global Health in the University of California, San Francisco. His work focuses on developing and implementing statistical algorithms for infectious diseases surveillance. He is interested in the automation and deployment of statistics and machine learning tools for risk prevention and response.  Before joining UCSF, Ricardo worked for the National Institute of Geography and Statistics in Mexico, as technical coordinator, overseeing methodologies for information production regarding the topics of Geography and Environment. He also worked as Credit Risk Analyst at the Banking and Securities Commission in Mexico.  

 

Alemayehu Midekisa, PhD  Email: Alemayehu.Midekisa@ucsf.edu Office Hours: Fridays 3-4pm PST 

Alemayehu Midekisa, PhD, is a Geospatial Research Specialist at the UCSF Global Health Group’s Malaria Elimination Initiative (MEI). In this role, Alemayehu is co-leading the development of automated malaria risk mapping using cloud-computing and near-real time satellite observations. He is also co-leading various other projects that involve big geospatial earth observation data. Prior to joining the MEI, Alemayehu's doctoral research examined the spatial and temporal drivers of malaria in the highlands of East Africa using satellite observations, for which he was a NASA Earth and 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

Space Science Fellow. His broader research interests include the application of earth observation satellite data to quantify and monitor the distribution of vector-borne diseases. Alemayehu has a PhD in Geospatial Sciences and Engineering from South Dakota State University and an MSc in Remote Sensing and GIS from Addis Ababa University.   

 

Everleigh Stokes, MPH candidate  Email: eis5187@berkeley.edu Office Hours: Wednesdays 5:30pm - 6:30pm PST 

Everleigh Stokes is a second-year Master of Public Health candidate in Global Health & Environment at the University of California-Berkeley School of Public Health. Her primary interests are conducting spatial-temporal analysis and designing data visualizations for health and climate data. She has an extensive background in using R, ArcGIS, and Tableau to build maps, visualizations, and dashboards. Ultimately, her goal is to use these resources to inform decision making around resource allocation and disease management. Everleigh earned her B.S. in Geographic Information Systems (GIS), with an emphasis on Sustainability and Global Entrepreneurship, from Penn State University. 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

Instructional Design Information 

 

 

Michelle Ruiz, MA.ed 

m.ruiz@berkeley.edu  

Michelle Ruiz guides the instructional team in instructional design concepts, educational technology tools, digital learning methods, and alternates proctored exam facilitation with other members of the digital learning team. Michelle oversees educational technology in OOMPH courses and binational online courses from the UC - Mexico health initiative. She is passionate about creating inclusive online courses and applying her media computing skills to public health education and research. She led the design and video production of nutrition education programs at UC San Diego’s Center for Community Health and the design and video production of EnviRN-Evidence, an online program for health professionals about environmental health risks funded by the National Library of Medicine. Before joining OOMPH, she was an instructional designer at University of San Francisco where she designed online academic programs about Climate Change, Health, and Nursing.  

 

Course Format  

This eight-week course is designed to introduce students to methods for the analysis of spatial data. The course will cover theory and methods in spatial statistics, and the use of statistical software for the visualization, exploration and analysis of spatial data. The course will expose students to real-world datasets and applications of spatial analysis drawn from public health and environmental health. Each week, approximately two readings drawn from articles and texts will be assigned that 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

explore current applications of spatial analysis in public health from a variety of sources. Lectures will cover core statistical theories and techniques (generalized linear models, geostatistics, cluster analysis) as applied to spatial problems, and discussions will emphasize strategies for effectively communicating and collaborating with scientists across health-related disciplines where spatial statistics are used (e.g., public health, medicine, economics, management and policy). Course materials will support students in selecting appropriate analytical techniques, and readings will expose students to examples of the interpretation of results and outcomes of spatial statistical analysis, helping students build skills in the translation of these results into ideas and findings that can be broadly understood by the public health community. The final project will be completed in groups of up to 5 students, and will require students to articulate an application of spatial data analysis in public health; develop a specific spatial analysis question; present a review of background information on how public health scientists or practitioners have approached the spatial analysis question being posed; and present findings of quantitative explorations of question investigated through the application of spatial data science. The graded project will address these items, and will be submitted in the form of a journal-esque article with supporting media. The instructors will assist students in acquiring a publicly available spatial data sets for their final project, or students can bring in their own data to work with as they build skills in the course and complete their final project.  

See course site weekly pages for resources and detailed instructions for each session. All work in this class should be 

done independently although a course forum is available to post questions. Credit is given for answering questions in the 

forum. 

 

Course Schedule  

Date  Activities  Assignments  Lead  Learning outcome 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

10.22.18  Introduction to geospatial analysis: theory, methods and practice. Introduction to spatial data visualization and analysis in R. 

· Assignment 1: Using suggested or other open data, visualize spatial data to highlight a hypothesis regarding a spatial process in public health (Due 10/29) 

HS  - Familiarity with different types of spatial data 

- Understanding of key concepts in spatial epidemiology 

- Knowledge of how to load and visualize spatial data in R 

10.29.18  Manipulating spatial data: relating, extracting and summarizing spatial datasets.+ 

· Assignment 2: Complete coding assignment focused on manipulating and relating spatial datasets (Due 11/5) 

HS  - n understanding of how to manipulate (select, crop, resample) spatial data in AR 

- Ability to spatially relate different spatial data  

11.5.18  Spatial variation in risk: understanding patterns of risk from point data. 

· Submit Assignment 3: Complete coding assignment focused on kernel density estimates of risk and kriging (Due 11/12) 

HS  - An understanding of kernel density estimates 

- An understanding of interpolation techniques such as kriging 

11.12.18  Spatial cluster and point pattern analyses, with examples from cancer epidemiology 

· Submit Assignment 4: Identify clusters of disease risk from point prevalence and areal data (Due 11/19) 

HS  -An understanding of different ways to manipulate remote sensing data 

-Ability to classify multispectral satellite imagery and generate new spatial information  

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

11.19.18  Remote sensing and raster analysis for public health application: manipulating and classifying raster data. 

· Submit Assignment 5: Complete coding assignment focused on generating a land class raster from multi-spectral satellite imagery (Due 11/26) 

AM  - An understanding of different ways to assess global and local clustering 

- Ability to perform cluster analysis in R on areal and point level data 

11.26.18  Spatial regression analysis and interpretation, with examples from diarrheal diseases and chronic non-communicable diseases 

· Submit Assignment 6: Perform and summarize outputs of a spatial regression using point level data (Due 12/3) 

RAP  - An understanding of the concept of geostatistics. 

- Knowledge of how to model spatial dependence through a covariance matrix. 

- Introduction to Gaussian processes. 

- An understanding of cross-validation. 

12.03.18  Spatial regression analysis continued 

· Submit Assignment 7: Perform and summarize outputs of a spatial regression using areal/point level datauts of a spatial regression using point level data (Due 12/10) 

RAP  - An understanding of the difference between continuous spatial variation and discrete spatial variation. 

- Introduction to Markov Random Fields. 

- An understanding of the needs and caveats of using non-Gaussian likelihoods. 

- Introduction to splines. - Gain familiarity with the 

mgcv package. 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

12.10.18  Final assessment  · Submit Final Project (Due 12/19)  

HS   

 

 

Course Grading

Weekly Assignments 50%

Final Assessment 50%

Course Requirements 

 

Required Course Materials 

Microphone/headset for your computer   

 Windows 7 or higher required. If you do not have access to a PC, a MAC can be partitioned to run the software. A partitioned computer should have at least 8MB of RAM. During this course, you will be provided with links to PDF files of articles and other materials from the UC Berkeley Library 

Collection. Please make sure you understand and follow the University of California Library Conditions of Use. 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

Completion of Course Modules 

Students are expected to complete all modules, including viewing all lectures; completing all required readings and individual                                 

activities; and fully participating in class activities. 

Participation in Course Activities and Discussions 

Students are expected to complete each assignment on a per week basis. Additionally, students are expected to participate in                                     the class on-line forum (in bCourses) and discuss their progress at least every other week with the instructor via Skype. 

Class (Final) Project 

Due dates and submission details for the final project are posted in the bCourses site. 

 

Course Communication 

As we move through the course materials, we want to hear how the course is going for you, your questions as well as how                                               your personal and professional experiences add to our conversation.  You can learn a lot from discussing the material in this                                       course with each other and we encourage you to take advantage of the interactive components of the course to learn from                                         each other.   

Announcements 

Announcements will sent via email and then subsequently posted on the home page of the course site . Please check regularly                                         for updates.   

Course mail 

Course announcements will also be sent out through Canvas’ notification system. The default is to receive announcements via                                   the Course Mail system, so make sure to check your Course Mailbox for message or wherever you receive notifications.  

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

Office hours 

Instructions for making an office hours appointment are found on the course site. Use the Office Hours button in the left hand                                           navigation. 

 

Policies 

Late Assignments 

This is a fast-paced course with content that builds upon prior content each week, so we encourage students to meet deadlines for assignments and quizzes. Please communicate with instructors using Canvas Course Mail if you will not be able to meet course deadlines ahead of the deadlines. If a student does not communicate with instructors in advance of a deadline to notify them of a late submission, the following deductions from your total score will apply: 

 − One day late (includes any submission after the deadline) – 10% of your total score will be deducted − Two days late – 30% of your total score will be deducted − Three or more days late – no credit 

Policy on Sharing, Copying, or Reusing SPH Online Materials 

Per the School of Public Health handbook for online students, course materials provided to students for exclusive use within the course may not be shared outside the course without written consent from the instructor. This includes course videos, lectures, and any journal articles that are not freely available on the Internet.  

Disability support services 

If you need disability-related accommodations in this class contact the UC Berkeley Disabled Students Program (510) 642-0518 / web site: dsp.berkeley.edu).  DSP services include accommodation letters, assistive technology and access services.  An 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

accommodation letter is needed in order for the instructor to grant an accommodation (e.g. extended exam time). Student must be assessed every semester to receive an accommodation letter. 

Accommodation of religious creed 

If you need to reschedule a test or examination for religious reasons such as holidays, inform the course instructor by the second week of the course. More information is available in the Religious Creed Policy page. 

Course evaluations 

Course evaluations will be posted on the course site on Friday of Week 5 of the course and be available until the start of the final exam period.  You will receive notification when evaluations are available. While participation in course evaluations is not graded, it is an important service to the instructor, program and the university.  Your responses are anonymous and will not be available to the instructor until after final grades have been submitted.  Your feedback is essential for understanding how courses offered by OOMPH can be improved and I thank you in advance for you participation and feedback. 

Netiquette 

In an online environment it is not possible to read your body language, tone of voice, or facial expressions. Therefore, a special set of rules has emerged for online communications, called Netiquette. Here are some basic Netiquette guidelines that should be followed in this course. 

● Adhere to the same standards of behavior online that you follow in real life. Never mail or post anything you wouldn’t say to your reader’s face. 

● Before posting to a discussion board, you should read prior messages to get a sense of the flow and language of the ● Don’t be afraid to ask questions within the course discussion group, or to share what you know. ● If you post a different viewpoint, first acknowledge what someone else has said. If you disagree with someone, it is 

better to start a message by briefly restating what the other person has said in your own words. This lets the other person know that you are trying to understand him/her. 

● Support the points you make with examples or evidence from lecture, readings and/or from your own professional experience. 

● Email messages should be considered private and not shared with others or quoted without permission. However, whatever you post to a newsgroup or discussion board is public. You never know who might read what you posted. 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

● Consider that a post may be the first – and lasting - impression you make on someone: Make sure your postings contain correct information. Check your spelling. 

● Do not use ALL CAPS. It gives the impression that you are shouting. ● Do not send “Me Too!” or “Thank You” etc. messages to the entire group. Send those directly to the original poster. ● Cite all quotes, references and sources and respect copyright and license agreements. 

 

Expectations of Student Conduct 

As members of the academic community, students are responsible for upholding the standards of academic integrity.  The basic rules of academic study and inquiry call for honesty in the preparation of papers and assignments, acknowledging sources of ideas, and taking examinations on the foundation of one’s own knowledge. 

The Berkeley Campus Regulations Implementing University Policies, which address standards of student conduct, were amended in 1992 and are now published separately as “The Code of Student Conduct.”  The complete document is available here: http://sa.berkeley.edu/code-of-conduct.  

An excerpt from the introductory statement of principles is presented below: 

The University of California at Berkeley is committed to providing its students the very best education that is possible within our resources.  Thus, we try to attract the finest faculty members, we endeavor to maintain excellent classroom and laboratory facilities, and we support literally hundreds of co-curricular activities that enhance the quality of the Berkeley student’s experience.  Yet, for the campus to function as a university community, it is not enough for the faculty and administration to carry out their respective obligations.  It is equally important that every student assume his or her individual responsibilities. 

Foremost among these, of course, is the student’s responsibility to perform academically to the full extent of his or her ability.  In so doing, it is assumed that each student will observe the basic tenets of academic honesty.  Therefore, any act of cheating or misrepresenting one’s own or someone else’s academic work will be considered a very serious offense.  Intellectual products – including papers, exams, laboratory reports, articles, and books – are the heart and soul of any university’s academic life.  We cannot permit them to be willfully compromised or expropriated. 

Beyond our expectations of academic honesty – and of equal importance – is the assumption that the Berkeley student will accept his or her civil and civic responsibilities.  What are these responsibilities?  Simply put, they are the courtesies, 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

considerations, and gestures of respect towards other members of the campus community that allow us all to express our personal freedoms without trampling on those of others. 

The University is a fragile organism, requiring for its vitality consensus among its members concerning acceptable standards of conduct.  These standards must both underlie and promote a degree of tolerance far greater than that which is exhibited in society at large.  It is not coercive law that restrains our actions, it’s a shared common purpose. 

That purpose is understood as guaranteeing the right of every Berkeley student to pursue his or her personal path to an education, to ask his or her very own questions, and to express his or her private reflections – in short, to evolve as an individual without undue interference.  Any infringement of this right, whether in the classroom or elsewhere on campus, will be regarded as an offense against the entire campus community. 

UCB Code of Student Conduct, 1992, pp. 1 and 2 

Academic honesty 

The School of Public Health and the University of California, Berkeley place a high value on academic honesty, which prohibits cheating and plagiarism.  What is meant by “cheating is usually quite clear cut, but not so for “plagiarism”.  The following memo, prepared by Professor William Bicknell at the Boston University School of Public Health for orientation of students, defines plagiarism quite well.  Please read this carefully and discuss with your faculty adviser or with Associate Dean of Student Affairs if you have any questions. 

 “Plagiarism” a memo by Dr. William J. Bicknell:  The purpose of this memo is to make clear: What plagiarism is, how to avoid plagiarism and the consequences of plagiarism 

Misunderstanding is widespread about what plagiarism is and whether or not it is a serious offense.  It is a serious offense, and should be painstakingly avoided.  Acceptable practice on citing sources of information differs as one moves from an academic environment to the world of work.  There are also differences in custom between countries and cultures.  This memo outlines practices appropriate to a U.S. academic environment. 

What is plagiarism?  Plagiarism is using someone else’s work, words, or ideas without giving them proper credit.  An example of plagiarism, and an example of one acceptable way to avoid it, is shown below under the heading of Attachment 1. 

How to avoid Plagiarizing.  Here are some simple guidelines for avoiding plagiarism: 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

1. If you use a phrase, sentence or more from any source, you must put them in quotation marks and cite the source in footnote. 

2. If you recount someone else’s ideas in your own words (paraphrasing), you must provide a footnote at the end of the passage citing the source of the ideas. 

3. if you draw on someone else’s ideas, even though you neither quote nor paraphrase them precisely, one of the following is called for: 

● A footnote crediting the source of the ideas. 

● A direct reference to the source within the text (for example, “Seligman has repeatedly made the point that.”, with facts of publication (title, etc.) provided in a footnote or bibliography. 

  

Footnotes should be complete enough to enable the reader to accurately identify your sources.  In addition to articles and books, sources may be personal communication, unpublished data, working memos and internal documents.  A footnote should cite the author (if no author is named, the organization), as well as the title, date and page number(s).  a bibliography, listing your sources but not linking them to specific points in your text, may well be desirable but is not a substitute for footnotes. 

The Consequences of Plagiarism 

The consequences of plagiarism are serious.  Students can be expelled and lose all chance of completing their studies.  Even if 99 percent of a student’s work has been above reproach, proven plagiarism could easily result in a degree not being granted. 

Summary 

A good paper typically demonstrates grasp of concepts, originality and appropriate attention to detail.  The person who reads your paper assumes that the words and ideas originate with you unless you explicitly attribute them to others.  Whenever you draw on someone else’s work, it is your obligation to say so.  If you do not, you are operating under false pretenses.  That is plagiarism. 

Original Source 

PHW27C, Fall 2018 Course Syllabus Hugh, Mideska & Andrade Pacheco

 “Tribal pressures affect Kenyans’ behavior more than pronouncements arriving from the national seat of government but what ultimately counts is what an individual perceives as in his or her own best interest.  For more than 80 percent of Kenya’s people who live and work on the land, children are seen as essential to survival and status.  This is particularly true for women.  Children and young adults provide an extra labor needed during peak planting and harvest times when everyone in the household must work long hours every day.  For women, children are essential to lessen their heavy workload throughout the year: in a study of the Akamba tribe, three-quarters of the respondents gave this reason for having children.” 

From Frank L. Mott and Susan H. Mott, “Kenya’s Record Population Growth: A Dilemna of Development, Population Bulletin, Vol. 35, No. 3 (Population Reference Bureau, Inc., Washington, D.C., 1980): 7-8. 

Plagiarism 

Tribal pressures affect the Kenyan woman’s behavior more than pronouncements from the capital.  She will perceive what is in her best interest.  She sees children as essential to her survival and status.  They provide the extra labor needed during peak planning and harvest times when everyone in the family must work for long hours. 

Properly Footnoted Citation 

Why do Kenyans have so many children?  Mott and Mott write that “tribal pressures affect Kenyans’ behavior more than pronouncements arriving from the national seat of government but what ultimately counts is what the individual perceives as in his or her own best interest.”1  They point out that children are seen as necessary for a woman’s livelihood as well as her place in society.  Children work on the shamba and assist with all kinds of labor: planting, harvesting, fetching firewood and water.2 1 From Frank L. Mott and Susan H. Mott, “Kenya’s Record Publication Growth:  A Dilemma of Development, “Population Bulletin, Vol. 35, No. 3 (Population Reference Bureau, Inc., Washington, D.C., 1980): 7. 2 Ibid: 7-8