Exploratory data analysis in environmental health

ENV-444 / 4 credits

Teacher(s): Joost Stéphane, Nehme Mayssam Assem

Language: English

Summary

This course teaches how to apply exploratory spatial data analysis to health information. Teaching focuses on the role of GIS and spatial statistics in spatial epidemiology. It proposes a context to investigate the relationship between health, quality of life, and environmental characteristics.

Content

This course consists of a theoretical and of a practical part. Each week 45 minutes of ex-catedra teaching are used to present the history of the discipline (exploratory spatial data analysis and spatial epidemiology), the theoretical background and the main software used.
The theory is followed by 1h30 of practical work during which the notions covered by the theory are implemented in the computer lab. The data used are provided by current research case studies related to environmental health (e.g. sleep troubles and road traffic noise). Data exploration, geovisualization, geocomputation, communication (semiology of graphics) and representation (thematic mapping) are implemented in the context of exercises. In parallel, the data provided and the approaches taught have to be used to write scientific papers (1 short and 1 regular).

Keywords

Exploratory spatial data analysis; epidemiology; environmental health; spatial epidemiology; Exposome; Geocomputation; EDA; ESDA;
Geovisualization; GIS; Geoda; Thematic mapping; Semiology of graphics; Spatial statistics; Scientific paper writing

Learning Prerequisites

Required courses

It is compulsory that students have attended the course "Systèmes d'Information Géographique" (ENV-342). The notions taught during this course require a good knowledge of Geographic Information Systems and of GIScience basic concepts, as well as a perfect understanding of the english language.

Important concepts to start the course

Statistics; Spatial statistics; Geographic Information Systems; Epidemiology; Exposome; Environmental health

Learning Outcomes

By the end of the course, the student must be able to:

  • Investigate the variation of attributes according to the change of the location of a set of spatial units
  • Elaborate a research project based on the characteristics of a georeferenced data set available
  • Formulate hypotheses to be validated in the context of a research project
  • Report on the main results obtained in the context of a research project
  • Interpret the main results obtained based on the spatial distribution of the objects under investigation
  • Structure ideas and arguments in the context of the writing of short scientific papers
  • Produce adequate geospatial data sets for the processing of spatial statistics and association models

Transversal skills

  • Set objectives and design an action plan to reach those objectives.
  • Use a work methodology appropriate to the task.
  • Identify the different roles that are involved in well-functioning teams and assume different roles, including leadership roles.
  • Take feedback (critique) and respond in an appropriate manner.
  • Write a scientific or technical report.
  • Summarize an article or a technical report.
  • Negotiate effectively within the group.
  • Make an oral presentation.

Teaching methods

  • Ex-cathedra teaching
  • Exercises (practicals in geolab)
  • Collaborative work within groups
  • Writing of short scientific articles
  • Videos

Assessment methods

Continuous control during the semster:

  • Excercises (individual) = 20%
  • Writing a scientific article (group) = 30%

Oral exam:

  • Individual = 50%

Supervision

Office hours Yes
Assistants Yes
Forum Yes

Resources

Virtual desktop infrastructure (VDI)

No

Bibliography

- Anselin L, McCann M (2009) OpenGeoDa, Open Source Software for the Exploration and Visualization of Geospatial Data. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 550-551. ACM, New York, NY, USA.

- Cui Yuxia, Balshaw David M., Kwok Richard K., Thompson Claudia L., Collman Gwen W., & Birnbaum Linda S. (2016). The Exposome: Embracing the Complexity for Discovery in Environmental Health. Environmental Health Perspectives, 124(8), A137-A140. doi: 10.1289/EHP412

- Harris TM (2017) Exploratory Spatial Data Analysis: Tight Coupling Data and Space, Spatial Data Mining, and Hypothesis Generation. In: Regional Research Frontiers - Vol. 2, pp. 181-191. Springer, Cham.

- Morgenthaler, Stephan (2009) Exploratory data analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 1, 33-44

- Siroux, V., Agier, L., & Slama, R. (2016). The exposome concept: a challenge and a potential driver for environmental health research. European Respiratory Review, 25(140), 124-129. doi:10.1183/16000617.0034-2016

- Tukey JW (1980) We Need Both Exploratory and Confirmatory. The American Statistician, 34, 23-25.

Notes/Handbook

Lecture notes will be gradually distributed to students during the semester.

Websites

Moodle Link

In the programs

  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Exploratory data analysis in environmental health
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Exploratory data analysis in environmental health
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Exploratory data analysis in environmental health
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Exploratory data analysis in environmental health
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Exploratory data analysis in environmental health
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Exploratory data analysis in environmental health
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Type: optional
  • Semester: Fall
  • Exam form: Oral (winter session)
  • Subject examined: Exploratory data analysis in environmental health
  • Courses: 2 Hour(s) per week x 14 weeks
  • Project: 2 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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