Exploratory data analysis in environmental health
Summary
This course teaches how to apply exploratory spatial data analysis to health data. Teaching focuses on the basics of spatial statistics and of epidemiology, and proposes a context to analyse geodatasets making it possible to study the relationship between health and the environment.
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 data analysis and spatial epidemiology), of the main theoretical framework and of 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; environmental health; spatial epidemiology; Exposome; Geocomputation; EDA; ESDA; Geovisualization; GIS; Geoda; Thematic mapping; Semiology of graphics; Spatial statistics; Scientific paper writing
Learning Prerequisites
Recommended courses
- Systèmes d'Information Géographique (SIG), ENV-342, bachelor, 2ème année
- MOOCs Systèmes d'Information Géographique 1 et 2, sur Courseware
Important concepts to start the course
Statistics; Spatial statistics; Geographic Information Systems; Epidemiology
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)
- Writing of short scientific articles
- MOOC
Expected student activities
Attend and participate in theoretical courses, apply the instructions proposed during the practicals, write short articles, present a project orally, show initiative
Assessment methods
Continuous control during the semester:
- 2 exercises (individual) = 10%
- 1 short paper (individual) = 20%
- 1 long paper (group) = 30%
- 1 oral exam (individual) = 40%
Supervision
Office hours | Yes |
Assistants | Yes |
Forum | Yes |
Resources
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 GIS ¿09., 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), A137âA140. 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)Â 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), 124â129. doi: 10.1183/16000617.0034-2016
- Tukey JW (1980) We Need Both Exploratory and Confirmatory. The American Statistician, 34, 23-25.
Ressources en bibliothèque
- Anselin L, McCann M (2009) OpenGeoDa, Open Source Software for the Exploration and Visualization of Geospatial Data
- Morgenthaler, Stephan (2009) Exploratory data analysis
- Tukey JW (1980) We Need Both Exploratory and Confirmatory
Notes/Handbook
Lecture notes are 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
- Lecture: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Exploratory data analysis in environmental health
- Lecture: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Exploratory data analysis in environmental health
- Lecture: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Exploratory data analysis in environmental health
- Lecture: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Exploratory data analysis in environmental health
- Lecture: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Exploratory data analysis in environmental health
- Lecture: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
- Semester: Fall
- Exam form: Oral (winter session)
- Subject examined: Exploratory data analysis in environmental health
- Lecture: 1 Hour(s) per week x 14 weeks
- Project: 2 Hour(s) per week x 14 weeks
Reference week
Mo | Tu | We | Th | Fr | |
8-9 | |||||
9-10 | |||||
10-11 | |||||
11-12 | |||||
12-13 | |||||
13-14 | |||||
14-15 | |||||
15-16 | |||||
16-17 | |||||
17-18 | |||||
18-19 | |||||
19-20 | |||||
20-21 | |||||
21-22 |
Légendes:
Lecture
Exercise, TP
Project, other