ENV-444 / 4 crédits

Enseignant(s): Joost Stéphane, Guessous Idris

Langue: Anglais


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

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, 3ème année
  • MOOCs Systèmes d'Information Géographique 1 et 2, Coursera

Important concepts to start the course

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.
  • Make an oral presentation.
  • Write a scientific or technical report.
  • Summarize an article or a technical report.
  • Negotiate effectively within the group.

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), 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.

Ressources en bibliothèque

Notes/Handbook

Lecture notes are gradually distributed to students during the semester.

Websites

Moodle Link

Dans les plans d'études

  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Exploratory data analysis in environmental health
  • Cours: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Exploratory data analysis in environmental health
  • Cours: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Exploratory data analysis in environmental health
  • Cours: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Exploratory data analysis in environmental health
  • Cours: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines
  • Semestre: Automne
  • Forme de l'examen: Oral (session d'hiver)
  • Matière examinée: Exploratory data analysis in environmental health
  • Cours: 1 Heure(s) hebdo x 14 semaines
  • Projet: 2 Heure(s) hebdo x 14 semaines

Semaine de référence

 LuMaMeJeVe
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