**ENVR 468
Temporal GIS and Space/Time Geostatistics for the Environment and Public **

**(Short
title: Temporal GIS and Geostatistics)**

** **

**Fall 2018**, 3
semester hours, Tuesday Thursday 09:30AM-10:45AM

Lecture Location: McGavran-Greenberg 2304

Computer Lab Location: Health Sciences Library computer lab rooms 307

**Instructor Office
hours (MHRC 1303)**: Any time upon request to
Instructor

Instructor: Marc Serre

**Course description:**

The course focuses on the development of environmental Geostatistics and its
application in **temporal Geographical Information Systems** (TGIS).
TGIS describe environmental, epidemiological, economic, and social phenomena
distributed across space and time. The course introduces the ** arcGIS
software **to query and manipulate geographic data, it provides the concepts
and mathematical framework of

The
course starts with a 4 to 5 weeks review of basic GIS consisting in intensive
computer labs on the **ESRI ArcGIS
**software. Prior knowledge of GIS is highly recommended, but not required.
Lessons from these ArcGIS computer labs is tested in a homework where students
research and display maps of their own space/time environmental data using
basic ArcGIS functions (see Graph 1). In the remainder of the course we then
switch to using the

The** **concepts and mathematical formulation of **spatiotemporal
Geostatistics** are progressively introduced throughout the course. We start
with the concept of space/time distance. We then rapidly review multivariate
calculus (derivatives and integrals) and basic statistics (probability density
function, or pdf, and expected value) of random variables. Multivariate
calculus is a pre-requirement for this course, and prior introductory
statistics or probability courses are recommended, but not required. Using this
foundation in multivariate calculus and basic statistics, we then cover the
theory of spatiotemporal Geostatistics, which include 1) bivariate pdf and
conditional probabilities, 2) variability in space and time and covariance
function, 3) spatial and spatiotemporal random fields and 4) spatiotemporal
estimation and uncertainty assessment. The concepts of the **Bayesian
Maximum Entropy** (BME) method is presented, which provides a powerful
framework for space/time mapping, and leads to the classical **kriging**
methods as special cases.

The application* *consists of a **real-world mapping TGIS project**.
Using skills acquired in basic GIS (i.e. *arcGIS*), and in advanced TGIS
(i.e. *BMEGUI*) each students research a space/time dataset of concern for
society, s/he formulates the space/time mapping problem, and s/he uses concepts
and mathematical tools together with the *BME* method of space/time
Geostatistics to provide a realistic representation of the field over space and
time.

**Textbook recommended:**

George Christakos, Patrick Bogaert, and Marc Serre (2002) *Temporal
GIS: Advanced Functions for Field-Based Applications*, Springer-Verlag, New
York, N.Y., 250 p., CD ROM included

**Prerequisite:**

The prerequisite for this class is Calculus of Functions of One Variable I & II (MATH 231 & 232) and preferably a multivariate calculus course like MATH 233. An introductory course in Statistics or Probability is useful, but not required. Additionally, knowledge of GIS (from beginner to expert) is highly recommended, but not required.

**Philosophy of Grading and Course
Evaluation:**

The students should learn the concepts, and not use the tools as a black box. They will be graded on solving conceptual problems rather than just applying the programs. The students are expected to promptly do their homework, a class project, and fill out the course evaluation at the end of the semester. The grading will be as follow

Homework 50%

Student-defined project 50%

Filling out course evaluation: 1 point bonus

**Honor code:**

The homework and class project are open book. However each student is expected to do their homework and class project on their own, without outside help nor help from classmates. Classmates can form groups to study together and discuss problems together, but each student is expected to arrive at homework solutions on their own. A cluster of students providing the same erroneous solution should therefore be a rare event occurring by chance only. Likewise each project should be original compared to that of others.