ENVR† 765

Space/Time exposure mapping and risk assessment


Spring of even years, 3 semester hours


Instructor :† Marc Serre


Course description:


Using environmental monitoring data and health surveillance observations collected at sparse locations over space and time, how can we assess the exposure at unmonitored points, assess the associated human health risk across space and time, or directly construct disease space/time maps?




These questions arise in many environmental and health fields, such as environmental risk analysis, environmental epidemiology, and medical geography.† These are exactly the questions that we will visit in this course using a space/time exposure mapping and risk assessment framework, and itís implementation in the MATLAB© programming language.† This framework will consider the following issues







Each of these issues will be addressed using space/time statistics as well as computational lab applications (using MATLAB© programming) of space/time exposure mapping and risk assessment. Environmental monitoring data arises when there is a need to evaluate and control the source of exposure to some environmental contaminant, or when there is an interest to model the relationship between exposure and a suspected health effect.† The introduction to space/time statistics covered in this course will allow students to create exposure maps from space/time monitoring data.† In an epidemiologic context of exposure/health association analysis, we will see how these maps are useful to provide exposure estimates at the unsampled location and times of measured health outcomes.† When the exposure/health response curve can be constructed using published slope factors (such as those of EPA IRIS), we will investigate how exposure maps and the exposure/health response curve can be used to assess risks on human health and characterize the associated uncertainty, which has interesting policy implications. On the other hand, if health surveillance count data is available, we will look at how to map disease rates using an estimation framework that addresses the small count number problem.


The course is organized around 3 main themes: spatial statistics and risk assessment theory, computer programming, and a real-world application. The theory will include space/time random fields, linear and Bayesian geostatistics, and stochastic risk assessment. The computer programming will be done using the MATLAB language and spatiotemporal geostatistics BMElib package written in MATLAB.† Writing their own code in MATLAB, the students will learn how to use the BMElib package for the exploratory visualization of space/time data, for the modeling of spatiotemporal variability, and to implement geostatistical estimation so as to create exposure, risk and disease maps.† The aim of this course is to prepare its participants to use this toolbox and its concepts to analyze their own dataset in the context of exposure mapping and risk assessment, or of disease mapping.


As an example of application problem, we will consider throughout the course the exposure mapping and risk assessment analysis of air pollutants (particulate matter, ozone, lead, etc.) across a geographical region of interest (such as the United States).† This problem will illustrate all the steps of the exposure mapping and risk assessment framework, from analysis of space/time variability, modeling of data uncertainty, exposure mapping, integration of dose/response curves to estimate human health risk across space and time, and finally incorporation of population density to arrive at a population health impact assessment.† Other environmental and health variables will also be considered, such as environmental carcinogens, water quality parameters, and health outcome variables including asthma and HIV prevalence in North Carolina, and a historical mapping analysis of the Black Death in Europe.†


Possible class topics


Space/time random fields:

         Random variables,

         Random fields,

         Space/time variability,

         Covariance function


Linear geostatistics:

         The Best Linear Unbiased Estimator (BLUE),

         Simple kriging,

         Ordinary kriging,

         Universal kriging


Bayesian geostatistics:

         Bayesian Maximum Entropy (BME) theory,

         Bayesian Hierarchical modeling,

         Bayesian Melding


Stochastic risk assessment:

         The space/time risk assessment framework

         The EPA integrated risk information system (IRIS)

         Combining random variables (e.g. exposure and slope factors)


Disease mapping:

         The small count number problem

         Poisson kriging

         BME approaches to disease mapping





George Christakos, Patrick Bogaert, and Marc Serre (2001) Temporal GIS: Advanced Functions for Field-Based Applications, Springer-Verlag, New York, N.Y., 250 p., CD ROM included


Peter Diggle and Paulo Ribeiro Jr (2007), Model-Based Geostatistics, Springer Series in Statistics, 230 p.


Also very useful:

George Christakos, Ricardo Olea, Marc Serre, Hwa-Lung Yu and Linlin Wang (2005) Interdisciplinary Public Health Reasoning and Epidemic Modelling : The Case of Black Death. Springer-Verlag, New York, N.Y., 302 p.


Sudipto Banerjee, Bradley Carlin, and Alan Gelfand (2004) Hierarchical Modeling and Analysis for Spatial Data, Chapman & Hall/CRC, 452 p.


Lance Waller and Carol Gotway (2004) Applied Spatial Statistics for Public Health Data, Wiley, 494 p.



Multivariate Calculus (MATH 233 or equivalent)

A grasp of linear algebra (for example through MATH 547- Linear Algebra, or equivalent)

A grasp of statistics (for example through a prior course in statistics)

Experience with, or willingness to learn, the MATLAB computer language


Philosophy and grading:

The students should learn the concepts of spatial statistics and risk assessment, they should learn how to implement these concepts in the MATLAB programming language, and they should learn how to apply them on a real world exposure mapping and risk assessment project.† The students will do homework, an in-class exam, and a final project, which will approximately count for the final grade as follow:

Homework 60%

In-class exam and final project 40%