Cross-Disciplinary Statistical Ecology and Environmental Statistics

STATISTICS 524: ECOMETRICS
Credits: 3 Fall Registration Number: 195511
Time and Place: T R 8:00AM--9:15AM
(Will be rescheduled: suited to each one in class)
120 Thomas Building

STATISTICS 548: ENVIRONMETRICS
Credits: 3
Time and Place: T R 8:00AM--9:15AM
(Will be rescheduled: suited to each one in class)
219 Thomas Building

Prerequisite
Intermediate mathematics and statistics or permission of the instructor.

Instructor
G. P. Patil, Distinguished Professor and Director, Center for Statistical Ecology and Environmental Statistics, Department of Statistics, and Inter-College Program in Ecology

Course Materials
Selected reference books, journal articles, technical reports, lecture notes, and relevant instructional materials.

Instructional Objectives
The objective of the course is to introduce students to statistical methods and stochastic approaches that are important in ecological and environmental research, teaching, and service: problem formulation; observational economy; modeling, analysis, and synthesis; data acquisition, analysis, and decision making; regional policy with remote imagery; geoinformatic surveillance; hotspot detection and prioritization; early warning system; case studies.

Evaluation Methods of the Course
Creative homework, mid-semester disciplinary and interdisciplinary take-home, final suited to the class composition.

Course Outline
Motivation and emphasis on statistical, ecological, and environmental insights and skills in doing topics of the following kind suited to the class:
(1) environmental monitoring and assessment
(2) ecological sampling and observational economy
(3) ecological assessment and multi-scale analysis
(4) geo-spatial statistics and spatio-temporal analysis
(5) environmental data synthesis and statistical meta-analysis
(6) statistics in environmental toxicology and epidemiology
(7) environmental and ecological risk assessment
(8) modeling and simulation of landscape fragmentation for ecosystem health assessment
(9) classified multicategorical raster map analysis
(10) thematic map accuracy and change detection assessment
(11) regional policy with remote imagery and geospatial information
(12) computational ecometrics and environmetrics
(13) hotspot detection and early warning system
(14) prioritization without having to integrate multiple indicators
(15) geoinformatic biosurveillance and biosecurity

Geospatial data form the foundation of an information-based society. While it is exciting that we are alive in the age of information, and while it is unfortunate that we find ourselves in the crisis of environment, it is only a bliss to have the opportunity to more effectively serve the cross-disciplinary cause of statistics, ecology, environment and society in the research, training, and outreach setting.

If there is sufficient interest in the class, special emphasis can be on geoinformatic surveillance and security, with classroom instruction on geographic hotspot detection and prioritization methods, tools, and applications.

Hotspot means something unusual: anomaly, aberration, outbreak, elevated cluster, critical area, etc. The declared need may be for monitoring, etiology, management, or early warning. Responsible factors may be natural, accidental, or intentional.

Applications and case studies can be from: Biodiversity and threats to biodiversity, carbon management, coastal management, community growth for infrastructure, disaster management, homeland security, invasive species management, public health and environment, water management and conservation, etc.

The instructor has recently received a large grant from NSF Digital Government Program for Geoinformatic Surveillance Research. There is room and scope for graduate assistantships and internships as well as research experience for undergraduate (REU) awards.