GIS for Geoscientists Short Course

Sponsored by the Nevada Petroleum Society

March 30 & 31st (Friday & Saturday) 8:30 a.m. to 5:00 p.m.

At UNR Laxalt Mineral Engineering (LME) Building, Main entrance off quad and to the right, Room 315

Only $170 per person

Registered UNR students, NPS or GSN members $150

Given 5 times at the National GSA costing more than double

Includes large short course volume. Coffee provided, but Bring Lunch

Faculty Richard Bedell

Please contact for questions or space: tel. 233-2212, geocorp@home.com

Course Outline

I INTRODUCTION: What is GIS, Geoscience Case Studies and CAD vs. GIS, Imaging vs. GIS, Modeling vs. GIS

II DATA AVAILABILITY AND FORMAT: Types of spatial data available, formats, & where to get data.

III SPATIAL DATA MODELS: Raster vs. vector, topology, Fractals, N-dimensional models.

IV DATABASE MODELS: Database Management Systems design, review of relational algebra and the building of a true relational drill hole database. The importance of object oriented database models in GIS.

V MAP PROJECTIONS, DATUMS & GPS: Local Grids, Earth Curvature, Datums, map projections. How to make projections in a GIS, convert datums and projections, GPS.

VI SPATIAL ACCURACY & RECTIFICATION: Types of error, map digitizing, map generalization, projection changes, map accuracy standards, rectification, error documentation.

VII SPATIAL STATISTICS: Descriptive Statistics, Autocorrelation, regionalized variables

VIII SAMPLING AND INTERPOLATION: Sampling methodology, gridding and contouring

IX ANALYTICAL METHODS IN GIS: Spatial operators, Map algebra, Distance operators, Neighborhood operators, Frequency domain, geomorphometrics.

X PROBABILITY: Classical Probability, Statistical Probability, The integration of statistical and classical methods, Bayes Theorem

XI MODELING: Including: Maximum Likelihood, Dempster Shafer (including weights of evidence), Fuzzy Logic. Including a new method of normalization on a simple spread sheet so that allows the grading of data layers and therefore the full use of data distributions in modeling instead of the binary layers used in weights of evidence.