GIS Spatial Modeling is the process of modeling, examining, and interpreting geographic data.It uses a set of defined methodology and analytical procedures to derive information with spatial relationships between geographic phenomena. It can be useful for evaluating suitability and capability, for estimating and predicting, and for interpreting and understanding real world situations. There are four traditional types: spatial overlay surface analysis, linear analysis, and raster analysis.
Data with spatial relationships can be modeled in a GIS to provide images and relationships that can be interpreted to help solve problems and provide information in a way that data bases by them selves can not. The image to the right for example is a screen capture of a GIS thematic spatial model created from a database of precipitation measurements from various weather stations and data loggers spread out across the region. ESRI Geostatistical Analyst was used to create a model that can be easily used to depict the amount of precipitation that a community in the region would experience based on the data from the databases.
Below are two presentations that I gave on GIS Spatial Modeling, one is an informal more information based one that was used to train other students how to use the ESRI Geostatistical Analyst extension and the other is a more formal presentation that was open to all students and faculty at the campus.
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The document titled ‘Introduction & Simple Guide to Using the Leica Total Station’ published by Ted MacKinnon and Jonathan Murphy in 2004 was part of a requirement for the Applied Geomatics Research advanced Post Graduate Diploma at COGS.
(In 2011 it was revised and updated). The full version of the document can be downloaded by clicking here.