Course Outline: Introduction, Definitions, Key components, Functional
Subsystem, Raster Data Model, Vector Data Model, Attribute Data Model,
Data Acquisition Techniques, Data sources, Data capturing techniques
and procedures, Data Transformation, Visualization of spatial data,
Layers and Projections, Map Design: Symbols to Portray Points , Lines
and Volumes , Graphic Variables , Visual Hierarchy,
Data Classification Graphic Approach , Mathematical Approach, Spatial
Analysis: Overlay Analysis ,Spatial analysis, Neighborhood functions,
Network and overlay analysis, buffering, Spatial data Quality:
Components of Data Quality , Micro Level Components , Macro Level
Components , Usage Components Sources Of Error , Accuracy, Project
work.
Lab Outline: Introduction to GIS Lab (hardware / software),
Raster/Vector/Attribute Data Display, Scanning, Digitization, Coordinate
based point mapping, Raster / Vector Conversion, Data layer integration
and display of different projections, Map layout, Data Classification and
Thematic Mapping, Handling with Topological Errors, Overlay and network
analysis.
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