Week 1: Human cognition of the spatial world, Sensing and perceiving the world, Cognitive maps, Spatial learning and interpretation of maps, Defining a map, types.
Week 2: Space representation concepts, features and properties, Map projections, Georeferencing.
Week 3: Spatial Data Models, Information organization and data structure, Data and information, Geographic data and spatial information, Spatial data formats – raster and vector, Choice between raster and vector.
Week 4: Fundamental concepts of Geographic data, Spatial Query and Analysis – Types of spatial analysis
Week 5: Spatial Analysis – Components, Process, Basic spatial Analysis tools.
Week 6: Overlay Working in Spatial Analysis - Case Studies, Problems and challenges in spatial analysis, Queries and reasoning, Measurements and their application.
Week 7: Measurement, Transformations- types and their application in spatial science.
Week 8: Buffer and Interpolation - case study, Application of spatial analysis -urban planning, Public health management
Week 9: Application of spatial analysis- Agriculture and farming, water Analysis, Digital Elevation Model, Data Acquisition and Errors, Spatial Relations – Topological Relations, Data Standardization.
Week 10: Project Module: SIS Project Design and Management: Problem identification, evaluation strategy, execution and implementation, Lab Work - Introduction to QGIS and graphic user interface.
Week 11: Lab Work Map Georeferencing in QGIS, Spatial Analysis- Digitization, converting point data to digital format, creating line data.
Week 12: Lab Work Digitization-line, Area Data capture, Spatial relation Attributes, working with google earth- Data Export to kml and shp, Digitization, thematic map display.
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