Geospatial Data Analysis with Matlab

Geospatial is used to indicate that data that has a geographic component to it.  Geospatial analysis is the gathering, display, and manipulation of imagery, GPS, satellite photography and historical data, described explicitly in terms of geographic coordinates or implicitly, in terms of a street address, postal code, or forest stand identifier as they are applied to geographic models.

The many applications of geospatial analysis include crisis management, climate change modeling, weather monitoring, sales analysis, human population forecasting and animal population management.

Following is the list of topics under Geospatial Data Analysis which is prepared after detailed analysis of courses taught in multiple universities across the globe:

  • Analytical methodologies and model building
  • Core components of geospatial analysis, including distance and directional analysis, geometrical processing, map algebra, and grid models
  • Exploratory Spatial and Spatio-temporal Data Analysis (ESDA, ESTDA) and spatial statistics, including spatial autocorrelation and spatial regression
  • Geocomputational methods, including agent-based modelling, artifical neural networks and evolutionary computing
  • Geospatial analysis concepts
  • Network and locational analysis, including shortest path calculation, travelling salesman problems, facility location and arc routing
  • Surface analysis, including surface form and flow analysis, gridding and interpolation methods, and visibility analysis