GeoSpatial Data Analysis Using Markov Models
Prabha Selvaraj, P V Pradeepa · International journal of advanced research in computer science and electronics engineering · 2012
The advances in survey and data collection techniques over the last decade have dramatically enhanced our capabilities to collect terabytes of geographic data on a daily basis. However, the wealth of geographic data cannot be fully realized when information implicit in data is difficult to discern. This confronts us with an urgent need for new methods and tools that can intelligently and automatically transform geographic data into information and, furthermore, synthesize geographic knowledge. It calls for new approaches in geographic representation, query processing, spatial analysis, and data visualization. For spatial data, the assumption of independent samples is too constrained. Hence the application of Markov model (MM) for the analysis of spatial data is recommended in literatures. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data-driven inductive approaches to geographical analysis and modeling.