Grid Based Spatial Analysis

Diffusion modeling and Connectivity analysis can be effectively conducted from grid data. Grid analysis is suitable for these types of problems because of the grid's regular spatial configuration of geographic units.

Diffusion Modeling: It deals with the process underlying spatial distribution. The constant distance between adjacent units makes it possible to simulate the progression over geographic units at a consistent rate. Diffusion modeling has a variety of possible applications, including wildfire management, disease vector tracking, migration studies, and innovation diffusion research, among others.

Connectivity Analysis: Connectivity analysis evaluates inter separation distance, which is difficult to calculate in polygon coverage, but can be obtained much more effectively in a grid.

The connectivity of a landscape measures the degree to which surface features of a certain type are connected. Landscape connectivity is an important concern in environmental management. In some cases, effective management of natural resources requires maximum connectivity of specific features. For instance, a sufficiently large area of dense forests must be well connected to provide a habitat for some endangered species to survive. In such cases, forest management policies must be set to maintain the highest possible level to connectivity. Connectivity analysis is especially useful for natural resource and environmental management.


GIS is considered as a decision making tool in problem solving environment. Spatial analysis is a vital part of GIS and can be used for many applications like site suitability, natural resource monitoring, environmental disaster management and many more. Vector, raster based analysis functions and arithmetic, logical and conditional operations are used based on the recovered derivations.


Bonhan - Carter, G.F. 1994. Geographic Information Systems for Geoscientists. Love Printing Service Ltd., Ontario, Canada.

Burrough, P.A. 1987. Principles of Geographical Information System for Land Assessment. Oxford : Clardon Press.

Chung, Chang-Jo F. and Fabbri, A.G. 1993. The representation of Geoscience Information for data integration. NonrenewableResources, Vol. 2, No. 2, Oxford Univ. Press.

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