# Categorized of Point Patterns in GIS Data: Dispersion & Arrangement, Spatial Randomness, Quadrat Analysis and Nearest Neighbor Analysis (Especially for GATE-Geospatial 2022)

Get top class preparation for CTET/Paper-2 right from your home: get questions, notes, tests, video lectures and more- for all subjects of CTET/Paper-2.

Examrace Books on Mapping, GIS, and Remote Sensing prepares you throughly for a wide range of practical applications.

## Dispersion & Arrangement

Point patterns can be categorized as random, uniform, clustered or dispersed along the following two continuums:

- Random vs. Uniform
- Clustered vs. Dispersed

These two continuums are not related, and a pattern of points could be randomly distributed in a clustered way, or stratified and dispersed. This allows one to determine exactly how clustered something is, or to compare two sets of points.

### Complete Spatial Randomness

There are several techniques specifically designed for pattern analysis of point data. A concept that is common in these techniques is complete spatial randomness.

### Quadrat Analysis

In quadrat analysis you divide your study area into subsections of equal size, count the frequency of points in each subsection and then calculate the frequency of points in each subsection.

### Nearest Neighbor

In this analysis the distance of each point to its nearest neighbor is measured and the average nearest neighbor distance for all points is determined in given figure.