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Comprehensive Analysis of Point Pattern in Completely Censused Population Data

This study explores methods for analyzing point patterns in completely censused population data, comparing global and local patterns in 1D, 2D, and 3D dimensions. It covers univariate, bivariate, and multivariate analyses and distinguishes between types of point patterns (random, overdispersed, underdispersed). The methods include distance to neighbor sampling, refined nearest neighbor analysis, randomization, and second-order point pattern analysis using Ripley’s K statistic.

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Comprehensive Analysis of Point Pattern in Completely Censused Population Data

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  1. Point Pattern Analysis

  2. Methods for analyzing completely censused population data • Entire extent of study area or • Each unit of an array of contiguous sample units (e.g. quadrats) • Global vs. Local

  3. Types of Point data • Univariate, Bivariate, Multivariate • 1, 2, and 3 Dimensions (x,y,z)

  4. Point Pattern Analysis • Pattern may change with scale! • Test statistic calculated from data vs. expected value of statistic under CSR (complete spatial randomness)

  5. Types of Point Patterns • Random (CSR) • Overdispersed (spaced or regular) • Underdispersed (clumped or aggregated)

  6. Methods • Distance to neighbor • sample • Refined Nearest Neighbor • randomization • Second-order point pattern analysis

  7. Second-order Point Pattern Analysis: Ripley’s K “Used to analyse the mapped positions of events in the plane… and assume a complete census…”

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