Event Centric Modeling Approach in Colocation Pattern Snalysis from Spatial Data

TL;DR

This paper introduces an event-centric modeling approach for analyzing colocation patterns in spatial data, utilizing a novel distance-based method and a participation index to efficiently discover meaningful spatial co-location rules.

Contribution

It proposes a new distance-based approach and a participation index for mining spatial co-location patterns, addressing limitations of transaction-based methods in continuous geographic spaces.

Findings

Developed a distance-based co-location pattern mining algorithm

Introduced a participation index with anti-monotone property

Effectively identified spatial co-location rules in geographic data

Abstract

Spatial co-location patterns are the subsets of Boolean spatial features whose instances are often located in close geographic proximity. Co-location rules can be identified by spatial statistics or data mining approaches. In data mining method, Association rule-based approaches can be used which are further divided into transaction-based approaches and distance-based approaches. Transaction-based approaches focus on defining transactions over space so that an Apriori algorithm can be used. The natural notion of transactions is absent in spatial data sets which are embedded in continuous geographic space. A new distance -based approach is developed to mine co-location patterns from spatial data by using the concept of proximity neighborhood. A new interest measure, a participation index, is used for spatial co-location patterns as it possesses an anti-monotone property. An algorithm to discover co-location patterns are designed which generates candidate locations and their table instances. Finally the co-location rules are generated to identify the patterns.