Mining Maximal Dynamic Spatial Co-Location Patterns

TL;DR

This paper introduces a new approach for mining maximal dynamic spatial co-location patterns, addressing limitations of previous methods by improving pattern detection accuracy and computational efficiency through novel concepts and algorithms.

Contribution

The paper proposes the concept of dynamic spatial co-location patterns and an efficient algorithm for mining maximal patterns, enhancing pattern detection and reducing computational costs.

Findings

The proposed method effectively identifies meaningful dynamic co-location patterns.

The algorithm significantly improves mining efficiency over existing methods.

Experimental results validate the approach's accuracy and scalability.

Abstract

A spatial co-location pattern represents a subset of spatial features whose instances are prevalently located together in a geographic space. Although many algorithms of mining spatial co-location pattern have been proposed, there are still some problems: 1) they miss some meaningful patterns (e.g., {Ganoderma_lucidumnew, maple_treedead} and {water_hyacinthnew(increase), algaedead(decrease)}), and get the wrong conclusion that the instances of two or more features increase/decrease (i.e., new/dead) in the same/approximate proportion, which has no effect on prevalent patterns. 2) Since the number of prevalent spatial co-location patterns is very large, the efficiency of existing methods is very low to mine prevalent spatial co-location patterns. Therefore, first, we propose the concept of dynamic spatial co-location pattern that can reflect the dynamic relationships among spatial features. Second, we mine small number of prevalent maximal dynamic spatial co-location patterns which can derive all prevalent dynamic spatial co-location patterns, which can improve the efficiency of obtaining all prevalent dynamic spatial co-location patterns. Third, we propose an algorithm for mining prevalent maximal dynamic spatial co-location patterns and two pruning strategies. Finally, the effectiveness and efficiency of the method proposed as well as the pruning strategies are verified by extensive experiments over real/synthetic datasets.