A spatial co-location mining algorithm that includes adaptive proximity improvements and distant instance references

ABSTRACT

Spatial co-location pattern mining is employed to identify a group of spatial types whose instances are frequently located in spatial proximity. Current co-location mining methods have two limitations: (1) it is difficult to set an appropriate proximity threshold to identify close instances in an unknown region, and (2) such methods neglect the effects of the distance values between instances and long-distance instance effects on pattern significance. This paper proposes a novel maximal co-location algorithm to address these problems. To remove the first constraint, the algorithm uses Voronoi diagrams to extract the most related instance pairs of different types and their normalized distances, from which two distance-separating parameters are adaptively extracted using a statistical method. To remove the second constraint, the algorithm employs a reward-based verification based on distance-separating parameters to identify the prevalent patterns. Our experiments with both synthetic data and real data from Beijing, China, demonstrate that the algorithm can identify many interesting patterns that are neglected by traditional co-location methods.

KEYWORDS:

• Spatial data mining
• co-location pattern mining
• reward value
• Voronoi diagram
• generalized extreme value distribution

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [Grant Number 41701438]; The National Science-Technology Support Plan Projects of China [Grant Number 2015BAJ02B00]; The Special Foundation of the Chief of the Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences [Grant Number Y6SJ2800CX].