Detecting common features from point patterns for similarity measurement using matrix decomposition

ABSTRACT

Similarity of point patterns is critical to geographic information retrieval. Many methods depend on measuring the similarity between point patterns within the spatial database. However, previous researches mainly focus on point density which is only one aspect of point patterns. A point distribution can be complex by its numerous alignments of point groups, which usually imply different geographical meanings in reality. In this paper, we propose a new method that uses image analysis techniques to comprehensively consider the characteristics of a point pattern. Specifically, given a set of point datasets falling in the same region, our method first generates the point intensity surfaces to transform the original vector space to raster space; then, the method constructs a matrix to describe all the pattern-related information. Finally, the point pattern similarity is calculated by decomposing this matrix into the lower-order representation and the factorized basis features. Due to the use of matrix decomposition, the proposed method has the merits that it can eliminate noises from the original data and assess the similarity of two patterns with emphasis on their major features. As a case study, our method is effective in discovering regularity from the taxi pick-up/drop-off point datasets.

KEYWORDS:

• Map space
• spatial database
• pattern assessment
• spatial cognition
• point pattern

Acknowledgments

The authors appreciate the valuable comments of the anonymous reviewers and the editor, which greatly improved and strengthened this work. The project was supported by the National Natural Science Foundation of China (No.42071442); by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.CUG170640).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data and codes that support the findings of this study are available in [figshare.com] with the identifiers (https://doi.org/10.6084/m9.figshare.19470593.v2).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [42071442]; Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) [CUG170640]