A method to improve Ripley's function to analyze spatial pattern by factor analysis

Hongchun Qu; Qinhao Zhang

doi:10.1117/12.2682259

26 May 2023 A method to improve Ripley's function to analyze spatial pattern by factor analysis

Hongchun Qu, Qinhao Zhang

Proceedings Volume 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023); 127002D (2023) https://doi.org/10.1117/12.2682259
Event: International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 2023, Nanchang, China

Abstract

The Ripley function is a spatial point pattern analysis method, which is used to characterize point processes at different distance scales. However, to use the spatial point pattern analysis in combination with other spatial pattern analysis methods, or to analyze the spatial pattern of a population and individuals, if the spatial pattern is affected by many influencing factors, the introduction of the dimension of scale in the subsequent analysis will lead to a large number of dimensions to be examined and lack of correlation, which makes the study impossible. Therefore, in this study, the Ripley's function was improved by reducing the dimensionality of the calculated results through factor analysis, so that the results obtained from Ripley's K function only show one spatial pattern, thus eliminating the influence of scale on the spatial pattern, and expressing the spatial pattern that best reflects the current population or individual. Four standard spatial point pattern datasets were used to verify the ability of factor analysis to respond to spatial patterns after dimensionality reduction for Ripley'K, Ripley'L, and Ripley'G. The results showed that for aggregated point patterns the accuracy of the calculated results after dimensionality reduction reached 100%, where For the random point pattern, the average accuracy is also above 95%, which indicates that the applicability of factor analysis dimensionality reduction to spatial pattern models is in the random point pattern and aggregation pattern. In terms of different scale point sets, it has good adaptability to all three scale point set sizes. In this way the application scenario of Ripley function will be more extensive.

Citation Download Citation

Hongchun Qu and Qinhao Zhang "A method to improve Ripley's function to analyze spatial pattern by factor analysis", Proc. SPIE 12700, International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023), 127002D (26 May 2023); https://doi.org/10.1117/12.2682259

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
5 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Factor analysis

Reflection

Monte Carlo methods

Data modeling

Diseases and disorders

Statistical analysis

Automation

Show All Keywords

Keywords/Phrases

Search In:

Publication Years