Local Sparse Principal Component Analysis for Exploring the Spatial Distribution of Social Infrastructure

Seong-Yun Hong, Seonggook Moon (), Sang-Hyun Chi, Yoon-Jae Cho and Jeon-Young Kang
Additional contact information
Seong-Yun Hong: Department of Geography, Kyung Hee University, Seoul 02447, Republic of Korea
Seonggook Moon: LX Education Institute, Gongju-si 32522, Chungcheongnam-do, Republic of Korea
Sang-Hyun Chi: Department of Geography, Kyung Hee University, Seoul 02447, Republic of Korea
Yoon-Jae Cho: Department of Geography, Kyung Hee University, Seoul 02447, Republic of Korea
Jeon-Young Kang: Department of Geography Education, Kongju National University, Gongju-si 32522, Chungcheongnam-do, Republic of Korea

Land, 2022, vol. 11, issue 11, 1-16

Abstract: The primary purpose of this study is to develop a method that can assist in exploring infrastructure-related multidimensional data. The spatial distribution of social infrastructure, including housing and service facilities, is usually uneven across a nation. The underlying reasons behind the spatial configuration of infrastructure vary, and its comprehensive examination is crucial to understanding the true implications of their skewed distribution. However, simultaneous examination of all social infrastructure is not always straightforward due to the volume of data. The presence of strong correlations between the facilities may further impede the finding of meaningful patterns. To this end, we present an extension of PCA that constructs sparse principal components for local subsets of the data. To demonstrate its strengths and limitations, we apply it to a dataset on housing and service facilities in Korea. The results exhibit clear geographic patterns and offer valuable insights into the spatial patterns of social infrastructure, which the standard PCA only partly addressed. It provides empirical evidence that the proposed method can be an effective alternative to the traditional dimension reduction techniques for exploring spatial heterogeneity in massive multidimensional data.

Keywords: exploratory spatial analysis; principal component analysis; sparse loadings; spatial heterogeneity; spatial distribution; social infrastructure; urban analytics (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2073-445X/11/11/2034/pdf (application/pdf)
https://www.mdpi.com/2073-445X/11/11/2034/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:11:y:2022:i:11:p:2034-:d:971630

Access Statistics for this article

Land is currently edited by Ms. Carol Ma

More articles in Land from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().