K-Means Clustering algorithms in Urban studies: A Review of Unsupervised Machine Learning techniques

Bochra Hadj Kilani

No bs6wy, OSF Preprints from Center for Open Science

Abstract: In years there has been an increase, in the interest surrounding the utilization of unsupervised machine learning methods, particularly the application of K means clustering algorithms within urban studies. These techniques have demonstrated their usefulness, in examining and comprehending facets of planning including land usage patterns, transportation systems and population distribution. The objective of this article is to offer an overview of how K means clustering algorithm are employed in urban studies. The review examines the different methodologies and approaches employed in utilizing K-means clustering for urban analysis, highlighting its advantages and limitations. Additionally, the article discusses the specific challenges and considerations that arise when applying K-means clustering in urban studies, including data preprocessing, feature selection, and interpretation of the cluster results. The findings of this review demonstrate the wide range of applications of K-means clustering in urban studies, from identifying distinct land use categories to understanding the spatial distribution of social amenities. Furthermore, it is revealed that the use of K-means clustering in urban studies allows for the identification and characterization of hidden patterns and similarities among urban areas that might not be immediately apparent through traditional analysis methods. Overall, the use of K-means clustering algorithms provides a valuable tool for urban planners and researchers in gaining insights and making informed decisions in urban design.

Date: 2023-11-30
New Economics Papers: this item is included in nep-big, nep-cmp and nep-ure
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://osf.io/download/656a78c2a0121a0931317c35/

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:osf:osfxxx:bs6wy

DOI: 10.31219/osf.io/bs6wy

Access Statistics for this paper

More papers in OSF Preprints from Center for Open Science
Bibliographic data for series maintained by OSF ().