Next Article in Journal
Connectivity and Usership of Two Types of Multi-Modal Transportation Network: A Regional Trail and a Transit-Oriented Commercial Corridor
Previous Article in Journal
Designing a Model to Display the Relation between Social Vulnerability and Anthropogenic Risk of Wildfires in Galicia, Spain
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessArticle

Insights from Self-Organizing Maps for Predicting Accessibility Demand for Healthcare Infrastructure

1
School of Community and Regional Planning, Faculty of Applied Science, University of British Columbia, 6333 Memorial Road, Vancouver, BC V6T 1Z2, Canada
2
Department of Earth, Ocean and Atmospheric Sciences, Faculty of Science, 2020–2207 Main Mall, Vancouver, BC V6T 1Z4, Canada
*
Author to whom correspondence should be addressed.
Urban Sci. 2019, 3(1), 33; https://doi.org/10.3390/urbansci3010033
Received: 26 December 2018 / Revised: 1 March 2019 / Accepted: 7 March 2019 / Published: 12 March 2019
  |  
PDF [3387 KB, uploaded 12 March 2019]
  |     |  

Abstract

As urban populations grow worldwide, it becomes increasingly important to critically analyse accessibility—the ease with which residents can reach key places or opportunities. The combination of ‘big data’ and advances in computational techniques such as machine learning (ML) could be a boon for urban accessibility studies, yet their application in this field remains limited. In this study, we provided detailed predictions of healthcare accessibility across a rapidly growing city and related them to socio-economic factors using a combination of classical and modern data analysis methods. Using the City of Surrey (Canada) as a case study, we clustered high-resolution income data for 2016 and 2022 using principal component analysis (PCA) and a powerful ML clustering tool, the self-organising map (SOM). We then combined this with door-to-door travel times to hospitals and clinics, calculated using a simple open-source tool. Focusing our analysis on senior populations (65+ years), we found that higher income clusters are projected to become more prevalent across Surrey over our study period. Low income clusters have on average better accessibility to healthcare facilities than high income clusters in both 2016 and 2022. Population growth will be the biggest accessibility challenge in neighbourhoods with good existing access to healthcare, whereas income change (both positive and negative) will be most challenging in poorly connected neighbourhoods. A dual accessibility problem may arise in Surrey: first, large senior populations will reside in areas with access to numerous and close-by, clinics, putting pressure on existing facilities for specialised services. Second, lower-income seniors will increasingly reside in areas poorly connected to healthcare services, which may impact accessibility equity. We demonstrate that combining PCA and SOM clustering techniques results in novel insights for predicting accessibility at the neighbourhood level. This allows for robust planning policy recommendations to be drawn from large multivariate datasets. View Full-Text
Keywords: smart cities; machine learning; big data; clustering; principal component analysis (PCA) smart cities; machine learning; big data; clustering; principal component analysis (PCA)
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Mayaud, J.R.; Anderson, S.; Tran, M.; Radić, V. Insights from Self-Organizing Maps for Predicting Accessibility Demand for Healthcare Infrastructure. Urban Sci. 2019, 3, 33.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Urban Sci. EISSN 2413-8851 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top