Zooming into Socio-economic Inequalities: Using Urban Analytics to Track Vulnerabilities – A Case Study of Helsinki

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

12 Downloads (Pure)

Abstract

The Covid19 crisis has highlighted once more that socio-economic inequalities are a main driver of vulnerability. Especially in densely populated urban areas, however, these inequalities can drastically change even within neighbourhoods. To better prepare for urban crises, more granular techniques are needed to assess these vulnerabilities, and identify the main drivers that exacerbate inequality. Machine learning techniques enable us to extract this information from spatially geo-located datasets. In this paper, we present a prototypical study on how Principal Component Analysis (PCA) to analyse the distribution of labour and residential characteristics in the urban area of Helsinki, Finland. The main goals are twofold: 1) identify patterns of socio-economic activities, and 2) study spatial inequalities. Our analyses use a grid of 250x250 meters that covers the whole city of Helsinki, thereby providing a higher granularity than the neighbourhood-scale. The study yields four main findings. First, the descriptive statistical analysis detects inequalities in the labour and residential distributions. Second, relationships between the socio-economic variables exist in the geographic space. Third, the first two Principal Components (PCs) can extract most of the information about the socio-economic dataset. Fourth, the spatial analyses of the PCs identify differences between the Eastern and Western areas of Helsinki, which persist since the 1990s. Future studies will include further datasets related to the distribution of urban services and socio-technical indicators.
Original languageEnglish
Title of host publicationISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management
Place of PublicationBlacksburg, VA (USA)
PublisherISCRAM
Pages1028-1041
Volume18
Publication statusPublished - 2021

Publication series

Name
ISSN (Electronic)949373615

Keywords

  • machine learning (ML)
  • PCA
  • inequality
  • resilience
  • vulnerability
  • Urban analytics
  • GIS

Fingerprint

Dive into the research topics of 'Zooming into Socio-economic Inequalities: Using Urban Analytics to Track Vulnerabilities – A Case Study of Helsinki'. Together they form a unique fingerprint.

Cite this