Zooming into socio-economic inequalities: Using urban analytics to track vulnerabilities - A case study of helsinki

ABSTRACT

The Covid19 crisis has highlighted once more that socio-economic inequalities are a main driver of vulnerability. Especially in densely populated urban areas, these inequalities can drastically change even within neighbourhoods. However, conventionally such vulnerabilities are analysed at city or district scale. As such, new methods with higher granularity are needed to zoom into the spatial patterns locally. Machine learning techniques enable us to extract detailed spatial information from geo-located datasets. In this paper, we present a prototypical study that uses 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 and related vulnerabilities. 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 economic crisis in the 1990s, indicating clear path-dependencies. Future studies will include further datasets related to the distribution of urban services and socio-technical indicators. © 2021 Information Systems for Crisis Response and Management, ISCRAM. All rights reserved.