Abstract
In this article, we present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. An hourly population distribution dataset is provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The dataset is validated by comparing population register data from Statistics Finland for night hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city, and examine population variations relevant to spatial accessibility analyses, crisis management, planning and beyond.
Highlights
Background & SummaryIn this paper, we introduce a dynamic population distribution dataset based on mobile phone data from the Helsinki Metropolitan Area in Finland
We introduce a dynamic population distribution dataset based on mobile phone data from the Helsinki Metropolitan Area in Finland
After creating the physical surface layer, a geometric union was performed between the physical surface layer, source zone and target zone layers to create the disaggregated physical surface layer – a layer where physical surface layer units are divided into subunits so that each subunit is designated both to one unique source zone (j) and one unique target zone (z), see Fig. 6
Summary
We introduce a dynamic population distribution dataset based on mobile phone data from the Helsinki Metropolitan Area in Finland. Information about the actual human presence is often scarce and predominantly based on static population data derived from national population censuses and registers. Census and register data and ambient population modelling neglect the actual setting of temporarily incoming and outgoing population groups, such as tourists and visitors, commuting workers, the short-stay migrant workforce and unregistered people. This could be invaluable information e.g. for mitigating the ongoing COVID-19 pandemic[19]. We hope that the availability of this dataset facilitates the understanding of our dynamic society[19] and benefits later analyses for social good, whilst preserving privacy of mobile phone users[19]
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