Volume XLII-4/W11
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W11, 51-58, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W11-51-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W11, 51-58, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W11-51-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  20 Sep 2018

20 Sep 2018

EXPLORING SIMILARITIES AND VARIATIONS OF HUMAN MOBILITY PATTERNS IN THE CITY OF LONDON

P. Sulis and E. Manley P. Sulis and E. Manley
  • Centre for Advanced Spatial Analysis, University College London, UK

Keywords: time-series, cluster analysis, smart card data, human mobility, spatiotemporal patterns

Abstract. The availability of new spatial data represents an unprecedented opportunity to better understand and plan cities. In particular, extensive data sets of human mobility data supply new information that can empower urbanism research to unveil how people use and visit urban places over time, overcoming traditional limitations related to the lack of large, detailed data sets. In this work, we explore patterns of similarities and spatial differences in human mobility flows in London, analysing their temporal variations in relation to the liveliness measured in a number of places. Using data sourced from the Oyster smart card and Twitter, we perform a time-series cluster analysis, exploring the similarity of temporal trends amongst places assigned to each cluster. Results suggest that differences in patterns appear to be related to the central and peripheral location of places, which present two or more temporal trends over the week. The type of transport network connecting the places (Tube, Railways, etc.) also appears to be a factor in determining significant differences. This work contributes to current urbanism research investigating the daily rhythms in cities. It also explores how to use mobility data to classify places according to their temporal features, with the aim of enhancing conventional analysis tools and integrating them with new quantitative information and methods.