Quantitative evaluation of public spaces using crowd replication

Samuli Hemminki, Keisuke Kuribayashi, Shin'Ichi Konomi, Petteri Nurmi, Sasu Tarkoma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

We propose crowd replication as a low-effort, easy to implement and cost-effective mechanism for quantifying the uses, activities, and sociability of public spaces. Crowd replication combines mobile sensing, direct observation, and mathematical modeling to enable resource efficient and accurate quantification of public spaces. The core idea behind crowd replication is to instrument the researcher investigating a public space with sensors embedded on commodity devices and to engage him/her into imitation of people using the space. By combining the collected sensor data with a direct observations and population model, individual sensor traces can be generalized to capture the behavior of a larger population. We validate the use of crowd replication as a data collection mechanism through a field study conducted within an exemplary metropolitan urban space. Results of our evaluation show that crowd replication accurately captures real human dynamics (0.914 correlation between indicators estimated from crowd replication and visual surveillance) and captures data that is representative of the behavior of people within the public space.

Original languageEnglish
Title of host publication24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
EditorsMatthias Renz, Mohamed Ali, Shawn Newsam, Matthias Renz, Siva Ravada, Goce Trajcevski
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450345897
DOIs
Publication statusPublished - Oct 31 2016
Externally publishedYes
Event24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016 - Burlingame, United States
Duration: Oct 31 2016Nov 3 2016

Publication series

NameGIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems

Other

Other24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
CountryUnited States
CityBurlingame
Period10/31/1611/3/16

All Science Journal Classification (ASJC) codes

  • Earth-Surface Processes
  • Computer Science Applications
  • Modelling and Simulation
  • Computer Graphics and Computer-Aided Design
  • Information Systems

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