TY - GEN
T1 - Quantitative evaluation of public spaces using crowd replication
AU - Hemminki, Samuli
AU - Kuribayashi, Keisuke
AU - Konomi, Shin'Ichi
AU - Nurmi, Petteri
AU - Tarkoma, Sasu
N1 - Publisher Copyright:
© 2016 ACM.
PY - 2016/10/31
Y1 - 2016/10/31
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85011088666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85011088666&partnerID=8YFLogxK
U2 - 10.1145/2996913.2996946
DO - 10.1145/2996913.2996946
M3 - Conference contribution
AN - SCOPUS:85011088666
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
BT - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
A2 - Renz, Matthias
A2 - Ali, Mohamed
A2 - Newsam, Shawn
A2 - Renz, Matthias
A2 - Ravada, Siva
A2 - Trajcevski, Goce
PB - Association for Computing Machinery
T2 - 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2016
Y2 - 31 October 2016 through 3 November 2016
ER -