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Collabmap: crowdsourcing maps for emergency planning

Collabmap: crowdsourcing maps for emergency planning
Collabmap: crowdsourcing maps for emergency planning
In this paper, we present a software tool to help emergency planners at Hampshire County Council in the UK to create maps for high-fidelity crowd simulations that require evacuation routes from buildings to roads. The main feature of the system is a crowdsourcing mechanism that breaks down the problem of creating evacuation routes into microtasks that a contributor to the platform can execute in less than a minute. As part of the mechanism we developed a concensus-based trust mechanism that filters out incorrect contributions and ensures that the individual tasks are complete and correct. To drive people to contribute to the platform, we experimented with different incentive mechanisms and applied these over different time scales, the aim being to evaluate what incentives work with different types of crowds, including anonymous contributors from Amazon Mechanical Turk. The results of the 'in the wild' deployment of the system show that the system is effective at engaging contributors to perform tasks correctly and that users respond to incentives in different ways. More specifically, we show that purely social motives are not good enough to attract a large number of contributors and that contributors are averse to the uncertainty in winning rewards. When taken altogether, our results suggest that a combination of incentives may be the best approach to harnessing the maximum number of resources to get socially valuable tasks (such for planning applications) performed on a large scale.
978-1-4503-1889-1
326-335
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Huynh, Trung Dong
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Venanzi, Matteo
ba24a77f-31a6-4c05-a647-babf8f660440
Shi, Bing
62e2a794-c690-45cc-8da6-993a75ecff7e
Ramchurn, Sarvapali D.
1d62ae2a-a498-444e-912d-a6082d3aaea3
Huynh, Trung Dong
ddea6cf3-5a82-4c99-8883-7c31cf22dd36
Venanzi, Matteo
ba24a77f-31a6-4c05-a647-babf8f660440
Shi, Bing
62e2a794-c690-45cc-8da6-993a75ecff7e

Ramchurn, Sarvapali D., Huynh, Trung Dong, Venanzi, Matteo and Shi, Bing (2013) Collabmap: crowdsourcing maps for emergency planning. The 5th Annual ACM Web Science Conference, Paris, France. 02 - 04 May 2013. pp. 326-335 . (doi:10.1145/2464464.2464508).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper, we present a software tool to help emergency planners at Hampshire County Council in the UK to create maps for high-fidelity crowd simulations that require evacuation routes from buildings to roads. The main feature of the system is a crowdsourcing mechanism that breaks down the problem of creating evacuation routes into microtasks that a contributor to the platform can execute in less than a minute. As part of the mechanism we developed a concensus-based trust mechanism that filters out incorrect contributions and ensures that the individual tasks are complete and correct. To drive people to contribute to the platform, we experimented with different incentive mechanisms and applied these over different time scales, the aim being to evaluate what incentives work with different types of crowds, including anonymous contributors from Amazon Mechanical Turk. The results of the 'in the wild' deployment of the system show that the system is effective at engaging contributors to perform tasks correctly and that users respond to incentives in different ways. More specifically, we show that purely social motives are not good enough to attract a large number of contributors and that contributors are averse to the uncertainty in winning rewards. When taken altogether, our results suggest that a combination of incentives may be the best approach to harnessing the maximum number of resources to get socially valuable tasks (such for planning applications) performed on a large scale.

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More information

Published date: 3 May 2013
Venue - Dates: The 5th Annual ACM Web Science Conference, Paris, France, 2013-05-02 - 2013-05-04
Organisations: Web & Internet Science, Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 350677
URI: http://eprints.soton.ac.uk/id/eprint/350677
ISBN: 978-1-4503-1889-1
PURE UUID: 04ac9e69-c179-41c6-ab6b-d598e0b968bb
ORCID for Sarvapali D. Ramchurn: ORCID iD orcid.org/0000-0001-9686-4302
ORCID for Trung Dong Huynh: ORCID iD orcid.org/0000-0003-4937-2473

Catalogue record

Date deposited: 08 Apr 2013 13:24
Last modified: 15 Mar 2024 03:22

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Contributors

Author: Sarvapali D. Ramchurn ORCID iD
Author: Trung Dong Huynh ORCID iD
Author: Matteo Venanzi
Author: Bing Shi

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