Developing a multilevel distribuiting crowdsourcing system for aiding and rescuing to overcome widespread crises
DOI:
https://doi.org/10.15587/1729-4061.2020.201082Keywords:
spatial crowdsourcing, urban disaster management, spatial allocation, Multi-agent environment, Enterprise GISAbstract
Today, the management of different crises in urban areas is among the main challenges of societies due to their scope and limited resources. Using the crowd to solve these problems would be a proper solution. Crowdsourcing, due to a large number of people, the diversity of expertise, superficial dispersion and low cost, has long been considered. However, managing such a volume of people to restore the crisis situation has many problems that modern IT-based techniques in recent years have Facilitates the issue.
In this paper, a distributed geospatial system consisting of segments and different users is designed that can be used to manage the crowd to solve the problems of the urban crisis. The system consists of several subsystems and several user groups that operate on the basis of spatial crowdsourcing service.
The proposed new service is an atomic, consisting of a guiding section, an operational content, and a control segment. Operational content involves performing a simple activity. Solving complex issues involves the proper combination of simple services. After identifying the crisis environment with system elements, the system design a suitable combination of services for addressing regional issues and then allocate services to appropriate rescuers at the region level. The designed mechanism to allocate and combine services is based on a multidisciplinary agent environment.
In order to evaluate, in addition to designing software test scenarios, the system was tested during the Aqala flood of 2019 in Golestan province of Iran. The system accuracy in allocation was as well as its performance when the number of users increased. The system also considerably raised various quality indicators such as rescuer fatigue or mission latency. Furthermore, an innovated crowdsourcing evaluation method also announced the overall system success rate of 44.5 %
References
- Wolensky, R. P., Wolensky, K. C. (1990). Local government's problem with disaster management: a literature review and structural analysis. Review of Policy Research, 9 (4), 703–725. doi: https://doi.org/10.1111/j.1541-1338.1990.tb01074.x
- Kazemi, L., Shahabi, C. (2012). GeoCrowd: enabling query answering with spatial crowdsourcing. Proceedings of the 20th International Conference on Advances in Geographic Information Systems - SIGSPATIAL’12. doi: https://doi.org/10.1145/2424321.2424346
- Zhao, Y., Han, Q. (2016). Spatial crowdsourcing: current state and future directions. IEEE Communications Magazine, 54 (7), 102–107. doi: https://doi.org/10.1109/mcom.2016.7509386
- Liao, P., Wan, Y., Tang, P., Wu, C., Hu, Y., Zhang, S. (2019). Applying crowdsourcing techniques in urban planning: A bibliometric analysis of research and practice prospects. Cities, 94, 33–43. doi: https://doi.org/10.1016/j.cities.2019.05.024
- Chatfield, A. T., Reddick, C. G. (2018). All hands on deck to tweet #sandy: Networked governance of citizen coproduction in turbulent times. Government Information Quarterly, 35 (2), 259–272. doi: https://doi.org/10.1016/j.giq.2017.09.004
- William, S. (2013). On Language. New York Times Magazine.
- L French, E. L., Birchall, S. J., Landman, K., Brown, R. D. (2019). Designing public open space to support seismic resilience: A systematic review. International Journal of Disaster Risk Reduction, 34, 1–10. doi: https://doi.org/10.1016/j.ijdrr.2018.11.001
- Cai, L., Xu, J., Liu, J., Ma, T., Pei, T., Zhou, C. (2019). Sensing multiple semantics of urban space from crowdsourcing positioning data. Cities, 93, 31–42. doi: https://doi.org/10.1016/j.cities.2019.04.011
- Gizzi, F. T., Potenza, M. R., Zotta, C. (2016). The Insurance Market of Natural Hazards for Residential Properties in Italy. Open Journal of Earthquake Research, 05 (01), 35–61. doi: https://doi.org/10.4236/ojer.2016.51004
- Principles of Emergency Management Supplement (2007). doi: http://doi.org/10.13140/RG.2.2.32021.93925
- Alieinykov, I., Thamer, K. A., Zhuravskyi, Y., Sova, O., Smirnova, N., Zhyvotovskyi, R. et. al. (2019). Development of a method of fuzzy evaluation of information and analytical support of strategic management. Eastern-European Journal of Enterprise Technologies, 6 (2 (102)), 16–27. doi: https://doi.org/10.15587/1729-4061.2019.184394
- Camero, A., Alba, E. (2019). Smart City and information technology: A review. Cities, 93, 84–94. doi: https://doi.org/10.1016/j.cities.2019.04.014
- Zander, J. (2014). Smart Emergency Response System (SERS).
- The Smart Emergency Response System Using MATLAB and Simulink (2015).
- World volunteer (2016). World Volunteer Web/Index. Available at: http://www.worldvolunteerweb.org/
- Masli, M., Bouma, L., Owen, A., Terveen, L. (2013). Geowiki + route analysis = improved transportation planning. Proceedings of the 2013 Conference on Computer Supported Cooperative Work Companion - CSCW ’13. doi: https://doi.org/10.1145/2441955.2442008
- Volunteer of World (2016). United Nations. Available at: https://www.unv.org/
- Ahmed, S., Darsiti, M., Agustiawan, H. (2007). A development framewor for collaborative robots using feedback control.
- Samany, N. N. (2019). Automatic landmark extraction from geo-tagged social media photos using deep neural network. Cities, 93, 1–12. doi: https://doi.org/10.1016/j.cities.2019.04.012
- Lim, C., Kim, K.-J., Maglio, P. P. (2018). Smart cities with big data: Reference models, challenges, and considerations. Cities, 82, 86–99. doi: https://doi.org/10.1016/j.cities.2018.04.011
- Wei, L.-Y., Zheng, Y., Peng, W.-C. (2012). Constructing popular routes from uncertain trajectories. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ’12. doi: https://doi.org/10.1145/2339530.2339562
- Bao, J., Zheng, Y., Mokbel, M. F. (2012). Location-based and preference-aware recommendation using sparse geo-social networking data. Proceedings of the 20th International Conference on Advances in Geographic Information Systems - SIGSPATIAL ’12. doi: https://doi.org/10.1145/2424321.2424348
- Goodchild, M. F., Glennon, J. A. (2010). Crowdsourcing geographic information for disaster response: a research frontier. International Journal of Digital Earth, 3 (3), 231–241. doi: https://doi.org/10.1080/17538941003759255
- Mohammadi, N., Malek, M. (2014). Artificial intelligence-based solution to estimate the spatial accuracy of volunteered geographic data. Journal of Spatial Science, 60 (1), 119–135. doi: https://doi.org/10.1080/14498596.2014.927337
- Mohammadi, N., Malek, M. (2014). VGI and Reference Data Correspondence Based on Location-Orientation Rotary Descriptor and Segment Matching. Transactions in GIS, 19 (4), 619–639. doi: https://doi.org/10.1111/tgis.12116
- Vieweg, S., Hughes, A. L., Starbird, K., Palen, L. (2010). Microblogging during two natural hazards events. Proceedings of the 28th International Conference on Human Factors in Computing Systems - CHI ’10. doi: https://doi.org/10.1145/1753326.1753486
- Cutter, S. L. (2003). GI Science, Disasters, and Emergency Management. Transactions in GIS, 7 (4), 439–446. doi: https://doi.org/10.1111/1467-9671.00157
- Lee, R., Sumiya, K. (2010). Measuring geographical regularities of crowd behaviors for Twitter-based geo-social event detection. Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks - LBSN ’10. doi: https://doi.org/10.1145/1867699.1867701
- Li, L., Goodchild, M. F. (2010). The Role of Social Networks in Emergency Management. International Journal of Information Systems for Crisis Response and Management, 2 (4), 48–58. doi: https://doi.org/10.4018/jiscrm.2010100104
- Estellés-Arolas, E., González-Ladrón-de-Guevara, F. (2012). Towards an integrated crowdsourcing definition. Journal of Information Science, 38 (2), 189–200. doi: https://doi.org/10.1177/0165551512437638
- Hirth, M., Hoßfeld, T., Tran-Gia, P. (2011). Anatomy of a Crowdsourcing Platform - Using the Example of Microworkers.com. 2011 Fifth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing. doi: https://doi.org/10.1109/imis.2011.89
- Johnson, B. A., Scheyvens, H., Baqui Khalily, M. A., Onishi, A. (2019). Investigating the relationships between climate hazards and spatial accessibility to microfinance using geographically-weighted regression. International Journal of Disaster Risk Reduction, 33, 122–130. doi: https://doi.org/10.1016/j.ijdrr.2018.10.001
- Mohanty, A., Hussain, M., Mishra, M., Kattel, D. B., Pal, I. (2019). Exploring community resilience and early warning solution for flash floods, debris flow and landslides in conflict prone villages of Badakhshan, Afghanistan. International Journal of Disaster Risk Reduction, 33, 5–15. doi: https://doi.org/10.1016/j.ijdrr.2018.07.012
- Howe, J. (2006). The Rise of Crowdsourcing. Wired.
- Harvey, F. (2012). To Volunteer or to Contribute Locational Information? Towards Truth in Labeling for Crowdsourced Geographic Information. Crowdsourcing Geographic Knowledge, 31–42. doi: https://doi.org/10.1007/978-94-007-4587-2_3
- Stefanidis, A., Crooks, A., Radzikowski, J. (2011). Harvesting ambient geospatial information from social media feeds. GeoJournal, 78 (2), 319–338. doi: https://doi.org/10.1007/s10708-011-9438-2
- Caillou, P., Gaudou, B., Grignard, A., Truong, C. Q., Taillandier, P. (2017). A Simple-to-Use BDI Architecture for Agent-Based Modeling and Simulation. Advances in Social Simulation 2015, 15–28. doi: https://doi.org/10.1007/978-3-319-47253-9_2
- AnyLogic. Wikipedia. Available at: https://en.wikipedia.org/wiki/AnyLogic
- Simio SYNC. SIMIO. Available at: https://www.simio.com/index.php
- Afuah, A., Tucci, C. L. (2012). Crowdsourcing As a Solution to Distant Search. Academy of Management Review, 37 (3), 355–375. doi: https://doi.org/10.5465/amr.2010.0146
- De Vreede, T., Nguyen, C., de Vreede, G.-J., Boughzala, I., Oh, O., Reiter-Palmon, R. (2013). A Theoretical Model of User Engagement in Crowdsourcing. Collaboration and Technology, 94–109. doi: https://doi.org/10.1007/978-3-642-41347-6_8
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Hooshang Eivazy, Mohammad Reza Malek
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.