Data Fusion and Visualization towards City Disaster Management: Lisbon Case Study
DOI:
https://doi.org/10.4108/eetsc.v6i18.1374Keywords:
Disaster Management, Data mining, Smart City, CRISP-DMAbstract
INTRODUCTION: Due to the high level of unpredictability and the complexity of the information requirements, disaster management operations are information demanding. Emergency response planners should organize response operations efficiently and assign rescue teams to particular catastrophe areas with a high possibility of surviving. Making decisions becomes more difficult when the information provided is heterogeneous, out of date, and often fragmented.
OBJECTIVES: In this research work a data fusion of different information sources and a data visualization process was applied to provide a big picture about the disruptive events in a city. This high-level knowledge is important for emergency management authorities. This holistic process for managing, processing, and analysing the seven Vs (Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value) in order to generate actionable insights for disaster management.
METHODS: A CRISP-DM methodology over smart city-data was applied. The fusion approach was introduced to merge different data sources.
RESULTS: A set of visual tools in dashboards were produced to support the city municipality management process. Visualization of big picture based on different data available is the proposed work.
CONCLUSION: Through this research, it was verified that there are temporal and spatial patterns of occurrences that affected the city of Lisbon, with some types of occurrences having a higher incidence in certain periods of the year, such as floods and collapses that occur when there are high levels of precipitation. On the other hand, it was verified that the downtown area of the city is the most affected area.
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References
J. J. Wellington and P. Ramesh, “Role of Internet of Things in disaster management,” in 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Mar. 2017, pp. 1–4. doi: 10.1109/ICIIECS.2017.8275928.
C. Yang, G. Su, and J. Chen, “Using big data to enhance crisis response and disaster resilience for a smart city,” in 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Mar. 2017, pp. 504–507. doi: 10.1109/ICBDA.2017.8078684.
A. Zagorecki, D. Johnson, and J. Ristvej, “Data Mining and Machine Learning in the Context of Disaster and Crisis Management,” Int. J. Emerg. Manag., vol. 9, pp. 351–365, Jan. 2013, doi: 10.1504/IJEM.2013.059879.
“(PDF) Crowdsourcing Disaster Response.” https://www.researchgate.net/publication/268448750_Crowdsourcing_Disaster_Response (accessed Aug. 26, 2021).
S. A. Shah, D. Z. Seker, M. M. Rathore, S. Hameed, S. B. Yahia, and D. Draheim, “Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?,” IEEE Access, vol. 7, pp. 91885–91903, 2019, doi: 10.1109/ACCESS.2019.2928233.
S. Chaudhari, A. Bhagat, N. Tarbani, and M. Pund, “Dynamic Notifications in Smart Cities for Disaster Management,” in Computational Intelligence in Data Mining, Singapore, 2019, pp. 177–190. doi: 10.1007/978-981-10-8055-5_17.
S. A. Shah, D. Z. Seker, S. Hameed, and D. Draheim, “The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects,” IEEE Access, vol. 7, pp. 54595–54614, 2019, doi: 10.1109/ACCESS.2019.2913340.
T. Li et al., “Data-Driven Techniques in Disaster Information Management,” ACM Comput. Surv., vol. 50, no. 1, p. 1:1-1:45, Mar. 2017, doi: 10.1145/3017678.
D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” Int. J. Surg., [1] J. J. Wellington and P. Ramesh, “Role of Internet of Things in disaster management,” in 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Mar. 2017, pp. 1–4. doi: 10.1109/ICIIECS.2017.8275928.
C. Yang, G. Su, and J. Chen, “Using big data to enhance crisis response and disaster resilience for a smart city,” in 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), Mar. 2017, pp. 504–507. doi: 10.1109/ICBDA.2017.8078684.
A. Zagorecki, D. Johnson, and J. Ristvej, “Data Mining and Machine Learning in the Context of Disaster and Crisis Management,” Int. J. Emerg. Manag., vol. 9, pp. 351–365, Jan. 2013, doi: 10.1504/IJEM.2013.059879.
“(PDF) Crowdsourcing Disaster Response.” https://www.researchgate.net/publication/268448750_Crowdsourcing_Disaster_Response (accessed Aug. 26, 2021).
S. A. Shah, D. Z. Seker, M. M. Rathore, S. Hameed, S. B. Yahia, and D. Draheim, “Towards Disaster Resilient Smart Cities: Can Internet of Things and Big Data Analytics Be the Game Changers?,” IEEE Access, vol. 7, pp. 91885–91903, 2019, doi: 10.1109/ACCESS.2019.2928233.
S. Chaudhari, A. Bhagat, N. Tarbani, and M. Pund, “Dynamic Notifications in Smart Cities for Disaster Management,” in Computational Intelligence in Data Mining, Singapore, 2019, pp. 177–190. doi: 10.1007/978-981-10-8055-5_17.
S. A. Shah, D. Z. Seker, S. Hameed, and D. Draheim, “The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects,” IEEE Access, vol. 7, pp. 54595–54614, 2019, doi: 10.1109/ACCESS.2019.2913340.
T. Li et al., “Data-Driven Techniques in Disaster Information Management,” ACM Comput. Surv., vol. 50, no. 1, p. 1:1-1:45, Mar. 2017, doi: 10.1145/3017678.
D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement,” Int. J. Surg., vol. 8, no. 5, pp. 336–341, 2010, doi: 10.1016/j.ijsu.2010.02.007.
C. Okoli and K. Schabram, “A Guide to Conducting a Systematic Literature Review of Information Systems Research,” SSRN Electron. J., 2010, doi: 10.2139/ssrn.1954824.
“Scopus - Document search.” https://www.scopus.com/search/form.uri?display=basic&edit.scft=1#basic (accessed Aug. 09, 2021).
“About Google Scholar.” https://scholar.google.com/intl/en/scholar/about.html (accessed Aug. 09, 2021).
M.-C. Jeong and J. Kim, “Prediction and analysis of electrical accidents and risk due to climate change,” Int. J. Environ. Res. Public. Health, vol. 16, no. 16, 2019, doi: 10.3390/ijerph16162984.
M. F. Abdullah, M. Ibrahim, and H. Zulkifli, “Big Data Analytics Framework for Natural Disaster Management in Malaysia:,” in Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security, Porto, Portugal, 2017, pp. 406–411. doi: 10.5220/0006367204060411.
K. M. Briones-Estébanez and N. F. F. Ebecken, “Occurrence of emergencies and disaster analysis according to precipitation amount,” Nat. Hazards, vol. 85, no. 3, pp. 1437–1459, 2017, doi: 10.1007/s11069-016-2635-z.
J. Park et al., “Ensemble model development for the prediction of a disaster index in water treatment systems,” Water Switz., vol. 12, no. 11, pp. 1–19, 2020, doi: 10.3390/w12113195.
S. Saha, S. Shekhar, S. Sadhukhan, and P. Das, “An analytics dashboard visualization for flood decision support system,” J. Vis., vol. 21, no. 2, pp. 295–307, Apr. 2018, doi: 10.1007/s12650-017-0453-3.
R. C. dos Santos Alvalá et al., “Mapping characteristics of at-risk population to disasters in the context of Brazilian early warning system,” Int. J. Disaster Risk Reduct., vol. 41, p. 101326, Dec. 2019, doi: 10.1016/j.ijdrr.2019.101326.
S. Lee, S. Lee, M.-J. Lee, and H.-S. Jung, “Spatial Assessment of Urban Flood Susceptibility Using Data Mining and Geographic Information System (GIS) Tools,” Sustainability, vol. 10, no. 3, Art. no. 3, Mar. 2018, doi: 10.3390/su10030648.
Y. Liu, Z. Li, B. Wei, X. Li, and B. Fu, “Seismic vulnerability assessment at urban scale using data mining and GIScience technology: application to Urumqi (China),” Geomat. Nat. Hazards Risk, vol. 10, no. 1, pp. 958–985, Jan. 2019, doi: 10.1080/19475705.2018.1524400.
W. Chen, S. Zhang, R. Li, and H. Shahabi, “Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling,” Sci. Total Environ., vol. 644, pp. 1006–1018, Dec. 2018, doi: 10.1016/j.scitotenv.2018.06.389.
S. Smith et al., “Adoption of data-driven decision making in fire emergency management,” presented at the 24th European Conference on Information Systems, ECIS 2016, 2016.
F. F. Balahadia, B. G. Dadiz, R. R. Ramirez, M. Luvett, J. P. Lalata, and A. C. Lagman, “Application of Data Mining Approach for Profiling Fire Incidents Reports of Bureau of Fire and Protection,” in 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dec. 2019, pp. 713–717. doi: 10.1109/ICCIKE47802.2019.9004420.
A. Asgary, A. Ghaffari, and J. Levy, “Spatial and temporal analyses of structural fire incidents and their causes: A case of Toronto, Canada,” Fire Saf. J., vol. 45, no. 1, pp. 44–57, Jan. 2010, doi: 10.1016/j.firesaf.2009.10.002.
X. Liu, Y. Lu, Z. Xia, F. Li, and T. Zhang, “A Data Mining Method for Potential Fire Hazard Analysis of Urban Buildings based on Bayesian Network,” in Proceedings of the 2nd International Conference on Intelligent Information Processing, New York, NY, USA, Jul. 2017, pp. 1–6. doi: 10.1145/3144789.3144811.
E. W. Lee, G. Yeoh, M. Cook, and C. Lewis, “Data Mining on Fire Records of New South Wales, Sydney,” Procedia Eng., vol. 71, pp. 328–332, 2014, doi: 10.1016/j.proeng.2014.04.047.
Z. Wang, J. Xu, X. He, and Y. Wang, “Analysis of spatiotemporal influence patterns of toxic gas monitoring concentrations in an urban drainage network based on IoT and GIS,” Pattern Recognit. Lett., vol. 138, pp. 237–246, 2020, doi: 10.1016/j.patrec.2020.07.022.
L. B. Elvas, B. M. Mataloto, A. L. Martins, and J. C. Ferreira, “Disaster Management in Smart Cities,” Smart Cities, vol. 4, no. 2, Art. no. 2, Jun. 2021, doi: 10.3390/smartcities4020042.
J. Mesquitela, L. B. Elvas, J. C. Ferreira, and L. Nunes, “Data Analytics Process over Road Accidents Data—A Case Study of Lisbon City,” ISPRS Int. J. Geo-Inf., vol. 11, no. 2, Art. no. 2, Feb. 2022, doi: 10.3390/ijgi11020143.
L. B. Elvas, C. F. Marreiros, J. M. Dinis, M. C. Pereira, A. L. Martins, and J. C. Ferreira, “Data-Driven Approach for Incident Management in a Smart City,” Appl. Sci., vol. 10, no. 22, Art. no. 22, Jan. 2020, doi: 10.3390/app10228281.
“[PDF] Crisp-dm: towards a standard process modell for data mining | Semantic Scholar.” https://www.semanticscholar.org/paper/Crisp-dm%3A-towards-a-standard-process-modell-for-Wirth-Hipp/48b9293cfd4297f855867ca278f7069abc6a9c24 (accessed Aug. 27, 2021).
“Portal do INE.” https://www.ine.pt/xportal/xmain?xpid=INE&xpgid=ine_inst_legislacao&xlang=pt (accessed May 04, 2021).
“IPMA - Serviços.” https://www.ipma.pt/pt/produtoseservicos/index.jsp?page=dados.xml (accessed May 04, 2021).
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