Designing user-centered decision support systems for climate disasters: What information do communities and rescue responders need during floods?

Julia Hillin; Bahareh Alizadeh; Diya Li; Courtney M. Thompson; Michelle A. Meyer; Zhe Zhang; Amir H. Behzadan

doi:10.5055/jem.0741

Authors

Julia Hillin, MS
Bahareh Alizadeh, PhD Student
Diya Li, PhD Student
Courtney M. Thompson, PhD https://orcid.org/0000-0001-5082-4540
Michelle A. Meyer, PhD
Zhe Zhang, PhD
Amir H. Behzadan, PhD

DOI:

https://doi.org/10.5055/jem.0741

Keywords:

spatial decision support systems, community knowledge base, disaster response, disaster rescue operators

Abstract

Flooding events are the most common natural hazard globally, resulting in vast destruction and loss of life. An effective flood emergency response is necessary to lessen the negative impacts of flood disasters. However, disaster management and response efforts face a complex scenario. Simultaneously, regular citizens attempt to navigate the various sources of information being distributed and determine their best course of action. One thing is evident across all disaster scenarios: having accurate information and clear communication between citizens and rescue personnel is critical.

This research aims to identify the diverse needs of two groups, rescue operators and citizens, during flood disaster events by investigating the sources and types of information they rely on and information that would improve their responses in the future. This information can improve the design and implementation of existing and future spatial decision support systems (SDSSs) during flooding events. This research identifies information characteristics crucial for rescue operators and everyday citizens’ response and possible evacuation to flooding events by qualitatively coding survey responses from rescue responders and the public. The results show that including local input in SDSS development is crucial for improving higher-resolution flood risk quantification models. Doing so democratizes data collection and analysis, creates transparency and trust between people and governments, and leads to transformative solutions for the broader scientific community.

Author Biographies

Julia Hillin, MS

Department of Geography, Texas A&M University, College Station, Texas

Bahareh Alizadeh, PhD Student

Department of Construction Science, Texas A&M University, College Station, Texas

Diya Li, PhD Student

Department of Geography, Texas A&M University, College Station, Texas

Courtney M. Thompson, PhD

Affiliate Faculty, Department of Geography, Texas A&M University, College Station, Texas

Michelle A. Meyer, PhD

Associate Professor, Department of Landscape Architecture & Urban Planning, Texas A&M University, College Station, Texas

Zhe Zhang, PhD

Assistant Professor, Department of Geography, Texas A&M University, College Station, Texas

Amir H. Behzadan, PhD

Professor, Department of Civil, Environmental, and Architectural Engineering (CEAE), University of Colorado Boulder, Boulder, Colorado

References

Ahern M, Kovats RS, Wilkinson P, et al.: Global health impacts of floods: Epidemiologic evidence. Epidemiol Rev. 2005; 27(1): 36-46. DOI: 10.1093/epirev/mxi004.

Du W, FitzGerald GJ, Clark M, et al.: Health impacts of floods. Prehosp Disaster Med. 2010; 25(3): 265-272. DOI: 10.1017/S1049023X00008141.

Wing OEJ, Lehman W, Bates PD, et al.: Inequitable patterns of US flood risk in the anthropocene. Nat Clim Chang. 2022; 12(2): 156-162. DOI: 10.1038/s41558-021-01265-6.

Wahl T, Jain S, Bender J, et al.: Increasing risk of compound flooding from storm surge and rainfall for major US cities. Nat Clim Change. 2015; 5(12): 1093-1097. DOI: 10.1038/nclimate2736.

Salmoral G, Rivas Casado M, Muthusamy M, et al.: Guidelines for the use of unmanned aerial systems in flood emergency response. Water. 2020; 12(2): 521. DOI: 10.3390/w12020521.

Altay N: Issues in disaster relief logistics. Large-scale disasters: Prediction, control, and mitigation. 2008; 120-146.

Eller W, Gerber BJ, Branch LE: Voluntary nonprofit organizations and disaster management: Identifying the nature of intersector coordination and collaboration in disaster service assistance provision: Nonprofit service provision. Risk Hazards Crisis Public Policy. 2015; 6(2): 223-238. DOI: 10.1002/rhc3.12081.

Janssen M, Lee J, Bharosa N, et al.: Advances in multi-agency disaster management: Key elements in disaster research. Inf Syst Front. 2010; 12(1): 1-7. DOI: 10.1007/s10796-009-9176-x.

Gerber BJ: Disaster management in the United States: Examining key political and policy challenges. Policy Stud J. 2007; 35(2): 227-238. DOI: 10.1111/j.1541-0072.2007.00217.x.

Waugh WL Jr: Terrorism, homeland security and the national emergency management network. Public Organ Rev. 2003; 3(4): 373-385. DOI: 10.1023/B:PORJ.0000004815.29497.e5.

Lettieri E, Masella C, Radaelli G: Disaster management: Findings from a systematic review. Disaster Prev Manag Int J. 2009; 18(2): 117-136. DOI: 10.1108/09653560910953207.

Horan TA, Schooley BL: Time-critical information services. Commun ACM. 2007; 50(3): 73-78. DOI: 10.1145/1226736.1226738.

Chaudhary MT, Piracha A: Natural disasters—Origins-impacts, management. Encyclopedia. 2021; 1(4): 1101-1131. DOI: 10.3390/encyclopedia1040084.

National Research Council: Improving Disaster Management: The Role of IT in Mitigation, Preparedness, Response, and Recovery. Washington, DC: National Academies Press, 2007: 11824. DOI: 10.17226/11824.

McAtee KJ, Bedenbaugh R, Lakis DC, et al.: Emergency preparedness and disaster response: There’s an app for that 2.0. Prehosp Disaster Med. 2021; 17: 1-7. DOI: 10.1017/S1049023X21001321.

Mihunov VV, Lam NSN, Zou L, et al.: Use of twitter in disaster rescue: Lessons learned from Hurricane Harvey. Int J Digit Earth. 2020; 13(12): 1454-1466. DOI: 10.1080/17538947.2020.1729879.

Sakurai M, Murayama Y: Information technologies and disaster management—Benefits and issues. Prog Disaster Sci. 2019; 2: 100012. DOI: 10.1016/j.pdisas.2019.100012.

Leeuw S, Vis IFA, Jonkman SN: Exploring logistics aspects of flood emergency measures: Logistics aspects of flood emergency measures. J Contingencies Crisis Man. 2012; 20(3): 166-179. DOI: 10.1111/j.1468-5973.2012.00667.x.

Brody SD, Sebastian A, Blessing R, et al.: Case study results from southeast Houston, Texas: Identifying the impacts of residential location on flood risk and loss. J Flood Risk Manage. 2018; 11(S1): S110-S120. DOI: 10.1111/jfr3.12184.

Cutter SL: The landscape of disaster resilience indicators in the USA. Nat Hazards. 2016; 80(2): 741-758. DOI: 10.1007/s11069-015-1993-2.

Cutter SL, Ash KD, Emrich CT: Urban–rural differences in disaster resilience. Ann Am Assoc Geogr. 2016; 106(6): 1236-1252. DOI: 10.1080/24694452.2016.1194740.

Cox RS, Hamlen M: Community disaster resilience and the rural resilience index. Am Behav Sci. 2015; 59(2): 220-237. DOI: 10.1177/0002764214550297.

Kapucu N, Hawkins CV, Rivera FI: Disaster preparedness and resilience for rural communities. Risk Hazards Crisis Public Policy. 2013; 4(4): 215-233. DOI: 10.1002/rhc3.12043.

Tootle D: Disaster recovery in rural communities: A case study of Southwest Louisiana. J Rural Soc Sci. 2007; 22(23). Article 2.

Chikoto GL, Sadiq AA, Fordyce E: Disaster mitigation and preparedness: Comparison of nonprofit, public, and private organizations. Nonprofit Volunt Sect Q. 2013; 42(2): 391-410. DOI: 10.1177/0899764012452042.

Cvetkovic VM, Martinovic´ J: Innovative solutions for flood risk management. Int J Disaster Risk Manag. 2020; 2(2): 71-99. DOI: 10.18485/ijdrm.2020.2.2.5.

Ali K, Nguyen HX, Vien QT, et al.: Disaster management communication networks: Challenges and architecture design. In: 2015 IEEE International Conference on Pervasive Computing and Communication Workshops (PerCom Workshops). 2015: 537-542. DOI: 10.1109/PERCOMW.2015.7134094.

Zhang Z, Demšar U, Rantala J, et al.: A fuzzy multiple-attribute decision-making modelling for vulnerability analysis on the basis of population information for disaster management. Int J Geogr Inf Sci. 2014; 28(9): 1922-1939. DOI: 10.1080/13658816.2014.908472.

Zhao J, Zhang Z, Sullivan CJ: Identifying anomalous nuclear radioactive sources using Poisson kriging and mobile sensor networks. PLoS One. 2019; 14(5): e0216131. DOI: 10.1371/journal.pone.0216131.

Zhang W, Villarini G, Vecchi GA, et al.: Urbanization exacerbated the rainfall and flooding caused by Hurricane Harvey in Houston. Nature. 2018; 563(29): 384-388.

Seppänen H, Luokkala P, Zhang Z, et al.: Critical infrastructure vulnerability—A method for identifying the infrastructure service failure interdependencies. Int J Crit Infrastruct Prot. 2018; 22: 25-38. DOI: 10.1016/j.ijcip.2018.05.002.

Alizadeh Kharazi B, Li D, Zhang Z, et al.: Feasibility study of urban flood mapping using traffic signs for route optimization. In EG-ICE 2021 Workshop on Intelligent Computing in Engineering (2021) 572-581. 2021. Available at http://arxiv.org/abs/2109.11712. Accessed May 11, 2022.

Zhang Z, Zou L, Li W, et al.: Cyberinfrastructure and intelligent spatial decision support systems. Trans GIS. 2021; 25(4): 1651-1653. DOI: 10.1111/tgis.12835.

Aldrich DP: Challenges to coordination: Understanding intergovernmental friction during disasters. Int J Disaster Risk Sci. 2019; 10(3): 306-316. DOI: 10.1007/s13753-019-00225-1.

GNDR (Global Network of Civil Society Organizations): “Clouds but Little Rain…” Views from the frontline; a local perspective of progress towards implementation of the Hyogo framework for action. Gncsodr. 2009. Available at https://www.preventionweb.net/files/9822_9822VFLfullreport06091.pdf. Accessed May 23, 2022.

Benson C, Clay E: Economic and financial impacts of natural disasters: An assessment of their effects and options for mitigation: Synthesis report. London: Overseas Development Institute, 2003; 128.

Boin A, McConnell A, Hart P: Governing after crisis: The politics of investigation, accountability and learning. Cambridge: Cambridge University Press, 2008. DOI: 10.1017/CBO9780511756122.

Abdeen FN, Fernando T, Kulatunga U, et al.: Challenges in multi-agency collaboration in disaster management: A Sri Lankan perspective. Int J Disaster Risk Reduct. 2021; 62: 102399. DOI: 10.1016/j.ijdrr.2021.102399.

McEntire DA: Coordinating multi-organisational responses to disaster: Lessons from the March 28, 2000, Fort Worth Tornado. Disaster Prev Manag. 2002; 11(5): 369-379. DOI: 10.1108/09653560210453416.

McGuire M, Silvia C: The effect of problem severity, managerial and organizational capacity, and agency structure on intergovernmental collaboration: Evidence from local emergency management. Public Adm Rev. 2010; 70(2): 279-288. DOI: 10.1111/j.1540-6210.2010.02134.x.

Agranoff R, McGuire M: Collaborative Public Management: New Strategies for Local Governments. Washington, DC: Georgetown University Press, 2003.

Jung K, Song M: Linking emergency management networks to disaster resilience: Bonding and bridging strategy in hierarchical or horizontal collaboration networks. Qual Quant. 2015; 49(4): 1465-1483. DOI: 10.1007/s11135-014-0092-x.

Bae Y, Joo YM, Won SY: Decentralization and collaborative disaster governance: Evidence from South Korea. Habitat Int. 2016; 52: 50-56. DOI: 10.1016/j.habitatint.2015.08.027.

Annelli JF: National incident management system: A multiagency approach to emergency response in the United States of America. Rev Sci Tech. 2006; 25(1): 223-231. Available at http://hdl.handle.net/10113/36325. Accessed May 23, 2022.

Dwyer I, Owen C: Emergency incident management: An evolving incident control system framework. J Pac Rim Psychol. 2009; 3(2): 66-75. DOI: 10.1375/prp.3.2.66.

Flanagan J: Joint emergency services interoperability programme: Working together saving lives. J Paramed Pract. 2014; 6(6): 284-287. DOI: 10.12968/jpar.2014.6.6.284.

Akter S, Wamba SF: Big data and disaster management: A systematic review and agenda for future research. Ann Oper Res. 2019; 283(1): 939-959. DOI: 10.1007/s10479-017-2584-2.

Kanchanasut K, Tunpan A, Awal MA, et al.: DUMBONET: A multimedia communication system for collaborative emergency response operations in disaster-affected areas. Int J Emerg Manag. 2007; 4(4): 670. DOI: 10.1504/IJEM.2007.015736.

Gralla E, Goentzel J, Walle BV: Understanding the information needs of field-based decision-makers in humanitarian response to sudden onset disasters. In Palen L, Büscher M, Comes T, et al. (eds.): Decision Support Systems. - ISCRAM2015 Conference Proceedings. Kristiansand, Norway, May 24-27, 2015: 7.

Nunavath V, Prinz A: Data sources handling for emergency management: Supporting information availability and accessibility for emergency responders. In Yamamoto S (ed.): Human Interface and the Management of Information: Supporting Learning, Decision-Making and Collaboration. Berlin: Springer, 2017: 240-259. DOI: 10.1007/978-3-319-58524-6_21.

Thornburg KM: Disruptive Emergent Systems in Disaster Response. Monterey: Naval Postgraduate School, 2019. Available at https://apps.dtic.mil/sti/citations/AD1073687. Accessed January 27, 2022.

Park S, Baek F, Sohn J, et al.: Computer vision-based estimation of flood depth in flooded-vehicle images. J Comput Civ Eng. 2021; 35(2): 04020072. DOI: 10.1061/(ASCE)CP.1943-5487.0000956.

Alizadeh Kharazi B, Behzadan AH: Flood depth mapping in street photos with image processing and deep neural networks. Comput Environ Urban Syst. 2021; 88: 101628. DOI: 10.1016/j.compenvurbsys.2021.101628.

Pavkovic B, Berbakov L, Vrane S, et al.: Situation awareness and decision support tools for response phase of emergency management: A short survey. In 2014 25th International Workshop on Database and Expert Systems Applications. 2014: 154-159. DOI: 10.1109/DEXA.2014.43.

Zhang Z, Hu H, Yin D, et al.: A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management. Int J Digit Earth. 2019; 12(11): 1364-1381. DOI: 10.1080/17538947.2018.1543363.

Unsworth J: Our Cultural Commonwealth: The Report of the American Council of Learned Societies Commission on Cyberinfrastructure for the Humanities and Social Sciences. New York: American Council of Learned Societies (ACLS), 2006. Available at http://hdl.handle.net/2142/189. Accessed October 7, 2022.

Armstrong MP, Wang S, Zhang Z: The internet of things and fast data streams: Prospects for geospatial data science in emerging information ecosystems. Cartogr Geogr Inf Sci. 2019; 46(1): 39-56. DOI: 10.1080/15230406.2018.1503973.

Wang S, Anselin L, Bhaduri B, et al.: CyberGIS software: A synthetic review and integration roadmap. Int J Geogr Inf Sci. 2013; 27(11): 2122-2145. DOI: 10.1080/13658816.2013.776049.

Li X, Yu S, Huang X, et al.: Do underserved and socially vulnerable communities observe more crashes? A spatial examination of social vulnerability and crash risks in Texas. Accid Anal Prev. 2022; 173: 106721. DOI: 10.1016/j.aap.2022.106721.

Li D, Chaudhary H, Zhang Z: Modeling spatiotemporal pattern of depressive symptoms caused by COVID-19 using social media data mining. Int J Environ Res Public Health. 2020; 17(14): 4988. DOI: 10.3390/ijerph17144988.

Zhang Z, Yin D, Virrantaus K, et al.: Modeling human activity dynamics: An object-class oriented space-time composite model based on social media and urban infrastructure data. Comput Urban Sci. 2021; 1(1): 1-13. DOI: 10.1007/s43762-021-00006-x.

Saldaña J: The Coding Manual for Qualitative Researchers. Thousand Oaks, CA: Sage, 2021.

Mehrotra S, Qiu X, Cao Z, et al.: Technological challenges in emergency response [guest editors’ introduction]. IEEE Intell Syst. 2013; 28(4): 5-8.

Lazo J, Peacock W: Social science research needs for the Hurricane forecast and warning system: An Introduction. Natural Hazards Review. 2007; 8: 43-44. DOI: 10.1061/(ASCE)1527-6988(2007)8:3(43).

Dominguez C: Task force hopes to identify flood risk in the woodlands with new gauges. The Courier of Montgomery County. July 8, 2017. Available at https://www.yourconroenews.com/neighborhood/woodlands/news/article/Task-force-hopes-to-identifyflood-potential-in-11275030.php. Accessed April 29, 2022.