research-article

Insights and Opportunities for HCI Research into Hurricane Risk Communication

Authors:

Rebecca Bicksler,

Kaira Ray Villanueva,

Melissa BicaAuthors Info & Claims

CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

Article No.: 325, Pages 1 - 13

https://doi.org/10.1145/3491102.3502101

Published: 29 April 2022 Publication History

Abstract

Communicating risk to the public in the lead-up to tropical storms has the potential to significantly reduce the impacts on both livelihood and property. While significant research has been conducted in the storm risk community on how people receive, seek, and utilize risk information, given the importance of computing technologies and social media in these activities, human-centered design stands to make important contributions to this area. Drawing on an extensive literature review and 48 interviews with hurricane experts and members of the public, this paper makes three contributions. First, we provide a broad overview of hurricane risk communication. We then offer a set of guiding insights to inform HCI research work in this domain. Finally, we identify 6 opportunities that future human centered design work might pursue. In sum, this paper offers an invitation and a starting point for HCI to take up the problem of hurricane risk communication.

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References

[1]

Anderson, T.J., Kogan, M., Bica, M., Palen, L., Anderson, K.M., Morss, R., Demuth, J., Lazrus, H., Wilhelmi, O. and Henderson, J., 2016, May. Far Far Away in Far Rockaway: Responses to Risks and Impacts during Hurricane Sandy through First-Person Social Media Narratives. In ISCRAM.

[2]

Andrews, C.J., 2002. Humble analysis: the practice of joint fact-finding. Greenwood Publishing Group.

[3]

Arias, E., Eden, H., Fischer, G., Gorman, A. and Scharff, E., 2000. Transcending the individual human mind—creating shared understanding through collaborative design. ACM Transactions on Computer-Human Interaction (TOCHI), 7(1), pp.84-113.

Digital Library

[4]

Árvai, J. (2014). The end of risk communication as we know it. Journal of Risk Research, 17(10), 1245–1249. https://doi.org/10.1080/13669877.2014.919519

[5]

Baker, E. J. (1991). Hurricane evacuation behavior. International Journal of Mass Emergencies and Disasters, 9(2), 287–310.

[6]

Baker, E. J., K. Broad, J. Czajkowski, R. Meyer, and B. Orlove, 2012: Risk perceptions and prepared-ness among mid-Atlantic residents in advance of Hurricane Sandy: Preliminary report. Risk Manage-ment and Decision Processes Center, The Wharton School, University of Pennsylvania Working Paper 2012-18, 42 pp. [Available online at http://opim.wharton.upenn.edu/risk/library/WP2012-18_EJB-etal_RiskPerceptions-Sandy.pdf.]

[7]

Barlas, Y. and Carpenter, S., 1990. Philosophical roots of model validation: two paradigms. System Dynamics Review, 6(2), pp.148-166.

[8]

Bennett, C.L. and Rosner, D.K., 2019, May. The Promise of Empathy: Design, Disability, and Knowing the" Other". In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

[9]

Bennett, C.L. and Keyes, O., 2020. What is the point of fairness? disability, AI and the complexity of justice. ACM SIGACCESS Accessibility and Computing, (125), pp.1-1.

[10]

Bernhardt, J., Snellings, J., Smiros, A., Bermejo, I., Rienzo, A. and Swan, C., 2019. Communicating hurricane risk with virtual reality: A pilot project. Bulletin of the American Meteorological Society, 100(10), pp.1897-1902.

[11]

Bica, M., Demuth, J.L., Dykes, J.E. and Palen, L., 2019, May. Communicating Hurricane Risks: Multi-Method Examination of Risk Imagery Diffusion. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-13).

[12]

Bica, M., Weinberg, J., and Palen, L. Achieving Accuracy through Ambiguity: The Interactivity of Risk Communication in Severe Weather Events. Computer Supported Cooperative Work (forthcoming).

Digital Library

[13]

Blaikie, P., Cannon, T., Davis, I. and Wisner, B., 2014. At risk: natural hazards, people's vulnerability and disasters. Routledge.

[14]

Blandford, A., 2019. HCI for health and wellbeing: Challenges and opportunities. International Journal of Human-Computer Studies, 131, pp.41-51.

Digital Library

[15]

Bostrom, A., Morss, R.E., Lazo, J.K., Demuth, J.L., Lazrus, H. and Hudson, R., 2016. A mental models study of hurricane forecast and warning production, communication, and decision-making. Weather, Climate, and Society, 8(2), pp.111-129.

[16]

Bostrom, A., Morss, R., Lazo, J.K., Demuth, J. and Lazrus, H., 2018. Eyeing the storm: How residents of coastal Florida see hurricane forecasts and warnings. International journal of disaster risk reduction, 30, pp.105-119.

[17]

Bowker, G.C. and Star, S.L., 2000. Sorting things out: Classification and its consequences. MIT press.

[18]

Bowser, G. C., & Cutter, S. L. 2015. Stay or Go? Examining Decision Making and Behavior in Hurricane Evacuations. Environment: Science and Policy for Sustainable Development, 57(6), 28–41. https://doi.org/10.1080/00139157.2015.1089145.

[19]

Broad, K., Leiserowitz, A., Weinkle, J. and Steketee, M., 2007. Misinterpretations of the “cone of uncertainty” in Florida during the 2004 hurricane season. Bulletin of the American Meteorological Society, 88(5), pp.651-668.

[20]

Bruno, N., 2011. The post-journalist's toolbox-Trends in digital storytelling. Journal of Science Communication, 10(4), p.C03.

[21]

Calero Valdez, A., Ziefle, M. and Verbert, K., 2016, September. HCI for recommender systems: the past, the present and the future. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 123-126).

[22]

Campbell,R., Beardsley, D., and Tokar, S. Impact-based Forecasting and Warning: Weather Ready Nations. World Meteorological Organization. Bulletin nº : Vol 67 (2) - 2018.

[23]

CFI Group. 2019. National Weather Service Web Monitor Results. Ann Arbor, MI: CFI Group.

[24]

Civan-Hartzler, A., McDonald, D.W., Powell, C., Skeels, M.M., Mukai, M. and Pratt, W., 2010, April. Bringing the field into focus: user-centered design of a patient expertise locator. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1675-1684).

[25]

Costikyan, G., 2013. Uncertainty in games. MIT Press.

Digital Library

[26]

Cox, J., House, D., & Lindell, M. (2013). Visualizing Uncertainty in Predicted Hurricane Tracks. International Journal for Uncertainty Quantification, 3(2), 143–156. https://doi.org/10.1615/int.j.uncertaintyquantification.2012003966

[27]

Cutter, S.L. and Emrich, C.T., 2006. Moral hazard, social catastrophe: The changing face of vulnerability along the hurricane coasts. The Annals of the American Academy of Political and Social Science, 604(1), pp.102-112.

[28]

Dailey, D. and Starbird, K., 2014. Journalists as crowdsourcerers: Responding to crisis by reporting with a crowd. Computer Supported Cooperative Work (CSCW), 23(4-6), pp.445-481.

Digital Library

[29]

Dash, N., & Gladwin, H. 2007. Evacuation Decision Making and Behavioral Responses: Individual and Household. Natural Hazards Review, 8(3), 69–77. https://doi.org/10.1061/ASCE1527-698820078:369

[30]

Demeritt, D. and Nobert, S., 2014. Models of best practice in flood risk communication and management. Environmental Hazards, 13(4), pp.313-328.

[31]

Demuth, J.L., Morss, R.E., Morrow, B.H. and Lazo, J.K., 2012. Creation and communication of hurricane risk information. Bulletin of the American Meteorological Society, 93(8), pp.1133-1145.

[32]

Demuth, J. L., Morss, R. E., Lazo, J. K., & Trumbo, C. 2016. The Effects of Past Hurricane Experiences on Evacuation Intentions through Risk Perception and Efficacy Beliefs: A Mediation Analysis. Weather, Climate, and Society, 8(4), 327–344. https://doi.org/10.1175/WCAS-D-15-0074.1

[33]

Demuth, J.L., Morss, R.E., Palen, L., Anderson, K.M., Anderson, J., Kogan, M., Stowe, K., Bica, M., Lazrus, H., Wilhelmi, O. and Henderson, J., 2018. “Sometimes da# beachlife ain't always da wave”: Understanding people's evolving hurricane risk communication, risk assessments, and responses using Twitter narratives. Weather, climate, and society, 10(3), pp.537-560.

[34]

Doherty, G., Coyle, D. and Matthews, M., 2010. Design and evaluation guidelines for mental health technologies. Interacting with computers, 22(4), pp.243-252.

[35]

Donovan, A., Borie, M. and Blackburn, S., 2019. Changing the paradigm for risk communication: integrating sciences to understand cultures. Background paper for UNISDR Global Assessment of Risk.

[36]

Dow, K. and Cutter, S.L., 1998. Crying wolf: Repeat responses to hurricane evacuation orders. Coastal Management, 26(4), pp.237-252.

[37]

EM-DAT, C.R.E.D., 2020. The international disaster database. Center for Research on the Epidemiology of Disasters. Available at: https://www. emdat. be [Accessed on 09/04/2020].

[38]

Epstein, D.A., Ping, A., Fogarty, J. and Munson, S.A., 2015, September. A lived informatics model of personal informatics. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (pp. 731-742).

[39]

Fischer, F., 2000. Citizens, experts, and the environment: The politics of local knowledge. Duke University Press.

[40]

Fritz, C.E. and Williams, H.B., 1957. The human being in disasters: A research perspective. The Annals of the American Academy of Political and Social Science, 309(1), pp.42-51.

[41]

Gardony, A.L., Martis, S.B., Taylor, H.A. and Brunyé, T.T., 2018. Interaction strategies for effective augmented reality geo-visualization: Insights from spatial cognition. Human–Computer Interaction, pp.1-43.

[42]

Gaver, W.W., Beaver, J. and Benford, S., 2003, April. Ambiguity as a resource for design. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 233-240).

[43]

Gitelman, L., 2013. Raw data is an oxymoron. MIT press.

[44]

Greis, M., Hullman, J., Correll, M., Kay, M. and Shaer, O., 2017, May. Designing for Uncertainty in HCI: When Does Uncertainty Help?. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems (pp. 593-600).

[45]

Greis, M., Joshi, A., Singer, K., Schmidt, A. and Machulla, T., 2018, April. Uncertainty visualization influences how humans aggregate discrepant information. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-12).

[46]

Gui, X., Kou, Y., Pine, K.H. and Chen, Y., 2017, May. Managing uncertainty: using social media for risk assessment during a public health crisis. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (pp. 4520-4533).

[47]

Gui, X., Kou, Y., Pine, K., Ladaw, E., Kim, H., Suzuki-Gill, E. and Chen, Y., 2018, April. Multidimensional risk communication: public discourse on risks during an emerging epidemic. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (pp. 1-14).

[48]

Guo, S., Du, F., Malik, S., Koh, E., Kim, S., Liu, Z., Kim, D., Zha, H. and Cao, N., 2019, May. Visualizing uncertainty and alternatives in event sequence predictions. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-12).

[49]

Heidmann, F. and Schiewe, J., 2017. Human-Computer Interaction in Geovisualization. i-com, 16(3), pp.203-203.

[50]

Hinds, J. and Joinson, A., 2017, July. Radicalization, the internet and cybersecurity: Opportunities and challenges for HCI. In International Conference on Human Aspects of Information Security, Privacy, and Trust (pp. 481-493). Springer, Cham.

[51]

Hooke, W.H. and Pielke Jr, R.A., 2000. Short-term weather prediction: an orchestra in need of a conductor. Prediction: Science, Decision-Making, and the Future of Nature, pp.61-83.

[52]

Huang, S.K., Lindell, M.K. and Prater, C.S., 2016. Who leaves and who stays? A review and statistical meta-analysis of hurricane evacuation studies. Environment and Behavior, 48(8), pp.991-1029.

[53]

Hullman, J., Kay, M., Kim, Y.S. and Shrestha, S., 2017. Imagining replications: Graphical prediction & discrete visualizations improve recall & estimation of effect uncertainty. IEEE transactions on visualization and computer graphics, 24(1), pp.446-456.

[54]

Hullman, J., Qiao, X., Correll, M., Kale, A. and Kay, M., 2018. In pursuit of error: A survey of uncertainty visualization evaluation. IEEE transactions on visualization and computer graphics, 25(1), pp.903-913.

[55]

IFRC, 2018. World Disasters Report 2018. Leaving no one behind. Report.

[56]

Ikonen, P. and Uskali, T., 2020. AUGMENTED REALITY AS NEWS. Immersive Journalism as Storytelling: Ethics, Production, and Design.

[57]

Jansen, Y., Dragicevic, P., Isenberg, P., Alexander, J., Karnik, A., Kildal, J., Subramanian, S. and Hornbæk, K., 2015, April. Opportunities and challenges for data physicalization. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (pp. 3227-3236).

[58]

Karvonen, A. and Brand, R., 2012. Technical expertise, sustainability, and the politics of specialized knowledge. In Environmental Governance (pp. 52-73). Routledge.

[59]

Kaufhold, M.A., Haunschild, J. and Reuter, C., 2020. Warning the Public: A Survey on Attitudes, Expectations and Use of Mobile Crisis Apps in Germany. In ECIS.

[60]

Kay, M., Kola, T., Hullman, J.R. and Munson, S.A., 2016, May. When (ish) is my bus? user-centered visualizations of uncertainty in everyday, mobile predictive systems. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 5092-5103).

[61]

Kelman, I., 2020. Disaster by Choice: How our actions turn natural hazards into catastrophes. Oxford University Press.

[62]

Keyes, O., 2018. The misgendering machines: Trans/HCI implications of automatic gender recognition. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), pp.1-22.

Digital Library

[63]

Keyes, O., Hutson, J. and Durbin, M., 2019, May. A mulching proposal: Analysing and improving an algorithmic system for turning the elderly into high-nutrient slurry. In Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems (pp. 1-11).

[64]

Kim, J., Oh, H. and Yeh, T., 2015, January. A Study to Empower Children to Design Movable Tactile Pictures for Children with Visual Impairments. In Proceedings of the Ninth International Conference on Tangible, Embedded, and Embodied Interaction (pp. 703-708).

[65]

Knowles, B., Blair, L., Coulton, P. and Lochrie, M., 2014, April. Rethinking plan A for sustainable HCI. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 3593-3596).

[66]

Korfmacher, K.S., 2001. The politics of participation in watershed modeling. Environmental management, 27(2), pp.161-176.

[67]

Kossin, J.P., Knapp, K.R., Olander, T.L. and Velden, C.S., 2020. Global increase in major tropical cyclone exceedance probability over the past four decades. Proceedings of the National Academy of Sciences, 117(22), pp.11975-11980.

[68]

Kox, T., 2015. Criteria affecting people's decision to take protective measures during winter storm XAVER on 5 December 2013. In ISCRAM.

[69]

Kox, T. and Thieken, A.H., 2017. To act or not to act? Factors influencing the general public's decision about whether to take protective action against severe weather. Weather, climate, and society, 9(2), pp.299-315.

[70]

Lachlan, K.A., Spence, P.R., Lin, X., Najarian, K.M. and Greco, M.D., 2014. Twitter use during a weather event: Comparing content associated with localized and nonlocalized hashtags. Communication Studies, 65(5), pp.519-534.

[71]

Lazo, J.K., Bostrom, A., Morss, R.E., Demuth, J.L. and Lazrus, H., 2015. Factors affecting hurricane evacuation intentions. Risk analysis, 35(10), pp.1837-1857.

[72]

Lazrus, H., Morrow, B.H., Morss, R.E. and Lazo, J.K., 2012. Vulnerability beyond stereotypes: Context and agency in hurricane risk communication. Weather, Climate, and Society, 4(2), pp.103-109.

[73]

Logan, J.R. and Xu, Z., 2015. Vulnerability to hurricane damage on the US Gulf Coast since 1950. Geographical review, 105(2), pp.133-155.

[74]

Mamykina, L., Heitkemper, E.M., Smaldone, A.M., Kukafka, R., Cole-Lewis, H., Davidson, P.G., Mynatt, E.D., Tobin, J.N., Cassells, A., Goodman, C. and Hripcsak, G., 2016. Structured scaffolding for reflection and problem solving in diabetes self-management: qualitative study of mobile diabetes detective. Journal of the American Medical Informatics Association, 23(1), pp.129-136.

[75]

McCreary, S., 1999. Resolving science-intensive public policy disputes: Reflections on the New York bight initiative. The consensus building handbook: A comprehensive guide to reaching agreement, pp.829-858.

[76]

McGlade, J., Bankoff, G., Abrahams, J., Cooper-Knock, S.J., Cotecchia, F., Desanker, P., Erian, W., Gencer, E., Gibson, L., Girgin, S. and Hirsch, F., 2019. Global Assessment Report on Disaster Risk Reduction 2019.

[77]

Metaxa-Kakavouli, D., Maas, P. and Aldrich, D.P., 2018. How social ties influence hurricane evacuation behavior. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), pp.1-16.

[78]

Miles, S.B., 2000. Towards policy relevant environmental modeling: contextual validity and pragmatic models. US Department of the Interior, US Geological Survey.

[79]

Mileti, D.S. and Peek, S.C., L.(2000). The social psychology of public response to warnings of a nuclear power plant accident. journal of Hazardous Materials, 75.

[80]

Morrow, B.H. and Lazo, J.K., 2015. Effective tropical cyclone forecast and warning communication: Recent social science contributions. Tropical Cyclone Research and Review, 4(1), pp.38-48.

[81]

Morrow, B.H., Lazo, J.K., Rhome, J. and Feyen, J., 2015. Improving storm surge risk communication: Stakeholder perspectives. Bulletin of the American Meteorological Society, 96(1), pp.35-48.

[82]

Morss, R.E., Cuite, C.L., Demuth, J.L., Hallman, W.K. and Shwom, R.L., 2018. Is storm surge scary? The influence of hazard, impact, and fear-based messages and individual differences on responses to hurricane risks in the USA. International journal of disaster risk reduction, 30, pp.44-58.

[83]

Morss, R.E., Demuth, J.L., Lazo, J.K., Dickinson, K., Lazrus, H. and Morrow, B.H., 2016. Understanding public hurricane evacuation decisions and responses to forecast and warning messages. Weather and Forecasting, 31(2), pp.395-417.

[84]

Morss, R.E., Cuite, C.L., Demuth, J.L., Hallman, W.K. and Shwom, R.L., 2018. Is storm surge scary? The influence of hazard, impact, and fear-based messages and individual differences on responses to hurricane risks in the USA. International journal of disaster risk reduction, 30, pp.44-58.

[85]

Muller, M.J., 2003. Participatory design: the third space in HCI. Handbook of HCI. Mahway NJ USA: Erlbaum.

[86]

Myers, Laura. NOAA Weather Information and Dissemination All Hazards Stakeholder Project: Phase 1. University of Alabama: Center for Advanced Public Safety. (nd)

[87]

National Hurricane Center, 2020. Definition of the NHC Track Forecast Cone. Online at: https://www.nhc.noaa.gov/aboutcone.shtml. Accessed 9/13/2020.

[88]

National Oceanographic and Atmospheric Administration, 2016. Risk Communication and Behavior. Best Practices and Research Findings.

[89]

National Research Council. 1989. Improving Risk Communication Committee on Risk Perception and Communication, National Research Council. National Academy Press. http://www.nap.edu/catalog/1189.html

[90]

National Weather Service, 2020. Hurricane Threats and Impacts Graphics FAQ. Online at: https://www.weather.gov/media/srh/tropical/HTI_Explanation.pdf. Accessed 9/13/2020.

[91]

National Wildlife Federation. (2006). Increasing vulnerability to hurricanes: Global warming's wake-up call for the U.S. Gulf and Atlantic coasts. Retrieved from http://www.nwf.org/∼/media/PDFs/Global-Warming/Hurricanes_FNL_LoRes.ashx.

[92]

NOAA National Ocean Service Coastal Services Center, 2013. Storm Surge Marketing: Audience Analysis Final Report. Arlington, VA: Eastern Research Group, Inc. (2013).

[93]

Norton, B., 1996. Integration or reduction: Two approaches to environmental values. In A. Light and E. Katz (Ed.), Environmental Pragmatism (pp. 105-138). New York:Routledge.

[94]

Ogbonnaya-Ogburu, I.F., Smith, A.D., To, A. and Toyama, K., 2020, April. Critical Race Theory for HCI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16).

[95]

Oreskes, N., 1998. Evaluation (not validation) of quantitative models. Environmental health perspectives, 106(suppl 6), pp.1453-1460.

[96]

Padilla, L.M., Ruginski, I.T. and Creem-Regehr, S.H., 2017. Effects of ensemble and summary displays on interpretations of geospatial uncertainty data. Cognitive research: principles and implications, 2(1), pp.1-16.

[97]

Paehlke, R.C. and Paehlke, R., 2004. Democracy's dilemma: environment, social equity, and the global economy. MIT Press.

[98]

Palen, L., Anderson, K.M., Mark, G., Martin, J., Sicker, D., Palmer, M. and Grunwald, D., 2010. A vision for technology-mediated support for public participation & assistance in mass emergencies & disasters. ACM-BCS Visions of Computer Science 2010, pp.1-12.

[99]

Palen, L. and Anderson, K.M., 2016. Crisis informatics—New data for extraordinary times. Science, 353(6296), pp.224-225.

[100]

Palen, L. and Hughes, A.L., 2018. Social media in disaster communication. In Handbook of disaster research (pp. 497-518). Springer, Cham.

[101]

Park, E.S., Yoon, D.K. and Choi, Y.W., 2019. Leave no one behind: Experiences of persons with disability after the 2017 Pohang earthquake in South Korea. International Journal of Disaster Risk Reduction, 40, p.101261.

[102]

Peters, R.G., Covello, V.T. and McCallum, D.B., 1997. The determinants of trust and credibility in environmental risk communication: An empirical study. Risk analysis, 17(1), pp.43-54.

[103]

Pine, K.H., Lee, M., Whitman, S.A., Chen, Y. and Henne, K., 2021, May. Making Sense of Risk Information amidst Uncertainty: Individuals’ Perceived Risks Associated with the COVID-19 Pandemic. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-15).

[104]

Prasad, S., 2013. An Examination of Hurricane Vulnerability of the US Northeast. Population, 506, p.191.

[105]

Radford, L., Senkbeil, J. C., & Rockman, M. (2013). Suggestions for alternative tropical cyclone warning graphics in the USA. Disaster Prevention and Management: An International Journal, 22(3), 192–209. https://doi.org/10.1108/DPM-06-2012-0064

[106]

Radu, I. and Schneider, B., 2019, May. What Can We Learn from Augmented Reality (AR)? Benefits and Drawbacks of AR for Inquiry-based Learning of Physics. In 2019 CHI Conference on Human Factors in Computing Systems Proceedings (CHI 2019).

[107]

Reuter, C., Hughes, A.L. and Kaufhold, M.A., 2018. Social media in crisis management: An evaluation and analysis of crisis informatics research. International Journal of Human–Computer Interaction, 34(4), pp.280-294.

[108]

Rickard, L. N., Schuldt, J. P., Eosco, G. M., Scherer, C. W., & Daziano, R. A. (2017). The Proof is in the Picture: The Influence of Imagery and Experience in Perceptions of Hurricane Messaging. Weather, Climate, and Society, 9, 471–485. https://doi.org/10.1175/WCAS-D-16-0048.1.

[109]

Ruginski, I.T., Boone, A.P., Padilla, L.M., Liu, L., Heydari, N., Kramer, H.S., Hegarty, M., Thompson, W.B., House, D.H. and Creem-Regehr, S.H., 2016. Non-expert interpretations of hurricane forecast uncertainty visualizations. Spatial Cognition & Computation, 16(2), pp.154-172.

[110]

Sarewitz, D.R. and Byerly, R. eds., 2000. Prediction: science, decision making, and the future of nature. Island Press.

[111]

Schulze, K., Lorenz, D.F., Wenzel, B. and Voss, M., 2015. Disaster Myths and their Relevance for Warning Systems. In ISCRAM.

[112]

Shaer, O., Nov, O., Okerlund, J., Balestra, M., Stowell, E., Westendorf, L., Pollalis, C., Davis, J., Westort, L. and Ball, M., 2016, May. Genomix: A novel interaction tool for self-exploration of personal genomic data. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (pp. 661-672).

[113]

Sherman-Morris, K., Antonelli, K. B., & Williams, C. C. (2015). Measuring the Effectiveness of the Graphical Communication of Hurricane Storm Surge Threat. Weather, Climate, and Society, 7(1), 69–82. https://doi.org/10.1175/WCAS-D-13-00073.1

[114]

Simis, M.J., Madden, H., Cacciatore, M.A. and Yeo, S.K., 2016. The lure of rationality: Why does the deficit model persist in science communication?. Public understanding of science, 25(4), pp.400-414.

[115]

So, M., Franks, J.L., Cree, R.A. and Leeb, R.T., 2019. An evaluation of the literacy demands of online natural disaster preparedness materials for families. Disaster medicine and public health preparedness, pp.1-10.

[116]

Soden, R., Sprain, L. and Palen, L., 2017, May. Thin Grey Lines: Confrontations With Risk on Colorado's Front Range. In CHI (pp. 2042-2053).

[117]

Soden, R. and Lord, A., 2018. Mapping silences, reconfiguring loss: Practices of damage assessment & repair in post-earthquake Nepal. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), pp.1-21.

[118]

Soden, R. and Palen, L., 2018. Informating crisis: Expanding critical perspectives in crisis informatics. Proceedings of the ACM on human-computer interaction, 2(CSCW), pp.1-22.

[119]

Soden, R., Devendorf, L., Wong, R.Y., Chilton, L.B., Light, A. and Akama, Y., 2020, April. Embracing Uncertainty in HCI. In Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-8).

[120]

Spence, P.R., Lachlan, K., Burke, J.M. and Seeger, M.W., 2007. Media use and information needs of the disabled during a natural disaster. Journal of health care for the poor and underserved, 18(2), pp.394-404.

[121]

Starbird, K. and Stamberger, J., 2010, May. Tweak the tweet: Leveraging microblogging proliferation with a prescriptive syntax to support citizen reporting. In Proceedings of the 7th International ISCRAM Conference (Vol. 1, pp. 1-5). Seattle, WA: ISCRAM.

[122]

Starbird, K., Palen, L., Liu, S. B., Vieweg, S., Hughes, A., Schram, A., Anderson, K. M., Bagdouri, M., White, J., McTaggart, C., & Schenk, C. (2011). Promoting structured data in citizen communications during disaster response: An account of strategies for diffusion of the “Tweak the Tweet” syntax. In Crisis Information Management: Communication and Technologies (pp. 43–63). Elsevier Ltd. https://doi.org/10.1016/B978-1-84334-647-0.50003-5

[123]

Starbird, K., Dailey, D., Mohamed, O., Lee, G. and Spiro, E.S., 2018, April. Engage early, correct more: How journalists participate in false rumors online during crisis events. In Proceedings of the 2018 CHI conference on human factors in computing systems (pp. 1-12).

[124]

Stein, R.M., Dueñas‐Osorio, L. and Subramanian, D., 2010. Who evacuates when hurricanes approach? The role of risk, information, and location. Social science quarterly, 91(3), pp.816-834.

[125]

Tapia, A.H. and LaLone, N.J., 2019. Crowdsourcing investigations: Crowd participation in identifying the bomb and bomber from the Boston marathon bombing. In Crowdsourcing: Concepts, Methodologies, Tools, and Applications (pp. 1433-1450). IGI Global.

[126]

Van der Vink, G., Allen, R.M., Chapin, J., Crooks, M., Fraley, W., Krantz, J., Lavigne, A., LeCuyer, A., MacColl, E.K., Morgan, W.J. and Ries, B., 1998. Why the United States is becoming more vulnerable to natural disasters. Eos, Transactions American Geophysical Union, 79(44), pp.533-537.

[127]

Villegas, J., Matyas, C., Srinivasan, S., Cahyanto, I., Thapa, B. and Pennington-Gray, L., 2013. Cognitive and affective responses of Florida tourists after exposure to hurricane warning messages. Natural hazards, 66(1), pp.97-116.

[128]

Van Willigen, M., Edwards, T., Edwards, B. and Hessee, S., 2002. Riding out the storm: Experiences of the physically disabled during Hurricanes Bonnie, Dennis, and Floyd. Natural Hazards Review, 3(3), pp.98-106.

[129]

Voinov, A. and Gaddis, E.J.B., 2008. Lessons for successful participatory watershed modeling: a perspective from modeling practitioners. Ecological modelling, 216(2), pp.197-207.

[130]

Wachinger, G., Renn, O., Begg, C. and Kuhlicke, C., 2013. The risk perception paradox—implications for governance and communication of natural hazards. Risk analysis, 33(6), pp.1049-1065.

[131]

Whatmore, S.J. and Landström, C., 2011. Flood apprentices: an exercise in making things public. Economy and society, 40(4), pp.582-610.

[132]

White, J.I. and Palen, L., 2015, February. Expertise in the wired wild west. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (pp. 662-675).

[133]

Wu, H.-C., Lindell, M. K., Prater, C. S., & Samuelson, C. D. (2014). Effects of Track and Threat Information on Judgments of Hurricane Strike Probability. Risk Analysis, 34(6), 1025–1039. https://doi.org/10.1111/risa.12128

[134]

Zade, H., Shah, K., Rangarajan, V., Kshirsagar, P., Imran, M., & Starbird, K. (2018). From Situational Awareness to Actionability. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1–18. https://doi.org/10.1145/3274464

Digital Library

Cited By

Jit SSpinney JChandra PChilton LSoden R(2024)Writing out the Storm: Designing and Evaluating Tools for Weather Risk MessagingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641926(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641926
Simko LSri Ramulu HKohno TAcar Y(2023)The Use and Non-Use of Technology During HurricanesProceedings of the ACM on Human-Computer Interaction10.1145/36102157:CSCW2(1-54)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610215
Prabhudesai SYang LAsthana SHuan XLiao QBanovic N(2023)Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision MakingProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584033(379-396)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584033

Index Terms

Insights and Opportunities for HCI Research into Hurricane Risk Communication
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. HCI design and evaluation methods
    2. HCI theory, concepts and models
  2. Interaction design
    1. Interaction design process and methods
      1. User centered design
    2. Interaction design theory, concepts and paradigms

Index terms have been assigned to the content through auto-classification.

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CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems

April 2022

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ISBN:9781450391573

DOI:10.1145/3491102

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Toyota Research Institute, USA
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Microsoft Research, USA
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University of Glasgow, UK
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University of Tokyo, Japan

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Jit SSpinney JChandra PChilton LSoden R(2024)Writing out the Storm: Designing and Evaluating Tools for Weather Risk MessagingProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3641926(1-16)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3641926
Simko LSri Ramulu HKohno TAcar Y(2023)The Use and Non-Use of Technology During HurricanesProceedings of the ACM on Human-Computer Interaction10.1145/36102157:CSCW2(1-54)Online publication date: 4-Oct-2023
https://dl.acm.org/doi/10.1145/3610215
Prabhudesai SYang LAsthana SHuan XLiao QBanovic N(2023)Understanding Uncertainty: How Lay Decision-makers Perceive and Interpret Uncertainty in Human-AI Decision MakingProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584033(379-396)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584033

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