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Articles

Correlating Twitter Use with Disaster Resilience at Two Spatial Scales: A Case Study of Hurricane Sandy

ORCID Icon, ORCID Icon & ORCID Icon
Pages 1-20 | Received 15 Oct 2021, Accepted 02 Jan 2023, Published online: 09 Jan 2023

References

  • Adger, W. N., T. P. Hughes, C. Folke, S. R. Carpenter, and J. Rockström. 2005. “Social-Ecological Resilience to Coastal Disasters.” Science 309 (5737): 1036–1039. doi:10.1126/science.1112122.
  • Almatar, G. M., H. S. Alazmi, L. Li, and E. A. Fox. 2020. “Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait.” ISPRS International Journal of Geo-Information 9 (12): 702. doi:10.3390/ijgi9120702.
  • Battersby, S. E., M. E. Hodgson, and J. Wang. 2012. “Spatial Resolution Imagery Requirements for Identifying Structure Damage in a Hurricane Disaster.” Photogrammetric Engineering & Remote Sensing 78 (6): 625–635. doi:10.14358/PERS.78.6.625.
  • Bian, L., and S. J. Walsh. 1993. “Scale Dependencies of Vegetation and Topography in a Mountainous Environment of Montana.” The Professional Geographer 45 (1): 1–11. doi:10.1111/j.0033-0124.1993.00001.x.
  • Blake, E. S., T. B. Kimberlain, R. J. Berg, J. P. Cangialosi, and J. L. Beven II. 2013. Tropical Cyclone Report: Hurricane Sandy (al182012). National Hurricane Center: National Oceanic and Atmospheric Administration, National Hurricane Center 22–29 October 2012 No. AL182012
  • Cai, H., N. S. N. Lam, Y. Qiang, L. Zou, R. M. Correll, and V. Mihunov. 2018. “A Synthesis of Disaster Resilience Measurement Methods and Indices.” International Journal of Disaster Risk Reduction 31: 844–855. doi:10.1016/j.ijdrr.2018.07.015.
  • Cai, H., N. S. N. Lam, L. Zou, Y. Qiang, and K. Li. 2016. “Assessing Community Resilience to Coastal Hazards in the Lower Mississippi River Basin.” Water 8 (2): 46. doi:10.3390/w8020046.
  • Cheng, T., and M. Adepeju. 2014. “Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection.” Plos One 9 (6): e100465. doi:10.1371/journal.pone.0100465.
  • Chen, D., D. A. Stow, and P. Gong. 2004. “Examining the Effect of Spatial Resolution and Texture Window Size on Classification Accuracy: An Urban Environment Case.” International Journal of Remote Sensing 25 (11): 2177–2192. doi:10.1080/01431160310001618464.
  • Cui, P., and D. Li. 2019. “Measuring the Disaster Resilience of an Urban Community Using ANP-FCE Method from the Perspective of Capitals.” Social Science Quarterly 100 (6): 2059–2077. doi:10.1111/ssqu.12699.
  • Cutter, S. L., C. G. Burton, and C. T. Emrich. 2010. “Disaster Resilience Indicators for Benchmarking Baseline Conditions.” Journal of Homeland Security and Emergency Management 7 (1). doi:10.2202/1547-7355.1732.
  • Earle, P. S., D. C. Bowden, and M. Guy. 2012. “Twitter Earthquake Detection: Earthquake Monitoring in a Social World.” Annals of Geophysics 54 (6). doi:10.4401/ag-5364.
  • Federal Emergency Management Agency (FEMA). 2006. Principles of Emergency Management: Independent Study Manual [online]. Homeland Security Digital Library, Accessed 13 Feb 2021. https://www.hsdl.org/?abstract&did=
  • Federal Emergency Management Agency (FEMA). 2017. Remembering Sandy Five Years Later, Accessed 5 May 2021. https://www.fema.gov/press-release/20210318/remembering-sandy-five-years-later
  • Federal Emergency Management Agency (FEMA). 2021. “OpenFema Data Sets” Accessed 5 May 2021. https://www.fema.gov/about/openfema/data-sets
  • Hutto, C., and E. Gilbert. 2014. “VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text.” Proceedings of the International AAAI Conference on Web and Social Media, 8(1), 216-225.
  • Jaidka, K., S. Giorgi, H. A. Schwartz, M. L. Kern, L. H. Ungar, and J. C. Eichstaedt. 2020. “Estimating Geographic Subjective Well-Being from Twitter: A Comparison of Dictionary and Data-Driven Language Methods.” Proceedings of the National Academy of Sciences 117 (19): 10165–10171. doi:10.1073/pnas.1906364117.
  • Jiang, Y., Z. Li, and X. Ye. 2019. “Understanding Demographic and Socioeconomic Biases of Geotagged Twitter Users at the County Level.” Cartography and Geographic Information Science 46 (3): 228–242. doi:10.1080/15230406.2018.1434834.
  • Jongman, B., J. Wagemaker, B. R. Romero, and E. C. De Perez. 2015. “Early Flood Detection for Rapid Humanitarian Response: Harnessing Near Real-Time Satellite and Twitter Signals.” ISPRS International Journal of Geo-Information 4 (4): 2246–2266. doi:10.3390/ijgi4042246.
  • Kent, J. D., and H. T. Capello Jr. 2013. “Spatial Patterns and Demographic Indicators of Effective Social Media Content During the Horsethief Canyon Fire of 2012.” Cartography and Geographic Information Science 40 (2): 78–89. doi:10.1080/15230406.2013.776727.
  • Knapp, K. R., M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann. 2010. “The International Best Track Archive for Climate Stewardship (IBTrAcs): Unifying Tropical Cyclone Data.” Bulletin of the American Meteorological Society 91 (3): 363–376. doi:10.1175/2009BAMS2755.1.
  • Knowles, J. T., and M. Leitner. 2007. “Visual Representations of the Spatial Relationship Between Bermuda High Strengths and Hurricane Tracks.” Cartographic Perspectives 56: 37–51. doi:10.14714/CP56.316.
  • Kruse, F. A., C. C. Clasen, A. M. Kim, and S. C. Carlisle. 2012. “Effects of Spatial and Spectral Resolution on Remote Sensing for Disaster Response.” Presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 7086–7089.
  • Kwan, M. P. 2012. “The Uncertain Geographic Context Problem.” Annals of the Association of American Geographers 102 (5): 958–968. doi:10.1080/00045608.2012.687349.
  • Lam, N. S. N. 2012. “Geospatial Methods for Reducing Uncertainties in Environmental Health Risk Assessment: Challenges and Opportunities.” Annals of the Association of American Geographers 102 (5): 942–950. doi:10.1080/00045608.2012.674900.
  • Lam, N. 2019. “Resolution.” Geographic Information Science & Technology Body of Knowledge. https://gistbok.ucgis.org/bok-topics/resolution
  • Lam, N. S. N., H. Arenas, P. L. Brito, and K. B. Liu. 2014. “Assessment of Vulnerability and Adaptive Capacity to Coastal Hazards in the Caribbean Region.” Journal of Coastal Research 70: 473–478. doi:10.2112/SI70-080.1.
  • Lam, N. S. N., W. Cheng, L. Zou, and H. Cai. 2018. “Effects of Landscape Fragmentation on Land Loss.” Remote sensing of environment 209: 253–262. doi:10.1016/j.rse.2017.12.034.
  • Lam, N. S. N., C. David, Q. Dale, B. Daniel, and M. Robert. 2004. “Scale.” In A Research Agenda for Geographic Information Science edited by Robert B. McMaster 7 E. Lynn Usery, 93–128. Boca Raton: CRC Press.
  • Lam, N. S. N., Y. Qiang, H. Arenas, P. Brito, and K. Liu. 2015. “Mapping and Assessing Coastal Resilience in the Caribbean Region.” Cartography and Geographic Information Science 42 (4): 315–322. doi:10.1080/15230406.2015.1040999.
  • Lam, N. S. N., and D. A. Quattrochi. 1992. “On the Issues of Scale, Resolution, and Fractal Analysis in the Mapping Sciences.” The Professional Geographer 44 (1): 88–98. doi:10.1111/j.0033-0124.1992.00088.x.
  • Lam, N. S. N., M. Reams, K. Li, C. Li, and L. P. Mata. 2016. “Measuring Community Resilience to Coastal Hazards Along the Northern Gulf of Mexico.” Natural Hazards Review 17 (1): 04015013. doi:10.1061/(ASCE)NH.1527-6996.0000193.
  • Li, C. 2013. “Community resilience to coastal hazards: an analysis of two geographical scales in Louisiana.” Master thesis, Louisiana State University.
  • Li, L., M. F. Goodchild, and B. Xu. 2013. “Spatial, Temporal, and Socioeconomic Patterns in the Use of Twitter and Flickr.” Cartography and Geographic Information Science 40 (2): 61–77. doi:10.1080/15230406.2013.777139.
  • Li, X., N. S. N. Lam, Y. Qiang, K. Li, L. Yin, S. Liu, and W. Zheng. 2016. “Measuring County Resilience After the 2008 Wenchuan Earthquake.” International Journal of Disaster Risk Science 7 (4): 393–412. doi:10.1007/s13753-016-0109-2.
  • Li, K., N. S. N. Lam, Y. Qiang, L. Zou, and H. Cai. 2015. “A Cyberinfrastructure for Community Resilience Assessment and Visualization.” Cartography and Geographic Information Science 42 (sup1): 34–39. doi:10.1080/15230406.2015.1060113.
  • Liu, X., B. Kar, C. Zhang, and D. M. Cochran. 2019. “Assessing Relevance of Tweets for Risk Communication.” International Journal of Digital Earth 12 (7): 781–801. doi:10.1080/17538947.2018.1480670.
  • Madichetty, S., and S. Muthukumarasamy. 2020. “Classifying Informative and Non-Informative Tweets from the Twitter by Adapting Image Features During Disaster.” Multimedia Tools and Applications 79 (39–40): 28901–28923. doi:10.1007/s11042-020-09343-1.
  • Malik, M., H. Lamba, C. Nakos, and J. Pfeffer. 2015. “Population Bias in Geotagged Tweets.” Proceedings of the International AAAI Conference on Web and Social Media 9 (4): 18–27. doi:10.1609/icwsm.v9i4.14688.
  • Mihunov, V. V., and N. S. N. Lam. 2020. “Modeling the Dynamics of Drought Resilience in South-Central United States Using a Bayesian Network.” Applied Geography 120: 102224. doi:10.1016/j.apgeog.2020.102224.
  • Mihunov, V. V., N. S. N. Lam, L. Zou, Z. Wang, and K. Wang. 2020. “Use of Twitter in Disaster Rescue: Lessons Learned from Hurricane Harvey.” International Journal of Digital Earth 13 (12): 1454–1466. doi:10.1080/17538947.2020.1729879.
  • Moreno, J., A. Lara, and M. Torres. 2019. “Community Resilience in Response to the 2010 Tsunami in Chile: The Survival of a Small-Scale Fishing Community.” International Journal of Disaster Risk Reduction 33: 376–384. doi:10.1016/j.ijdrr.2018.10.024.
  • National Academies of Sciences, Engineering, and Medicine. 2019. Building and Measuring Community Resilience: Actions for Communities and the Gulf Research Program. Washington, DC: The National Academies Press. doi:10.17226/25383.
  • National Oceanic and Atmospheric Administration (NOAA). 2012. “Hurricane SANDY” National Hurricane Center, NOAA. Accessed September 22, 2017. http://www.nhc.noaa.gov/archive/2012/al18/al182012.public.029.shtml?
  • National Research Council. 2012. Disaster Resilience: A National Imperative. Washington, DC: The National Academies Press. doi:10.17226/13457.
  • Niles, M. T., B. F. Emery, A. J. Reagan, P. S. Dodds, and C. M. Danforth. 2019. “Social Media Usage Patterns During Natural Hazards.” Plos One 14 (2): e0210484. doi:10.1371/journal.pone.0210484.
  • Openshaw, S. 1984. “Ecological Fallacies and the Analysis of Areal Census Data.” Environment and Planning A: Economy and Space 16 (1): 17–31. doi:10.1068/a160017.
  • Petkova, E. P., J. Beedasy, E. J. Oh, J. J. Sury, E. M. Sehnert, W. -Y. Tsai, and M. J. Reilly. 2018. “Long-Term Recovery from Hurricane Sandy: Evidence from a Survey in New York City.” Disaster Medicine and Public Health Preparedness 12 (2): 172–175. doi:10.1017/dmp.2017.57.
  • Pourebrahim, N., S. Sultana, J. Edwards, A. Gochanour, and S. Mohanty. 2019. “Understanding Communication Dynamics on Twitter During Natural Disasters: A Case Study of Hurricane Sandy.” International Journal of Disaster Risk Reduction 37: 101176. doi:10.1016/j.ijdrr.2019.101176.
  • Schreck, C., and National Center for Atmospheric Research Staff (Eds). “Last Modified 06 Nov 2013. “The Climate Data Guide: IBTrAcs: Tropical Cyclone Best Track Data” Accessed 1 April 2021. https://climatedataguide.ucar.edu/climate-data/ibtracs-tropical-cyclone-best-track-data
  • Shan, S., F. Zhao, Y. Wei, and M. Liu. 2019. “Disaster Management 2.0: A Real-Time Disaster Damage Assessment Model Based on Mobile Social Media Data—a Case Study of Weibo (Chinese Twitter).” Safety Science 115: 393–413. doi:10.1016/j.ssci.2019.02.029.
  • Sloan, L., J. Morgan, P. Burnap, and M. Williams. 2015. “Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data.” Plos One 10 (3): e0115545. doi:10.1371/journal.pone.0115545.
  • Tang, Z., L. Zhang, F. Xu, and H. Vo. 2015. “Examining the Role of Social Media in California’s Drought Risk Management in 2014.” Natural Hazards 79 (1): 171–193. doi:10.1007/s11069-015-1835-2.
  • Tsou, M. H., H. Zhang, and C. T. Jung, 2017. Identifying Data Noises, User Biases, and System Errors in Geo-Tagged Twitter Messages ( Tweets). arXiv:1712.02433.
  • Vongkusolkit, J., and Q. Huang. 2021. “Situational Awareness Extraction: A Comprehensive Review of Social Media Data Classification During Natural Hazards.” Annals of GIS 27 (1): 5–28. doi:10.1080/19475683.2020.1817146.
  • Wang, Z., N. S. N. Lam, N. Obradovich, and X. Ye. 2019. “Are Vulnerable Communities Digitally Left Behind in Social Responses to Natural Disasters? Evidence from Hurricane Sandy with Twitter Data.” Applied Geography 108: 1–8. doi:10.1016/j.apgeog.2019.05.001.
  • Wang, K., N. S. N. Lam, L. Zou, and V. Mihunov. 2021. “Twitter Use in Hurricane Isaac and Its Implications for Disaster Resilience.” ISPRS International Journal of Geo-Information 10 (3): 116. doi:10.3390/ijgi10030116.
  • Zou, L., N. S. N. Lam, H. Cai, and Y. Qiang. 2018. “Mining Twitter Data for Improved Understanding of Disaster Resilience.” Annals of the American Association of Geographers 0 (0): 1–20.
  • Zou, L., N. S. N. Lam, S. Shams, H. Cai, M. A. Meyer, S. Yang, K. Lee, S. J. Park, and M. A. Reams. 2019. “Social and Geographical Disparities in Twitter Use During Hurricane Harvey.” International Journal of Digital Earth 12 (11): 1300–1318. doi:10.1080/17538947.2018.1545878.