Assessing Community Needs in Disasters: Transfer Learning for Fusing Limited Georeferenced Data from Crowdsourced Applications on the Community Level
Publication: Journal of Management in Engineering
Volume 40, Issue 6
Abstract
The effectiveness of infrastructure resilience relies on the seamless extraction of information, timely acquisition of critical knowledge, and heightened situational awareness. The ongoing utilization of digital citizen communication through social media with response organizations during disasters remains a valuable avenue for disseminating information, ensuring the effective utilization of public resources in emergency response to crisis events. Public agencies can use this information to examine community sentiments and discussions to assess, determine, and prioritize critical areas in need of assistance. However, there are limitations on harnessing precise geolocation information from social media, as well as a lack of mitigating bias of machine learning models used during such events. These limitations can restrict emergency management personnel’s ability to locate and promptly delineate actionable insights. Here, we propose a semisupervised machine learning model that utilizes approaches such as transfer learning, topic modeling (i.e., Latent Dirichlet Allocation), and natural language processing to augment data from historical and current social media posts (i.e., Twitter) with community-driven application alerts (i.e., Waze) to achieve further evidence on the location and context of emergency events. The model is designed to also mitigate machine learning bias using the Wells–Du Bois protocol. A framework was developed for this process and is illustrated through a case study on Hurricane Ian and three previous hurricanes that occurred in Florida. This fusion provides increased situational awareness and may enhance the speed of emergency response. This study establishes a foundation for equitable, real-time crisis event detection, expanding organizations’ response capacity in allocating resources and reducing harmful effects of disaster, particularly within public infrastructure systems.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions. The Waze and Twitter data are restricted due to the privacy of the API and feed access of the data streams.
Acknowledgments
This study is supported by the Georgia Department of Transportation under Grant No. 20–13. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Georgia Department of Transportation.
References
Adeola, F. O., and J. S. Picou. 2017. “Hurricane Katrina-linked environmental injustice: Race, class, and place differentials in attitudes.” Disasters 41 (2): 228–257. https://doi.org/10.1111/disa.12204.
Albahri, A. S., et al. 2023. “A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion.” Inf. Fusion 96 (Jun): 156–191. https://doi.org/10.1016/j.inffus.2023.03.008.
Alexander, D. E. 2014. “Social media in disaster risk reduction and crisis management.” Sci. Eng. Ethics 20 (3): 717–733. https://doi.org/10.1007/s11948-013-9502-z.
Amin-Naseri, M., P. Chakraborty, A. Sharma, S. B. Gilbert, and M. Hong. 2018. “Evaluating the reliability, coverage, and added value of crowdsourced traffic incident reports from Waze.” Transp. Res. Rec. 2672 (43): 34–43. https://doi.org/10.1177/0361198118790619.
Athuraliya, C. D., M. K. H. Gunasekara, S. Perera, and S. Suhothayan. 2015. “Real-time natural language processing for crowdsourced road traffic alerts.” In Proc., 2015 15th Int. Conf. on Advances in ICT for Emerging Regions (ICTer), 58–62. New York: IEEE.
Awasthi, P., A. Beutel, M. Kleindessner, J. Morgenstern, and X. Wang. 2021. “Evaluating fairness of machine learning models under uncertain and incomplete information.” In Proc., FAccT ’21: Proc., 2021 ACM Conf. on Fairness, Accountability, and Transparency, 206–214. New York: IEEE.
Azhar, S., Z. Riaz, and D. Robinson. 2019. “Integration of social media in day-to-day operations of construction firms.” J. Manage. Eng. 35 (1): 06018003. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000660.
Bodenreider, C., L. Wright, O. Barr, K. Xu, and S. Wilson. 2019. “Assessment of social, economic, and geographic vulnerability pre-and post-Hurricane Harvey in Houston, Texas.” Environ. Justice 12 (4): 182–193. https://doi.org/10.1089/env.2019.0001.
Bozeman, J. F. 2024. “Bolstering integrity in environmental data science and machine learning requires understanding socioecological inequity.” Front. Environ. Sci. Eng. 18 (5): 1–9. https://doi.org/10.1007/s11783-024-1825-2.
Brunila, M., R. Zhao, A. Mircea, S. Lumley, and R. Sieber. 2021. “Bridging the gap between supervised classification and unsupervised topic modelling for social-media assisted crisis management.” Preprint, submitted March 22, 2021. http://arxiv.org/abs/2103.11835.
Chair, S., M. Charrad, and N. B. B. Saoud. 2019. “Towards a social media-based framework for disaster communication.” Procedia Comput. Sci. 164 (Dec): 271–278. https://doi.org/10.1016/j.procs.2019.12.183.
Chen, X., and T. Wang. 2017. “Combining active learning and semi-supervised learning by using selective label spreading.” In Proc., 2017 IEEE Int. Conf. on Data Mining Workshops (ICDMW), 850–857. New York: IEEE.
Chen, Y., and W. Ji. 2021. “Enhancing situational assessment of critical infrastructure following disasters using social media.” J. Manage. Eng. 37 (6): 04021058. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000955.
Chen, Y., Q. Wang, and W. Ji. 2020. “Rapid assessment of disaster impacts on highways using social media.” J. Manage. Eng. 36 (5): 04020068. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000836.
Chen, Z., J. M. Zhang, M. Hort, F. Sarro, and M. Harman. 2022. “Fairness testing: A comprehensive survey and analysis of trends.” Preprint, submitted April 23, 2018. http://arxiv.org/abs/2207.10223.
Chowdhury, S., and J. Zhu. 2023. “Investigation of critical factors for future-proofed transportation infrastructure planning using topic modeling and association rule mining.” J. Comput. Civ. Eng. 37 (1): 04022044. https://doi.org/10.1061/(ASCE)CP.1943-5487.0001059.
CRED (Centre for Research on the Epidemiology of Disasters). 2009. “EM-DAT glossary.” EM-DAT The International Disaster Database Centre for Research on the Epidemiology of Disasters–CRED. Accessed March 9, 2023. https://www.emdat.be/Glossary.
Dhakal, S., L. Zhang, and X. Lv. 2021. “Understanding infrastructure resilience, social equity, and their interrelationships: Exploratory study using social media data in Hurricane Michael.” Nat. Hazards Rev. 22 (4): 04021045. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000512.
Dixon, S. J. 2023. “Distribution of X (formerly Twitter) users worldwide as of January 2023, by gender.” Statista. Accessed October 6, 2023. https://www.statista.com/statistics/828092/distribution-of-users-on-twitter-worldwide-gender/.
ETC (Eastern Transportation Coalition). 2017. Going my WAZE to closing real time data gaps: Crowdsourcing summit summary brief, 1–19. Philadelphia, PA: ETC.
Fan, C., Y. Jiang, and A. Mostafavi. 2020. “Social sensing in disaster city digital twin: Integrated textual–visual–geo framework for situational awareness during built environment disruptions.” J. Manage. Eng. 36 (3): 04020002. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000745.
FastText. 2023. “Fasttext.” PyPI. Accessed April 12, 2023. https://pypi.org/project/fasttext/.
FEMA. 2021. “FEMA issues public notice for 21 Georgia counties.” FEMA. Accessed March 9, 2023. https://www.fema.gov/press-release/20210211/fema-issues-public-notice-21-georgia-counties.
FEMA. 2023a. “Declared disasters.” FEMA. Accessed March 9, 2023. https://www.fema.gov/disaster/declarations.
FEMA. 2023b. “Glossary.” FEMA. Accessed March 9, 2023. https://training.fema.gov/programs/emischool/el361toolkit/glossary.htm.
FEMA. 2023c. “How a disaster gets declared.” FEMA. Accessed March 9, 2023. https://www.fema.gov/disasters/how-declared.
FEMA. 2023d. “2022–2026 FEMA strategic plan: Building the FEMA our nation needs and deserves.” FEMA. Accessed March 9, 2023. https://www.fema.gov/about/strategic-plan.
Gillborn, D., P. Warmington, and S. Demack. 2018. “Quantcrit: Education, policy, ‘big data’ and principles for a critical race theory of statistics.” Race Ethnicity Educ. 21 (2): 158–179. https://doi.org/10.1080/13613324.2017.1377417.
Griego, A. L., A. B. Flores, T. W. Collins, and S. E. Grineski. 2020. “Social vulnerability, disaster assistance, and recovery: A population-based study of Hurricane Harvey in greater Houston, Texas.” Int. J. Disaster Risk Reduct. 51 (Mar): 101766. https://doi.org/10.1016/j.ijdrr.2020.101766.
Gu, Y., Z. Qian, and F. Chen. 2016. “From Twitter to detector: Real-time traffic incident detection using social media data.” Transp. Res. Part C Emerging Technol. 67 (Jun): 321–342. https://doi.org/10.1016/j.trc.2016.02.011.
Halse, S. E., R. Grace, J. Kropczynski, and A. Tapia. 2019. “Simulating real-time twitter data from historical datasets.” In Proc., 16th ISCRAM Conf., edited by Z. Franco, J. J. González, and J. H. Canós, 780–787. València, Spain: Information Systems for Crisis Response and Management.
Hamideh, S., and J. Rongerude. 2018. “Social vulnerability and participation in disaster recovery decisions: Public housing in galveston after Hurricane Ike.” Nat. Hazards 93 (Dec): 1629–1648. https://doi.org/10.1007/s11069-018-3371-3.
Henchey, M. J., R. Batta, A. Blatt, M. Flanigan, and K. Majka. 2014. “A simulation approach to study emergency response.” J. Simul. 8 (2): 115–128. https://doi.org/10.1057/jos.2013.20.
Hooshmand, A., and R. Sharma. 2019. “Energy predictive models with limited data using transfer learning.” In Proc., e-Energy ’19: Proc., 10th ACM Int. Conf. on Future Energy Systems, 1–5. New York: Association for Computing Machinery.
Hort, M., Z. Chen, J. M. Zhang, F. Sarro, and M. Harman. 2022. “Bias mitigation for machine learning classifiers: A comprehensive survey.” Preprint, submitted April 23, 2018. http://arxiv.org/abs/2207.07068.
Kim, J. H., and E. J. Sutley. 2021. “Implementation of social equity metrics in an engineering-based framework for distributing disaster resources.” Int. J. Disaster Risk Reduct. 64 (Dec): 102485. https://doi.org/10.1016/j.ijdrr.2021.102485.
Kumar, D., and S. V. Ukkusuri. 2020. “Enhancing demographic coverage of Hurricane evacuation behavior modeling using social media.” J. Comput. Sci. 45 (Dec): 101184. https://doi.org/10.1016/j.jocs.2020.101184.
Lieberknecht, K., D. Zoll, J. Jiao, and K. Castles. 2021. “Hurricane harvey: Equal opportunity storm or disparate disaster?” Local Environ. 26 (2): 216–238. https://doi.org/10.1080/13549839.2021.1886063.
Lindsay, B. R. 2011. Social media and disasters: Current uses, future options, and policy considerations. Washington, DC: Congressional Research Service.
Loos, S., D. Lallemant, J. Baker, J. McCaughey, S. H. Yun, N. Budhathoki, F. Khan, and R. Singh. 2020. “G-DIF: A geospatial data integration framework to rapidly estimate post-earthquake damage.” Earthquake Spectra 36 (4): 1695–1718. https://doi.org/10.1177/8755293020926190.
Loos, S., J. Levitt, K. Tomozawa, J. Baker, and D. Lallemant. 2022. “Efficacy of damage data integration: A comparative analysis of four major earthquakes.” Nat. Hazards Rev. 23 (4): 04022026. https://doi.org/10.1061/(ASCE)NH.1527-6996.0000581.
Lovari, A., and S. A. Bowen. 2019. “Social media in disaster communication: A case study of strategies, barriers, and ethical implications.” J. Public Aff. 20 (1): 1–9. https://doi.org/10.1002/pa.1967.
Maurer, S. M. 2020. “Evolving approaches to place tagging in social media.” J. Urban Plan. Dev. 146 (3): 04020023. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000583.
Mhatre, M., D. Phondekar, P. Kadam, A. Chawathe, and K. Ghag. 2017. “Dimensionality reduction for sentiment analysis using pre-processing techniques.” In Proc., IEEE Int. Conf. on Computing Methodologies and Communication (ICCMC), 16–21. New York: IEEE.
Mohammadi, N., and J. E. Taylor. 2017. “Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction.” Appl. Energy 195 (Mar): 810–818. https://doi.org/10.1016/j.apenergy.2017.03.044.
Monroe-White, T., and J. Lecy. 2022. “The wells-du bois protocol for machine learning bias: Building critical quantitative foundations for third sector scholarship.” VOLUNTAS 34 (1): 170–184. https://doi.org/10.1007/s11266-022-00479-2.
Nargesian, F., A. Asudeh, and H. V. Jagadish. 2022. “Responsible dataintegration: Next-generation challenges.” In Proc., SIGMOD ’22: Proc., 2022 Int. Conf. on Management of Data, 1–7. New York: Association for Computing Machinery.
National Advisory Council. 2020. National advisory council report to the FEMA administrator FEMA. Washington, DC: FEMA.
Nejat, A., L. Solitare, E. Pettitt, and H. Mohsenian-Rad. 2022. “Equitable community resilience: The case of winter storm Uri in Texas.” Int. J. Disaster Risk Reduct. 77 (Mar): 103070. https://doi.org/10.1016/j.ijdrr.2022.103070.
Neyshabur, B., H. Sedghi, and C. Zhang. 2020. “What is being transferred in transfer learning?” In Proc., 34th Conf. on Neural Information Processing Systems (NeurIPS 2020), 1–12. Vancouver, Canada: Neural Information Processing Systems.
NOAA US Department of Commerce. 2020a. “Hurricane zeta 2020.” NOAA’s National Weather Service. Accessed May 5, 2023. https://www.weather.gov/tae/zeta2020#:∼:text=George%20Island%2031%20MPH%200139,28%2029.79N%2F84.88W%20.
NOAA US Department of Commerce. 2020b. “Tropical storm zeta affects north and central Georgia.” Accessed May 5, 2023. https://www.weather.gov/ffc/TropicalStormZeta.
NOAA US Department of Commerce. 2022. “Hurricane Ian’s path of destruction.” National Environmental Satellite Data and Information Service. Accessed March 8, 2023. https://www.nesdis.noaa.gov/news/hurricane-ians-path-of-destruction.
Ogie, R. I., and N. Verstaevel. 2020. “Disaster informatics: An overview.” Prog. Disaster Sci. 7 (Mar): 100111. https://doi.org/10.1016/j.pdisas.2020.100111.
Peirelinck, T., H. Kazmi, B. V. Mbuwir, C. Hermans, F. Spiessens, J. Suykens, and G. Deconinck. 2022. “Transfer learning in demand response: A review of algorithms for data-efficient modelling and control.” Energy AI 7 (Jan): 100126. https://doi.org/10.1016/j.egyai.2021.100126.
Peng, Y., Y. Zhang, Y. Tang, and S. Li. 2011. “An incident information management framework based on data integration, data mining, and multi-criteria decision making.” Decis. Support Syst. 51 (2): 316–327. https://doi.org/10.1016/j.dss.2010.11.025.
Pinto, G., Z. Wang, A. Roy, T. Hong, and A. Capozzoli. 2022. “Transfer learning for smart buildings: A critical review of algorithms, applications, and future perspectives.” Adv. Appl. Energy 5 (Jun): 100084. https://doi.org/10.1016/j.adapen.2022.100084.
Pollard, J. A., T. Spencer, and S. Jude. 2018. “Big data approaches for coastal flood risk assessment and emergency response.” WIREs Clim. Change 9 (5): 1–14. https://doi.org/10.1002/wcc.543.
Psyllidis, A., A. Bozzon, S. Bocconi, and C. Titos Bolivar. 2015. “A platform for urban analytics and semantic data integration in city planning.” In Proc., CAAD Futures 2015: Computer-Aided Architectural Design Futures. The Next City-New Technologies and the Future of the Built Environment, 21–36. New York: Springer.
Purohit, H., R. Kanagasabai, and N. Deshpande. 2019. “Towards next generation knowledge graphs for disaster management.” In Proc., 2019 IEEE 13th Int. Conf. on Semantic Computing (ICSC), 474–477. New York: IEEE.
Qi, S., D. G. Brown, Q. Tian, L. Jiang, T. Zhao, and K. M. Bergen. 2009. “Inundation extent and flood frequency mapping using landsat imagery and digital elevation models.” GISci. Remote Sens. 46 (1): 101–127. https://doi.org/10.2747/1548-1603.46.1.101.
Ribeiro, M., K. Grolinger, H. F. ElYamany, W. A. Higashino, and M. A. M. Capretz. 2018. “Transfer learning with seasonal and trend adjustment for cross-building energy forecasting.” Energy Build. 165 (Mar): 352–363. https://doi.org/10.1016/j.enbuild.2018.01.034.
Romanowski, C., R. Raj, J. Schneider, S. Mishra, V. Shivshankar, S. Ayengar, and F. Cueva. 2015. “Regional response to large-scale emergency events: Building on historical data.” Int. J. Crit. Infrastruct. Protect. 11 (Dec): 12–21. https://doi.org/10.1016/j.ijcip.2015.07.003.
Rossi, C., F. S. Acerbo, K. Ylinen, I. Juga, P. Nurmi, A. Bosca, F. Tarasconi, M. Cristoforetti, and A. Alikadic. 2018. “Early detection and information extraction for weather-induced floods using social media streams.” Int. J. Disaster Risk Reduct. 30 (Apr): 145–157. https://doi.org/10.1016/j.ijdrr.2018.03.002.
Roy, K. C., S. Hasan, A. M. Mohaimin, and M. Cebrian. 2020. “Understanding the efficiency of social media based crisis communication during hurricane sandy.” Int. J. Inf. Manage. 52 (Jun): 102060. https://doi.org/10.1016/j.ijinfomgt.2019.102060.
Salas, A., P. Georgakis, and Y. Petalas. 2017. “Incident detection using data from social media.” In Proc., IEEE Conf. on Intelligent Transportation Systems (ITSC), 751–755. New York: IEEE.
Salley, C., N. Mohammadi, and J. E. Taylor. 2022. “Semi-supervised machine learning framework for fusing georeferenced data from social media and community-driven applications.” In Proc., Computing in Civil Engineering 2021, 114–122. Reston, VA: ASCE.
Salley, C., N. Mohammadi, and J. E. Taylor. 2024. “Protecting critical infrastructure: NLP-based automated information retrieval to generate hypothetical cyberattack scenarios.” J. Infrastruct. Syst. 30 (3): 04024008. https://doi.org/10.1061/JITSE4.ISENG-2407.
Samuels, R., and J. E. Taylor. 2020. “Deepening the divide: Crises disproportionately silence vulnerable populations on social media.” J. Manage. Eng. 36 (6): 04020083. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000848.
Schmidtlein, M. C., C. Finch, and S. L. Cutter. 2008. “Disaster declarations and major hazard occurrences in the United States.” Prof. Geogr. 60 (1): 1–14. https://doi.org/10.1080/00330120701715143.
Sidauruk, A. 2018. “Congestion correlation and classification from Twitter and Waze Map using artificial neural network.” In Proc., 3rd Int. Conf. on Information Technology, Information Systems and Electrical Engineering, 224–229. New York: IEEE.
Smith, C. 2023. “Waze statistics and user count (2023).” DMR. Accessed April 12, 2023. https://expandedramblings.com/index.php/waze-statistics-facts/.
Sujon, M., and F. Dai. 2021. “Social media mining for understanding traffic safety culture in Washington State using Twitter data.” J. Comput. Civ. Eng. 35 (1): 04020059. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000943.
Sullivan, L., and E. Schwartz. 2018. “FEMA report acknowledges failures in Puerto Rico disaster response.” NPR. Accessed April 16, 2023. https://www.npr.org/2018/07/13/628861808/fema-report-acknowledges-failures-in-puerto-rico-disaster-response.
Tang, L., Y. Zhang, F. Dai, Y. Yoon, Y. Song, and R. S. Sharma. 2017. “Social media data analytics for the U.S. construction industry: Preliminary study on Twitter.” J. Manage. Eng. 33 (6): 04017038. https://doi.org/10.1061/(ASCE)ME.1943-5479.0000554.
Tankovska, H. 2022. “Twitter: Number of users worldwide 2019–2024.” Statista. Accessed April 12, 2023. https://www.statista.com/statistics/303681/twitter-users-worldwide/.
Tien, I., A. Musaev, D. Benas, A. Ghadi, S. Goodman, and C. Pu. 2016. “Detection of damage and failure events of critical public infrastructure using social sensor big data.” In Proc., Int. Conf. on Internet of Things and Big Data (IoTBD 2016), 435–440. Rome: IoTBD.
Twitter Developer Platform. 2023. “Advanced filtering with Geo Data. Developer Platform, Twitter.” Accessed March 7, 2023. https://developer.twitter.com/en/docs/tutorials/advanced-filtering-for-geo-data#:~:text=You%20may%20find%20bounding%20boxes,sqft%20(25%20x%2025).
UNDRR (United Nations Office for Disaster Risk Reduction). 2016. “Terminology.” UNDRR. Accessed March 9, 2023. https://www.undrr.org/terminology.
Verma, S., S. Vieweg, W. Corvey, L. Palen, J. Martin, M. Palmer, A. Schram, and K. Anderson. 2011. “Natural language processing to the rescue? Extracting ‘situational awareness’ tweets during mass emergency.” In Proc., Int. AAAI Conf. on Web and Social Media, 385–392. Washington, DC: Association for the Advancement of Artificial Intelligence.
Wang, Q., and J. E. Taylor. 2016. “Process map for urban-human mobility and civil infrastructure data collection using geosocial networking platforms.” J. Comput. Civ. Eng. 30 (2): 04015004. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000469.
Wang, Y., and J. E. Taylor. 2018. “Coupling sentiment and human mobility in natural disasters: A twitter-based study of the 2014 South Napa Earthquake.” Nat. Hazards 92 (Mar): 907–925. https://doi.org/10.1007/s11069-018-3231-1.
Wang, Y., and J. E. Taylor. 2019. “DUET: Data-driven approach based on latent Dirichlet allocation topic modeling.” J. Comput. Civ. Eng. 33 (3): 04019023. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000819.
Waze. 2017. Waze traffic-data specification document, 1–21. Mountain View, CA: Waze.
Wright, B. 2011. “Race, place, and the environment in the Aftermath of Katrina.” Anthropol. Work Rev. 32 (1): 4–8. https://doi.org/10.1111/j.1548-1417.2011.01052.x.
Wu, D., and Y. Cui. 2018. “Disaster early warning and damage assessment analysis using social media data and geo-location information.” Decis. Support Syst. 111 (Dec): 48–59. https://doi.org/10.1016/j.dss.2018.04.005.
Yang, Y., C. Zhang, C. Fan, A. Mostafavi, and X. Hu. 2020. “Towards fairness-aware disaster informatics: An interdisciplinary perspective.” IEEE Access 8 (Dec): 1–15. https://doi.org/10.1109/ACCESS.2020.3035714.
Yin, J., A. Lampert, M. Cameron, B. Robinson, and R. Power. 2012. “Using social media to enhance emergency situation awareness.” IEEE Intell. Syst. 27 (6): 52–59. https://doi.org/10.1109/MIS.2012.6.
Yu, J. J. Q. 2019. “Online traffic speed estimation for urban road networks with few data: A transfer learning approach.” In Proc., 2019 IEEE Intelligent Transportation Systems Conference (ITSC), 4024–4029. New York: IEEE.
Yuan, F., and R. Liu. 2018. “Integration of social media and unmanned aerial vehicles (UAVs) for rapid damage assessment in Hurricane Matthew.” In Proc., Construction Research Congress 2018, 513–523. Reston, VA: ASCE.
Yuen, D. A., B. J. Kadlec, E. F. Bollig, W. Dzwinel, Z. A. Garbow, and C. R. Silva. 2005. “Clustering and visualization of earthquake data in a grid environment.” Visual Geosci. 10 (1): 1–12. https://doi.org/10.1007/s10069-005-0023-z.
Zhou, D., O. Bousquet, T. Navin Lal, J. Weston, and B. Schölkopf. 2004. “Learning with local and global consistency.” In Vol. 16 of Proc., Advances in Neural Information Processing Systems, 1–8. Cambridge, MA: MIT Press.
Zou, L., N. S. Lam, H. Cai, and Y. Qiang. 2018. “Mining Twitter data for improved understanding of disaster resilience.” Ann. Am. Assoc. Geogr. 108 (5): 1422–1441. https://doi.org/10.1080/24694452.2017.1421897.
Zubiaga, A., A. Voss, R. Procter, M. Liakata, B. Wang, and A. Tsakalidis. 2017. “Towards real-time, country-level location classification of worldwide tweets.” IEEE Trans. Knowl. Data Eng. 29 (9): 2053–2066. https://doi.org/10.1109/TKDE.2017.2698463.
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Journal of Management in Engineering
Volume 40 • Issue 6 • November 2024
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© 2024 American Society of Civil Engineers.
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Received: Feb 1, 2024
Accepted: May 28, 2024
Published online: Aug 23, 2024
Published in print: Nov 1, 2024
Discussion open until: Jan 23, 2025
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