Unpacking the role of volunteered geographic information in disaster management: focus on data quality

References

• Abrahams J. 2001. Disaster management in Australia: the national emergency management system. Emerg Med (Fremantle). 13(2):165–173. doi: 10.1046/j.1442-2026.2001.00205.x.
   PubMedGoogle Scholar
• Ahmad K, Pogorelov K, Riegler M, Conci N, Halvorsen P. 2019. Social media and satellites: disaster event detection, linking and summarisation. Multimed Tools Appl. 78(3):2837–2875. doi: 10.1007/s11042-018-5982-9.
   Web of Science ®Google Scholar
• Ahmad K, Riegler M, Pogorelov K, Conci N, Halvorsen P, De Natale F. 2017. Jord: a system for collecting information and monitoring natural disasters by linking social media with satellite imagery. Proceedings of the. 15th International Workshop on Content-Based Multimedia Indexing; Jun 19–21; Florence, Italy. New York: ACM. p. 1–6. doi: 10.1145/3078971.3079013.
   Google Scholar
• Aissi S, Sboui T. 2017. Towards evaluating geospatial metadata quality in the context of VGI. Proc Comput Sci. 109:686–691. doi: 10.1016/j.procs.2017.05.377.
   Google Scholar
• Albuquerque J, Herfort B, Eckle M. 2016. The tasks of the crowd: a typology of tasks in geographic information crowdsourcing and a case study in humanitarian mapping. Remote Sens. 8(10):859. doi: 10.3390/rs8100859.
   Google Scholar
• Antoniou V, Skopeliti A. 2015. Measures and indicators of VGI quality: an overview. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. II-3/W5:345–351. doi: 10.5194/isprsannals-II-3-W5-345-2015.
   Google Scholar
• Arapostathis SG. 2020. Fundamentals of volunteered geographic information in disaster management related to floods. Flood impact mitigation and resilience enhancement. London: IntechOpen. doi: 10.5772/intechopen.92225.
   Google Scholar
• Aria M, Cuccurullo C. 2017. Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr. 11(4):959–975. doi: 10.1016/j.joi.2017.08.007.
   Web of Science ®Google Scholar
• Bajracharya B, Childs I, Hastings P. 2011. Climate change adaptation through land use planning and disaster management: local government perspectives from Queensland. Proceedings of 17th Pacific Rim Real Estate Society Conference; Jan 16–19; Gold Coast, Australia. Sydney, Australia: PRRES Inc. p. 16–19.
   Google Scholar
• Becker D, Bendett S. 2015. Crowdsourcing solutions for disaster response: examples and lessons for the US government. Proc Eng. 107:27–33. doi: 10.1016/j.proeng.2015.06.055.
   Google Scholar
• Bekker I, Felus Y. 2019. Quality control for crowdsourcing large-scale topographic maps. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLII-2/W13(2/W13):1201–1205. doi: 10.5194/isprs-archives-XLII-2-W13-1201-2019.
   Google Scholar
• Buffi G. 2018. [Assessment of seismic behaviour of large concrete dams by means of geomatic techniques and finite element modelling] [doctoral dissertation]. Florence, Italy: University of Florence.
   Google Scholar
• Burns R. 2015. Rethinking big data in digital humanitarianism: practices, epistemologies, and social relations. GeoJournal. 80(4):477–490. doi: 10.1007/s10708-014-9599-x.
   Web of Science ®Google Scholar
• Capineri C. 2016. The nature of volunteered geographic information. European handbook of crowdsourced geographic information. London: Ubiquity Press. p. 15–33. doi: 10.5334/bax.b.
   Google Scholar
• Camponovo ME, Freundschuh SM. 2014. Assessing uncertainty in VGI for emergency response. Cartograph Geographic Inform Sci. 41(5):440–455. doi: 10.1080/15230406.2014.950332.
   Web of Science ®Google Scholar
• Carter WN. 2008. Disaster management: a disaster manager’s handbook. Manila, Philippines: Asian Development Bank.
   Google Scholar
• Castillo C. 2016. Big crisis data: social media in disasters and time-critical situations. Cambridge: Cambridge University Press.
   Google Scholar
• Cetl V, Ioannidis C, Dalyot S, Doytsher Y, Felus Y, Haklay M, Mueller H, Potsiou C, Rispoli E, Siriba D. 2019. New trends in geospatial information: the land surveyors role in the era of crowdsourcing and VGI. FIG Publication No 73. ISBN 978-87-92853-86-8. https://www.fig.net/resources/publications/figpub/pub73/figpub73.asp.
   Google Scholar
• Čekada MT, Lisec A. 2019. Opportunities for using the volunteered geographic information within the national spatial infrastructure. Geod Vestn. 63(02):199–212. doi: 10.15292/geodetski-vestnik.2019.02.199-212.
   Google Scholar
• Chen H, Zhang WC, Deng C, Nie N, Yi L. 2017. Volunteered geographic information for disaster management with application to earthquake disaster databank and sharing platform. IOP Conf Ser Earth Environ Sci. 57(1):012015. doi: 10.1088/1755-1315/57/1/012015.
   Google Scholar
• Chow TE, Chien J, Meitzen K. 2023. Validating the quality of volunteered geographic information (VGI) for flood modeling of Hurricane Harvey in Houston, Texas. Hydrology. 10(5):113. doi: 10.3390/hydrology10050113.
   Web of Science ®Google Scholar
• Coppola DP. 2006. Introduction to international disaster management. Amsterdam, Netherlands: Elsevier. doi: 10.1016/b978-0-12-801477-6.09002-x.
   Google Scholar
• Cronstedt M. 2002. Prevention, preparedness, response, recovery-an outdated concept? Austral J Emergency Manag. 17:10–13.
   Google Scholar
• Danylo O, Moorthy I, Sturn T, See L, Laso Bayas J-C, Domian D, Fraisl D, Giovando C, Girardot B, Kapur R, et al. 2018. The Picture Pile tool for rapid image assessment: a demonstration using Hurricane Matthew. ISPRS Ann Photogramm Remote Sens Spatial Inf Sci. IV-4:27–32. doi: 10.5194/isprs-annals-IV-4-27-2018.
   Google Scholar
• Demetriou D, Campagna M, Racetin I, Konecny M. 2017. Integrating spatial data infrastructures (SDIs) with volunteered geographic information (VGI) for creating a global GIS platform. In: Foody G, See L, Fritz, Mooney P, Olteanu-Raimond AM, Fonte CC, Antoniou V, editors. Mapping and the citizen sensor. London: Ubiquity Press. p. 273–297. doi: 10.5334/bbf.l.
   Google Scholar
• Demšar J, Curk T, Erjavec A, Gorup Č, Hočevar T, Milutinovič M, Možina M, Polajnar M, Toplak M, Starič A, Štajdohar M, Umek L, Žagar L, Žbontar J, Žitnik M, Zupan B. 2013. Orange: data mining toolbox in Python. J Mach Learn Res. 14(1):2349–2353.
   Google Scholar
• Dittus M, Quattrone G, Capra L. 2017. Mass participation during emergency response: event-centric crowdsourcing in humanitarian mapping. Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, Feb 25–Mar 1; Portland, Oregon. New York: ACM. p. 1290–1303. doi: 10.1145/2998181.2998216.
   Google Scholar
• Ehnis CF. 2018. [Social media within emergency management organisations-A case study exploring social media utilisation for emergency and disaster management] [doctoral dissertation]. New South Wales, Australia: The University of Sydney Business School.
   Google Scholar
• Fekete A, Tzavella K, Armas I, Binner J, Garschagen M, Giupponi C, Mojtahed V, Pettita M, Schneiderbauer S, Serre D. 2015. Critical data source; tool or even infrastructure? Challenges of geographic information systems and remote sensing for disaster risk governance. ISPRS Int J Geo-Inf. 4(4):1848–1869. doi: 10.3390/ijgi4041848.
   Web of Science ®Google Scholar
• Feng Y, Huang X, Sester M. 2022. Extraction and analysis of natural disaster-related VGI from social media: review, opportunities and challenges. Int J Geograph Inform Sci. 36(7):1275–1316. doi: 10.1080/13658816.2022.2048835.
   Web of Science ®Google Scholar
• Feng Y, Sester M. 2018. Extraction of pluvial flood relevant volunteered geographic information (VGI) by deep learning from user generated texts and photos. ISPRS Int J Geo-Inf. 7(2):39. doi: 10.3390/ijgi7020039.
   Web of Science ®Google Scholar
• Fonte CC, Antoniou V, Bastin L, Estima J, Arsanjani JJ, Bayas JCL, See L, Vatseva R. 2017. Assessing VGI data quality. In: Foody G, See L, Fritz S, Mooney P, Olteanu-Raimond AM Fonte CC, Antoniou V, editors. Mapping and the citizen sensor. London: Ubiquity Press. p. 137–163. doi: 10.5334/bbf.g.
   Google Scholar
• Foody GM. 2014. Rating crowdsourced annotations: evaluating contributions of variable quality and completeness. Int J Digital Earth. 7(8):650–670. doi: 10.1080/17538947.2013.839008.
   Web of Science ®Google Scholar
• Foody GM, See L, Fritz S, van der Velde M, Perger C, Schill C, Boyd DS, Comber A. 2015. Accurate attribute mapping from volunteered geographic information: issues of volunteer quantity and quality. Cartograph J. 52(4):336–344. doi: 10.1179/1743277413y.0000000070.
   Web of Science ®Google Scholar
• Foody G, See L, Fritz S, Moorthy I, Perger C, Schill C, Boyd D. 2018. Increasing the accuracy of crowdsourced information on land cover via a voting procedure weighted by information inferred from the contributed data. ISPRS Int J Geo-Inf. 7(3):80. doi: 10.3390/ijgi7030080.
   Web of Science ®Google Scholar
• Fritz S, See L, Brovelli M, See L, Fritz S. 2017. Motivating and sustaining participation in VGI. In: Foody G, Mooney P, Olteanu-Raimond AM, Fonte CC, Antoniou V, editors. Mapping and the citizen sensor. London: Ubiquity Press. p. 93–117. doi: 10.5334/bbf.e.
   Google Scholar
• Fitriany AA, Flatau PJ, Khoirunurrofik K, Riama NF. 2021. Assessment on the use of meteorological and social media information for forest fire detection and prediction in Riau, Indonesia. Sustainability. 13(20):11188. doi: 10.3390/su132011188.
   Web of Science ®Google Scholar
• Galasso F, Rongier C, Lauras M, Gourc D. 2011. Disaster management control: decision support through performance assessment. IFAC Proc Vol. 44(1):10758–10763. doi: 10.3182/20110828-6-IT-1002.01621.
   Google Scholar
• Goodchild MF, Glennon JA. 2010. Crowdsourcing geographic information for disaster response: a research frontier. Int J Digital Earth. 3(3):231–241. doi: 10.1080/17538941003759255.
   Web of Science ®Google Scholar
• Goodchild MF. 2007. Citizens as sensors: the world of volunteered geography. GeoJournal. 69(4):211–221. doi: 10.1007/s10708-007-9111-y.
   Google Scholar
• Gómez-BarrÓn JP, Manso-Callejo MÁ, Alcarria R. 2019. Needs, drivers, participants and engagement actions: a framework for motivating contributions to volunteered geographic information systems. J Geogr Syst. 21(1):5–41. doi: 10.1007/s10109-018-00289-5.
   Web of Science ®Google Scholar
• Rosario Michel G, Gonzalez-Campos ME, Manzano Aybar F, Jiménez Durán T, Crompvoets J. 2023. Identifying critical factors to enhance SDI performance for facilitating disaster risk management in small island developing states. Surv Rev. 55(389):114–126. doi: 10.1080/00396265.2021.2024969.
   Web of Science ®Google Scholar
• Haklay M. 2013. CitSciand volunteered geographic information: overview and typology of participation. Crowdsourcing geographic knowledge. Dordrecht, the Netherlands: Springer Netherlands; p. 105–122. doi: 10.1007/978-94-007-4587-2_7.
   Google Scholar
• Haworth B. 2016. Emergency management perspectives on volunteered geographic information: opportunities, challenges, and change. Comput Environ Urban Syst. 57:189–198. doi: 10.1016/j.compenvurbsys.2016.02.009.
   Web of Science ®Google Scholar
• Haworth B, Bruce E. 2015. A review of volunteered geographic information for disaster management. Geograph Compass. 9(5):237–250. doi: 10.1111/gec3.12213.
   Web of Science ®Google Scholar
• Haworth B, Bruce E, Whittaker J, Read R. 2018. The good, the bad, and the uncertain: contributions of volunteered geographic information to community disaster resilience. Front Earth Sci. 6:183. doi: 10.3389/feart.2018.00183.
   Web of Science ®Google Scholar
• Hedberg TD, Krima S, Camelio JA. 2019. Method for enabling a root of trust in support of product data certification and traceability. J Comput Inf Sci Eng. 19(4):1. doi: 10.1115/1.4042839.
   Web of Science ®Google Scholar
• Heinzelman J, Waters C. 2010. Crowdsourcing crisis information in disaster-affected Haiti. Washington (DC): US Institute of Peace.
   Google Scholar
• Hicks A, Barclay J, Chilvers J, Armijos MT, Oven K, Simmons P, Haklay M. 2019. Global mapping of citizen science projects for disaster risk reduction. Front Earth Sci. 7. doi: 10.3389/feart.2019.00226.
   Web of Science ®Google Scholar
• Hjorth L, Kim KH. 2011. The mourning after: a case study of social media in the 3.11 earthquake disaster in Japan. Telev New Media. 12(6):552–559. doi: 10.1177/1527476411418351.
   Web of Science ®Google Scholar
• Hodges A. 2000. Emergency risk management. Risk Manag. 2(4):7–18. doi: 10.1057/palgrave.rm.8240064.
   Google Scholar
• Imran M, Castillo C, Lucas J, Meier P, Vieweg S. 2014. AIDR: artificial intelligence for disaster response. Proceedings of the 23rd International Conference on World Wide Web; Apl 7–11; Seoul, Korea. New York: ACM. p. 159–162. doi: 10.1145/2567948.2577034.
   Google Scholar
• ISO. 2013. ISO 19157:2013–Geographic information–data quality. Geneva, Switzerland: International Organization for Standardization.
   Google Scholar
• Jeansoulin R. 2016. Review of forty years of technological changes in geomatics toward the big data paradigm. ISPRS Int J Geo-Inf. 5(9):155. doi: 10.3390/ijgi5090155.
   Web of Science ®Google Scholar
• Kankanamge N, Yigitcanlar T, Goonetilleke A, Kamruzzaman M. 2019. Can volunteer crowdsourcing reduce disaster risk? A systematic review of the literature. Int J Disaster Risk Reduct. 35:101097. doi: 10.1016/j.ijdrr.2019.101097.
   Web of Science ®Google Scholar
• Kashian A, Rajabifard A, Richter KF, Chen Y. 2019. Automatic analysis of positional plausibility for points of interest in OpenStreetMap using coexistence patterns. Int J Geograph Inform Sci. 33(7):1420–1443. doi: 10.1080/13658816.2019.1584803.
   Web of Science ®Google Scholar
• Kocaman S, Anbaroglu B, Gokceoglu C, Altan O. 2018. A review on citizen science (CITSCI) applications for disaster management. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLII-3/W4:301–306. doi: 10.5194/isprs-archives-XLII-3-W4-301-2018.
   Google Scholar
• Koswatte S, McDougall K, Liu X. 2015. SDI and crowdsourced spatial information management automation for disaster management. Survey Rev. 47(344):307–315. doi: 10.1179/1752270615y.0000000008.
   Web of Science ®Google Scholar
• Koswatte S, McDougall K, Liu X. 2018. VGI and crowdsourced data credibility analysis using spam email detection techniques. Int J Digital Earth. 11(5):520–532. doi: 10.1080/17538947.2017.1341558.
   Web of Science ®Google Scholar
• Laylavi F, Rajabifard A, Kalantari M. 2016. A multi-element approach to location inference of twitter: a case for emergency response. ISPRS Int J Geo-Inf. 5(5):56. doi: 10.3390/ijgi5050056.
   Web of Science ®Google Scholar
• Li H, Zech J, Ludwig C, Fendrich S, Shapiro A, Schultz M, Zipf A. 2021. Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning. Int J Appl Earth Obs Geoinf. 104:102571. doi: 10.1016/j.jag.2021.102571.
   Web of Science ®Google Scholar
• Li Z, Wang C, Emrich CT, Guo D. 2018. A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods. Cartograph Geograph Inform Sci. 45(2):97–110. doi: 10.1080/15230406.2016.1271356.
   Web of Science ®Google Scholar
• Lin WY, Wu TH, Tsai MH, Hsu WC, Chou YT, Kang SC. 2018. Filtering disaster responses using crowdsourcing. Autom Constr. 91:182–192. doi: 10.1016/j.autcon.2018.03.016.
   Web of Science ®Google Scholar
• Lowrie C, Kruczkiewicz A, McClain SN, Nielsen M, Mason SJ. 2022. Evaluating the usefulness of VGI from Waze for the reporting of flash floods. Sci Rep. 12(1):5268. doi: 10.1038/s41598-022-08751-7.
   PubMed Web of Science ®Google Scholar
• Manfré LA, Hirata E, Silva JB, Shinohara EJ, Giannotti MA, Larocca APC, Quintanilha JA. 2012. An analysis of geospatial technologies for risk and natural disaster management. ISPRS Int J Geo-Inf. 1(2):166–185. doi: 10.3390/ijgi1020166.
   Web of Science ®Google Scholar
• Meier P. 2015. Digital humanitarians. How big data is changing the face of humanitarian. Boca Raton (FL): CRC Press.
   Google Scholar
• Mejri O, Menoni S, Matias K, Aminoltaheri N. 2017. Crisis information to support spatial planning in post disaster recovery. Int J Disaster Risk Reduct. 22:46–61. doi: 10.1016/j.ijdrr.2017.02.007.
   Web of Science ®Google Scholar
• Mirbabaie M, Stieglitz S, Volkeri S. 2016. Volunteered geographic information and its implications for disaster management. 49th Hawaii International Conference on System Sciences (HICSS); Jan 5–8; Koloa, US. New Jersey: IEEE. p. 207–216. doi: 10.1109/hicss.2016.33.
   Google Scholar
• Moghadas M, Rajabifard A, Fekete A, Kötter T. 2022. A framework for scaling urban transformative resilience through utilizing volunteered geographic information. ISPRS Int J Geo-Inf. 11(2):114. doi: 10.3390/ijgi11020114.
   Web of Science ®Google Scholar
• Moghadas M, Fekete A, Rajabifard A, Kötter T. 2023. The wisdom of crowds for improved disaster resilience: a near-real-time analysis of crowdsourced social media data on the 2021 flood in Germany. GeoJournal. 88(4):4215–4241. doi: 10.1007/s10708-023-10858-x.
   Web of Science ®Google Scholar
• Mooney P, Minghini M, Laakso M, Antoniou V, Olteanu-Raimond AM, Skopeliti A. 2016. Towards a protocol for the collection of VGI vector data. ISPRS Int J Geo-Inf. 5(11):217. doi: 10.3390/ijgi5110217.
   Web of Science ®Google Scholar
• Mooney P, Sun H, Yan L. 2011. VGI as a dynamically updating data source in location-based services in urban environments. Proceedings of the 2nd International Workshop on Ubiquitous Crowdsourcing; Sep 17–21; Beijing, China. New York: ACM. p. 13–16. doi: 10.1145/2030100.2030105.
   Google Scholar
• Mulder F, Ferguson J, Groenewegen P, Boersma K, Wolbers J. 2016. Questioning big data: crowdsourcing crisis data towards an inclusive humanitarian response. Big Data Soc. 3(2):205395171666205. doi: 10.1177/2053951716662054.
   Google Scholar
• Munro R, Schnoebelen T, Erle S. 2013. Quality analysis after action report for the crowdsourced aerial imagery assessment following hurricane sandy. Proceedings of the 10th International ISCRAM Conference; May 12–15; Baden-Baden, Germany. Baden-Baden, Germany: Karlsruher Institut fur Technologie.
   Google Scholar
• Neto FRA, Santos CA. 2018. Understanding crowdsourcing projects: a systematic review of tendencies, workflow, and quality management. Inf Process Manage. 54(4):490–506. doi: 10.1016/j.ipm.2018.03.006.
   Web of Science ®Google Scholar
• Ostermann FO, Spinsanti L. 2011. A conceptual workflow for automatically assessing the quality of volunteered geographic information for crisis management. Proceedings of AGILE 2011; Aug 7–13; Salt Lake City. New Jersey: IEEE. p. 1–6.
   Google Scholar
• Pepe A, Bonano M, Bordogna G, Brovelli M, Calo F, Carrara P, Congedo L, Frigerio L, Imperatore P, Lanari R, Lanucara S, Manzo M, Munafo M. 2018. The 'urban geomatics for bulk information generation, data assessment and technology awareness’ project: detection, representation and analysis of the urban scenario changes. IGARSS 2018–International Geoscience and Remote Sensing Symposium; Jul 22–27; Valencia, Spain. New Jersey: IEEE. doi:10.1109/igarss.2018.8518843.
   Google Scholar
• Pezzica C, Piemonte A, Bleil de Souza C, Cutini V. 2019. Photogrammetry as a participatory recovery tool after disasters: a grounded framework for future guidelines. Int Arch Photogramm Remote Sens Spatial Inf Sci. XLII-2/W15:921–928. doi: 10.5194/isprs-archives-XLII-2-W15-921-2019.
   Google Scholar
• Poorazizi ME, Hunter AJ, Steiniger S. 2015. A volunteered geographic information framework to enable bottom-up disaster management platforms. ISPRS Int J Geo-Inf. 4(3):1389–1422. doi: 10.3390/ijgi4031389.
   Web of Science ®Google Scholar
• Poser K, Dransch D. 2010. Volunteered geographic information for disaster management with application to rapid flood damage estimation. Geomatica. 64(1):89–98.
   Google Scholar
• Priya S, Bhanu M, Dandapat SK, Ghosh K, Chandra J. 2018. Characterising infrastructure damage after earthquake: a split-query based IR approach. ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM); Aug 28–31; Barcelona, Spain. New Jersey: IEEE. p. 202–209.
   Google Scholar
• Raja G, Thomas A. 2019. Safer: crowdsourcing based disaster monitoring system using software defined fog computing. Mobile Netw Appl. 24(5):1414–1424. doi: 10.1007/s11036-019-01267-x.
   Web of Science ®Google Scholar
• Riccardi MT. 2016. The power of crowdsourcing in disaster response operations. Int J Disaster Risk Reduct. 20:123–128. doi: 10.1016/j.ijdrr.2016.11.001.
   Web of Science ®Google Scholar
• Rogers P. 2011. Development of Resilient Australia: enhancing the PPRR approach with anticipation, assessment and registration of risks. Austral J Emergency Manag. 26: 54–58. doi: 10.3316/ielapa.80630554640444
   Google Scholar
• Sachdeva S, McCaffrey S, Locke D. 2017. Social media approaches to modeling wildfire smoke dispersion: spatiotemporal and social scientific investigations. Inform Commun Soc. 20(8):1146–1161. doi: 10.1080/1369118X.2016.1218528.
   Web of Science ®Google Scholar
• See L, Mooney P, Foody G, Bastin L, Comber A, Estima J, Fritz S, Kerle N, Jiang B, Laakso M, et al. 2016. Crowdsourcing, citizen science or volunteered geographic information? The current state of crowdsourced geographic information. ISPRS Int J Geo-Inf. 5(5):55. doi: 10.3390/ijgi5050055.
   Web of Science ®Google Scholar
• Senaratne H, Mobasheri A, Ali AL, Capineri C, Haklay M. 2017. A review of volunteered geographic information quality assessment methods. Int J Geograph Inform Sci. 31(1):139–167. doi: 10.1080/13658816.2016.1189556.
   Web of Science ®Google Scholar
• Siebritz L, Sithole G, Zlatanova S. 2012. Assessment of the homogeneity of volunteered geographic information in South Africa. XXII ISPRS Congress, Commission IV, Melbourne; Australia 25 August-1 September 2012; IAPRS XXXIX-B4 2012. French, German: International Society for Photogrammetry and Remote Sensing (ISPRS). doi: 10.5194/isprsarchives-XXXIX-B4-553-2012.
   Google Scholar
• Solís P, McCusker B, Menkiti N, Cowan N, Blevins C. 2018. Engaging global youth in participatory spatial data creation for the UN sustainable development goals: the case of open mapping for malaria prevention. Appl Geogr. 98:143–155. doi: 10.1016/j.apgeog.2018.07.013.
   Web of Science ®Google Scholar
• Tavra M, Racetin I, Peroš J. 2021. The role of crowdsourcing and social media in crisis mapping: a case study of a wildfire reaching Croatian City of Split. Geoenviron Dis. 8(1):1–16. doi: 10.1186/s40677-021-00181-3.
   Google Scholar
• Toujani R, Akaichi J. 2019. Event news detection and citizens community structure for disaster management in social networks. Online Inform Rev. 43(1):113–132. doi: 10.1108/OIR-03-2018-0091.
   Web of Science ®Google Scholar
• Tsai PH, Lin YJ, Ou YZ, Chu ETH, Liu JW. 2014. A framework for fusion of human sensor and physical sensor data. IEEE Trans Syst Man Cybern Syst. 44(9):1248–1261. doi: 10.1109/TSMC.2014.2309090.
   Web of Science ®Google Scholar
• Tzavella K, Skopeliti A, Fekete A. 2022. Volunteered geographic information use in crisis, emergency and disaster management: a scoping review and a web atlas. Geo-Spatial Inform Sci. 1–32. doi: 10.1080/10095020.2022.2139642.
   Google Scholar
• Verplanke J, McCall MK, Uberhuaga C, Rambaldi G, Haklay M. 2016. A shared perspective for PGIS and VGI. Cartograph J. 53(4):308–317. doi: 10.1080/00087041.2016.1227552.
   Web of Science ®Google Scholar
• Vijaya AS, Bateja R. 2017. A review on hierarchical clustering algorithms. J Eng Appl Sci. 12(24):7501–7507. doi: 10.36478/jeasci.2017.7501.7507.
   Google Scholar
• Wahlström M. 2015. New Sendai framework strengthens focus on reducing disaster risk. Int J Disaster Risk Sci. 6(2):200–201. doi: 10.1007/s13753-015-0057-2.
   Web of Science ®Google Scholar
• Xiao Y, Huang Q, Wu K. 2015. Understanding social media data for disaster management. Nat Hazards. 79(3):1663–1679. doi: 10.1016/b978-0-12-819047-0.00015-9.
   Web of Science ®Google Scholar
• Yan Y, Eckle M, Kuo CL, Herfort B, Fan H, Zipf A. 2017. Monitoring and assessing post-disaster tourism recovery using geotagged social media data. ISPRS Int J Geo-Inf. 6(5):144. doi: 10.3390/ijgi6050144.
   Web of Science ®Google Scholar
• Yan Y, Feng CC, Huang W, Fan H, Wang YC, Zipf A. 2020. Volunteered geographic information research in the first decade: a narrative review of selected journal articles in GIScience. Int J Geograph Inform Sci. 34(9):1765–1791. doi: 10.1080/13658816.2020.1730848.
   Web of Science ®Google Scholar
• Yang J, Yu M, Qin H, Lu M, Yang C. 2019. A twitter data credibility framework—Hurricane Harvey as a use case. IJGI. 8(3):111. doi: 10.3390/ijgi8030111.
   Google Scholar
• Yao W, Zhang C, Saravanan S, Huang R, Mostafavi A. 2020. Weakly-supervised fine-grained event recognition on social media texts for disaster management. AAAI. 34(01):532–539. doi: 10.1609/aaai.v34i01.5391.
   Google Scholar
• Yuan F, Liu R. 2018. Crowdsourcing for forensic disaster investigations: hurricane Harvey case study. Nat Hazards. 93(3):1529–1546. doi: 10.1007/s11069-018-3366-0.
   Web of Science ®Google Scholar
• Yuan J, Chowdhury PKR, McKee J, Yang HL, Weaver J, Bhaduri B. 2018. Exploiting deep learning and volunteered geographic information for mapping buildings in Kano, Nigeria. Sci Data. 5(1):180217. doi: 10.1038/sdata.2018.217.
   PubMedGoogle Scholar
• Zahra K, Ostermann FO, Purves RS. 2017. Geographic variability of Twitter usage characteristics during disaster events. Geospatial Inform Sci. 20(3):231–240. doi: 10.1080/10095020.2017.1371903.
   Google Scholar
• Zhang C, Fan C, Yao W, Hu X, Mostafavi A. 2019. Social media for intelligent public information and warning in disasters: an interdisciplinary review. Int J Inf Manage. 49:190–207. doi: 10.1016/j.ijinfomgt.2019.04.004.
   Web of Science ®Google Scholar
• Zhang C, Liu L, Lei D, Yuan Q, Zhuang H, Hanratty T, Han J. 2017. Triovecevent: embedding-based online local event detection in geo-tagged tweet streams. Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; Aug 13–17; Halifax, NS Canada. New York: ACM. p. 595–604.
   Google Scholar
• Zhang C, Zhou G, Yuan Q, Zhuang H, Zheng Y, Kaplan L, Wang S, Han J. 2016. Geoburst: real-time local event detection in geo-tagged tweet streams. Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval; Jul 17–21; Pisa, Italy. New Jersey: IEEE. p. 513–522. doi:10.1145/2911451.2911519.
   Google Scholar
• Zhang D, Zhang Y, Li Q, Plummer T, Wang D. 2019. Crowdlearn: A crowd-ai hybrid system for deep learning-based damage assessment applications. 39th International Conference on Distributed Computing Systems (ICDCS); July 7–9; Dallas, Texas. New Jersey: IEEE. p. 1221–1232.
   Google Scholar
• Zook M, Graham M, Shelton T, Gorman S. 2010. Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Med Health Policy. 2(2):7–33. doi: 10.2202/1948-4682.1069.
   Google Scholar