Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’
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Data-driven public policies may only succeed in economically advantaged, urban, and data-rich areas if efforts are not made to improve data collection elsewhere.
“data-driven public policies may only succeed in economically advantaged, urban, and data-rich areas (Hecht and Stephens, 2014; Shelton et al., 2014), if efforts are not made to improve data collection elsewhere”
From Social Data: Biases, Methodological Pitfalls, and Ethical Boundaries (2019) · cites this paper as Shelton et al., 2014
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Ash, J., Kitchin, R., & Leszczynski, A. (2016) · Progress in Human Geography · doi:10.1177/0309132516664800 · Cited by 772 · see citing works
“…cast a far larger Twitter ‘data shadow’ than the most severely affected, more socioeconomically deprived areas of the tri-state coastline, giving the impression that Manhattan was more deserving of an earlier and/or more concentrated emergency response than was merited (Shelton et al ., 2014).”
BackgroundSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kıcıman, E. (2019) · Frontiers in Big Data · doi:10.3389/fdata.2019.00013 · Cited by 763 · see citing works
“For example, data-driven public policies may only succeed in economically advantaged, urban, and data-rich areas (Hecht and Stephens, 2014; Shelton et al., 2014), if efforts are not made to improve data collection elsewhere (see the “digital divide” on section 9.4).”
BackgroundInfluentialThe role of Big Data in explaining disaster resilience in supply chains for sustainability
Παπαδόπουλος, Θ., Gunasekaran, A., Dubey, R., Nezih Altay, A., Childe, S. J., & Wamba, S. F. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.03.059 · Cited by 669 · see citing works
“…of the role of Big Data for sustainability and resilience, and more importantly, are studies looking at Big Data and disaster management (Chae et al., 2014; Shelton et al., 2014), there have studies that utilize Big Data (unstructured data) to explain disaster resilience for sustainability.”
InfluentialApplications of artificial intelligence for disaster management
Sun, W., Bocchini, P., & Davison, B. D. (2020) · Natural Hazards · doi:10.1007/s11069-020-04124-3 · Cited by 491 · see citing works
“When assessing the disaster-induced impact on human, sentiment analyses of social media data can track human activity pattern throughout the recovery (Caragea et al. 2014; Hasan and Ukkusuri 2014; Shelton et al. 2014; Resch et al. 2018; Liu et al. 2019b).”
BackgroundShelton, T., Poorthuis, A., & Zook, M. (2015) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2015.02.020 · Cited by 388 · see citing works
Self-citationBig data and disaster management: a systematic review and agenda for future research
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“The impacts of disaster events can disrupt the progress and developmental efforts of nations, often pushing them many years back (Smith and Matthews, 2015; Huang and Cervone, 2016).”
BackgroundSocial media analytics for natural disaster management
Wang, Z., & Ye, X. (2017) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2017.1367003 · Cited by 316 · see citing works
Social-media data for urban sustainability
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“The work of Hochman and Manovich (2013) on Instagram signatures in Tel Aviv and that of the Floating Sheep collective on the digital shadows of Hurricane Sandy (Shelton et al., 2014), for example, attest to the ways in which spatial media are leveraged as mediums through which to foreground particular places at partic…”
BackgroundInfluentialMining Twitter Data for Improved Understanding of Disaster Resilience
Zou, L., Lam, N., Cai, H., & Qiang, Y. (2018) · Annals of the American Association of Geographers · doi:10.1080/24694452.2017.1421897 · Cited by 229 · see citing works
“previous investigations: hurricane, Sandy, flood, and storm (Shelton et al. 2014).”
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“These (im)mutable augmentations of information about places matter because they shape how we are able to find and understand different parts of the world (Shelton et al. 2014).”
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“2014a), • natural disasters, crises, and disaster response (Morstatter et al. 2014; Lin and Margolin 2014; Shelton et al. 2014; Sylvester et al. 2014; Kumar, Hu, and Liu 2014), and”
BackgroundQuestioning Big Data: Crowdsourcing crisis data towards an inclusive humanitarian response
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“…scholarly domains, such as new media studies (e.g., Parks, 2009), critical cartography (e.g., Crampton et al., 2013), geoweb studies (e.g., Burns, 2015; Shelton et al., 2014), spatial technologies (e.g., Elwood, 2007), development studies (Avgerou, 2008) and organization sciences (Leonardi, 2011).”
BackgroundLeveraging Twitter to gauge evacuation compliance: Spatiotemporal analysis of Hurricane Matthew
Martín, Y., Li, Z., & Cutter, S. L. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181701 · Cited by 157 · see citing works
“Following Shelton et al. [39], we normalized the Twitter activity by the Twitter population (active Twitter users from September 15 th to October 15 th in the study area) to reduce the population bias (Eq 1).”
BackgroundMethodUpscaling urban data science for global climate solutions
Creutzig, F., Lohrey, S., Bai, X., Baklanov, A., Dawson, R., Dhakal, S., Lamb, W. F., McPhearson, T., Minx, J., Munoz, E., et al. (2019) · Global Sustainability · doi:10.1017/sus.2018.16 · Cited by 137 · see citing works
Glitch epistemologies for computational cities
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“…data generation (e.g. Rabari and Storper, 2015); marketspaces for digital services (e.g. Artioli, 2018); experimental sites for digital technologies and technology capital (e.g. Rodrigues et al., 2020); and spatialities that cast disproportionately large data shadows (e.g. Shelton et al., 2014).”
BackgroundKogan, M., Palen, L. A., & Anderson, K. M. (2015) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2675133.2675218 · Cited by 129 · see citing works
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“…studies analyzed Twitter activities during Hurricane Sandy and concluded that monitoring activities on social media can help estimate damage and monitor post-disaster recovery status (Guan and Chen 2014; Kryvasheyeu et al. 2015, 2016; Shelton et al. 2014; Wang and Taylor 2014; Zou et al. 2018).”
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“computing [[108]], the development of technologies for use in constrained resource environments [[123]], the ongoing legacy of colonialism [[94]], and designing with/for Anthropocene futures [[78]].”
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“Further, social media content is strongly influenced by short-lived feedback loops (Shelton et al., 2014).”
BackgroundType, tweet, tap, and pass: How smart city technology is creating a transactional citizen
Johnson, P. A., Robinson, P. J., & Philpot, S. L. (2019) · Government Information Quarterly · doi:10.1016/j.giq.2019.101414 · Cited by 101 · see citing works
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“Big data comes with big invisibilities that tend to reflect existing inequalities whereby there is an increasing geographical divide in terms of access to data and data about marginalized places (Graham et al., 2015; Shelton et al., 2014a; Thatcher, 2014).”
BackgroundInfluentialSocial Data: Biases, Methodological Pitfalls, and Ethical Boundaries
Olteanu, A., Castillo, C., Diaz, F., & Kiciman, E. (2016) · doi:10.2139/ssrn.2886526 · Cited by 763 · see citing works
Social media processes in disasters: Implications of emergent technology use
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Hazardous Weather Prediction and Communication in the Modern Information Environment
Morss, R. E., Demuth, J. L., Lazrus, H., Palen, L. A., Barton, C. M., Davis, C. A., Snyder, C., Wilhelmi, O. V., Anderson, K. M., Ahijevych, D. A., et al. (2017) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-16-0058.1 · Cited by 88 · see citing works
“For example, much of the current work using Twitter data for hazardous weather research focuses on questions about how many people are tweeting, where, and how frequently (Lachlan et al. 2014; Shelton et al. 2014; Ripberger et al. 2014; Silva et al. 2015; Kryvasheyeu et al. 2016).”
BackgroundApplying big data beyond small problems in climate research
Knüsel, B., Zumwald, M., Baumberger, C., Hadorn, G. H., Fischer, E. M., Bresch, D. N., & Knutti, R. (2019) · Nature Climate Change · doi:10.1038/s41558-019-0404-1 · Cited by 82 · see citing works
A geospatial infodemic: Mapping Twitter conspiracy theories of COVID-19
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“Especially during a crisis, Twitter can only reflect the situated geographic and social practices of the people producing it (Shelton et al., 2014).”
BackgroundEvidence and future potential of mobile phone data for disease disaster management
Cinnamon, J., Jones, S. K., & Adger, W. N. (2016) · Geoforum · doi:10.1016/j.geoforum.2016.07.019 · Cited by 80 · see citing works
“…collected, can be made available very rapidly once the right agreements are in place, and have little to no on-going data collection costs, since the data are generated automatically (Lydersen, 2014; Tatem et al. , 2009), which opens up new opportunities for information generation in an emergency.”
Reynard, D., & Shirgaokar, M. (2019) · Transportation Research Part D Transport and Environment · doi:10.1016/j.trd.2019.03.002 · Cited by 80 · see citing works
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Ecology-centred accounting for biodiversity in the production of a blanket bog
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“20 catalyst for encouraging multi-dimensional thinking (Chettiparamb, 2013; Mayer, 2008; Shelton et al., 2014).”
BackgroundCOVID-19 is spatial: Ensuring that mobile Big Data is used for social good
Poom, A., Järv, O., Zook, M., & Toivonen, T. (2020) · Big Data & Society · doi:10.1177/2053951720952088 · Cited by 77 · see citing works
“…solutions (Positium, 2019) and environmental management (Heikinheimo et al., 2020; Poom et al., 2017), characterizing tourist behavior (Campagna et al., 2015; Raun et al., 2016; Saluveer et al., 2020), or reflecting the lived experiences of people in case of disruptions (Shelton et al., 2014).”
BackgroundSelf-citationChristaller and “big data”: recalibrating central place theory via the geoweb
van Meeteren, M., & Poorthuis, A. (2017) · Urban Geography · doi:10.1080/02723638.2017.1298017 · Cited by 72 · see citing works
“georeferenced tweets for Shelton et al. (2015) to draw inferences about the spatial mobility of these groups.”
BackgroundSelf-citationIntroduction: Spatial Big Data and everyday life
Leszczynski, A., & Crampton, J. W. (2016) · Big Data & Society · doi:10.1177/2053951716661366 · Cited by 71 · see citing works
“…interval of an odds ratio, which give greater weight to locations characterized by heightened levels of Twitter activity which also experience greater relative degrees of tweeting about a particular topic (for examples of this approach, see Poorthuis et al., 2016; Shelton et al., 2014, 2015).”
BackgroundCai, Z., Tang, Y., & Zhan, Q. (2021) · Building and Environment · doi:10.1016/j.buildenv.2021.107729 · Cited by 68 · see citing works
“Therefore, to fill this gap, some studies have begun using big data based on mobile users to explore the distribution of populations and their characteristics [38,39].”
BackgroundMapping the popularity of urban restaurants using social media data
Zhai, S., Xu, X., Yang, L., Zhou, M., Zhang, L., & Qiu, B. (2015) · Applied Geography · doi:10.1016/j.apgeog.2015.06.006 · Cited by 67 · see citing works
Uncertainty in geospatial health: challenges and opportunities ahead
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“2), and it experienced significant impacts from Sandy (Shelton et al. 2014; Superstorm Research Lab 2013).”
BackgroundInfluentialFerreira, D., Vale, M., do Carmo, R. M., Encalada-Abarca, L., & Marcolin, C. B. (2021) · Geoforum · doi:10.1016/j.geoforum.2021.05.002 · Cited by 60 · see citing works
Towards Scalable and Dynamic Social Sensing Using A Distributed Computing Framework
Zhang, D., Zheng, C., Wang, D., Thain, D., Mu, X., Madey, G., & Huang, C. (2017) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2017.196 · Cited by 58 · see citing works
“First, different topics or events are likely to generate different amounts of social sensing data (e.g., Hurricane Matthew generates way more tweets than a local traffic accident) [31].”
BackgroundMaking Big Data Small: Strategies to Expand Urban and Geographical Research Using Social Media
Poorthuis, A., & Zook, M. (2017) · Journal of Urban Technology · doi:10.1080/10630732.2017.1335153 · Cited by 57 · see citing works
“In addition, we have used DOLLY to search and extract relevant data for our own articles (Crampton et al., 2013; Poorthuis and Zook, 2014; 2015; Poorthuis et al., 2016; Shelton, Poorthuis, Graham, and Zook, 2014; Zook and Poorthuis, 2014).”
MethodSelf-citationLeszczynski, A. (2017) · Progress in Human Geography · doi:10.1177/0309132517711779 · Cited by 56 · see citing works
“…and local (Cranshaw et al., 2012; Hawelka et al., 2014; Shelton et al., 2015), urban income inequality and segregation (Indaco and Manovich, forthcoming; Shelton et al., 2014, 2015), urban/rural divides (Hecht and Stephens, 2014), and even the happiness of city dwellers (Manovich et al., 2014).”
BackgroundCollaborative Science to Enhance Coastal Resilience and Adaptation
Nichols, C. R., Wright, L. D., Bainbridge, S. J., Cosby, A. G., Hénaff, A., Loftis, J. D., Cocquempot, L., Katragadda, S., Méndez, G. R., Letortu, P., et al. (2019) · Frontiers in Marine Science · doi:10.3389/fmars.2019.00404 · Cited by 56 · see citing works
“When combined with the network and content analyses, the spatial analysis of human-generated big data can reveal whether differences in social networks, information diffusion and messaging are shaped by spatial proximity or by some other form of place-based process (Starbird and Palen, 2010; Shelton et al., 2014; Koga…”
BackgroundSoden, R., & Owen, E. (2021) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3479862 · Cited by 55 · see citing works
“demonstrated that technologies intervene in these politics and can unwittingly reinforce existing inequalities [5][44].”
BackgroundSachdeva, S. S., McCaffrey, S., & Locke, D. H. (2016) · Information Communication & Society · doi:10.1080/1369118x.2016.1218528 · Cited by 53 · see citing works
A Twitter Data Credibility Framework—Hurricane Harvey as a Use Case
Yang, J., Yu, M., Qin, H., Lu, M., & Yang, C. P. (2019) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi8030111 · Cited by 53 · see citing works
Analyzing the spread of tweets in response to Paris attacks
Cvetojevic, S., & Hochmair, H. H. (2018) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2018.03.010 · Cited by 52 · see citing works
Unresolved issues in regional economic resilience: Conceptual ways forward
Lemke, L. K.-G., Sakdapolrak, P., & Trippl, M. (2023) · Progress in Human Geography · doi:10.1177/03091325231191242 · Cited by 52 · see citing works
“The authors further expose the relationship between material and closely related digital manifestations and complex spatialities (Shelton et al., 2014).”
BackgroundKangana, N., Kankanamge, N., Silva, C. K. D., Goonetilleke, A., Mahamood, R., & Ranasinghe, D. (2024) · Smart Cities · doi:10.3390/smartcities7060147 · Cited by 52 · see citing works
“Problems can occur when attempting to map user-generated data due to difficulties in comprehending socio-spatial links that go beyond the conventional use of latitude and longitude coordinates [154].”
Tavra, M., Racetin, I., & Peroš, J. (2021) · Geoenvironmental Disasters · doi:10.1186/s40677-021-00181-3 · Cited by 47 · see citing works
“Analysing the data collected by crowdsourcing, the existence of more complex spatialities than longitude and latitude becomes evident (Shelton et al., 2014).”
BackgroundInfluentialSmart Disaster Detection and Response System for Smart Cities
Boukerche, A., & Coutinho, R. W. L. (2018) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2018.8538356 · Cited by 46 · see citing works
“The proper processing of and knowledge extraction from multimedia big data will be used for providing improved information for civilians and search and rescue teams [6], to assist the preparation of disaster recovery plans [19], and to better assess the socio-spatial impact of disasters [20].”
MethodSpatialities of data: mapping social media ‘beyond the geotag’
Shelton, T. (2016) · GeoJournal · doi:10.1007/s10708-016-9713-3 · Cited by 42 · see citing works
“…new sources of digital data to rethink the boundedness of conventional Cartesian spatial categories (cf. Cranshaw et al. 2012; Stefanidis et al. 2013; Shelton et al. 2014, 2015), this paper has tried to demonstrate how these point-based datasets can actually be incorporated into understandings of…”
BackgroundSelf-citationWhen a disaster happens, we are ready: Location mention recognition from crisis tweets
Suwaileh, R., Elsayed, T., Imran, M., & Sajjad, H. (2022) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2022.103107 · Cited by 45 · see citing works
Analyzing Refugee Migration Patterns Using Geo-tagged Tweets
Hübl, F., Cvetojevic, S., Hochmair, H. H., & Paulus, G. (2017) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi6100302 · Cited by 44 · see citing works
Mapping Silences, Reconfiguring Loss
Soden, R., & Lord, A. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274430 · Cited by 39 · see citing works
“Building on research in human geography and critical data studies [[7],[10],[18],[54]], we find that these activities offer a means of studying the data politics of humanitarian technologies.”
BackgroundUsing Twitter to Analyze the Effect of Hurricanes on Human Mobility Patterns
Ahmouda, A., Hochmair, H. H., & Cvetojevic, S. (2019) · Urban Science · doi:10.3390/urbansci3030087 · Cited by 37 · see citing works
“disasters including wildfires, earthquakes, floods, storms, tsunamis, and hurricanes [6,7].”
BackgroundQuantitative geography III: Future challenges and challenging futures
Wolf, L. J., Fox, S., Harris, R., Johnston, R. J., Jones, K., Manley, D., Tranos, E., & Wang, W. W. (2020) · Progress in Human Geography · doi:10.1177/0309132520924722 · Cited by 37 · see citing works
“Indeed, bias from non-representative samples (Boeing and Waddell, 2017; Folch et al., 2018; Zhang and Zhu, 2018) or spatial disparity in coverage (Shelton et al., 2014) is increasingly well studied.”
BackgroundDisaster damage assessment based on fine-grained topics in social media
Dou, M., Wang, Y., Gu, Y., Dong, S., Qiao, M., & Deng, Y. (2021) · Computers & Geosciences · doi:10.1016/j.cageo.2021.104893 · Cited by 37 · see citing works
Scheele, C., Yu, M., & Huang, Q. (2021) · International Journal of Digital Earth · doi:10.1080/17538947.2021.1968048 · Cited by 35 · see citing works
Diversity in volunteered geographic information: comparing OpenStreetMap and Wikimapia in Jerusalem
Bittner, C. (2016) · GeoJournal · doi:10.1007/s10708-016-9721-3 · Cited by 35 · see citing works
Amézquita-Sánchez, J. P., Valtierra‐Rodriguez, M., & Adeli, H. (2017) · Scientia Iranica · doi:10.24200/sci.2017.4589 · Cited by 34 · see citing works
Challenges of using big data for humanitarian relief: lessons from the literature
Sharma, P., & Joshi, A. (2019) · Journal of Humanitarian Logistics and Supply Chain Management · doi:10.1108/jhlscm-05-2018-0031 · Cited by 34 · see citing works
OpenStreetMap in Israel and Palestine – ‘Game changer’ or reproducer of contested cartographies?
Bittner, C. (2016) · Political Geography · doi:10.1016/j.polgeo.2016.11.010 · Cited by 34 · see citing works
Robertson, C., & Feick, R. (2018) · Transactions in GIS · doi:10.1111/tgis.12321 · Cited by 34 · see citing works
“…risk associated with the 2014 West African Ebola outbreak (Fung, Tse, Cheung, Miu, & Fu, 2014), and share information concerning natural disasters (Goodchild & Glennon, 2010; Shelton et al., 2014) and human emer-gencies such as football-related riots in Lexington, KY (Crampton et al., 2013).”
BackgroundNguyen, T. V., Han, H., & Sahito, N. (2019) · Sustainability · doi:10.3390/su11215967 · Cited by 33 · see citing works
“traditional data, which is often combined and outdated [29].”
BackgroundJärv, O., Tominga, A., Müürisepp, K., & Silm, S. (2021) · Journal of Location Based Services · doi:10.1080/17489725.2021.1887526 · Cited by 32 · see citing works
“Finally, social media data allow spatio-temporal knowledge to be combined with social content (Banda et al. 2020; Shelton et al. 2014).”
BackgroundUsing Geotagged Digital Social Data in Geographic ResearchPreprint
Poorthuis, A., Zook, M., Shelton, T., Graham, M., & Stephens, M. (2014) · SSRN Electronic Journal · Cited by 31 · see citing works
Self-citationYu, Z., Zhan, J., Yao, Z., & Peng, J. (2024) · Natural Hazards · doi:10.1007/s11069-024-06518-z · Cited by 30 · see citing works
“For instance, in October 2012, the United States utilized social media for emergency response and post-disaster reconstruction during Hurricane Sandy (Shelton et al. 2014).”
Software engineering for big data projects: Domains, methodologies and gaps
Kumar, V. D., & Alencar, P. (2016) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2016.7840938 · Cited by 30 · see citing works
Streets of London: Using Flickr and OpenStreetMap to build an interactive image of the city
Bahrehdar, A. R., Adams, B., & Purves, R. S. (2020) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2020.101524 · Cited by 30 · see citing works
“However, we note that though these data are suitable for exploring broad scale patterns of, for example language use or segregation in cities (Shelton et al., 2014), they have shortcomings with respect to fi ne-grained analysis (Lansley & Longley, 2016).”
BackgroundZhao, X., Yu, F., Zhang, X., Chen, J., & Li, P. (2025) · npj Heritage Science · doi:10.1038/s40494-025-01964-9 · Cited by 28 · see citing works
Taylor, K. N. (2015) · Administration & Society · doi:10.1177/0095399715622231 · Cited by 28 · see citing works
Cheng, J., Dong, Y., Qie, K., & Wang, J. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115704 · Cited by 26 · see citing works
A graph-based semi-supervised approach to classification learning in digital geographies
Liu, P., & Sabbata, S. D. (2020) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2020.101583 · Cited by 26 · see citing works
The Disaster and Climate Change Artathon
Soden, R., Hamel, P., Lallemant, D., & Pierce, J. (2020) · Designing Interactive Systems Conference · doi:10.1145/3357236.3395461 · Cited by 25 · see citing works
“Third, studies have shown that despite their supposed neutrality, such systems inevitably contain biases that can exacerbate or reinforce existing inequalities [10][16][70].”
BackgroundUitermark, J., & van Meeteren, M. (2021) · Tijdschrift voor economische en sociale geografie = Journal of economic and social geography = Revue de geographie economique et humaine = Zeitschrift fur okonomische und soziale Geographie = Revista de geografia economica y social · doi:10.1111/tesg.12480 · Cited by 27 · see citing works
“Computational social scientists tend to consider network data as ‘trace data’ or ‘data shadows’ (Shelton et al .”
BackgroundWhat is the place for space in epidemiology?
Eberth, J. M., Kramer, M. R., Delmelle, E. M., & Kirby, R. S. (2021) · Annals of Epidemiology · doi:10.1016/j.annepidem.2021.08.022 · Cited by 25 · see citing works
Geosocial capta in geographical research – a critical analysis
Rzeszewski, M. (2016) · Cartography and Geographic Information Science · doi:10.1080/15230406.2016.1229221 · Cited by 25 · see citing works
Grounding Big Data on Climate-Induced Human Mobility
Boas, I., Dahm, R. J., & Wrathall, D. J. (2019) · Geographical Review · doi:10.1111/gere.12355 · Cited by 25 · see citing works
Buckle, C. (2020) · Geoforum · doi:10.1016/j.geoforum.2020.03.004 · Cited by 24 · see citing works
Casero-Ripollés, A., Micó, J.-L., & Bosch, M. D. (2020) · Media and Communication · doi:10.17645/mac.v8i4.3145 · Cited by 24 · see citing works
Vayansky, I., Kumar, S., & Li, Z. (2019) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas48451.2019.8937859 · Cited by 24 · see citing works
“This research model was applied to twitter data collected during Hurricane Sandy, and although negative tweets were shown to consistently cluster in closer proximity to the storm, both positive and negative clustering tendencies were shown to increase at the points of Sandy’s maximum impact and then disperse over the…”
MethodAI-driven social media text analysis during crisis: A review for natural disasters and pandemics
Wahid, J. A., Xu, M., Ayoub, M., Jiang, X., Lei, S., Gao, Y., Hussain, S., & Yu, Y. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112774 · Cited by 26 · see citing works
Utilizing Geo-tagged Tweets to Understand Evacuation Dynamics during Emergencies
Kumar, D., & Ukkusuri, S. V. (2018) · International World Wide Web Conference (WWW) · doi:10.1145/3184558.3191619 · Cited by 23 · see citing works
“Geo-tagged social media posts have been successfully used to identify spatial patterns of users during emergency situations [22, 37, 38].”
BackgroundDagan, D. T., & Wilkins, E. J. (2023) · Journal of Outdoor Recreation and Tourism · doi:10.1016/j.jort.2023.100668 · Cited by 23 · see citing works
The Nature of Volunteered Geographic Information
Capineri, C. (2016) · Ubiquity Press eBooks · doi:10.5334/bax.b · Cited by 23 · see citing works
Hultquist, C., & Cervone, G. (2020) · Remote Sensing · doi:10.3390/rs12050834 · Cited by 23 · see citing works
Robertson, C., & Feick, R. (2015) · Cartography and Geographic Information Science · doi:10.1080/15230406.2015.1088801 · Cited by 22 · see citing works
“The implications of unevenness in these information overlays include differential advantages for certain functions, groups, and geographies (Kelley 2013; Shelton et al. 2014; Crang, Crosbie, and Graham.”
BackgroundInfluentialDiscovery of local topics by using latent spatio-temporal relationships in geo-social media
Kim, K., Kojima, I., & Ogawa, H. (2016) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2016.1146956 · Cited by 21 · see citing works
The object of mobile spatial data, the subject in mobile spatial research
Thatcher, J. (2016) · Big Data & Society · doi:10.1177/2053951716659092 · Cited by 21 · see citing works
“For example, Shelton et al.’s (2014) analysis of Hurricane Sandy tweets and Baginski et al.’s (2014) look at Yelp data both demonstrate the socioeconomic privileges inherent in the production of mobile spatial data.”
BackgroundEcolinguistics, GIS, and Corpus Linguistics for the Analysis of the Rosemont Copper Mine Debate
Poole, R. (2017) · Environmental Communication · doi:10.1080/17524032.2016.1275735 · Cited by 20 · see citing works
The power of big data mining to improve the health care system in the United Arab Emirates
Alhajaj, K. E., & Moonesar, I. A. (2023) · Journal Of Big Data · doi:10.1186/s40537-022-00681-5 · Cited by 22 · see citing works
Betwixt and between: Triple liminality and liminal agency in the Swedish gig economy
Weidenstedt, L., Geißinger, A., Leick, B., & Nazeer, N. (2023) · Environment and Planning A Economy and Space · doi:10.1177/0308518x231172984 · Cited by 20 · see citing works
“In the labour geography literature, the TPSN framework has been applied to the socio-spatialities of natural hazards and diseases (Kuebart and Stabler, 2020; Shelton et al., 2014), as well as energy and water politics (Beveridge et al., 2017; Gailing et al., 2020).”
MethodBehaviour Analysis Using Tweet Data and geo-tag Data in a Natural Disaster
Hara, Y. (2015) · Transportation research procedia · doi:10.1016/j.trpro.2015.12.033 · Cited by 20 · see citing works
Spatial Assessment of Community Resilience from 2012 Hurricane Sandy Using Nighttime Light
Xu, J., & Qiang, Y. (2021) · Remote Sensing · doi:10.3390/rs13204128 · Cited by 19 · see citing works
“The selection of keywords refers to previous studies that utilize Twitter data to study natural disasters [54,57,58].”
Background
- Authors
- Shelton, T., Poorthuis, A., Graham, M., & Zook, M.
- Affiliations
- Clark University (US)
- University of Kentucky (US)
- University of Oxford (GB)
- Corresponding author
- Shelton, Taylor
- Venue
- Geoforum
- Publisher
- Elsevier BV
- Published
- 2014-03
- Volume
- 52
- Pages
- 167-179
- ISSN
- 0016-7185 (ISSN-L)
- OpenAlex
- W2062751763
- Cited by
- 256
- References
- 48
- Citations by year
- 2026: 9 · 2025: 13 · 2024: 16 · 2023: 15 · 2022: 14 · 2021: 28 · 2020: 28 · 2019: 31 · 2018: 23 · 2017: 27 · 2016: 23 · 2015: 21 · 2014: 7 · 2013: 1
- FWCI
- 170.34
- Type
- journal article
- Language
- en
- Open access
- green
- papers.ssrn.com
- Locations
- Geoforum (publishedVersion)
- SSRN Electronic Journal (submittedVersion)
- Topics
- Social Sciences › Geography, Planning and Development › Geographic Information Systems Studies
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Keywords
- Hurricane Sandy, big data, Twitter, social media, georeferenced records, spatiality, New York metropolitan area, disaster response
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