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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 (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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Cites
boyd, D., & Crawford, K. (2012) · Information, Communication & Society · doi:10.1080/1369118x.2012.678878 · Cited by 5,479
Citizens as sensors: the world of volunteered geography
Goodchild, M. F. (2007) · GeoJournal · doi:10.1007/s10708-007-9111-y · Cited by 4,611
Big data: a revolution that will transform how we live, work, and think
(2013) · Choice Reviews Online · doi:10.5860/choice.50-6804 · Cited by 3,657
Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A.-L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al. (2009) · Science · doi:10.1126/science.1167742 · Cited by 3,365
Park, K. (2012) · Localities · doi:10.15299/local.2012.11.2.401 · Cited by 2,891
Massey, D. (1991) · doi:10.4324/9780203931950-45 · Cited by 1,857
The Fourth Paradigm – Data-Intensive Scientific Discovery
Hey, T. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-33299-9_1 · Cited by 1,656
Haklay, M. (2010) · Environment and Planning B Planning and Design · doi:10.1068/b35097 · Cited by 1,646
Theorizing Sociospatial Relations
Jessop, B., Brenner, N., & Jones, M. (2008) · Environment and Planning D: Society and Space · doi:10.1068/d9107 · Cited by 1,252
"The End of Theory: The Data Deluge Makes the Scientific Method Obsolete"
Willighagen, E. (2008) · doi:10.59350/j4d10-0jt05 · Cited by 1,057
Research works printed but not linked: 1
- Red Cross, n.d. The American Red Cross and Dell Launch First-Of-Its-Kind Social Media Digital Operations Center for Humanitarian Relief. <http://www.redcross.org/news/press-release/The-American-Red-Cross-and-Dell-Launch-First-Of-Its-Kind-Social-Media-Digital-Operations-Center-for-Humanitarian-Relief>.
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Digital turn, digital geographies?
Ash, J., Kitchin, R., & Leszczynski, A. (2016) · Progress in Human Geography · doi:10.1177/0309132516664800 · Cited by 772
“…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
“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
“…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
“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 414
Self-citationBig data and disaster management: a systematic review and agenda for future research
Akter, S., & Wamba, S. F. (2017) · Annals of Operations Research · doi:10.1007/s10479-017-2584-2 · Cited by 355
“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
Social-media data for urban sustainability
Ilieva, R. T., & McPhearson, T. (2018) · Nature Sustainability · doi:10.1038/s41893-018-0153-6 · Cited by 291
Leszczynski, A. (2014) · Progress in Human Geography · doi:10.1177/0309132514558443 · Cited by 229
“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 243
“previous investigations: hurricane, Sandy, flood, and storm (Shelton et al. 2014).”
BackgroundMethodResultInfluentialPourebrahim, N., Sultana, S., Edwards, J., Gochanour, A., & Mohanty, S. D. (2019) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2019.101176 · Cited by 195
Graham, M., Sabbata, S. D., & Zook, M. (2015) · Geo Geography and Environment · doi:10.1002/geo2.8 · Cited by 181
“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).”
BackgroundSelf-citationJapec, L., Kreuter, F., Berg, M., Biemer, P. P., Decker, P. T., Lampe, C. A. C., Lane, J. I., O’Neil, C., & Usher, A. (2015) · Public Opinion Quarterly · doi:10.1093/poq/nfv039 · Cited by 188
Population Bias in Geotagged Tweets
Malik, M. M., Lamba, H., Nakos, C., & Pfeffer, J. (2021) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v9i4.14688 · Cited by 172
“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
Mulder, F., Ferguson, J., Groenewegen, P., Boersma, K., & Wolbers, J. (2016) · Big Data & Society · doi:10.1177/2053951716662054 · Cited by 158
“…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 167
“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
Glitch epistemologies for computational cities
Leszczynski, A., & Elwood, S. A. (2022) · Dialogues in Human Geography · doi:10.1177/20438206221075714 · Cited by 134
“…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 142
Social and geographical disparities in Twitter use during Hurricane Harvey
Zou, L., Lam, N., Shams, S., Cai, H., Meyer, M. A., Yang, S., Lee, K., Park, S., & Reams, M. A. (2018) · International Journal of Digital Earth · doi:10.1080/17538947.2018.1545878 · Cited by 119
“…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).”
BackgroundInfluentialSoden, R., & Palen, L. A. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274431 · Cited by 109
“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]].”
BackgroundGranell, C., & Ostermann, F. (2016) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2016.01.006 · Cited by 106
“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 107
How to Study the City on Instagram
Boy, J. D., & Uitermark, J. (2016) · PLoS ONE · doi:10.1371/journal.pone.0158161 · Cited by 98
Geographies of infrastructure II: Concrete, cloud and layered (in)visibilities
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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
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Murthy, D., & Gross, A. (2016) · Social Science Research · doi:10.1016/j.ssresearch.2016.09.015 · Cited by 93
Mudigonda, S., Özbay, K. M. A., & Bartin, B. (2018) · Journal of Transportation Safety & Security · doi:10.1080/19439962.2018.1436105 · Cited by 85
Hazardous Weather Prediction and Communication in the Modern Information Environment
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“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 87
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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).”
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Cinnamon, J., Jones, S. K., & Adger, W. N. (2016) · Geoforum · doi:10.1016/j.geoforum.2016.07.019 · Cited by 80
“…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
Richterich, A. (2018) · University of Westminster Press eBooks · doi:10.16997/book14 · Cited by 120
Ecology-centred accounting for biodiversity in the production of a blanket bog
Cuckston, T. (2017) · Accounting Auditing & Accountability Journal · doi:10.1108/aaaj-12-2015-2330 · Cited by 76
“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
“…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
“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
“…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
“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
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Delmelle, E. M., Desjardins, M. R., Jung, P. H., Owusu, C., Lan, Y., Hohl, A., & Dony, C. C. (2021) · Annals of Epidemiology · doi:10.1016/j.annepidem.2021.10.002 · Cited by 71
Gruebner, O., Lowe, S. R., Sykora, M. D., Shankardass, K., Subramanian, S. V., & Galea, S. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15102275 · Cited by 64
Demuth, J. L., Morss, R. E., Palen, L. A., Anderson, K. M., Anderson, J., Kogan, M., Stowe, K., Bica, M., Lazrus, H., Wilhelmi, O. V., et al. (2018) · Weather Climate and Society · doi:10.1175/wcas-d-17-0126.1 · Cited by 61
“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 71
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
“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
“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
“…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
“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
“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 60
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
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
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 57
“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 79
“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
“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
“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
“…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
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
Mapping Silences, Reconfiguring Loss
Soden, R., & Lord, A. (2018) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3274430 · Cited by 39
“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 40
“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
“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
Scheele, C., Yu, M., & Huang, Q. (2021) · International Journal of Digital Earth · doi:10.1080/17538947.2021.1968048 · Cited by 35
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
Amézquita-Sánchez, J. P., Valtierra‐Rodriguez, M., & Adeli, H. (2017) · Scientia Iranica · doi:10.24200/sci.2017.4589 · Cited by 34
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
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
Robertson, C., & Feick, R. (2018) · Transactions in GIS · doi:10.1111/tgis.12321 · Cited by 34
“…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
“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 37
“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
Self-citationYu, Z., Zhan, J., Yao, Z., & Peng, J. (2024) · Natural Hazards · doi:10.1007/s11069-024-06518-z · Cited by 30
“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
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
“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
Taylor, K. N. (2015) · Administration & Society · doi:10.1177/0095399715622231 · Cited by 28
Cheng, J., Dong, Y., Qie, K., & Wang, J. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.115704 · Cited by 26
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
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
“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 · doi:10.1111/tesg.12480 · Cited by 28
“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 30
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
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
Buckle, C. (2020) · Geoforum · doi:10.1016/j.geoforum.2020.03.004 · Cited by 25
Casero-Ripollés, A., Micó, J.-L., & Bosch, M. D. (2020) · Media and Communication · doi:10.17645/mac.v8i4.3145 · Cited by 29
Vayansky, I., Kumar, S., & Li, Z. (2019) · IEEE International Symposium on Technology and Society (ISTAS) · doi:10.1109/istas48451.2019.8937859 · Cited by 24
“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 27
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
“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
The Nature of Volunteered Geographic Information
Capineri, C. (2016) · Ubiquity Press eBooks · doi:10.5334/bax.b · Cited by 23
Hultquist, C., & Cervone, G. (2020) · Remote Sensing · doi:10.3390/rs12050834 · Cited by 23
Robertson, C., & Feick, R. (2015) · Cartography and Geographic Information Science · doi:10.1080/15230406.2015.1088801 · Cited by 22
“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
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
“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
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
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 22
“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
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
“The selection of keywords refers to previous studies that utilize Twitter data to study natural disasters [54,57,58].”
Background
Metadata
- 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
- 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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