The DOI matches its Crossref record; 48 of the 48 references it cites were found in a registry or an index.
Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’
Open accessOverview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Citations
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>.
Cited by
Sorted by citations
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-citation
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
