Cited by 256Geographic Information Systems StudiesHuman Mobility and Location-Based AnalysisBig Data Technologies and Applications

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Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’

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Shelton, Taylor, Poorthuis, Ate, Graham, Mark, Zook, Matthew·2014·Geoforum

DOI 10.1016/j.geoforum.2014.01.006

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CRITICAL QUESTIONS FOR BIG DATA: Provocations for a cultural, technological, and scholarly phenomenon
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Theorizing Sociospatial Relations
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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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Tweeting Blame in a Federalist System: Attributions for Disaster Response in Social Media Following Hurricane Sandy
Canales, K. L., Pope, J. V., & Maestas, C. D. (2019) · Social Science Quarterly · doi:10.1111/ssqu.12669 · Cited by 18
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“For example, geotagging of tweets is nontrivial because of the uncertainties in their location and timing [24].”
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Zhai, W., Yu, H., & Song, Y. (2023) · Annals of the American Association of Geographers · doi:10.1080/24694452.2023.2271549 · Cited by 16
“Previous studies have highlighted the complexity inherent in social media data and the difficulties of conducting quantitative analysis solely based on it (Shelton et al. 2014).”
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Exodus 2.0: crowdsourcing geographical and social trails of mass migration
Curry, T., Croitoru, A., Crooks, A. T., & Stefanidis, A. (2018) · Journal of Geographical Systems · doi:10.1007/s10109-018-0278-1 · Cited by 17
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Anderson, J., Sáez, G. C., Anderson, K. M., Palen, L. A., & Morss, R. E. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.275 · Cited by 16
“of understanding the various contained geographies without more context and qualitative investigation [5].”
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Crowd Dynamics: Conflicts, Contradictions, and Community in Crowdsourcing
Hansson, K. E., & Ludwig, T. G. (2018) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-018-9343-z · Cited by 16
“Cultural geographers have also pointed out the hegemonic discourses and sociospatial relations in the geographic web (Crampton et al. 2013; Shelton et al. 2014; Soden and Palen 2014; Zook et al. 2015).”
Background
A place-based analysis of #BlackLivesMatter and counter-protest content on Twitter
Haffner, M. (2018) · GeoJournal · doi:10.1007/s10708-018-9919-7 · Cited by 15
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Information as Intervention: How Hurricane Risk Communication Interacted with Vulnerability and Capacities in Superstorm Sandy
Lazrus, H., Wilhelmi, O. V., Morss, R. E., Henderson, J., & Dietrich, A. S. (2020) · International Journal of Mass Emergencies & Disasters · doi:10.1177/028072702003800106 · Cited by 16
“Since our aim in the focus groups was to gather data about hurricane risk communication and decision making from people who were likely absent from other work analyzing Twitter data (Anderson et al. 2016; Bica et al. 2019; Demuth et al. 2018; Lachlan et al. 2014; Martin, Li, and Cutter 2017; Morss et al. 2017; Niles e…”
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Measuring the Impact of Natural Hazards with Citizen Science: The Case of Flooded Area Estimation Using Twitter
Bruneau, P., Brangbour, E., Marchand‐Maillet, S., Hostache, R., Chini, M., Pelich, R., Matgen, P., & Tamisier, T. (2021) · Remote Sensing · doi:10.3390/rs13061153 · Cited by 15
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Yao, Q., Li, R. Y. M., & Song, L. (2022) · Frontiers in Environmental Science · doi:10.3389/fenvs.2022.969039 · Cited by 15
“In order to have enough data that can be filtered or sampled, previous studies suggested using the keywords that return all potentially relevant tweets (Shelton et al., 2014; Grace et al., 2019).”
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Societal Attention to Heat Waves Can Indicate Public Health Impacts
Bogdanovich, E., Guenther, L., Reichstein, M., Frank, D., Ruhrmann, G., Brenning, A., Denissen, J., & Orth, R. (2023) · Weather Climate and Society · doi:10.1175/wcas-d-22-0147.1 · Cited by 13
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Enablers in Crisis Information Management: A Literature Review
Lee, N.-S., Hirschmeier, S., Müller, S., & Luz, L. J. (2017) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2017.033 · Cited by 15
“Social media-based location recognition [24, 37, 48, 61, 86, 89] [24, 61, 86, 89]”
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Expanding Awareness: Comparing Location, Keyword, and Network Filtering Methods to Collect Hyperlocal Social Media Data
Grace, R., Halse, S. E., Aurite, W., Montarnal, A., & Tapia, A. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.325 · Cited by 15
“[6, 11, 19], to include the types of information users post in a geographic area [29].”
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Reading urban land use through spatio-temporal and content analysis of geotagged Twitter data
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“One study found that 50% of deaths from Hurricane Sandy occurred in an area with a complete lack of Twitter activity (Shelton et al., 2014).”
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The effect of intra-urban mobility flows on the spatial heterogeneity of social media activity: investigating the response to rainfall events
de Andrade, S. C., de Albuquerque, J. P., Restrepo‐Estrada, C., Westerholt, R., Morales, C. A., Mendiondo, E. M., & Delbem, A. C. B. (2021) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2021.1957898 · Cited by 14
“…is that social media activity is spatially heterogeneous and influenced by several exogenous factors such as socio-economic inequalities (Shelton et al. 2014, Malik et al. 2015, Sloan 2017, Jiang et al. 2019), the complexity of the urban topology and social structures (Steiger et al.…”
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A Light-Weight and Quality-Aware Online Adaptive Sampling Approach for Streaming Social Sensing in Cloud Computing
Zhang, Y., Zhang, D., Vance, N., Li, Q., & Wang, D. (2018) · IEEE International Conference on Parallel and Distributed Systems (ICPADS) · doi:10.1109/padsw.2018.8644560 · Cited by 14
“It is quite challenging or even impossible to extensively examine every single tweet in the data stream to obtain the real-time situation awareness [14].”
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“These reconfigurations suggest terminology that acknowledges the ways in which the digital-material is liminal, in-between, lurking (seemingly invisibly) in the data shadows (Shelton et al. 2014) like digital ghosts and zombies that inhabit our cities.”
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Network Connections and Neighbourhood Perception: Using Social Media Postings to Capture Attitudes among Twitter Users in Estonia
Hess, D. B., Iacobucci, E., & Väiko, A. (2017) · Architecture and Urban Planning · doi:10.1515/aup-2017-0010 · Cited by 14
“Recently, researchers have argued that effective use of such data sources must go beyond simple, sometimes undertheorized quantitative analyses, emphasizing the role that theory and qualitative methods should play in examining social media data [25].”
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Mining social media for disaster management: Leveraging social media data for community recovery
Shibuya, Y. (2017) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2017.8258286 · Cited by 14
“For example, with the aim of improving the response phase, research has been conducted regarding how information is spread over social media during disasters [26]– [30]; how affected people use social media during disasters [31], [32], and how social media contents changes as time passes [33], [34].”
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Enhancing demographic coverage of hurricane evacuation behavior modeling using social media
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Geovisualization Using Hexagonal Tessellation for Spatiotemporal Earthquake Data Analysis in Indonesia
Dharmawan, R. D., Suharyadi, S., & Farda, N. M. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-7242-0_15 · Cited by 13
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Sanderson, R., Franklin, R. S., MacKinnon, D., & Matthews, J. (2024) · GeoJournal · doi:10.1007/s10708-024-11010-z · Cited by 14
“Following the work of Shelton et al. (2014), this study used geo-tagged tweets and tweets with information provided at POI level to maintain accuracy and avoid ‘false hot spots’ (Huck et al., 2015).”
Influential
Infrastructuring Community Fridges for Food Commoning
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“Previous studies critiqued the online-only community collaboration, which inadvertently created barriers for some marginalized individuals [23, 104, 107].”
Background
Temporal patterns and life cycle dynamics of social media user activity during disasters: A data-driven approach for effective crisis communication
Aziz, R. A., Agarwal, P. K., McGuinness, J., Karmaker, C. L., & Zhuang, J. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124454 · Cited by 11
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Background
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Bennett, K., Gardner, Z., & Sabbata, S. D. (2022) · Social & Cultural Geography · doi:10.1080/14649365.2022.2065699 · Cited by 11
“Interesting research agendas (Shelton et al., 2014), though, have demonstrated the importance of thinking about the complex sociospatial relations of a geotagged tweet beyond its simple relationship with a location.”
Background
Knowledge and Digital Technology
Glückler, J., & Panitz, R. (2024) · Knowledge and space · doi:10.1007/978-3-031-39101-9 · Cited by 11
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“Visions about a near-total state of data capture through sensors can collide with concerns about inequality in accessing data (cf. Shelton et al., 2014).”
Translocal networked public spheres: Spatial arrangements of metropolitan Twitter
Waldherr, A., Stoltenberg, D., Maier, D., Keinert, A., & Pfetsch, B. (2023) · New Media & Society · doi:10.1177/14614448231156579 · Cited by 16
“…observed repeatedly and in different social, cultural, and situational contexts, for example, in the aftermath of disasters (Lin and Margolin, 2014; Shelton et al., 2014; Zou et al., 2019), during protests (Bastos and Mercea, 2016; Van Haperen et al., 2018), or connecting dias-poras (Afeworki,…”
The Rise of Big Data in Communication Sciences: A Bibliometric Mapping of the Literature
Karaboğa, T., Karaboğa, H. A., & Şehitoğlu, Y. (2020) · Connectist Istanbul University Journal of Communication Sciences · doi:10.26650/connectist2020-0083 · Cited by 12
Deepening the Divide: Crises Disproportionately Silence Vulnerable Populations on Social Media
Samuels, R., & Taylor, J. E. (2020) · Journal of Management in Engineering · doi:10.1061/(asce)me.1943-5479.0000848 · Cited by 10
Big Data, Surveillance and Crisis Management
Fonio, C., & Boersma, K. (2017) · Routledge eBooks · doi:10.4324/9781315638423 · Cited by 10
Weighting critical infrastructure dependencies to facilitate evacuations
Foytik, P. B., & Robinson, R. M. (2017) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2017.11.019 · Cited by 10
What we know and what we think we know: Revealing misconceptions about coastal management for sandy beaches along the U.S. Atlantic Seaboard
Charbonneau, B. R., Cochran, C., & Avenarius, C. B. (2019) · Journal of Environmental Management · doi:10.1016/j.jenvman.2019.04.008 · Cited by 9
Crisis social media data labeled for storm-related information and toponym usage
Grace, R. (2020) · Data in Brief · doi:10.1016/j.dib.2020.105595 · Cited by 10
Big Data
Kitchin, R. (2016) · International Encyclopedia of Geography · doi:10.1002/9781118786352.wbieg0145 · Cited by 10
Reckoning with the digital turn in electoral geography
Temple, L. (2023) · Progress in Human Geography · doi:10.1177/03091325231170328 · Cited by 9
Tweeting after an earthquake: user localization and communication patterns during the 2012 Emilia seismic sequence
Comunello, F., Parisi, L., Lauciani, V., Magnoni, F., & Casarotti, E. (2016) · Annals of Geophysics · doi:10.4401/ag-6945 · Cited by 9
Machine Learning-Based Models for Assessing Impacts Before, During and After Hurricane Florence
Harvey, J. L., Kumar, S., & Bao, S. (2019) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci44817.2019.9003061 · Cited by 9
An Automated Approach to Identifying Corporate Editing
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“…of offline inequalities in the form of exclusion of women, non-urbanites, and people in the global south (Stephens 2013; Quinn 2017; Gilbert 2010; Shelton et al. 2014; Graham et al. 2014; Burns and Meek 2015) due to structural barriers, such as, internet access, free time to participate,…”
Background
Foundations of Responsive Crisis Management: Institutional Design and Information
Boersma, K., & Wolbers, J. (2021) · Oxford University Press eBooks · doi:10.1093/acrefore/9780190228637.013.1610 · Cited by 9
Sophy
Kim, K., Ogawa, H., Nakamura, A., & Kojima, I. (2014) · Workshop on Location-based Social Networks · doi:10.1145/2755492.2755498 · Cited by 8
“The geography of data shadows drawn from social media appears to be quite effective at reflecting experiences of real-world events[21].”
Background
Investigating an Architectural Framework for Small Data Platforms
Thinyane, M. (2017) · UNU Collections (United Nations University) · Cited by 8
A geographical and content-based approach to prioritize relevant and reliable tweets for emergency management
Suarez, A. M., & Clarke, K. (2022) · Cartography and Geographic Information Science · doi:10.1080/15230406.2022.2081257 · Cited by 8
“Second, since hexagons are much less distracting to map readers as opposed to square cells, they ease the identification of spatial patterns (Battersby et al., 2016; Carr et al., 1992; Shelton et al., 2014).”
Background
Tracking Hurricane Dorian in GDELT and Twitter
OWUOR, I., Hochmair, H. H., & Cvetojevic, S. (2020) · AGILE GIScience Series · doi:10.5194/agile-giss-1-19-2020 · Cited by 8
“The enormous growth of data generated by social media results in an abundance of online information that facilitates event detection and related applications, such as emergency, traffic, and health and management, rescue operations [14], tracking of refugee movements [13], delineation of areas affected by local events…”
Background
Environmental Justice and Emerging Information Communication Technology: A Review for U.S. Natural Disaster Management
Cutts, B. B., Sinclair, K. M., Strauch, M. A., Slivnick, B., & Emmons, Z. (2015) · Environmental Justice · doi:10.1089/env.2015.0012 · Cited by 7
Toward a More Socially Impactful Geographical Analysis
Delmelle, E. C. (2019) · Geographical Analysis · doi:10.1111/gean.12213 · Cited by 7
Exploring geospatial characteristics of hashtag activism in Ferguson, Missouri: An application of social disorganization theory
Bunting, A. M., & Stamatel, J. P. (2019) · Geoforum · doi:10.1016/j.geoforum.2019.06.008 · Cited by 6
Tipping the scales: how geographical scale affects the interpretation of social media behavior in crisis research
Samuels, R., Xie, J., Mohammadi, N., & Taylor, J. E. (2022) · Natural Hazards · doi:10.1007/s11069-021-05193-8 · Cited by 7
“County-scale and state-scale correlations have been found to be moderately strong (Guan and Chen 2014; Shelton et al. 2014; Kryvasheyeu et al. 2016); however, each author notes the influence of scale on their analyses.”
Background
Tracing environmental narratives: a web-based tool for the analysis and visualization of georeferenced narratives
Zhou, X., & Li, D. (2017) · GeoJournal · doi:10.1007/s10708-017-9774-y · Cited by 8
“…data from volunteered geographic information, e.g., Twitter and Flickr, along with qualitative methods to examine tourist and consumption habits, social and religious expressions, and responses to events and natural disasters (Goodchild and Glennon 2010; Shelton et al. 2014; Zook and Graham 2010).”
What ChatGPT “Thinks” About Your Country? Sentiments and Frames of AI Geographies
Manor, I., & Segev, E. (2025) · Policy & Internet · doi:10.1002/poi3.70013 · Cited by 8
“The production and reproduction of country images, be it through the news, social media, guidebooks, or maps, further shapes how individuals view those countries (Graham and Zook 2013; Shelton et al. 2014; Graham et al. 2015).”
Towards a study of everyday geographic information: Bringing the everyday into view
Sabbata, S. D., Bennett, K., & Gardner, Z. (2023) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083231217606 · Cited by 6
“…eruptions and storms are perfect case studies in the ﬁ eld of Volunteered Geographic Information (VGI) which developed from the concept of citizens as sensors, as they are likely to leave a geographic digital shadow aligned with, but not fully matching, their geographic one (Shelton et al., 2014).”
Background
Choice, Negotiation, and Pluralism: a Conceptual Framework for Participatory Technologies in Museum Collections
Hansson, K. E., & Dahlgren, A. N. (2022) · Computer Supported Cooperative Work (CSCW) · doi:10.1007/s10606-022-09441-8 · Cited by 10
“Cultural geographers have also pointed out the hegemonic discourses in the geographic web (Crampton et al., 2013; Shelton et al., 2014; Soden and Palen, 2014; Zook et al., 2015).”
Background
A scalable architecture for real-time analysis of microblogging data
Cavalin, P., de Bayser, M. G., Moraes, T. G. P., de Oliveira, F. S., Pinhanez, C. S., Rademaker, A., & de Paula, R. A. (2015) · IBM Journal of Research and Development · doi:10.1147/jrd.2015.2408911 · Cited by 6
Analyzing Big Data with the Hybrid Interval Regression Methods
Huang, C.-H., Yang, K., & Kao, H.-Y. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/243921 · Cited by 6
“One of the major applications of the future parallel, distributed, and cloud systems is in big data analytic [2–5].”
Background
Territoire 2.0 et information géographique involontaire : gestion des crises et réseaux sociaux numériques
Santoni, V. (2022) · Annales de Géographie · doi:10.3917/ag.743.0072 · Cited by 6
Spatio-temporal patterns of disaster impact and recovery in YouTube content
Hladík, J., Heřman, L., Snopková, D., & Konečný, M. (2024) · International Journal of Digital Earth · doi:10.1080/17538947.2024.2426518 · Cited by 5
“…management, social media data (SMD) has garnered significant attention as a valuable source of spatially-related information (Resch, Uslander, and Havas 2018; Shelton et al. 2014; Wang et al. 2019; Wang, Lam, and Mihunov 2023), as they provide user-generated content that can be near real-time.”
Researching from Home, Inside, and the Online: Methodological Lessons from the Pandemic
Monnier-Reyna, M. (2024) · The Professional Geographer · doi:10.1080/00330124.2023.2295343 · Cited by 5
“Conversation focuses on big data (Crampton et al. 2013; Shelton et al. 2014), and geotagged data (Graham, Hale, and Gaffney 2014; Luo et al. 2016) and the platform of Twitter (Takhteyev, Gruzd, and Wellman 2012; Crampton et al. 2013; Graham, Hale, and Gaffney 2014; Shelton et al. 2014; Jongman et…”
Information visualisation for social media analytics
Dahyot, R., Brady, C., Bourges, C., & Bülbül, A. (2015) · International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) · doi:10.1109/iwcim.2015.7347082 · Cited by 5
“…simple entertainment for users, by exploiting the strong connection between the virtual world where these data and the physical reality [1 ] To navigate efficiently into datasets harvested from the web, it is also important to de ploy visualisation capabilities to generate insights and…”
Background
Media społecznościowe w czasie pandemii – przykład profili wrocławskich rad osiedla na Facebooku
Janc, K., & Jurkowski, W. (2020) · Rozwój Regionalny i Polityka Regionalna · doi:10.14746/rrpr.2020.51.03 · Cited by 5
Introducing a more-than-quantitative approach to explore emerging structures of feeling in the everyday
Bennett, K., & Sabbata, S. D. (2023) · Emotion, space and society · doi:10.1016/j.emospa.2023.100965 · Cited by 5
Addressing the Information Needs of Crisis-Affected Communities: The Interplay of Legacy Media and Social Media in a Rural Disaster
Dailey, D., & Starbird, K. (2016) · Palgrave Macmillan US eBooks · doi:10.1057/978-1-349-94925-0_18 · Cited by 5
Toward a Typology of Participation in Crowdwork
Hansson, K. E., Muller, M. J., Aitamurto, T., Light, A., Mazarakis, A., Gupta, N., & Ludwig, T. G. (2016) · ACM Conference on Computer Supported Cooperative Work (CSCW) · doi:10.1145/2818052.2855510 · Cited by 5
“The power imbalances and socio-spatial relations in the geographic web also affect the ways maps are developed [9, 35, 41], and may affect classification systems that influence the organization of knowledge for millions of people [7].”
Background
Humanitarian Mapping
Cinnamon, J. (2019) · Elsevier eBooks · doi:10.1016/b978-0-08-102295-5.10559-1 · Cited by 5
Resonance+: Operationalizing Protective Action Decision Model for Finding Information Useful for Public Information Officers
Wang, D., & Kogan, M. (2025) · ACM Transactions on Social Computing · doi:10.1145/3708492 · Cited by 4
“Moreover, adoption and use of social media platforms often follow “long-standing patterns of sociospatial inequality, producing uneven data shadows” [71] and potentially excluding the voices of the most vulnerable populations in natural disasters [3].”
A Cloud Native Machine Learning based Approach for Detection and Impact of Cyclone and Hurricanes on Coastal Areas of Pacific and Atlantic Ocean
Joshi, D., Sayed, F., Jain, H., Beri, J., Bandi, Y., & Karamchandani, S. (2021) · Journal of University of Shanghai for Science and Technology · doi:10.51201/jusst/21/07149 · Cited by 4
Home Tweet Home: Can Social Media Define a Community?
Rock, A. E., & Taber, J. (2020) · Journal of Appalachian Studies · doi:10.5406/jappastud.26.1.0087 · Cited by 4
The digital life of the #migrantcaravan: Contextualizing Twitter as a spatial technology
Walker, M. A., & Boamah, E. F. (2020) · Big Data & Society · doi:10.1177/2053951720978485 · Cited by 4
“…i related to the #migrantcaravan, p is the sum of all tweets about the #migrantcaravan; ri is the number of random tweets in hexagon i and r is the sum of all random tweets (for description and application of this normalization methods, see Shelton et al., 2014; Poorthuis et al., 2016, 2020).”
BackgroundInfluential
TOURISTS GEOVISUALIZATION ANALYSIS UTILIZING INSTAGRAM DATA IN CENTRAL JAVA PROVINCE AND SPECIAL REGION OF YOGYAKARTA
Rofi’i, A., Wibowo, T. W., Sudaryatno, & Farda, N. M. (2019) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-4-w16-535-2019 · Cited by 4
“Meanwhile, size of tessellation cell 2 km refers to the rule of Shelton et al (2014) which states that the size of hexagonal tessellation cell in national scale is 65 km whereas for urban areas is 2 km (Shelton, et al , 2014).”
BackgroundMethodInfluential
Data Census of a Geographically-Bounded Tweet Set to Enhance Common Operational Picture Tools
Elrod, N. J., Hall, H., Mahajan, P., Grace, R., & Kropczynski, J. (2021) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2021.275 · Cited by 4
“These hyperlocal observations compliment national and global analyses of geotagging behavior in previous benchmarked studies [11, 12], and fill in gaps on geographic information behavior in crisis informatics literature [13, 45].”
Background
Identifying Locally- and Globally-Distinctive Urban Place Descriptors from Heterogeneous User-Generated Content
Feick, R., & Robertson, C. (2015) · Advances in geographic information science · doi:10.1007/978-3-319-19950-4_4 · Cited by 4
Crowd Dynamics
Hansson, K. E., Muller, M. J., Aitamurto, T., Irani, L. C., Mazarakis, A., Gupta, N., & Ludwig, T. G. (2016) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/2851581.2856505 · Cited by 3
“Cultural geographers have also pointed out the hegemonic discourses and socio-spatial relations in the geographic web [8, 31, 28, 33].”
Background
Encoding and compressing hexagonal raster data by the Gosper curve
Xin, R., Tinghua Ai, A., & Ai, B. (2019) · Transactions in GIS · doi:10.1111/tgis.12569 · Cited by 3
“…an import‐ ant role in the analysis of natural research fields, such as seismic data analysis (Dharmawan & Farda, 2017), hurricane data analysis (Shelton, Poorthuis, Graham, & Zook, 2014), social research fields such as store retail data analysis (Polisciuc, Maçãs, Assunção, & Macado, 2016),…”
Background
Empowering health geography research with location-based social media data: innovative food word expansion and energy density prediction via word embedding and machine learning
Wang, J., Kim, G., & Chang, K. C. (2023) · International Journal of Health Geographics · doi:10.1186/s12942-023-00344-5 · Cited by 3
“…multiple fields have benefited from mining this rapidly available data to profile different environments, including political analyses [29–32], natural hazards and disaster management [33–35], epidemiology outbreak tracking [36–39], medical and pharmaceutical services [40, 41], and others [42–44].”
Background
SPATIAL-TEMPORAL ANALYSIS OF OPENSTREETMAP DATA AFTER NATURAL DISASTERS: A CASE STUDY OF HAITI UNDER HURRICANE MATTHEW
Xu, J., Li, L., & Zhou, Q. (2017) · The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-archives-xlii-2-w7-1401-2017 · Cited by 3
“Tweeting were vastly concentrated in areas hit hardest by the Hurricane Sandy (Taylor Shelton et al.2014) and population of re-tweeting activity in geographically vulnerable places differs from that of the general (Marina Kogan et al. 2015).”
Background
Primerjava uporabe prostovoljnih geografskih informacij za spremljanje poplav in potresov // Comparison of the volunteered geographic information usage to monitor floods and earthquakes
Čekada, M. T., & Radovan, D. (2019) · Geografski vestnik · doi:10.3986/gv91207 · Cited by 4
Gender Imbalance and Spatiotemporal Patterns of Contributions to Citizen Science Projects: The Case of Zooniverse
Ibrahim, K. H., Khodursky, S., & Yasseri, T. (2021) · Frontiers in Physics · doi:10.3389/fphy.2021.650720 · Cited by 3
Talking to My Community Elsewhere
Maier, D., Stoltenberg, D., Pfetsch, B., & Waldherr, A. (2021) · Routledge eBooks · doi:10.4324/9781003036159-17 · Cited by 3

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
ORCID: Shelton, Taylor
Poorthuis, Ate
Graham, Mark
Zook, Matthew
Venue: Geoforum
Publisher: Elsevier BV
Published: 2014-03
Volume: 52
Pages: 167-179
ISSN: 0016-7185 (ISSN-L)
DOI: 10.1016/j.geoforum.2014.01.006
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
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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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Mapping the data shadows of Hurricane Sandy: Uncovering the sociospatial dimensions of ‘big data’

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