Geospatial big data handling theory and methods: A review and research challenges

The Fractal Geometry of Nature

Mandelbrot, B. B., & WHEELER, J. A. (1983) · American Journal of Physics · doi:10.1119/1.13295 · Cited by 21,964

Mining association rules between sets of items in large databases

Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907

Anomaly detection: A survey

Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687

Amazon's Mechanical Turk: A New Source of Inexpensive, Yet High-Quality, Data?

Buhrmester, M., Kwang, T., & Gosling, S. D. (2011) · Perspectives on Psychological Science · doi:10.1177/1745691610393980 · Cited by 9,996

Untitled work

(2011) · Cited by 5,203

Random forests for classification in ecology

Cutler, D. R., Edwards, T. C., Beard, K. H., Cutler, A., Hess, K. T., Gibson, J., & Lawler, J. J. (2007) · Ecology · doi:10.1890/07-0539.1 · Cited by 4,950

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

Big data: A survey

Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913

How Nature Works

Bak, P. (1996) · Springer eBooks · doi:10.1007/978-1-4757-5426-1 · Cited by 2,830

Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy

Bonney, R. E., Cooper, C. B., Dickinson, J. L., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009) · BioScience · doi:10.1525/bio.2009.59.11.9 · Cited by 2,542

Big Data, new epistemologies and paradigm shifts

Kitchin, R. (2014) · Big Data & Society · doi:10.1177/2053951714528481 · Cited by 2,355

A new dawn for citizen science

SILVERTOWN, J. W. (2009) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2009.03.017 · Cited by 2,199

Towards an integrated crowdsourcing definition

Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. (2012) · Journal of Information Science · doi:10.1177/0165551512437638 · Cited by 1,859

How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets

Haklay, M. (2010) · Environment and Planning B Planning and Design · doi:10.1068/b35097 · Cited by 1,646

ST-DBSCAN: An algorithm for clustering spatial–temporal data

Birant, D., & Kut, A. (2006) · Data & Knowledge Engineering · doi:10.1016/j.datak.2006.01.013 · Cited by 1,437

Public Participation Geographic Information Systems: A Literature Review and Framework

Sieber, R. E. (2006) · Annals of the Association of American Geographers · doi:10.1111/j.1467-8306.2006.00702.x · Cited by 1,214

A Space–Time Permutation Scan Statistic for Disease Outbreak Detection

Kulldorff, M., Heffernan, R. T., Hartman, J., Assunção, R., & Mostashari, F. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020059 · Cited by 1,142

Specification and Estimation of Spatial Panel Data Models

Elhorst, J. P. (2003) · International Regional Science Review · doi:10.1177/0160017603253791 · Cited by 1,114

Crowdsourcing geographic information for disaster response: a research frontier

Goodchild, M. F., & Glennon, J. A. (2010) · International Journal of Digital Earth · doi:10.1080/17538941003759255 · Cited by 910

Researching Volunteered Geographic Information: Spatial Data, Geographic Research, and New Social Practice

Elwood, S. A., Goodchild, M. F., & Sui, D. Z. (2011) · Annals of the Association of American Geographers · doi:10.1080/00045608.2011.595657 · Cited by 865

Online-SVR for short-term traffic flow prediction under typical and atypical traffic conditions

de Castro-Neto, M. M., Jeong, Y., Jeong, M. K., & Han, L. D. (2008) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.07.069 · Cited by 845

Assuring the quality of volunteered geographic information

Goodchild, M. F., & Li, L. (2012) · Spatial Statistics · doi:10.1016/j.spasta.2012.03.002 · Cited by 834

Why Functional Programming Matters

Hughes, J. (1989) · The Computer Journal · doi:10.1093/comjnl/32.2.98 · Cited by 741

Using social media to quantify nature-based tourism and recreation

Wood, S. A., Guerry, A. D., Silver, J. M., & Lacayo, M. (2013) · Scientific Reports · doi:10.1038/srep02976 · Cited by 724

Remote sensing big data computing: Challenges and opportunities

Ma, Y., Wu, H., Wang, L., Huang, B., Ranjan, R. K., Zomaya, A. Y., & Jie, W. (2014) · Future Generation Computer Systems · doi:10.1016/j.future.2014.10.029 · Cited by 681

A review of overview+detail, zooming, and focus+context interfaces

Cockburn, A., Karlson, A. K., & Bederson, B. B. (2009) · ACM Computing Surveys · doi:10.1145/1456650.1456652 · Cited by 662

The (mis)behavior of markets: a fractal view of risk, ruin, and reward

(2005) · Choice Reviews Online · doi:10.5860/choice.42-3541 · Cited by 620

The history of public participation in ecological research

Miller‐Rushing, A. J., Primack, R. B., & Bonney, R. E. (2012) · Frontiers in Ecology and the Environment · doi:10.1890/110278 · Cited by 599

Big data and human geography: Opportunities, challenges and risks

Kitchin, R. (2013) · Dialogues in Human Geography · doi:10.1177/2043820613513388 · Cited by 593

Harvesting ambient geospatial information from social media feeds

Stefanidis, A., Crooks, A. T., & Radzikowski, J. (2011) · GeoJournal · doi:10.1007/s10708-011-9438-2 · Cited by 568

A review of informal volunteerism in emergencies and disasters: Definition, opportunities and challenges

Whittaker, J., McLennan, B. J., & Handmer, J. W. (2015) · International Journal of Disaster Risk Reduction · doi:10.1016/j.ijdrr.2015.07.010 · Cited by 552

Crowdsourcing geospatial data

Heipke, C. (2010) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2010.06.005 · Cited by 470

#Earthquake: Twitter as a Distributed Sensor System

Crooks, A. T., Croitoru, A., Stefanidis, A., & Radzikowski, J. (2012) · Transactions in GIS · doi:10.1111/j.1467-9671.2012.01359.x · Cited by 470

A Comprehensive Framework for Intrinsic OpenStreetMap Quality Analysis

Barron, C., Neis, P., & Zipf, A. (2013) · Transactions in GIS · doi:10.1111/tgis.12073 · Cited by 424

Nonlinear black-box models in system identification: Mathematical foundations

Juditsky, A., Hjalmarsson, H., Albert Benveniste, A., Delyon, B., Lennart Ljung, A., Sjoberg, J. S., & Zhang, Q. (1995) · Automatica · doi:10.1016/0005-1098(95)00119-1 · Cited by 404

The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007–2011

Neis, P., Zielstra, D., & Zipf, A. (2011) · Future Internet · doi:10.3390/fi4010001 · Cited by 402

How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information

Haklay, M., Basiouka, S., Antoniou, V., & Ather, A. (2010) · The Cartographic Journal · doi:10.1179/000870410x12911304958827 · Cited by 402

Data-driven geography

Miller, H. J., & Goodchild, M. F. (2015) · GeoJournal · doi:10.1007/s10708-014-9602-6 · Cited by 391

The Cognitive Science of Visual‐Spatial Displays: Implications for Design

Hegarty, M. (2011) · Topics in Cognitive Science · doi:10.1111/j.1756-8765.2011.01150.x · Cited by 388

Quality Control in Crowdsourcing Systems: Issues and Directions

Allahbakhsh, M., Benatallah, B., Ignjatović, A., Motahari‐Nezhad, H. R., Bertino, E., & Dustdar, S. (2013) · IEEE Internet Computing · doi:10.1109/mic.2013.20 · Cited by 384

Head/Tail Breaks: A New Classification Scheme for Data with a Heavy-Tailed Distribution

Jiang, B. (2012) · The Professional Geographer · doi:10.1080/00330124.2012.700499 · Cited by 380

Crowdsourcing for climate and atmospheric sciences: current status and future potential

Muller, C., Chapman, L., Johnston, S., Christopher, K. I. D. D., Illingworth, S., Foody, G. M., Overeem, A., & Leigh, R. J. (2015) · International Journal of Climatology · doi:10.1002/joc.4210 · Cited by 371

Beyond the geotag: situating ‘big data’ and leveraging the potential of the geoweb

Crampton, J. W., Graham, M., Poorthuis, A., Shelton, T., Stephens, M., Wilson, M. W., & Zook, M. (2013) · Cartography and Geographic Information Science · doi:10.1080/15230406.2013.777137 · Cited by 369

A Three-Stage Iterative Procedure for Space-Time Modeling

Pfeifer, P. E., & Deutsch, S. J. (1980) · Technometrics · doi:10.2307/1268381 · Cited by 351

Cognitive Map-Design Research in the Twentieth Century: Theoretical and Empirical Approaches

Montello, D. R. (2002) · Cartography and Geographic Information Science · doi:10.1559/152304002782008503 · Cited by 344

Rules of Crowdsourcing: Models, Issues, and Systems of Control

Saxton, G. D., Oh, O., & Kishore, R. (2013) · Information Systems Management · doi:10.1080/10580530.2013.739883 · Cited by 343

Cognitive and Usability Issues in Geovisualization

Slocum, T. A., Blok, C. A., Jiang, B., Koussoulakou, A., Montello, D. R., Fuhrmann, S., & Hedley, N. (2001) · Cartography and Geographic Information Science · doi:10.1559/152304001782173998 · Cited by 340

User-Generated Content

Krumm, J. C., Davies, N., & Narayanaswami, C. (2008) · IEEE Pervasive Computing · doi:10.1109/mprv.2008.85 · Cited by 335

Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics

Nakaya, T., & Yano, K. (2010) · Transactions in GIS · doi:10.1111/j.1467-9671.2010.01194.x · Cited by 322

Big data classification

Suthaharan, S. (2014) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/2627534.2627557 · Cited by 320

imMens: Real‐time Visual Querying of Big Data

Liu, Z., Jiang, B., & Heer, J. (2013) · Computer Graphics Forum · doi:10.1111/cgf.12129 · Cited by 319

Recent Developments and Future Trends in Volunteered Geographic Information Research: The Case of OpenStreetMap

Neis, P., & Zielstra, D. (2014) · Future Internet · doi:10.3390/fi6010076 · Cited by 288

An ontology of slums for image-based classification

Kohli, D., Sliuzas, R., Kerle, N., & Stein, A. (2011) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2011.11.001 · Cited by 286

A Comparative Study of Proprietary Geodata and Volunteered Geographic Information for Germany

Zielstra, D., & Zipf, A. (2010) · Cited by 284

Neogeography and the Delusion of Democratisation

Haklay, M. (2013) · Environment and Planning A Economy and Space · doi:10.1068/a45184 · Cited by 272

Discovering data quality rules

Chiang, F., & Miller, R. J. (2008) · Proceedings of the VLDB Endowment · doi:10.14778/1453856.1453980 · Cited by 267

Data quality: The other face of Big Data

Saha, B., & Srivastava, D. (2014) · 2014 IEEE 30th International Conference on Data Engineering · doi:10.1109/icde.2014.6816764 · Cited by 262

Smart Cities, Big Data

Batty, M. (2012) · Environment and Planning B Planning and Design · doi:10.1068/b3902ed · Cited by 257

Developing a conceptual framework for visually-enabled geocollaboration

MacEachren, A. M., & Brewer, I. (2004) · International Journal of Geographical Information Systems · doi:10.1080/13658810310001596094 · Cited by 207

Creating the UK National Statistics 2001 Output Area Classification

Vickers, D., & Rees, P. (2007) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/j.1467-985x.2007.00466.x · Cited by 196

Location Privacy and Location-Aware Computing

Duckham, M., & Kulik, L. (2006) · CRC Press eBooks · doi:10.1201/9781420008609-11 · Cited by 195

Citizen science on a smartphone: Participants’ motivations and learning

Land‐Zandstra, A. M., Devilee, J. L. A., Snik, F., Buurmeijer, F., & van den Broek, J. M. (2015) · Public Understanding of Science · doi:10.1177/0963662515602406 · Cited by 194

Urban social vulnerability assessment with physical proxies and spatial metrics derived from air- and spaceborne imagery and GIS data

Ebert, A., Kerle, N., & Stein, A. A. (2008) · Natural Hazards · doi:10.1007/s11069-008-9264-0 · Cited by 193

Comparing the Quality of Crowdsourced Data Contributed by Expert and Non-Experts

See, L., Comber, A., Salk, C. F., Fritz, S., van der Velde, M., Perger, C., Schill, C., McCallum, I., Kraxner, F., & Obersteiner, M. H. (2013) · PLoS ONE · doi:10.1371/journal.pone.0069958 · Cited by 191

Identifying patterns in spatial information: A survey of methods

Shekhar, S., Evans, M. R., Kang, J. M., & Mohan, P. (2011) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.25 · Cited by 190

An Empirically-Derived Taxonomy of Interaction Primitives for Interactive Cartography and Geovisualization

Roth, R. E. (2013) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2013.130 · Cited by 189

The Geotemporal Demographics of Twitter Usage

Longley, P., Adnan, M., & Lansley, G. (2015) · Environment and Planning A Economy and Space · doi:10.1068/a130122p · Cited by 189

Big-Data Visualization

Keim, D., Qu, H.-M., & Ma, K. (2013) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2013.54 · Cited by 181

Science 2.0

Shneiderman, B. (2008) · Science · doi:10.1126/science.1153539 · Cited by 178

MEASURES AND INDICATORS OF VGI QUALITY: AN OVERVIEW

Antoniou, V., & Skopeliti, A. (2015) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-3-w5-345-2015 · Cited by 176

Ht-Index for Quantifying the Fractal or Scaling Structure of Geographic Features

Jiang, B., & Yin, J. (2013) · Annals of the Association of American Geographers · doi:10.1080/00045608.2013.834239 · Cited by 174

Tiers of ontology and consistency constraints in geographical information systems

Frank, A. U. (2001) · International Journal of Geographical Information Systems · doi:10.1080/13658810110061144 · Cited by 167

Modifiable Temporal Unit Problem (MTUP) and Its Effect on Space-Time Cluster Detection

Cheng, T., & Adepeju, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0100465 · Cited by 167

Exploring the efficiency of users' visual analytics strategies based on sequence analysis of eye movement recordings

Çöltekin, A., Fabrikant, S. I., & Lacayo, M. (2010) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2010.511718 · Cited by 166

Comparison of Volunteered Geographic Information Data Contributions and Community Development for Selected World Regions

Neis, P., Zielstra, D., & Zipf, A. (2013) · Future Internet · doi:10.3390/fi5020282 · Cited by 166

Big data: Distilling meaning from data

Frankell, F. C., & Reid, R. (2008) · Nature · doi:10.1038/455030a · Cited by 162

Applications of the MapReduce programming framework to clinical big data analysis: current landscape and future trends

Mohammed, E. A., Far, B. H., & Naugler, C. T. (2014) · BioData Mining · doi:10.1186/1756-0381-7-22 · Cited by 150

Scaling of geographic space from the perspective of city and field blocks and using volunteered geographic information

Jiang, B., & Liu, X. (2011) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2011.575074 · Cited by 150

Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems

Zhang, L., Stoffel, A., Behrisch, M., Mittelstädt, S., Schreck, T., Pompl, R., Weber, S., Last, H., & Keim, D. A. (2012) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2012.6400554 · Cited by 146

Privacy in Location-Aware Computing Environments

Anthony, D. L., Henderson, T., & Kotz, D. F. (2007) · IEEE Pervasive Computing · doi:10.1109/mprv.2007.83 · Cited by 145

The quality of big (geo)data

Goodchild, M. F. (2013) · Dialogues in Human Geography · doi:10.1177/2043820613513392 · Cited by 145

Quality Evaluation of VGI Using Authoritative Data—A Comparison with Land Use Data in Southern Germany

Dorn, H., Törnros, T., & Zipf, A. (2015) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi4031657 · Cited by 139

Geospatial analysis requires a different way of thinking: the problem of spatial heterogeneity

Jiang, B. (2014) · GeoJournal · doi:10.1007/s10708-014-9537-y · Cited by 137

Mining Association Rules in Spatio‐Temporal Data: An Analysis of Urban Socioeconomic and Land Cover Change

Mennis, J. L., & Liu, J. W. (2005) · Transactions in GIS · doi:10.1111/j.1467-9671.2005.00202.x · Cited by 134

Spatial big-data challenges intersecting mobility and cloud computing

Shekhar, S., Gunturi, V., Evans, M. R., & Yang, K. (2012) · ACM International Workshop on Data Engineering for Wireless and Mobile Access (MobiDE) · doi:10.1145/2258056.2258058 · Cited by 132

The Evolution of Natural Cities from the Perspective of Location-Based Social Media

Jiang, B., & Miao, Y. (2014) · The Professional Geographer · doi:10.1080/00330124.2014.968886 · Cited by 131

A trust and reputation model for filtering and classifying knowledge about urban growth

Bishr, M., & Mantelas, L. A. (2008) · GeoJournal · doi:10.1007/s10708-008-9182-4 · Cited by 126

A general-purpose parallel raster processing programming library test application using a geographic cellular automata model

Guan, Q., & Clarke, K. (2010) · International Journal of Geographical Information Systems · doi:10.1080/13658810902984228 · Cited by 126

Usability of VGI for validation of land cover maps

Fonte, C. C., Bastin, L., See, L., Foody, G. M., & Lupia, F. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1018266 · Cited by 103

Head/tail breaks for visualization of city structure and dynamics

Jiang, B. (2014) · Cities · doi:10.1016/j.cities.2014.11.013 · Cited by 98

Space–Time Geostatistics for Geography: A Case Study of Radiation Monitoring Across Parts of Germany. 地理学的时空地统计学：横跨德国部分区域的辐射监测的案例研究

Heuvelink, G. B. M., & Griffith, D. A. (2010) · Geographical Analysis · doi:10.1111/j.1538-4632.2010.00788.x · Cited by 94

A system of types and operators for handling vague spatial objects

Dilo, A., de By, R. A., & Stein, A. A. (2007) · International Journal of Geographical Information Systems · doi:10.1080/13658810601037096 · Cited by 92

Collaborative damage mapping for emergency response: the role of Cognitive Systems Engineering

Kerle, N., & Hoffman, R. R. (2013) · Natural hazards and earth system sciences · doi:10.5194/nhess-13-97-2013 · Cited by 90

Big data visual analytics for exploratory earth system simulation analysis

Steed, C. A., Ricciuto, D. M., Shipman, G. M., Smith, B., THORNTON, P. E., Wang, D., Shi, X., & Williams, D. N. (2013) · Computers & Geosciences · doi:10.1016/j.cageo.2013.07.025 · Cited by 86

A Dynamic Spatial Weight Matrix and Localized Space–Time Autoregressive Integrated Moving Average for Network Modeling

Cheng, T., Wang, J., Haworth, J., Heydecker, B. G., & Chow, A. H. F. (2014) · Geographical Analysis · doi:10.1111/gean.12026 · Cited by 84

Revisiting Web Cartography in the United States: the Rise of User-Centered Design

Tsou, M. (2011) · Cartography and Geographic Information Science · doi:10.1559/15230406382250 · Cited by 82

The Scale of VGI in Map Production: A Perspective on European National Mapping Agencies

Olteanu‐Raimond, A., Hart, G., Foody, G. M., Touya, G., Kellenberger, T., & Demetriou, D. (2016) · Transactions in GIS · doi:10.1111/tgis.12189 · Cited by 82

The Sensor Web: systems of sensor systems

van Zyl, T. L., Simonis, I., & McFerren, G. (2008) · International Journal of Digital Earth · doi:10.1080/17538940802439549 · Cited by 77

Accurate Attribute Mapping from Volunteered Geographic Information: Issues of Volunteer Quantity and Quality

Foody, G. M., See, L., Fritz, S., van der Velde, M., Perger, C., Schill, C., Boyd, D. S., & Comber, A. (2014) · The Cartographic Journal · doi:10.1080/00087041.2015.1108658 · Cited by 75

Towards Ubiquitous Cartography

Gärtner, G., Bennett, D. A., & Morita, T. (2007) · Cartography and Geographic Information Science · doi:10.1559/152304007782382963 · Cited by 73

Legal issues with volunteered geographic information

Scassa, T. (2012) · Canadian Geographies / Géographies canadiennes · doi:10.1111/j.1541-0064.2012.00444.x · Cited by 72

CloST

Tan, H., Luo, W., & Ni, L. M.-S. (2012) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2396761.2398589 · Cited by 72

Confronting the data-divide in a time of spatial turns and volunteered geographic information

Cinnamon, J., & Schuurman, N. (2012) · GeoJournal · doi:10.1007/s10708-012-9458-6 · Cited by 72

Local online kernel ridge regression for forecasting of urban travel times

Haworth, J., Shawe-Taylor, J. S., Cheng, T., & Wang, J. (2014) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2014.05.015 · Cited by 72

Big Data

Sugumaran, R., Burnett, J., & Armstrong, M. P. (2014) · CRC Press eBooks · doi:10.1201/b16524 · Cited by 72

Grid-enabling Geographically Weighted Regression: A Case Study of Participation in Higher Education in England

Harris, R., Singleton, A. D., Grose, D., Brunsdon, C., & Longley, P. (2010) · Transactions in GIS · doi:10.1111/j.1467-9671.2009.01181.x · Cited by 71

Understanding Science 2.0: Crowdsourcing and Open Innovation in the Scientific Method

Bücheler, T., & Sieg, J. H. (2011) · Procedia Computer Science · doi:10.1016/j.procs.2011.09.014 · Cited by 70

Tracing the German centennial flood in the stream of tweets

Fuchs, G., Andrienko, N. V., Andrienko, G., Bothe, S., & Stange, H. (2013) · ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information (GEOCROWD) · doi:10.1145/2534732.2534741 · Cited by 66

Verifying quantitative reliability for programs that execute on unreliable hardware

Carbin, M., Misailovíc, S., & Rinard, M. (2013) · ACM SIGPLAN Notices · doi:10.1145/2544173.2509546 · Cited by 65

Customizing Computational Methods for Visual Analytics with Big Data

Choo, J., & Park, H. (2013) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2013.39 · Cited by 62

Volunteered Geographic Information: Towards the establishment of a new paradigm

Jiang, B., & Thill, J. (2015) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2015.09.011 · Cited by 61

Predicting human mobility with activity changes

Huang, W., Li, S., Liu, X., & Yifang Ban, A. (2015) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2015.1033421 · Cited by 58

A Task Analysis Approach to the Visualization of Geographic Data

Knapp, L. (1995) · Springer eBooks · doi:10.1007/978-94-011-0103-5_25 · Cited by 58

Design and Implementation of a Model, Web-based, GIS-Enabled Cancer Atlas

MacEachren, A. M., Crawford, S., Akella, M., & Lengerich, E. J. (2008) · The Cartographic Journal · doi:10.1179/174327708x347755 · Cited by 56

On predicting and improving the quality of Volunteer Geographic Information projects

Bordogna, G., Carrara, P., Criscuolo, L., Pepe, M., & Rampini, A. (2014) · International Journal of Digital Earth · doi:10.1080/17538947.2014.976774 · Cited by 55

Quality Analysis of OpenStreetMap Data Based on Application Needs

Mondzech, J., & Sester, M. (2011) · Cartographica The International Journal for Geographic Information and Geovisualization · doi:10.3138/carto.46.2.115 · Cited by 55

A parallel computing approach to fast geostatistical areal interpolation

Guan, Q., Kyriakidis, P. C., & Goodchild, M. F. (2011) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2011.563744 · Cited by 55

Efficient spatial query processing for big data

Lee, K., Ganti, R., Srivatsa, M., & Liu, L. (2014) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2666310.2666481 · Cited by 54

Towards (Re)Constructing Narratives from Georeferenced Photographs through Visual Analytics

Straumann, R. K., Çöltekin, A., & Andrienko, G. (2014) · The Cartographic Journal · doi:10.1179/1743277414y.0000000079 · Cited by 53

The Australia urban research gateway

Sinnott, R., Bayliss, C., Bromage, A., Galang, G., Grazioli, G., Greenwood, P., Macaulay, A., Morandini, L., Nogoorani, G., Niño-Ruiz, M., et al. (2014) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3282 · Cited by 52

Choosing and using geospatial displays: Effects of design on performance and metacognition.

Hegarty, M., Smallman, H. S., & Stull, A. T. (2012) · Journal of Experimental Psychology Applied · doi:10.1037/a0026625 · Cited by 52

Using volunteered information to map the Queensland floods

McDougall, K. (2011) · University of Southern Queensland ePrints (University of Southern Queensland) · Cited by 49

Learning wind fields with multiple kernels

Foresti, L., Tuia, D., Kanevski, M., & Pozdnoukhov, A. (2010) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-010-0405-0 · Cited by 42

Image Mining for Modeling of Forest Fires From Meteosat Images

Rajasekar, U., Bijker, W., & Stein, A. A. (2007) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2006.883460 · Cited by 42

Decentralized Spatial Computing

Duckham, M. (2012) · Springer eBooks · doi:10.1007/978-3-642-30853-6 · Cited by 41

Spatial Data Mining

Wang, S., & Yuan, H. (2014) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.2014100103 · Cited by 40

Amateur or Professional: Assessing the Expertise of Major Contributors in OpenStreetMap Based on Contributing Behaviors

Yang, A., Fan, H., & Jing, N. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5020021 · Cited by 40

Spatiotemporal Data Mining

Cheng, T., Haworth, J., Anbaroğlu, B., Tanaksaranond, G., & Wang, J. (2013) · Springer eBooks · doi:10.1007/978-3-642-23430-9_68 · Cited by 39

The Big Picture for Big Data: Visualization

Shneiderman, B. (2014) · Science · doi:10.1126/science.343.6172.730-a · Cited by 37

Temporal Analysis on Contribution Inequality in OpenStreetMap: A Comparative Study for Four Countries

Yang, A., Fan, H., Jing, N., Sun, Y., & Zipf, A. (2016) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi5010005 · Cited by 35

VAST Challenge 2012: Visual analytics for big data

Cook, K. A., Grinstein, G., Whiting, M., Cooper, M., Havig, P. R., Liggett, K. K., Nebesh, B., & Paul, C. L. (2012) · IEEE Conference on Visual Analytics Science and Technology (VAST) · doi:10.1109/vast.2012.6400529 · Cited by 34

A model for external drift kriging with uncertain covariates applied to air quality measurements and dispersion model output

van de Kassteele, J., & Stein, A. A. (2005) · Environmetrics · doi:10.1002/env.771 · Cited by 33

Geospatial Metadata 2.0 – An approach for Volunteered Geographic Information

Kalantari, M., Rajabifard, A., Olfat, H., & WILLIAMSON, I. P. (2014) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2014.06.005 · Cited by 32

Handling Big Data Efficiently by Using Map Reduce Technique

Maitrey, S., & Jha, C. K. (2015) · doi:10.1109/cict.2015.140 · Cited by 31

Space-time statistics for environmental and agricultural related phenomena

Stein, A., van Groenigen, J. W., Jeger, M. J., & Hoosbeek, M. R. (1998) · Environmental and Ecological Statistics · doi:10.1023/a:1009614821618 · Cited by 29

Data Quality Ontology: An Ontology for Imperfect Knowledge

Frank, A. U. (2007) · Springer eBooks · doi:10.1007/978-3-540-74788-8_25 · Cited by 29

Information Overload: Causes, Symptoms and Solutions

Ruff, J. A. (2002) · Cited by 29

Citizens as Database: Conscious Ubiquity in Data Collection

Richter, K., & Winter, S. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-22922-0_27 · Cited by 28

The Next Generation of Atlas User Interfaces: A User Study with “Digital Natives”

Schnürer, R., Sieber, R., & Çöltekin, A. (2014) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-319-07926-4_3 · Cited by 27

High Quality Geographic Services and Bandwidth Limitations

Çöltekin, A., & Reichenbacher, T. (2011) · Future Internet · doi:10.3390/fi3040379 · Cited by 26

Semantic Interoperability of Sensor Data with Volunteered Geographic Information: A Unified Model

Bakillah, M., Liang, S. H. L., Zipf, A., & Arsanjani, J. J. (2013) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi2030766 · Cited by 25

Planet Labs’ Remote Sensing Satellite System

Marshall, W. S., & Boshuizen, C. R. (2013) · Digital Commons - USU (Utah State University) · Cited by 25

High-Performance Computing and Geography: Developments, Issues, and Case Studies

Turton, I., & Openshaw, S. (1998) · Environment and Planning A Economy and Space · doi:10.1068/a301839 · Cited by 24

Visualisation Tools for Understanding Big Data

Cheshire, J. A., & Batty, M. (2012) · Environment and Planning B Planning and Design · doi:10.1068/b3903ed · Cited by 23

Spatio-temporal Data Mining

(2011) · Springer eBooks · doi:10.1007/springerreference_62968 · Cited by 22

Micro-mapping with smartphones for monitoring agricultural development

Frommberger, L., Schmid, F., & Cai, C. (2013) · ACM Symposium on Computing for Development (ACM DEV) · doi:10.1145/2442882.2442934 · Cited by 21

Advances in geocomputation (1996–2011)

Cheng, T., Haworth, J., & Manley, E. (2012) · Computers Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2012.10.002 · Cited by 21

Assessing volunteered geographic information for rapid flood damage estimation

Poser, K., Kreibich, H., & Dransch, D. (2009) · Publication Database GFZ (GFZ German Research Centre for Geosciences) · Cited by 18

Approximate voronoi cell computation on spatial data streams

Sharifzadeh, M., & Shahabi, C. (2007) · The VLDB Journal · doi:10.1007/s00778-007-0081-y · Cited by 18

Benchmarking Spatial Big Data

Shekhar, S., Evans, M. R., Gunturi, V., Yang, K., & Cugler, D. C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-53974-9_8 · Cited by 17

Streaming big data with self-adjusting computation

Acar, U. A., & Chen, Y. (2013) · doi:10.1145/2429376.2429382 · Cited by 17

SksOpen: Efficient Indexing, Querying, and Visualization of Geo-spatial Big Data

Lu, Y., Zhang, M., Witherspoon, S., Yesha, Y., Yesha, Y., & Rishe, N. D. (2013) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2013.196 · Cited by 16

Space-variant image coding for stereoscopic media

Çöltekin, A. (2009) · Picture Coding Symposium · doi:10.1109/pcs.2009.5167396 · Cited by 15

Geographical Information as ‘Personal Information’

Scassa, T. (2010) · Oxford University Commonwealth Law Journal · doi:10.5235/147293410794895322 · Cited by 13

Visualizing and quantifying the movement of vegetative drought using remote-sensing data and GIS

Rulinda, C. M., Stein, A. A., & Turdukulov, U. (2012) · International Journal of Geographical Information Systems · doi:10.1080/13658816.2012.723712 · Cited by 12

An application of problem and product ontologies for the revision of beach nourishments

van de Vlag, D. E., Vasseur, B., Stein, A., & Jeansoulin, R. (2005) · International Journal of Geographical Information Systems · doi:10.1080/13658810500032404 · Cited by 12

Action and interaction in volunteered geographic information: a workshop review

Mooney, P., Rehrl, K., & Hochmair, H. H. (2013) · Journal of Location Based Services · doi:10.1080/17489725.2013.859310 · Cited by 11

Legal Issues in Maps Built on Third Party Base Layers

Saunders, A., Scassa, T., & Lauriault, T. P. (2012) · GEOMATICA · doi:10.5623/cig2012-054 · Cited by 10

AN OPEN SOURCE GEOVISUAL ANALYTICS TOOLBOX FOR MULTIVARIATE SPATIO-TEMPORAL DATA IN ENVIRONMENTAL CHANGE MODELLING

Bernasocchi, M., Çöltekin, A., & Gruber, S. (2012) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-i-2-123-2012 · Cited by 9

LTE RF subsystem power consumption modeling

Musiige, D., Vincent, L., Anton, F., & Mioc, D. (2012) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce.2012.6379941 · Cited by 9

Streaming Algorithms for Data in Motion

Hoffmann, M., Muthukrishnan, S., & Raman, R. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-74450-4_27 · Cited by 8

AROM: Processing big data with Data Flow Graphs and functional programming

Tran, N.-L., Skhiri, S., Lesuisse, A., & Zimányi, E. (2012) · IEEE International Conference on Cloud Computing Technology and Science · doi:10.1109/cloudcom.2012.6427487 · Cited by 8

Area of interest based interaction and GeoVisualization with WebGL

Bektaş, K., & Çöltekin, A. (2012) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-69876 · Cited by 8

Rainfall Estimation with a Geosensor Network of Cars - Theoretical Considerations and First Results

Fitzner, D., Sester, M., Haberlandt, U., & Rabiei, E. (2013) · Photogrammetrie - Fernerkundung - Geoinformation · doi:10.1127/1432-8364/2013/0161 · Cited by 8

Spatial Data Mining in the Context of Big Data

Wang, S., & Yuan, H. (2013) · International Conference on Parallel and Distributed Systems · doi:10.1109/icpads.2013.88 · Cited by 7

Modeling Dynamic Beach Objects Using Spatio-Temporal Ontologies

van de Vlag, D. E. (2006) · Journal of Environmental Informatics · doi:10.3808/jei.200600074 · Cited by 5

The 1996 Geographical Association Land Use-UK Survey: A 'Geographical Commitment'

Walford, R. (1999) · International Research in Geographical and Environmental Education · doi:10.1080/10382049908667621 · Cited by 4

Q&A: The data visualizer

Hoffman, J. (2012) · Nature · doi:10.1038/486033a · Cited by 4

KEYNOTE - Collaborative Positioning - Concepts and Approaches for More Robust Positioning

Kealy, A., & Rabiain, A. H. (2015) · Proceedings of the ION 2015 Pacific PNT Meeting · Cited by 4

User–Defined Rules Made Simple with Functional Programming

Mintchev, S. (2014) · Lecture notes in business information processing · doi:10.1007/978-3-319-06695-0_20 · Cited by 3

Rf Power Consumption Emulation Optimized With Interval Valued Homotopies

Musiige, D., Anton, F., Yatskevich, V., Vincent, L., Mioc, D., & Pierre, N. (2011) · Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) · doi:10.5281/zenodo.1057358 · Cited by 3

Dimensionality Reduction Algorithm Based on Density Portrayal

Liu, S. (2011) · Jisuanji gongcheng · Cited by 2

Using a moving window SVM classification to infer travel mode from GPS data

Bolbol, A., Cheng, T., & Haworth, J. (2011) · UCL Discovery (University College London) · Cited by 2

Untitled work

(1949) · Max Planck Digital Library

Remote sensing for agricultural applications: A meta-review

Weiss, M., Jacob, F., & Duveiller, G. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.111402 · Cited by 1,948

Google Earth Engine for geo-big data applications: A meta-analysis and systematic review

Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L. J., Adeli, S., & Brisco, B. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.04.001 · Cited by 1,437

Key issues in rigorous accuracy assessment of land cover products

Stehman, S. V., & Foody, G. M. (2019) · Remote Sensing of Environment · doi:10.1016/j.rse.2019.05.018 · Cited by 727

Social media data for conservation science: A methodological overview

Toivonen, T., Heikinheimo, V., Fink, C., Hausmann, A., Hiippala, T., Järv, O., Tenkanen, H., & Minin, E. D. (2019) · Biological Conservation · doi:10.1016/j.biocon.2019.01.023 · Cited by 504

Machine learning in modelling land-use and land cover-change (LULCC): Current status, challenges and prospects

Wang, J., Bretz, M., Dewan, M. A. A., & Delavar, M. A. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.153559 · Cited by 447

Spatial Data Handling in Big Data Era

Zhou, C., Su, F., Harvey, F., & Xu, J. H. (2017) · Advances in geographic information science · doi:10.1007/978-981-10-4424-3 · Cited by 24

Geospatial Big Data Analytics Applications Trends, Challenges & Opportunities

Ali, T. A. T. (2022) · HAL

Theoretical foundations and methodological advancements in geospatial big data analytics and challenges

Fentie, A. Y., & Minale, A. S. (2026) · Discover Sustainability · doi:10.1007/s43621-026-04379-z · Cited by 1

Introduction to Big Data Computing for Geospatial Applications

Li, Z., Tang, W., Huang, Q., Shook, E., & Guan, Q. (2020) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi9080487 · Cited by 21

Big Data breaking barriers - first steps on a long trail

Schade, S. (2015) · 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/isprsarchives-xl-7-w3-691-2015 · Cited by 13