Considerations on Geospatial Big Data

Geospatial data, as a significant portion of big data, has recently gained the full attention of researchers. However, few researchers focus on the evolution of geospatial data and its scientific research methodologies. When entering into the big data era, fully understanding the changing research paradigm associated with geospatial data will definitely benefit future research on big data. In this paper, we look deep into these issues by examining the components and features of geospatial big data, reviewing relevant scientific research methodologies, and examining the evolving pattern of geospatial data in the scope of the four 'science paradigms'. This paper proposes that geospatial big data has significantly shifted the scientific research methodology from 'hypothesis to data' to 'data to questions' and it is important to explore the generality of growing geospatial data 'from bottom to top'. Particularly, four research areas that mostly reflect data-driven geospatial research are proposed: spatial correlation, spatial analytics, spatial visualization, and scientific knowledge discovery. It is also pointed out that privacy and quality issues of geospatial data may require more attention in the future. Also, some challenges and thoughts are raised for future discussion.

[1]  M. Goodchild,et al.  Volunteered Geographic Information, the Exaflood, and the Growing Digital Divide , 2013 .

[2]  Roberto Canavosio-Zuzelski,et al.  A Photogrammetric Approach for Assessing Positional Accuracy of OpenStreetMap© Roads , 2013, ISPRS Int. J. Geo Inf..

[3]  S. Schade,et al.  BIG DATA BREAKING BARRIERS - FIRST STEPS ON A LONG TRAIL , 2015 .

[4]  Michael F. Goodchild,et al.  Assuring the quality of volunteered geographic information , 2012 .

[5]  Hugh G. Gauch,et al.  Scientific method in practice , 2002 .

[6]  M. Goodchild,et al.  Prospects for VGI Research and the Emerging Fourth Paradigm , 2013 .

[7]  Miriam J. Metzger,et al.  The credibility of volunteered geographic information , 2008 .

[8]  Ranga Raju Vatsavai,et al.  Spatiotemporal data mining in the era of big spatial data: algorithms and applications , 2012, BigSpatial '12.

[9]  H. R. Miller,et al.  The Data Avalanche is Here: Shouldn’t We Be Digging? , 2010 .

[10]  Huadong Guo,et al.  Scientific big data and Digital Earth , 2014 .

[11]  Dino Pedreschi,et al.  Trajectory pattern mining , 2007, KDD '07.

[12]  Akinori Asahara,et al.  Pedestrian-movement prediction based on mixed Markov-chain model , 2011, GIS.

[13]  Xing Xie,et al.  Mining correlation between locations using human location history , 2009, GIS.

[14]  Athanasios V. Vasilakos,et al.  Big data: From beginning to future , 2016, Int. J. Inf. Manag..

[15]  Robert Hecht,et al.  Measuring Completeness of Building Footprints in OpenStreetMap over Space and Time , 2013, ISPRS Int. J. Geo Inf..

[16]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[17]  Xing Xie,et al.  Mining user similarity based on location history , 2008, GIS '08.

[18]  M. Goodchild.  Citizens as sensors: the world of volunteered geography , 2007 .

[19]  Michael F. Goodchild,et al.  The convergence of GIS and social media: challenges for GIScience , 2011, Int. J. Geogr. Inf. Sci..

[20]  Jae-Gil Lee,et al.  Geospatial Big Data: Challenges and Opportunities , 2015, Big Data Res..

[21]  M. Haklay.  How Good is Volunteered Geographical Information? A Comparative Study of OpenStreetMap and Ordnance Survey Datasets , 2010 .

[22]  Yan Peng,et al.  Processing of earth observation big data: Challenges and countermeasures , 2015 .

[23]  H. Fromm,et al.  Big Data—Technologies and Potential , 2014 .

[24]  Thad Starner,et al.  Using GPS to learn significant locations and predict movement across multiple users , 2003, Personal and Ubiquitous Computing.

[25]  Christian Heipke,et al.  Crowdsourcing geospatial data , 2010 .

[26]  M M Waldrop.  Learning to drink from a fire hose. , 1990, Science.

[27]  David Coleman,et al.  Volunteered Geographic Information: the nature and motivation of produsers , 2009, Int. J. Spatial Data Infrastructures Res..

[28]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[29]  T Van Zyl.  Machine learning on geospatial big data , 2014 .

[30]  Steffen Fritz,et al.  Assessing quality of volunteer crowdsourcing contributions: lessons from the Cropland Capture game , 2016, Int. J. Digit. Earth.

[31]  Michael F. Goodchild,et al.  Spatial decision support systems: a research agenda , 1989 .

[32]  Vyron Antoniou,et al.  How Many Volunteers Does it Take to Map an Area Well? The Validity of Linus’ Law to Volunteered Geographic Information , 2010 .

[33]  Michael F. Goodchild,et al.  Twenty years of progress: GIScience in 2010 , 2010, J. Spatial Inf. Sci..

[34]  H. Miller.  Place‐Based versus People‐Based Geographic Information Science , 2007 .

[35]  Chaowei Phil Yang,et al.  Introduction to big geospatial data research , 2014, Ann. GIS.

[36]  Anthony Townsend,et al.  Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia , 2013 .

[37]  Shashi Shekhar,et al.  Encyclopedia of GIS , 2007, Encyclopedia of GIS.

[38]  Thomas Seidl,et al.  Insights and Knowledge Discovery from Big Geospatial Data Using TMC-Pattern , 2014 .

[39]  M. Goodchild,et al.  Data-driven geography , 2014, GeoJournal.

[40]  G. King,et al.  Ensuring the Data-Rich Future of the Social Sciences , 2011, Science.

[41]  Pascal Neis,et al.  The Street Network Evolution of Crowdsourced Maps: OpenStreetMap in Germany 2007-2011 , 2011, Future Internet.

[42]  Stefano Nativi,et al.  Big Data challenges in building the Global Earth Observation System of Systems , 2015, Environ. Model. Softw..

[43]  Michael F. Goodchild,et al.  The quality of big (geo)data , 2013 .

[44]  Michael Cox,et al.  Application-controlled demand paging for out-of-core visualization , 1997 .

[45]  John C. Tang,et al.  Reflecting on the DARPA Red Balloon Challenge , 2011, Commun. ACM.