The Internet of Things and fast data streams: prospects for geospatial data science in emerging information ecosystems

References

• Agarwal, Y., & Dey, A. K. (2016). Toward building a safe, secure, and easy-to-use Internet of things infrastructure. Computer, 49(4), 88–91. doi:10.1109/MC.2016.111
   Web of Science ®Google Scholar
• Aggarwal, C., Han, J., Wang, J., & Yu, P. (2007). On clustering massive data streams: A summarizing paradigm. In C. Aggarwal (Ed.), Data streams: Models and algorithms (pp. 9–38). New York: Springer.
   Google Scholar
• Aggarwal, C., & Yu, P. (2007). A survey of synopsis construction in data streams. In C. Aggarwal (Ed.), Data streams: Models and algorithms (pp. 169–207). New York: Springer. doi: 10.1007/978-0-387-47534-9_9
   Google Scholar
• Amdahl, G. M. (1967). Validity of the single-processor approach to achieving large-scale computing capabilities. Proceedings of the American Federation of Information Processing Societies Conference (pp. 483–485). Reston, VA: AFIPS Press.
   Google Scholar
• Armstrong, M. P. (1988). Temporality in spatial databases. In GIS/LIS 88 proceedings: Accessing the world (Vol. II, pp.880–889). Falls Church, VA: American Society for Photogrammetry and Remote Sensing.
   Google Scholar
• Armstrong, M. P. (2000). Geography and computational science. Annals of the Association of American Geographers, 90(1), 146–156. doi:10.1111/0004-5608.00190
   Web of Science ®Google Scholar
• Armstrong, M. P., & Densham, P. J. (1992). Domain decomposition for parallel processing of spatial problems. Computers, Environment and Urban Systems, Part B. 16(6), 497–513. doi:10.1016/0198-9715(92)90041-O
   Web of Science ®Google Scholar
• Beaumont, J. R. (1989). Towards an integrated information system for retail management. Environment and Planning A, 21(3), 299–309. doi:10.1068/a210299
   Web of Science ®Google Scholar
• Bennett, D. A., & Armstrong, M. P. (1989). An inductive bit-mapped classifier for terrain feature extraction. In Proceedings of GIS/LIS ‘89 (Vol. 1, pp.59–68). Bethesda, MD: American Congress on Surveying and Mapping.
   Google Scholar
• Bennett, D. A., & Armstrong, M. P. (1996). An inductive knowledge based approach to terrain feature extraction. Cartography and Geographic Information Systems, 23(1), 3–19. doi:10.1559/152304096782512177
   Google Scholar
• Bennett, D. A., Xiao, N., & Armstrong, M. P. (2004). Exploring the geographic consequences of public policies using evolutionary algorithms. Annals of the Association of American Geographers, 94(4), 827–847.doi:10.1111/j.1467-8306.2004.00437.x
   Web of Science ®Google Scholar
• Berry, B. J. L. (1964). Approaches to regional analysis: A synthesis. Annals of the Association of American Geographers, 54, 2–11. doi:10.1111/j.1467-8306.1964.tb00469.x
   Web of Science ®Google Scholar
• Bertino, E., Nepal, S., & Ranjan, R. (2015). Building sensor-based big data cyberinfrastructures. IEEE Cloud Computing, 2(5), 64–69.doi:10.1109/MCC.2015.106
   Web of Science ®Google Scholar
• Bloom, B. (1970). Space/time trade-offs in hash coding with allowable errors. Communications of the Association for Computing Machinery, 13(7), 422–426. doi:10.1145/362686.362692
   Web of Science ®Google Scholar
• Box, G. E. P. (1976). Science and statistics. Journal of the American Statistical Association, 71(356), 791–799. doi:10.1080/01621459.1976.10480949
   Web of Science ®Google Scholar
• Brown, A., Franken, P., Moross, J., Dolezal, N., & Bonner, S. (2017). The safecast report. https://blog.safecast.org/wp-content/uploads/2017/10/safecastreport2017-part1safecastproject-final-171004011228.pdf
   Google Scholar
• Butler, D. (2014, January 09). Many eyes on Earth. Nature, 505, 143–144. https://www.nature.com/polopoly_fs/1.14475!/menu/main/topColumns/topLeftColumn/pdf/505143a.pdf
   PubMed Web of Science ®Google Scholar
• Calkins, H. W. (1990). Creating large digital files from mapped data. In D. J. Peuquet & D. F. Marble (Eds.), Introductory readings in geographic information systems (pp. 209–214). New York: Taylor & Francis.
   Google Scholar
• Calo, R. (2018). Is the law ready for driverless cars? Communications of the Association for Computing Machinery, 61(5), 34–36. doi:10.1145/3199599
   Web of Science ®Google Scholar
• Cao, H., Mamoulis, N., & Cheung, D. W. (2009). Periodic pattern discovery from trajectories of moving objects. In H. J. Miller & J. Han (Eds.), Geographic data mining and knowledge discovery (2nd ed., pp. 389–408). Boca Raton, FL: CRC Press. https://www.taylorfrancis.com/books/9781420073980/chapters/10.1201%2F9781420073980-19
   Google Scholar
• Chorley, R. J., & Haggett, P. (Eds.). (1967). Models in geography. London: Methuen.
   Google Scholar
• Cormode, G., Garofalakis, M., Haas, P., & Jermaine, C. (2012). Synopses for massive data: Samples, histograms, wavelets and sketches. Boston: Now Publishers.
   Google Scholar
• Cormode, G., Korn, F., Muthukrishnan, S., & Srivastava, D. (2008). Finding hierarchical heavy hitters in streaming data. ACM Transactions on Knowledge Discovery from Data (TKDD), 1(4), Article 16 48. doi:10.1145/1324172.1324174
   Google Scholar
• daCosta, F. (2013). Rethinking the Internet of Things: A scalable approach to connecting everything. New York: Springer.
   Google Scholar
• de Selding, P. B. (2015). Facebook-Eutelsat internet deal leaves industry awaiting encore. SpaceNews, 26(36), 1, 5.
   Google Scholar
• Deakin, M. (Ed.). (2013). Smart cities: Governing, modelling and analysing the transition. New York: Routledge.
   Google Scholar
• Denning, P. J., & Lewis, T. G. (2017). Exponential laws of computing growth. Communications of the Association for Computing Machinery, 60(1), 54–65. doi:10.1145/2976758
   Web of Science ®Google Scholar
• Derrac, J., García, S., & Herrera, F. (2014). Fuzzy nearest neighbor algorithms: Taxonomy, experimental analysis and prospects. Information Sciences, 260, 98–119.
   Web of Science ®Google Scholar
• Dhar, V. (2013). Data science and prediction. Communications of the Association for Computing Machinery, 56(12), 64–73. doi:10.1145/2500499
   Web of Science ®Google Scholar
• Eck, J. E., Chainey, S., Cameron, J. G., Leitner, M., & Wilson, R. E. (2005). Mapping crime: Understanding hot spots. U.S. Department of Justice, National Institute of Justice, NCJ 209393 http://www.ncjrs.gov/pdffiles1/nij/209393.pdf
   Google Scholar
• Eckhoff, D., & Sommer, C. (2014). Driving for big data? Privacy concerns in vehicular networking. IEEE Security and Privacy, 12(1), 77–79. doi:10.1109/MSP.2014.2
   Web of Science ®Google Scholar
• Ellis, B. (2014). Real-time analytics: Techniques to analyze and visualize streaming data. Indianapolis, IN: Wiley.
   Google Scholar
• Fekete, J.-D., & Primet, R. (2016). Progressive analytics: A computation paradigm for exploratory data analysis. https://arxiv.org/abs/1607.05162.
   Google Scholar
• Forrest, S., & Mitchell, M. (2016). Adaptive computation: The multidisciplinary legacy of John H. Holland. Communications of the Association for Computing Machinery, 59(8), 58–63. doi:10.1145/2964342
   Web of Science ®Google Scholar
• Foust, J. (2015). Orbital debris questions unanswered. SpaceNews, 26(23), 1,4.
   Google Scholar
• Gahegan, M. (2000a). On the application of inductive machine learning tools to geographical analysis. Geographical Analysis, 32(1), 113–139. doi:10.1111/j.1538-4632.2000.tb00420.x
   Google Scholar
• Gahegan, M. (2000b). The case of inductive and visual techniques in the analysis of spatial data. Journal of Geographical Systems, 2(1), 77–83. doi:10.1007/s101090050033
   Google Scholar
• Gahegan, M. (2003). Is inductive machine learning just another wild goose (or might it lay the golden egg)? International Journal of Geographical Information Science, 17(1), 69–92. doi:10.1080/713811742
   Web of Science ®Google Scholar
• GAO (Government Accountability Office). (2013). In-car location-based services: Companies are taking steps to protect privacy, but some risks may not be clear to consumers. In GAO-14-81. Washington, DC: GAO.
   Google Scholar
• Gupta, M., Holloway, C., Heravi, B., & Hailes, S. (2015). A comparison between smartphone sensors and bespoke sensor devices for wheelchair accessibility studies. IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, 7–9 April. doi:10.1109/ISSNIP.2015.7106900
   Google Scholar
• Hägerstrand, T. (1967). The computer and the geographer. Transactions of the Institute of British Geographers, 42, 1–19. https://www.jstor.org/stable/621369
   Google Scholar
• Hägerstrand, T. (1970). What about people in regional science? Papers of the Regional Science Association, 24(1), 6–21. doi:10.1111/j.1435-5597.1970.tb01464.x
   Google Scholar
• Haggett, P., & Chorley, R. J. (1967). Models, paradigms and the new geography. In Models in Geography (pp. 19–41). London: Methuen.
   Google Scholar
• Hegeman, J. W., Sardeshmukh, V. B., Sugumaran, R., & Armstrong, M. P. (2014). Distributed LiDAR data processing in a high-memory cloud-computing environment. Annals of GIS, 20(4), 255–264. doi:10.1080/19475683.2014.923046
   Google Scholar
• Helsel, D. R. (2005). Nondetects and data analysis: Statistics for censored environmental data. Hoboken, NJ: Wiley.
   Google Scholar
• Henry, C. (2018). Constellation consternation. SpaceNews, 29(4), 13–19.
   Google Scholar
• Hey, T., Tansley, S., & Tolle, K. (Eds.). (2009). The fourth paradigm: Data-intensive scientific discovery. Redmond, WA: Microsoft Research.
   Google Scholar
• Holland, J.H., Holyoak, K.J., Nisbett, R.E., & Thagard, P.R. (1986). Induction: Processes of inference, learning and discovery. Cambridge, MA: MIT Press.
   Google Scholar
• Honda, L., Perkins, J., & Sun, S. (2013). Interpretations of de-orbit, deactivation and shutdown guidelines applicable to GEO satellites. Proceedings, IEEE Aerospace Conference, Big Sky, Montana, March 2–9. ISBN: 978-1-4673-1811-2.
   Google Scholar
• IEEE. (1990). IEEE standard glossary of software engineering terminology ( Standard 610.121990). New York: IEEE
   Google Scholar
• Ilyas, M., Alwakeel, S., Alwakeel, M., & Aggoune, E. (Eds.). (2014). Sensor networks for sustainable development. Boca Raton, FL: CRC Press.
   Google Scholar
• Järpe, E. (2002). Surveillance, environmental. In A. H. El-Shaarawi & W. W. Piegorsch (Eds.), Encyclopedia of environmetrics (Vol. 4, pp. 2150–2153). New York: Wiley.
   Google Scholar
• Jarr, S. (2015). Fast data and the new enterprise data architecture. Sebastopol, CA: O’Reilly
   Google Scholar
• Keller, J., Gray, M., & Givens, J. (1985). A fuzzy K-nearest neighbor algorithm. IEEE Transactions on Systems, Man, and Cybernetics, 15(4), 580–585. doi:10.1109/TSMC.1985.6313426
   Web of Science ®Google Scholar
• Kirkpatrick, K. (2015). The moral challenges of driverless cars. Communications of the Association for Computing Machinery, 58(8), 19–20. doi:10.1145/2788477
   Web of Science ®Google Scholar
• Kugler, L. (2015). Is “good enough” computing good enough? Communications of the Association for Computing Machinery, 58(5), 12–14. doi:10.1145/2742482
   Web of Science ®Google Scholar
• Kwan, M. P. (1998). Space-time and integral measures of individual accessibility: A comparative analysis using a point-based framework. Geographical Analysis, 30, 191–217. doi:10.1111/j.1538-4632.1998.tb00396.x
   Web of Science ®Google Scholar
• Langran, G., (1988). Temporal design tradeoffs. In Proceedings of GIS/LIS’88, Volume 2 (pp. 890–899). Falls Church, VA: ACSM.
   Google Scholar
• Langran, G. (1992). Time in Geographic Information Systems. Bristol, PA: Taylor & Francis.
   Google Scholar
• Langran, G., & Chrisman, N. R. (1988). A framework for temporal geographic information. Cartographica, 25(3), 1–14. doi:10.3138/K877-7273-2238-5Q6V
   Google Scholar
• Laube, P., & Duckham, M. (2009). Decentralized spatial data mining for geosensor networks. In H. J. Miller & J. Han (Eds.), Geographic data mining and knowledge discovery (2nd ed., pp. 409–430). Boca Raton, FL: CRC Press.
   Google Scholar
• Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., … Cheng, T. (2016). Geospatial big data handling theory and methods: A review and research. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133. doi:10.1016/j.isprsjprs.2015.10.012
   Web of Science ®Google Scholar
• Lochert, C., Scheuermann, B., & Mauve, M. (2010). Information dissemination in VANETs. In H. Hartenstein & K. P. Laberteaux (Eds.), VANET: Vehicular applications and inter-networking technologies (pp. 49–80). Chichester, UK: Wiley.
   Google Scholar
• Lowry, I. S. (1968). A short course in model design. In B. J. L. Berry & D. F. Marble (Eds.), Spatial analysis: A reader in statistical geography (pp. 53–64). Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Mandl, K., Overhage, J., Wagner, M., Lober, W., Sebastiani, P., Mostashari, F., … Grannis, S. (2004). Implementing syndromic surveillance: A practical guide informed by early experience. Journal of the American Medical Informatics Association, 11(2), 141–150. doi:10.1197/jamia.M1356
   PubMed Web of Science ®Google Scholar
• Marble, D. F. (2015). Computational geography as a new modality. In M. Monmonier (Ed.), The history of cartography Volume 6, Part 1. Chicago: University of Chicago Press. 488–492.
   Google Scholar
• Marble, D. F., & Anderson, B. M. (1972). LANDUSE: A computer program for laboratory use in economic geography courses. Commission on College Geography Technical Paper No. 8. Washington, DC: Association of American Geographers.
   Google Scholar
• Miller, H. J. (1991). Modeling accessibility using space-time prism concepts within a GIS. International Journal of Geographical Information Systems, 5, 287–301. doi:10.1080/02693799108927856
   Web of Science ®Google Scholar
• Miller, H. J. (2005). A measurement theory for time geography. Geographical Analysis, 37, 17–45. doi:10.1111/j.1538-4632.2005.00575.x
   Web of Science ®Google Scholar
• Miller, H. J. (2010). The data avalanche is here. Shouldn’t we be digging? Journal of Regional Science, 50, 181–201. doi:10.1111/j.1467-9787.2009.00641.x
   Web of Science ®Google Scholar
• Miller, H. J., & Han, J. (2009). Geographic data mining and knowledge discovery: An overview. In H. J. Miller & J. Han (Eds.), Geographic data mining and knowledge discovery (2nd ed., pp. 1–26). Boca Raton, FL: CRC Press.
   Google Scholar
• Mohring, K., Myers, T., Atkinson, I., VanDerWal, J., & van der Valk, S. (2015). Sensors in heat: A pilot study for high resolution urban sensing in an integrated streetlight platform. International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, 7–9 April. doi:10.1109/ISSNIP.2015.7106908
   Google Scholar
• Mone, G. (2015). The new smart cities. Communications of the Association for Computing Machinery, 58(7), 20–21. doi:10.1145/2771297
   Web of Science ®Google Scholar
• Moore, G. (1965). Cramming more components onto integrated circuits. Electronics, 38(8), 114–117. doi:10.1109/N-SSC.2006.4785860
   Web of Science ®Google Scholar
• Moreau, T., Sampson, A., & Ceze, L. (2015). Approximate computing: Making mobile systems more efficient. IEEE Pervasive Computing, 14(2), 9–13. doi:10.1109/MPRV.2015.25
   Web of Science ®Google Scholar
• Narkhede, N., Shapira, G., & Palino, T. (2017). Kafka: The definitive guide. Sebastopol, CA: O’Reilly.
   Google Scholar
• NAS (National Academies of Sciences, Engineering, and Medicine). (2016). Future directions for NSF advanced computing infrastructure to support U.S. Science and engineering in 2017–2020. Washington, DC: The National Academies Press. doi:10.17226/21886
   Google Scholar
• Nayak, A., & Stojmenovic, I. (2010). Wireless sensor and actuator networks: Algorithms and protocols for scalable coordination and data communication. Hoboken, NJ: Wiley.
   Google Scholar
• NIST (National Institute of Standards and Technology). (2015). DRAFT NIST big data interoperability framework: Volume 1, definitions. NIST Special Publication 1500-1. Gaithersburg, MD: NIST. doi:10.6028/NIST.SP.1500-1
   Google Scholar
• Noor, T. H., Sheng, Q. Z., Maamar, Z., & Zeadally, S. (2016). Managing trust in the cloud: State of the art and research challenges. Computer, 49(2), 34–45. doi:10.1109/MC.2016.57
   Web of Science ®Google Scholar
• NRC (National Research Council). (2013). Frontiers in massive data analysis. Washington, DC: The National Academies Press. doi:10.17226/18374
   Google Scholar
• Obama, B. (2015). https://www.whitehouse.gov/the-press-office/2015/07/29/executive-order-creating-national-strategic-computing-initiative
   Google Scholar
• Openshaw, S. (1995). Developing automated and smart spatial pattern exploration tools for geographical information systems applications. Journal of the Royal Statistical Society, Series D (The Statistician), 44(1), 3–16. doi:10.2307/2348611
   Google Scholar
• Openshaw, S. (1998). Towards a more computationally minded scientific human geography. Environment and Planning A, 30, 317–332. doi:10.1068/a300317
   Web of Science ®Google Scholar
• Peuquet, D. J. (1994). It’s about time: A conceptual framework for the representation of temporal dynamics in geographic information systems. Annals of the Association of American Geographers, 84(3), 441–462. doi:10.1111/j.1467-8306.1994.tb01869.x
   Web of Science ®Google Scholar
• Peuquet, D. J., & Duan, N. (1995). An event-based spatiotemporal data model (ESTDM) for temporal analysis of geographical data. International Journal of Geographical Information Systems, 9(7), 7–24. doi:10.1080/02693799508902022
   Google Scholar
• Porter, M. E., & Heppelmann, J. E. (2014). How smart connected devices are transforming competition. Harvard Business Review, (Nov.), 70–86. https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition
   Google Scholar
• Rajasegarar, S., Zhang, P., Zhou, Y., Karunasekera, S., Leckie, C., & Palaniswami, M. (2014). High resolution spatio-temporal monitoring of air pollutants using wireless sensor networks. Proceedings, IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, 21–24 April. doi:10.1109/ISSNIP.2014.6827607
   Google Scholar
• Reed, D. A., & Dongarra, J. (2015). Exascale computing and big data. Communications of the Association for Computing Machinery, 58(7), 56–68. doi:10.1145/2699414
   Web of Science ®Google Scholar
• Rogerson, P. A. (2009). Monitoring changes in spatial patterns. In S. A. Fotheringham & P. A. Rogerson (Eds.), The SAGE handbook of spatial analysis (pp. 343–354). Thousand Oaks, CA: Sage.
   Google Scholar
• Romero, J. (2015). On New York’s Lake George, researchers fire up a state-of-the-art observatory. http://www.sciencemag.org/news/2015/07/new-york-s-lake-george-researchers-fire-state-art-observatory doi:10.1126/science.aac8828
   Google Scholar
• Rubinfeld, R., & Shapira, A. (2011). Sublinear time algorithms. SIAM Journal of Discrete Mathematics, 25(4), 1562–1588. doi:10.1137/100791075
   Web of Science ®Google Scholar
• Serpanos, D. (2018). The cyber-physical systems revolution. IEEE Computing Edge, 4(5), 10–13. doi:10.1109/MC.2018.1731058
   Google Scholar
• Shaw, S.-L. (2006). What about ‘time’ in transportation geography? Journal of Transport Geography, 14(3), 237–240. doi:10.1016/j.jtrangeo.2006.02.009
   Web of Science ®Google Scholar
• Shekhar, S., Evans, M. R., Gunturi, V., Yang, K., & Cugler, D. C. (2014). Benchmarking spatial big data. In T. Rabl, M. Poess, C. Baru, & H. A. Jacobsen (Eds.), Lecture notes in computer science: Vol. 8163. Specifying big data benchmarks (pp. 81–93). Berlin: Springer.
   Google Scholar
• Shi, W., & Dustdar, S. (2016). The promise of edge computing. Computer, 49(5), 78–81. doi:10.1109/MC.2016.145
   Web of Science ®Google Scholar
• Silva, J. A., Faria, E. R., Barros, R. C., Hruschka, E. R., de Carvalho, A. C., & Gama, J. (2013). Data stream clustering: A survey. ACM Computing Surveys, 46(1), Article 13. doi:10.1145/2522968.2522981
   Web of Science ®Google Scholar
• Struchen, R., Vial, F., & Andersson, M. G. (2017). Value of evidence from syndromic surveillance with cumulative evidence from multiple data streams with delayed reporting. https://www.nature.com/articles/s41598-017-01259-5 doi:10.1038/s41598-017-01259-5
   Google Scholar
• Thrift, N. (1977). An introduction to time geography. In Concepts and techniques in modern geography (CATMOG) No 13. Norwich, UK: Geo Abstracts.
   Google Scholar
• Tomlinson, R. F. (Ed.). (1970). Environment information systems. Williamsville, NY: International Geographical Union Commission on Geographical Data Sensing and Processing.
   Google Scholar
• Torrens, P. M. (2010). Geography and computational social science. GeoJournal, 75(2), 133–148. doi:10.1007/s10708-010-9361-y
   Google Scholar
• Tsai, V. J. D. (1993). Delaunay triangulations in TIN creation: An overview and a linear-time algorithm. International Journal of Geographical Information Science, 7(6), 501–524. doi:10.1080/02693799308901979
   Web of Science ®Google Scholar
• Turkay, C., Kaya, E., Balcisoy, S., & Hauser, H. (2017). Designing progressive and interactive analytics processes for high-dimensional data analysis. IEEE Transactions on Visualization and Computer Graphics, 23(1), 131–140. doi:10.1109/TVCG.2016.2598470
   PubMed Web of Science ®Google Scholar
• Vilajosana, I., Llosa, J., Martinez, B., Domingo-Prieto, M., Angles, A., & Vilajosana, X. (2013, June). Bootstrapping smart cities through a self-sustainable model based on big data flows. IEEE Communications Magazine, pp. 128–134. doi:10.1109/MCOM.2013.6525605
   Google Scholar
• Vitter, J. S. (1985). Random sampling with a reservoir. ACM Transactions on Mathematical Software, 11(1), 37–57. doi:10.1145/3147.3165
   Web of Science ®Google Scholar
• Wachowicz, M. (1999). Object-oriented design for temporal GIS. London Taylor & Francis.
   Google Scholar
• Wang, S. (2013). CyberGIS: Blueprint for integrated and scalable geospatial software ecosystems. International Journal of Geographical Information Science, 27(11), 2119–2121. doi:10.1080/13658816.2013.841318
   Web of Science ®Google Scholar
• Wang, S. (2016). CyberGIS and spatial data science. GeoJournal, 81(6), 965–968. doi:10.1007/s10708-016-9740-0.pdf
   Web of Science ®Google Scholar
• Wang, S. (2017). CyberGIS. In D. Richardson, N. Castree, M. F. Goodchild, A. L. Kobayashi, W. Liu, & R. Marston (Eds.), The international encyclopedia of geography: People, the earth, environment, and technology. Hoboken, NJ: Wiley. doi: 10.1002/9781118786352.wbieg0931.
   Google Scholar
• Wang, S., Anselin, L., Bhaduri, B., Crosby, C., Goodchild, M. F., Liu, Y., & Nyerges, T. L. (2013). CyberGIS software: A synthetic review and integration roadmap. International Journal of Geographical Information Science, 27(11), 2122–2145. doi:10.1080/13658816.2013.776049
   Web of Science ®Google Scholar
• Wang, S., & Armstrong, M. P. (2003). A quadtree approach to domain decomposition for spatial interpolation in grid computing environments. Parallel Computing, 29(10), 1481–1504. doi:10.1016/j.parco.2003.04.003
   Web of Science ®Google Scholar
• Wang, S., & Armstrong, M. P. (2009). A theoretical approach to the use of cyberinfrastructure in geographical analysis. International Journal of Geographical Information Science, 23(2), 169–193. doi:10.1080/13658810801918509
   Web of Science ®Google Scholar
• Wang, W., & Wets, G. (Eds.). (2013). Computational Intelligence for Traffic and Mobility. Paris: Atlantis.
   Google Scholar
• Wang, X. (2018). Data acquisition in vehicular ad hoc networks. Communications of the Association for Computing Machinery, 61(5), 83–88. doi:10.1145/3197544
   Web of Science ®Google Scholar
• Weinberg, S. (2015). To explain the world: The discovery of modern science. New York: HarperCollins.
   Google Scholar
• Wiener, P., Stein, M., Seebacher, D., Bruns, J., Frank, M., Simko, V., … Mimis, J. (2016). BigGIS: A continuous refinement approach to master heterogeneity and uncertainty in spatio-temporal big data (vision paper). GIS ‘16 Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Article No. 8. doi:10.1145/2996913.2996931
   Google Scholar
• Worboys, M. F. (1994). A unified model for spatial and temporal information. The Computer Journal, 37(1), 26–34. doi:10.1093/comjnl/37.1.26
   Web of Science ®Google Scholar
• Wu, H., Guan, X., & Gong, J. (2011). ParaStream: A parallel streaming Delaunay triangulation algorithm for LiDAR points on multicore architectures. Computers and Geosciences, 37(9), 1355–1363. doi:10.1016/j.cageo.2011.01.008
   Web of Science ®Google Scholar
• Xiong, D., & Marble, D. F. (1996). Strategies for real-time spatial analysis using massively parallel SIMD computers: An application to urban traffic flow analysis. International Journal of Geographical Information Systems, 10(6), 769–789. doi:10.1080/02693799608902109
   Web of Science ®Google Scholar
• Yan, J., Cowles, M. K., Wang, S., & Armstrong, M. P. (2007). Parallelizing MCMC for Bayesian spatiotemporal geostatistical models. Statistics and Computing, 17(4), 323–335. doi:10.1007/s11222-007-9022-2
   Web of Science ®Google Scholar
• Yang, C., Xu, Y., & Nebert, D. (2013). Redefining the possibility of digital Earth and geosciences with spatial cloud computing. International Journal of Digital Earth, 6(4), 297–312. doi:10.1080/17538947.2013.769783
   Web of Science ®Google Scholar
• Yang, C., Yu, M., Jiang, Y., & Li, Y. (2017). Utilizing cloud computing to address big geospatial data challenges. Computers, environment and urban systems, 61, 120–128. doi:10.1016/j.compenvurbsys.2016.10.010
   Web of Science ®Google Scholar
• Yin, D., Liu, Y., Padmanabhan, A., Terstriep, J., Rush, J., & Wang, S. (2017). A cyberGIS-jupyter framework for geospatial analytics at scale. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, 18:1–18: 8. New York: ACM. doi:10.1145/3093338.3093378
   Google Scholar
• Yuan, M. (2009). Toward knowledge discovery about geographic dynamics in spatiotemporal databases. In H. J. Miller & J. Han (Eds.), Geographic data mining and knowledge discovery (2nd ed., pp. 347–365). Boca Raton, FL: CRC Press.
   Google Scholar
• Yuan, M., & Stewart, K. (Eds.). (2007). Computation and visualization for understanding dynamics in geographic domains: A research agenda. Boca Raton, FL: CRC Press.
   Google Scholar
• Zhang, H., Chen, G., Ooi, B., Tan, K., & Zhang, M. (2015). In-memory big data management and processing: A survey. IEEE Transactions on Knowledge and Data Engineering, 27(7), 1920–1948. doi:10.1109/TKDE.2015.2427795
   Web of Science ®Google Scholar
• Zhang, Z., Demsǎ, U., Rantala, J., & Virrantaus, K. (2014). A fuzzy multiple-attribute decision making modelling for vulnerability analysis on the basis of population information for disaster management. International Journal of Geographical Information Science, 28(9), 1922–1939. doi:10.1080/13658816.2014.908472
   Web of Science ®Google Scholar
• Zhang, Z., Demsǎ, U., Wang, S., & Virrantaus, K. (2018). A spatial fuzzy influence diagram for modelling spatial objects’ dependencies: A case study on tree-related electric outages. International Journal of Geographical Information Science, 32(2), 349–366. doi:10.1080/13658816.2017.1385789
   Web of Science ®Google Scholar