Abstract
As location-aware applications and location-based services continue to increase in popularity, data sources describing a range of dynamic processes occurring in near real-time over multiple spatial and temporal scales are becoming the norm. At the same time, existing frameworks useful for understanding these dynamic spatio-temporal data, such as time geography, are unable to scale to the high volume, velocity, and variety of these emerging data sources. In this paper, we introduce a computational framework that turns time geography into a scalable analysis tool that can handle large and rapidly changing datasets. The Hierarchical Prism Tree (HPT) is a dynamic data structure for fast queries on spatio-temporal objects based on time geographic principles and theories, which takes advantage of recent advances in moving object databases and computer graphics. We demonstrate the utility of our proposed HPT using two common time geography tasks (finding similar trajectories and mapping potential space-time interactions), taking advantage of open data on space-time vehicle emissions from the EnviroCar platform.
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Although some parallel versions of kd-trees [35] do show promise.
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See for example, http://www.realtimerendering.com/intersections.html.
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ICARUS analyzes the migratory behavior of animals such as birds and bats:
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Argos is a global, satellite-based platform widely used in animal tracking:
References
Batty, M.: Smart cities, big data. Environ. Plan. 39(2), 191–193 (2012)
Yang, C., Raskin, R., Goodchild, M., Gahegan, M.: Geospatial cyberinfrastructure: past, present and future. Comput. Environ. Urban Syst. 34(4), 264–277 (2010). Geospatial Cyberinfrastructure
Miller, H.J.: A measurement theory for time geography. Geogr. Anal. 37(1), 17–45 (2005)
Hägerstrand, T.: What about people in regional science? Papers Reg. Sci. Assoc. 24, 7–21 (1970)
Miller, H.J.: What about people in geographic information science? In: Fisher, P., Unwin, D. (eds.) Representing GIS, pp. 215–242. Wiley, Hoboken (2005)
Shaw, S.L.: Guest editorial introduction: time geography - its past, present and future. J. Transp. Geogr. 23, 1–4 (2012). Special Issue on Time Geography
Crease, P., Reichenbacher, T.: Linking time geography and activity theory to support the activities of mobile information seekers. Trans. GIS 17(4), 507–525 (2013)
Raubal, M., Miller, H.J., Bridwell, S.: User-centred time geography for location-based services. Geogr. Ann.: Ser. B Hum. Geogr. 86(4), 245–265 (2004)
Kwan, M.P.: Gender and individual access to urban opportunities: a study using space-time measures. Prof. Geogr. 51(2), 210–227 (1999)
Miller, H.J.: Modelling accessibility using space-time prism concepts within geographical information systems. Int. J. Geogr. Inf. Syst. 5(3), 287–301 (1991)
Raubal, M., Winter, S., Teßmann, S., Gaisbauer, C.: Time geography for ad-hoc shared-ride trip planning in mobile geosensor networks. ISPRS J. Photogramm. Remote Sens. 62(5), 366–381 (2007)
Winter, S., Raubal, M.: Time geography for ad-hoc shared-ride trip planning. In: 7th International Conference on Mobile Data Management 2006, MDM 2006 (2006)
Rainham, D., McDowell, I., Krewski, D., Sawada, M.: Conceptualizing the healthscape: contributions of time geography, location technologies and spatial ecology to place and health research. Soc. Sci. Med. 70(5), 668–676 (2010)
Bröring, A., Remke, A., Stasch, C., Autermann, C., Rieke, M., Möllers, J.: EnviroCar: a citizen science platform for analyzing and mapping crowd-sourced car sensor data. Trans. GIS 19(3), 362–376 (2015)
Winter, S., Yin, Z.C.: The elements of probabilistic time geography. GeoInformatica 15(3), 417–434 (2011)
Samet, H.: Applications of Spatial Data Structures. Addison-Wesley, Boston (1990)
Myllymaki, J., Kaufman, J.: High-performance spatial indexing for location-based services. In: Proceedings of 12th International Conference on World Wide Web, WWW 2003, pp. 112–117. ACM, New York (2003)
Gustafsson, T., Hansson, J.: Dynamic on-demand updating of data in real-time database systems. In: Proceedings of 2004 ACM Symposium on Applied Computing, SAC 2004, pp. 846–853. ACM, New York (2004)
Papadias, D., Tao, Y., Kanis, P., Zhang, J.: Indexing spatio-temporal data warehouses. In: Proceedings of 18th International Conference on Data Engineering 2002, pp. 166–175 (2002)
Theodoridis, Y., Sellis, T., Papadopoulos, A., Manolopoulos, Y.: Specifications for efficient indexing in spatiotemporal databases. In: Proceedings of 10th International Conference on Scientific and Statistical Database Management 1998, pp. 123–132, Jul 1998
Wang, W., Yang, J., Muntz, R.: Pk-tree: a spatial index structure for high dimensional point data. In: Tanaka, K., Ghandeharizadeh, S., Kambayashi, Y. (eds.) Information Organization and Databases: Foundations of Data Organization. SISECS, vol. 579. Springer, Berlin (2000)
Tayeb, J., Ulusoy, Ö., Wolfson, O.: A quadtree-based dynamic attribute indexing method. Comput. J. 41(3), 185–200 (1998)
Navarro, G., Reyes, N.: Dynamic spatial approximation trees for massive data. In: 2nd International Workshop on Similarity Search and Applications, SISAP, pp. 81–88, August 2009
Navarro, G., Reyes, N.: Dynamic spatial approximation trees. J. Exp. Algorithmics 12, 1.5:1–1.5:68 (2008)
Bo, Z., Fu-ling, B.: Dynamic quadtree spatial index algorithm for mobile GIS. Comput. Eng. 33(15), 86 (2007)
Xia, Y., Prabhakar, S.: Q+rtree: efficient indexing for moving object databases. In: Proceedings of 8th International Conference on Database Systems for Advanced Applications 2003 (DASFAA 2003), pp. 175–182, March 2003
Myllymaki, J., Kaufman, J.H.: DynaMark: a benchmark for dynamic spatial indexing. In: Chen, M.-S., Chrysanthis, P.K., Sloman, M., Zaslavsky, A. (eds.) MDM 2003. LNCS, vol. 2574, pp. 92–105. Springer, Heidelberg (2003)
Myllymaki, J., Kaufman, J.: Locus: a testbed for dynamic spatial indexing. IEEE Data Eng. Bull. Spec. Issue Index. Mov. Objects 25, 48–55 (2002)
Zhu, Q., Gong, J., Zhang, Y.: An efficient 3D r-tree spatial index method for virtual geographic environments. J. Photogramm. Remote Sens. 62(3), 217–224 (2007)
Ize, T., Wald, I., Parker, S.G.: Asynchronous BVH construction for ray tracing dynamic scenes on parallel multi-core architectures. In: Proceedings of 7th Eurographics Conference on Parallel Graphics and Visualization, EGPGV 2007, pp. 101–108. Eurographics Association, Aire-la-Ville (2007)
Glassner, A.S.: An Introduction to Ray Tracing. Academic Press Ltd., London (1989)
Stich, M., Friedrich, H., Dietrich, A.: Spatial splits in bounding volume hierarchies. In: Proceedings of Conference on High Performance Graphics 2009, HPG 2009, pp. 7–13. ACM, New York (2009)
Maneewongvatana, S., Mount, D.M.: Analysis of approximate nearest neighbor searching with clustered point sets. CoRR cs.CG/9901013 (1999)
Vinkler, M., Havran, V., Bittner, J.: Bounding volume hierarchies versus kd-trees on contemporary many-core architectures. In: Proceedings of 30th Spring Conference on Computer Graphics. SCCG 2014, pp. 29–36. ACM, New York (2014)
Shevtsov, M., Soupikov, A., Kapustin, A.: Highly parallel fast kd-tree construction for interactive ray tracing of dynamic scenes. Comput. Graph. Forum 26(3), 395–404 (2007)
He, L., Ortiz, R., Enquobahrie, A., Manocha, D.: Interactive continuous collision detection for topology changing models using dynamic clustering. In: Proceedings of 19th Symposium on Interactive 3D Graphics and Games, i3D 2015, pp. 47–54. ACM, New York (2015)
Stein, C., Limper, M., Kuijper, A.: Spatial data structures for accelerated 3D visibility computation to enable large model visualization on the web. In: Proceedings of 19th International ACM Conference on 3D Web Technologies, Web3D 2014, pp. 53–61. ACM, New York (2014)
Kopta, D., Ize, T., Spjut, J., Brunvand, E., Davis, A., Kensler, A.: Fast, effective BVH updates for animated scenes. In: Proceedings of ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, I3D 2012, pp. 197–204. ACM, New York (2012)
Yoon, S.E., Curtis, S., Manocha, D.: Ray tracing dynamic scenes using selective restructuring. In: Proceedings of 18th Eurographics Conference on Rendering Techniques, EGSR 2007, pp. 73–84. Eurographics Association, Aire-la-Ville (2007)
Karras, T., Aila, T.: Fast parallel construction of high-quality bounding volume hierarchies. In: Proceedings of 5th High-Performance Graphics Conference, HPG 2013, pp. 89–99. ACM, New York (2013)
Miller, H., Raubal, M., Jaegal, Y.: Measuring space-time prism similarity through temporal profile curves. In: 19th AGILE Conference on Geographic Information Science - Geospatial Data in a Changing World, p. 19 (2016)
Keßler, C., Farmer, C.J.Q.: Querying and integrating spatial-temporal information on the web of data via time geography. Web Semant.: Sci. Serv. Agents World Wide Web 35(1), 25–34 (2015)
Schwesinger, U., Siegwart, R., Furgale, P.: Fast collision detection through bounding volume hierarchies in workspace-time space for sampling-based motion planners. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 63–68, May 2015
Long, J., Nelson, T.: Home range and habitat analysis using dynamic time geography. J. Wildl. Manag. 79(3), 481–490 (2015)
Long, J.A., Nelson, T.A.: Measuring dynamic interaction in movement data. Trans. GIS 17(1), 62–77 (2013)
Larsson, T., Akenine-Möller, T.: A dynamic bounding volume hierarchy for generalized collision detection. Comput. Graph. 30(3), 450–459 (2006)
Sinha, G., Mark, D.M.: Measuring similarity between geospatial lifelines in studies of environmental health. J. Geogr. Syst. 7(1), 115–136 (2005)
Gao, P., Kupfer, J.A., Zhu, X., Guo, D.: Quantifying animal trajectories using spatial aggregation and sequence analysis: a case study of differentiating trajectories of multiple species. Geogr. Anal. 48, 275–291 (2016)
Demšar, U., Virrantaus, K.: Space-time density of trajectories: exploring spatio-temporal patterns in movement data. Int. J. Geogr. Inf. Sci. 24(10), 1527–1542 (2010)
Long, J.A., Webb, S.L., Nelson, T.A., Gee, K.L.: Mapping areas of spatial-temporal overlap from wildlife tracking data. Mov. Ecol. 3(1), 1–14 (2015)
Ram, P., Lee, D., March, W., Gray, A.G.: Linear-time algorithms for pairwise statistical problems. In: Advances in Neural Information Processing Systems (NIPS), December 2009, vol. 22. MIT Press (2010)
Gray, A.G., Moore, A.W.: \(N\)-body problems in statistical learning. In: Leen, T.K., Dietterich, T.G., Tresp, V. (eds.) Advances in Neural Information Processing Systems (NIPS), December 2000, vol. 13. MIT Press (2001)
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Farmer, C.J.Q., Keßler, C. (2016). Hierarchical Prism Trees for Scalable Time Geographic Analysis. In: Miller, J., O'Sullivan, D., Wiegand, N. (eds) Geographic Information Science. GIScience 2016. Lecture Notes in Computer Science(), vol 9927. Springer, Cham. https://doi.org/10.1007/978-3-319-45738-3_3
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