VIII. DISCUSSIONS AND CONCLUSIONS
In this study, we have presented taxonomy for the geospatial data and GIS systems. Based on this taxonomy, the
available literature are studied and categorized. The geo-spatial data is one of the major contributor towards the big data paradigm and hence research for newer techniques for storage of data and newer systems plays an important role in the scientific community.
This research received funding from the Netherlands Organisation for Scientific Research (NWO) in the framework of the Indo Dutch Science Industry Collaboration programme [NWO, Den Haag, PO Box 93138,NL-2509 AC The Hague, The Netherlands]. We are thankful to NWO, Royal Shell and Prof. Sebastian Meijer, the Principal Investigator of this project.
 Laney, Douglas. 3D Data Management: Controlling Data Volume, Velocity and Variety. Gartner. Retrieved 6 February 2001.
 Tao Wang and Xi Chen and An-ming Bao and Wei-sheng Wang, A new geospatial data model to facilitate geographic data mining and knowledge discovery, IEEE International Conference on Systems, Man
 J. Lacasta and F. J. Lopez-Pellicer and W. Renteria-Agualimpia and J. Nogueras-Iso, Improving the visibility of geospatial data on the Web, IEEE/ACM Joint Conference on Digital Libraries, 155-164, 2014
 Chi Ren Shyu and Matthew N. Klaric and Grant J. Scott and Adrian, S. Barb and Curt H. Davis and Kannappan Palaniappan, GeoIRIS: Geospatial Information Retrieval and Indexing System Content Mining, Semantics Modeling, and Complex Queries,IEEE Trans. Geoscience and Remote Sensing, 45, 4, 839–852, 2007
 A. Subramanian, IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Empowering geo spatial analysis with big data platform: Natural resource management, 1-6, 2016
 S. Roy and S. Das, 2015 4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, Spatial data infrastructures: Its metadata and analysis, 43-51, 2015
 Klein, Levente J. and Marianno, Fernando J. and Albrecht, Conrad M. and Freitag, Marcus and Lu, Siyuan and Hinds, Nigel and Shao, Xiaoyan and Rodriguez, Sergio Bermudez and Hamann, Hendrik F., Big Data, IEEE, PAIRS: A scalable geo-spatial data analytics platform, 1290-1298, 2015
 Xin Chen and Hoang Vo and Fusheng Wang, BigSpatial’14 3rd ACM SIGSPATIAL International Workshop on Analytics for BigGeospatial Data, High Performance integrated spatial big data analytics, 11-14, 2014
 Stefan Hagedorn and Philipp Gotze and Kai-Uwe Sattler, International Conference on Extending Database Technology, Big Spatial Data Processing Frameworks: Feature and Performance Evaluation, 2017
 A. Aji, F. Wang et al., Hadoop-GIS: A High Performance Spatial Data Warehousing System over Mapreduce, VLDB, pp. 1009 -1020, 2013.
 A. Eldawy and M. F. Mokbel, A Demonstration of SpatialHadoop: An Efficient MapReduce Framework for Spatial Data, VLDB, 2013.
 S. You, J. Zhang, and L. Gruenwald, Large-Scale Spatial Join Query Processing in Cloud, ICDE, 2015.
 J. Yu, J. Wu, and M. Sarwat, GeoSpark: A Cluster Computing Framework for Processing Large-Scale Spatial Data. SIGSPATIAL, p. 70, 2015
 Jae-GilLee, Minseo Kang, Big Data Research, Geospatial Big Data: Challenges and Opportunities, 2, 74-81, 2015
 David Haynes and Suprio Ray and Steven M. Manson and Ankit Soni, High performance analysis of big spatial data, IEEE International Conference on Big Data, Big Data 2015, 1953-1957, 2015
 Rabindra K. Barik and Harishchandra Dubey and Arun B. Samaddar and Rajan D. Gupta and Prakash K. Ray, FogGIS: Fog Computing for Geospatial Big Data Analytics, CoRR, abs/1701.02601, 2017
 Harald Bosch and Dennis Thom and Michael Worner and Steffen Koch and Edwin Puttmann and Dominik Jackle and Thomas Ertl, ScatterBlogs: Geo-spatial document analysis, IEEE Conference on Visual Analytics Science and Technology, 309-310, 2011
 Qiulei Guo and Balaji Palanisamy and Hassan A. Karimi, A MapReduce Algorithm for Polygon Retrieval in Geospatial Analysis, 8th IEEE International Conference on Cloud Computing, 901-908, 2015
 Zhen LIU and Huadong GUO and Changlin WANG, Considerations on Geospatial Big Data, IOP Conference Series: Earth and Environmental Science, 46, 1, 12-58, 2016
 Manfred M. Fischer and Jinfeng Wang, Spatial Data Analysis: Models, Methods and Techniques (Springerbriefs in Regional Science) (1st ed.), Springer Publishing Company, Incorporated, 2011
 Ranga Raju Vatsavai, Auroop Ganguly, Varun Chandola, Anthony Stefanidis, Scott Klasky, and Shashi Shekhar, Spatiotemporal data mining in the era of big spatial data: algorithms and applications. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data (BigSpatial ’12) ACM, New York, NY, USA, 1-10, 2012
 Kaiser, Mark S, Soumendra N Lahiri, and Daniel J Nordman, Goodness of Fit Tests for a Class of Markov Random Field Models. The Annals of Statistics, 40(1), Institute of Mathematical Statistics, 10430, 2012
 Andrea Kaplan, Mark Kaiser, Soumendra N Lahiri and Daniel Nordman, A Simple, Fast Sampler for Simulating Spatial Data and Other Markovian Data Structures, JSM, Section on Statistical Computing, 2017
 B. B. Mandelbrot, Fractals and chaos, the Mandelbrot set and beyond: selecta volume C. New York, N.Y., Springer, 2004.
 S. N. Rasband, Chaotic dynamics of nonlinear systems. New York, Wiley, 1990.
 P. Frankhauser, Fractal Geometry of Urban Patterns and their Morphogenesis, Discrete Dynamics in Nature and Society, vol. 2, pp. 127145, 1998.
 A. Clauset, C. R. Shalizi, and M. E. J. Newman, Power-Law Distributions in Empirical Data, SIAM Review, vol. 51, no. 4, pp. 661703, Nov. 2009.
 Qiulei Guo, Balaji Palanisamy, Hassan A. Karimi, A MapReduce Algorithm for Polygon Retrieval in Geospatial Analysis, CLOUD, 901- 908, 2015
 H. Wang, Y. Lu, Y. Guang, E. Edrosa, M. Zhang, R. Camarca, Y. Yesha, T. Lucic, N. Rishe, Epidemiological Data Analysis in TerraFly Geo-Spatial Cloud, International Conference on Machine Learning and Applications, 485-490, 2013
 Mingjin Zhang, Huibo Wang, Yun Lu, Tao Li, Yudong Guang, Chang Liu, Erik Edrosa, Hongtai Li, Naphtali Rishe, TerraFly GeoCloud, ACM Transactions on Intelligent Systems and Technology, vol. 6, pp. 1, 2015
 D. Lopez, M. Gunasekaran, B. S. Murugan, H. Kaur and K. M. Abbas, Spatial big data analytics of influenza epidemic in Vellore, India, 2014 IEEE International Conference on Big Data (Big Data), 19-24, 2014
 M. L. Martnez et al., Geospatial Recommender System for the Location of Health Services, 14th International Conference on Computational Science and Its Applications, Guimaraes, 200-203, 2014
 M. Zoran, Use of geospatial and in situ information for seismic hazard assessment in Vrancea area, Romania, Second Workshop on Use of Remote Sensing Techniques for Monitoring Volcanoes and Seismogenic
Areas, Naples, 1-5, 2008
 Jaishree. S. R, Vindya. T and Sandhya. M. K, Micro-seismic Zonation based on Geospatial Data using GIS Technology, Proceedings of National Conference on Communication and Informatics, 93-96, 2016
 Thomas Saaty L, Decision making with the analytic hierarchial process, International Journal of Service Sciences, Pittsburgh, USA, 1, 1, 83-98, 2008
 S. Shekhar, V. Gunturi, M. R. Evans, and K. Yang, Spatial big-data challenges intersecting mobility and cloud computing. In Proceedings of the Eleventh ACM International Workshop on Data Engineering for Wireless and Mobile Access, MobiDE ’12, 1-6, 2012
 A. Stefanidis, A. Crooks, and J. Radzikowski, Harvesting ambient geospatial information from social media feeds. GeoJournal, 1-20, 2011