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An Integrated Approach of GIS and Spatial Data Mining in Big Data

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Hemlata Goyal, Chilka Sharma, Nisheeth Joshi
10.5120/ijca2017914012

Hemlata Goyal, Chilka Sharma and Nisheeth Joshi. An Integrated Approach of GIS and Spatial Data Mining in Big Data. International Journal of Computer Applications 169(11):1-6, July 2017. BibTeX

@article{10.5120/ijca2017914012,
	author = {Hemlata Goyal and Chilka Sharma and Nisheeth Joshi},
	title = {An Integrated Approach of GIS and Spatial Data Mining in Big Data},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {169},
	number = {11},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume169/number11/28026-2017914012},
	doi = {10.5120/ijca2017914012},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

An explosive growth of spatial data has been demanding to Spatial Data Mining (SDM) technology, emerging as a innovative area for spatial data analysis. Geographical Information System (GIS) contains heterogeneous data from multidisciplinary sources in different formats. Geodatabase is the repository of GIS data, representing spatial attributes, with respect to location. Rapidly increasing satellite imagery and geodatabases generates huge data volume related to real world and natural resources such as soil, water, temperature, vegetation, forest cover etc. Inferring information from geodatabases has gained value using computational algorithms. The intent of this paper is to introduce with GIS, and spatial data mining, GIS and SDM tools, algorithmic approaches, issues and challenges, and role of spatial association rule mining in big data of GIS.

References

  1. Maguire, D. J., Goodchild, M. F., and Rhind, D. W. 1991. Geographical Information Systems, pp.9-20
  2. Heywood, I., Cornelius, S., Carver, S., Raju, and S. An Introduction to Geographical Information Systems, Second Edition, Pearson Education, pp.10.
  3. Sengchuan, T. 2003. Spatial Data Mining: Clustering of Hot Spots and Pattern Recognition. IEEE. pp.3685-3687
  4. Liang,Y., and Fuling, B. 2007. An Incremental Data Mining Method for Spatial Association Rule in GIS Based Fireproof System. IEEE. pp.5983-5986.
  5. Jayasinghe, P.K.S.C., and Masao, Y. 2013. Spatial data mining technique to evaluate forest extent changes using GIS and Remote Sensing.
  6. Wei, X., Yong, Q., Houkuan, and H. 2003. The Application of Spatial Data Mining in Railway Geographic Information Systems. IEEE. pp.1467-1471
  7. Marzolf, F., Trépanier, M., and Langevin. 2006. A Road network monitoring algorithms and a case study. Journal of Computer and Operation Research, pp.3494–3507.
  8. Yuanzhi, Z., XieKunqing, M., Xiujun, X., Dan, C., and Tang S. 2005. Spatial Data Cube: Provides Better Support for Spatial Data Mining. IEEE. pp.795-798
  9. Rub, G., and Brenning, A. 2010. Data Mining in Precision Agriculture: Management of Spatial Information, Computational Intelligence for Knowledge Based System Design, Volume 6178, pp. 350-359.
  10. Stathakis, D., Savin, I., and Nègre T. Neuro-Fuzzy Modeling for Crop Yield Prediction, The International Archives of the Photogrammetry, Remote Sensing and Spatial Info. Sc., Vol. 34, pp.1-4.
  11. Vaagh, Y. 2012. The application of a visual data mining framework to determine soil, climate and land use relationships. Journal of Procedia Eng. 32, pp.299–306 .
  12. Buhalis, D., and Law, R. 2008. Progress in information technology and tourism management,The state of eTourism research. Journal of Tourism Mgmt.
  13. Chakraaborty, A., Mandal, J.K., Chandrabanshi, S.B., and Sarkaar, S. 2013. A GIS Anchored system for selection of utility service stations through Hierarchical Clustering. International Conference on Computational Intelligence: Modeling techniques and Application, CIMTA
  14. Kashid, S.S., and Maity, R., 2012. Prediction of monthly rainfall on homogenous monsoon regions of India based on large scale circulation patterns using Genetic Programming. Journal of Hydrology, pp.26-41.
  15. Vyas, P., 2015. To predict rainfall in desert area of Rajasthan using data mining techniques. vol.3, no.5.
  16. Priya, R.L., and Manimannan, G., 2014. Rainfall fluctuation and regionwise classificatrion in Tamilnadu using geographical information system. IOSR Journal of Mathematics (IOSR-JM), vol. 10, pp.5-12.
  17. Teegavarapu, R. S. V., 2009. Estimation of missing precipitation records integrating surface interpolation techniques and spatio-temporal association rules. Journal of Hydroinformatics, vol. 11, no. 2, pp.133–146.
  18. http://www.newdesignfile.com/postpic/2013/04/vector-and-raster-data-gis_132173.JPG
  19. http://www.vermessungsseiten.de/gis/vector_raster.gif
  20. Niebles, J.C., Wang, H., and Fei-Fei, L. 2008 Unsupervised learning of human action categories using spatial–temporal words. Int. J. Compute. Vis.79(3), pp.299–318.
  21. Shekhar, S., Zhang, P., Huang, Y., and Vatsavai, R.R., (2003): Trends in spatial data mining.
  22. Lee, A.J.T., Hong, R.W., Ko, W.M., Tsao, W.K., and Lin, H.H. 2007. Mining spatial association rules in image databases. Inform. Sci. 177, pp.1593–1608.
  23. Du, S., Qin, Q., Wang, Q., and Ma, H. 2008. Evaluating structural and topological consistency of complex regions with broad boundaries in multi-resolution spatial databases. Information Sci. 178, pp.52–68.
  24. Pop III, A., Burnett, R.T., Thurston, M.J., and Calle, E.E., and Krewski. 2004. Cardiovascular Mortality and Long-Term Exposure to Particulate Air Pollution. Circulation 109, pp.71–77.
  25. Egenhofer, M. 1994. Spatial SQL A Query and Presentation Language. IEEE Transactions and Data Engineering 6, pp.86–95.
  26. Spatial Data Mining, Winter School on ‘Data Mining Techniques and Tools for Knowledge Discovery in Agricultural Datasets, pp. 153-166.
  27. Dunham, M.H. 2006. Basic Data Mining Tasks. Singapore, Pearson Education.
  28. Mennis, J., and Liu, J. W. 2005. Mining Association Rules in SpatioTemporal Data: An Analysis of Urban Socioeconomic and Land Cover Change. Transactions in GIS, 9(1), pp.5-17.
  29. Shekhar, S., Evans, M. R., Kang, J. M., and Mohan, P. 2011. Identifying Patterns in Spatial Information: A Survey of Methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(3), pp.193-214.
  30. Brinkoff, T., and Kriegel, H.P., 1994. The Impact of Global Clustering on Spatial Database Systems. Proceedings of the 2Uth VLDB Conference, pp.168–179.
  31. Moens S., Aksehirli E., and Goethals B. 2013. Frequent Itemset Mining for Big Data, IEEE Int. Conf. on Big Data, IEEE, pp.111-118.

Keywords

GIS, SDM, Geodatabases, Spatial and Nonspatial data, Bigdata, MRPrePost