Spatial Data Mining towards Geospatial Data Analysis for Discovery of Spatial Correlations

Authors

  • D. V. Lalitha Parameswari Associate Professor, Department of CSE, GNITS, Hyderabad
  • Ch. Mallikarjuna Rao Department of Computer Science and Engineering, GRIET, Bachupally, Hyderabad
  • Bh. Prashanthi Department of Computer Science and Engineering, GRIET, Bachupally, Hyderabad
  • D. Ushasree4 Department of Computer Science and Engineering, GRIET, Bachupally, Hyderabad
  • B.Indu Priya Department of Computer Science and Engineering, GRIET, Bachupally, Hyderabad

Keywords:

Spatial Data Mining, Geospatial Data Analysis, G Statistic, ZG Score Computations, Spatial Correlation Analysis

Abstract

Spatial data provides geographical correlations that can be discovered through Spatial Data Mining (SDM). Such discovery can have potential benefits as it bestows necessary knowledge to understand the patterns. There are many existing methods for correlation analysis. In this paper we focused on the discover of spatial correlations based on G statistic and ZG score computations. We proposed a framework for geospatial data analytics. We also proposed an algorithm known as Spatial Data Mining for Spatial Correlations Discovery (SDM-SCD). This algorithm is meant for spatial correlation analysis and Principal Component Analysis (PCA) to discover trends pertaining to spatial correlations in the given Twitter data based on given words. The algorithm performs spatial correlation analysis based on given words and the location from which such tweet has originated. Experimental results revealed that our framework is useful for geospatial data analysis.

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References

Ali Soltani;Christopher James Pettit;Mohammad Heydari;Fatemeh Aghaei; (2021). Housing price variations using spatio-temporal data mining techniques . Journal of Housing and the Built Environment, p1-29.

Song, Jinchao; Zhao, Chunli; Zhong, Shaopeng; Nielsen, Thomas Alexander Sick; Prishchepov, Alexander V. (2019). Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques. Computers, Environment and Urban Systems, 77, p1-12.

Shekhar, Shashi; Jiang, Zhe; Ali, Reem; Eftelioglu, Emre; Tang, Xun; Gunturi, Venkata; Zhou, Xun (2015). Spatiotemporal Data Mining: A Computational Perspective. ISPRS International Journal of Geo-Information, 4(4), p2306–2338.

Qiu, Qinjun; Xie, Zhong; Wu, Liang; Tao, Liufeng (2020). Automatic spatiotemporal and semantic information extraction from unstructured geoscience reports using text mining techniques. Earth Science Informatics, p1-18.

Wang, Senzhang; Cao, Jiannong; Yu, Philip (2020). Deep Learning for Spatio-Temporal Data Mining: A Survey. IEEE Transactions on Knowledge and Data Engineering, p1–20.

Dagaeva, Maria; Garaeva, Alina; Anikin, Igor; Makhmutova, Alisa; Minnikhanov, Rifkat (2019). Big spatio-temporal data mining for emergency management information systems. IET Intelligent Transport Systems, 13(11), p1649–1657.

Chu, Wesley W. (2014). [Studies in Big Data] Data Mining and Knowledge Discovery for Big Data Volume 1 || Spatio-temporal Data Mining for Climate Data: Advances, Challenges, and Opportunities. , p83–116.

Arguedas, Virginia Fernandez; Mazzarella, Fabio; Vespe, Michele (2015). [IEEE OCEANS 2015 - Genova - Spatio-temporal data mining for maritime situational awareness. , p1–8.

Ansari, Mohd Yousuf; Ahmad, Amir; Khan, Shehroz S.; Bhushan, Gopal; Mainuddin, (2019). Spatiotemporal clustering: a review. Artificial Intelligence Review, p1-43.

Ali Hamdi;Khaled Shaban;Abdelkarim Erradi;Amr Mohamed;Shakila Khan Rumi;Flora D. Salim; (2021). Spatiotemporal data mining: a survey on challenges and open problems . Artificial Intelligence Review, p1-48.

Das, Monidipa; Ghosh, Soumya K. (2016). Deep-STEP: A Deep Learning Approach for Spatiotemporal Prediction of Remote Sensing Data. IEEE Geoscience and Remote Sensing Letters, p1–5.

Agoua, Xwegnon Ghislain; Girard, Robin; Kariniotakis, Georges (2017). Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production. IEEE Transactions on Sustainable Energy, p1–9.

Li, Xiao-Li; Cao, Tru; Lim, Ee-Peng; Zhou, Zhi-Hua; Ho, Tu-Bao; Cheung, David (2015). [Lecture Notes in Computer Science] Trends and Applications in Knowledge Discovery and Data Mining Volume 9441 || Mining Massive-Scale Spatiotemporal Trajectories in Parallel: A Survey. , p41–52.

Aydin, Berkay; Kempton, Dustin; Akkineni, Vijay; Gopavaram, Shaktidhar Reddy; Pillai, Karthik Ganesan; Angryk, Rafal (2014). [IEEE 2014 IEEE International Conference on Big Data (Big Data) - Washington, DC, USA (2014.10.27-2014.10.30)] 2014 IEEE International Conference on Big Data (Big Data) - Spatiotemporal indexing techniques for efficiently mining spatiotemporal co-occurrence patterns. , p1–10.

Mohammed Ali Shaik;Dhanraj Verma;P Praveen;K Ranganath;Bonthala Prabhanjan Yadav; (2020). RNN based prediction of spatiotemporal data mining . IOP Conference Series: Materials Science and Engineering, p1-12.

Sheshasaayee, A., & Sridevi, D. (2016). Fuzzy C-means algorithm with gravitational search algorithm in spatial data mining. 2016 International Conference on Inventive Computation Technologies (ICICT). P1-5.

Shows Fuzzy C means algorithm’s functionality

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Published

17.02.2023

How to Cite

Parameswari, D. V. L. ., Rao, C. M. ., Prashanthi, B. ., Ushasree4, D. ., & Priya, B. . (2023). Spatial Data Mining towards Geospatial Data Analysis for Discovery of Spatial Correlations. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 847 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2898

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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