| Autor: |
Parameswari, D. V. Lalitha, Rao, Ch. Mallikarjuna, Prashanthi, Bh., Ushasree4, D., Priya, B.Indu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
International Journal of Intelligent Systems and Applications in Engineering; Vol. 11 No. 2 (2023); 847 – 853 |
| ISSN: |
2147-6799 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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