Exploratory Spatial Analysis of Pavo Cristatus (Peacock) Distribution in Coimbatore District of Tamil Nadu using Local Indicators
P. Mousi1, J. Ramsingh2, V. Bhuvaneswari3

1P.Mousi, Department of Computer Applications, Bharathiar University, Coimbatore, India.
2J.Ramsingh, Department of Computer Applications, Bharathiar University, Coimbatore, India.
3V.Bhuvaneswari, Department of Computer Applications, Bharathiar University, Coimbatore, India. 

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1905-1912 | Volume-8 Issue-12, October 2019. | Retrieval Number: L28821081219/2019©BEIESP | DOI: 10.35940/ijitee.L2882.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The presence of wildlife in each forest represents the prolific eco-system, wealth and fertility of the region. The literatures on the species distribution consider the global and climatic factors, with special attention to local condition, where the species exists. This study analyzes various factors (Environmental, Social, Economic, Institutional) associated with the existence of the species (Pavo Cristatus) within the forest range of Coimbatore region in two different years (2005 and 2013). A regional level perspective is carried out by investigating the factors associated with the distribution of Pavo Cristatus and estimates the impacts on the species found during analysis. The proposed analysis makes use of Exploratory Spatial Data Analysis (ESDA) to visualize and analyze the local spatial distribution of the species. The results reveal that no exclusive single aspect, but a collection of common factors regulates the species distribution. Further the analysis indicates that the augmentation of species distribution in an area is a healthy sign of the forest regions.
Keywords: Exploratory Spatial Data Analysis, Pavo Cristatus, K-nearest Neighbor, Local Indicators of Spatial Association, Multivariate Spatial Analysis.
Scope of the Article: Data Analytics