Measuring Spatial Dependencies of Various Spatial Objects Related To the Road Safety Discrepancies
Charankumar Ganteda1, Shobhalatha G2, Rajyalakshmi K3

1Charankumar Ganteda, Research scholar, Department of Mathematics, Srikrishna Devaraya University, Anantapuramu, India.
2Shobhalatha G, Department of Mathematics, Srikrishna Devaraya University, Anantapuramu, India.
3Rajyalakshmi K, Department of Mathematics, Koneru Lakshmaiah Education Foundation, Guntur, India

Manuscript received on 29 August 2019. | Revised Manuscript received on 12 September 2019. | Manuscript published on 30 September 2019. | PP: 362-365 | Volume-8 Issue-11, September 2019. | Retrieval Number: K13080981119/2019©BEIESP | DOI: 10.35940/ijitee.K1308.0981119
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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: Spatial analysis is very much useful and gives better results in analyzing the data related to graph theory in various fields with spatial contexts. Defining the spatial location of the entities being studied are the fundamental problem in the spatial analysis. Spatial graphs and mathematical tools plays a vital role in the analysis of spatial data. In this paper, we madse an attempt to understand the spatial graph properties and it can be used to describe, compare as well as to test specific hypothesis of road safety measures with respect to specific locations. The integration of Graph theory, statistical measure such as spatial autocorrelation and Geographical information system (GIS) provides a scientific platform for measuring the entities involved in road safety. In this connection, we collect the information in different specific locations related to several discrepancies which affect the road safety. Identify the locations with low safety in our study region and measured the similar objects which are close to other close objects by using Moran’s I index. Our main objective is to determine the observed spatial pattern of low safety values is equal to any other spatial pattern. Spatial dependencies with respect to the various discrepancies cause to the low safety and suggest measures to take the precautionary steps when moving from one location to another. From the examined results, we conclude that the similar values grouped together in a map and also observed that the spatial auto correlation, the qualities at one area do not rely upon qualities at other neighboring areas.
Keywords: Dijkstra’s algorithm, Moran’s I index, Road discrepancies, spatial auto correlation, Spatial structure.
Scope of the Article: