Safest Route Detection via Danger Index Calculation and K-Means Clustering

• Published in: Computers, materials & continua materials & continua, 2021, Vol.69 (2), p.2761-2777
• Main Authors: Puthige, Isha, Bansal, Kartikay, Bindra, Chahat, Kapur, Mahekk, Singh, Dilbag, Kumar Mishra, Vipul, Aggarwal, Apeksha, Lee, Jinhee, Kang, Byeong-Gwon, Nam, Yunyoung, R. Mostafa, Reham
• Format: Article
• Language: English
• Subjects: Algorithms; Cluster analysis; Clustering; Crime; Criminal statistics; Geographical locations; Social problems; Vector quantization
• ISSN: 1546-2226; 1546-2218; 1546-2226
• DOI: 10.32604/cmc.2021.018128

The study aims to formulate a solution for identifying the safest route between any two inputted Geographical locations. Using the New York City dataset, which provides us with location tagged crime statistics; we are implementing different clustering algorithms and analysed the results comparatively to discover the best-suited one. The results unveil the fact that the K-Means algorithm best suits for our needs and delivered the best results. Moreover, a comparative analysis has been performed among various clustering techniques to obtain best results. we compared all the achieved results and using the conclusions we have developed a user-friendly application to provide safe route to users. The successful implementation would hopefully aid us to curb the ever-increasing crime rates; as it aims to provide the user with a before-hand knowledge of the route they are about to take. A warning that the path is marked high on danger index would convey the basic hint for the user to decide which path to prefer. Thus, addressing a social problem which needs to be eradicated from our modern era.