TY  - EJOU
AU  - Puthige, Isha 
AU  - Bansal, Kartikay 
AU  - Bindra, Chahat 
AU  - Kapur, Mahekk 
AU  - Singh, Dilbag 
AU  - Mishra, Vipul Kumar 
AU  - Aggarwal, Apeksha 
AU  - Lee, Jinhee 
AU  - Kang, Byeong-Gwon 
AU  - Nam, Yunyoung 
AU  - Mostafa, Reham R. 

TI  - Safest Route Detection <i>via</i> Danger Index Calculation and K-Means Clustering
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 69
IS  - 2
SN  - 1546-2226

AB  - 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.
KW  - Agglomerative; clustering; crime rate; danger index; DBSCAN

DO  - 10.32604/cmc.2021.018128