This project consists of the data set and python codes.
In order to optimize tourism experience for the tourism industries and tourists per se, the road system can play a significant role in increasing convenience, comfort, safety, and marketing opportunities for the benefit of the users. Thus, this study tries to work on the urban road network dataset for Tokyo, particularly around the Tokyo Tower area, to fulfill the research problems that involve the road network system especially for tourism convenience in relation to the Tokyo 2020 Olympic. This study intends to answer research problems related to the identification of the most crucial roads around Tokyo Tower, suggestions for improvement of road traffic networks around this area and the identification of attractive spots or intersections for marketing opportunities. After preprocessing the data, the exploratory data analysis has been conducted on this dataset to gain better insight in terms of the network and node statistics. Furthermore, all the analyses, including finding the edge betweenness centrality, triadic closure frequency, and k-core node centric community detection, have been performed through Python programming languages to deal with the research problems!
An urban road network dataset for Tokyo city has been chosen and extracted from csun.uic.edu . This dataset consists of six columns, including Xcoord, Ycoord, START_NODE, END_NODE, EDGE, and LENGTH. The number of edges is 1035446 and the number of nodes is 354297.