Analysis of Regional Safety Index Based on Public Data: Focusing on Suwon City

The purpose of this paper is to analyze the correlation between the regional safety index, urban space, and time series data, to analyze the causes of vulnerable areas by subdividing the regional safety index, and to derive solutions. Public data was used for analysis, and spatial data such as police stations, security bells, and security facilities, and time-series and spatial composite data such as the number of police reports by time zone and floating population were used. In addition, Suwon, Gyeonggi-do, the city to be analyzed, is a large city with a population of over 1 million. It is located near Seoul and has a very high floating population as there are various landmarks, such as Samsung Electronics headquarters and Suwon Hwaseong, in the city. Therefore, it is an excellent city to analyze multiple characteristics that are difficult to obtain in other cities. To obtain various insights, we analyzed multiple methods, and in particular, techniques such as exploratory data analysis, visualization analysis, and machine learning were used.