## 3 Research Methodology
### Data Collection  
The dependent variable, commercial crime report density, is the number of commercial crime reports from each year divided by the area of their respective commercial districts. This normalization is performed in order to account for the variations in size among the commercial districts. These data were obtained from the Korean National Police Agency's Smart Policing Big Data Platform. While collecting the data, we excluded crimes deemed insufficiently related to commercial areas, as well as murder, voice phishing, economic crimes and suicide, for which the type of occurrence could not be specified. By considering prior studies, eight types of crimes (illegal street vendors, dining and dashing, minor quarrels, theft, drunkenness, assault, vagrancy and disturbing the peace) are categorized and considered commercial-related crimes [33; 34]. Commercial crime report data are different from the real crime statistics provided by the National Police Agency, predominantly focusing on five major crimes, and are more suitable for identifying trends in commercial crime, mainly consisting of minor crimes [35]. In addition, commercial crime report data are useful in examining changing patterns of crime occurrences over the course of a year in detail, and the actual crime statistics data are published only as the total number of crimes that occurred during the year. Independent variables were collected based on the features of each variable, which were classified into four characteristics that can affect the crime, and a total of 18 variables were utilized. For socioeconomic features, living population, youth sales ratio, middle-aged sales ratio, elderly sales ratio and land appraisal value were considered. Living population is a concept that encompasses resident and floating populations, including not only those who live within the commercial district but also those who visit the area for daily activities,  
Figure 1: Study area. such as education, work and other activities that generate administrative demand [36]. Living population data were collected and estimated based on the SK Telecom annual averages [37]. Based on a study that identified that the number of crimes may be impacted by different age usage, we employed three different age groups' average sales ratios [15]. Age groups are defined as 20-30 s (youth), 40-50 s (middle-age) and over 60s (elderly). Land appraisal value represents the economic level of a specific commercial district, and the data were obtained from the Individual Land Appraisal Value by using the median value of each commercial district. Neighborhood features include the commercial land ratio, land use mix index, commercial-to-housing ratio and the number of gathering facilities. The land use mix index was calculated by dividing the natural logarithm of the sum of four land use area ratios (residential, commercial, industrial and green spaces) by the total number of land uses. For example, when the area is fully covered by residential areas (100%), the value is 0. If an area has equal proportions of all land uses, the value is 1. Thus, a higher index indicates an area with more complex land uses. The commercial-to-housing ratio was derived by dividing the percentage of commercial building areas by residential building areas. Gathering facilities are the number of theaters and educational institutions within a commercial district, which induce the floating population to increment in shopping areas. Regarding park/greenery features, the number of neighborhood parks, children's parks and small parks within 400 m of each commercial district were used. Moreover, the green view index (GVI), representing the greenness rate of an area, was considered one of the park/greenery features. The GVI was calculated by using the Treepedia model, an object recognition computer vision technique, to measure the amount of street greenery in the image.