## 2 Literature Review
### Distinction of This Research  
Most previous studies on crime in Korea have focused on the physical aspects of the city as a whole. The majority of research has found similar results for factors influencing crime/the fear of crime by utilizing demographic and built environment factors, such as population density, foreign/elderly populations, income level and park features. However, existing studies have generally conducted their analyses only on a specific period (e.g., a certain year in which an accident occurred), and thus, there is a lack of research on how factors influencing crime have changed in response to social disasters such as COVID-19. Furthermore, as noted in existing theories of criminal behavior (such as the broken window theory), criminal behavior is influenced by the perceived degree of spatial awareness, yet there is a lack of research assessing the perceived aspects of crime in the literature. Therefore, the distinction of this study is as follows.  
First, we sought to identify changes in crime impact factors over time, focusing on the period of the COVID-19 outbreak and the implementation of social distancing policies. Unlike previous studies that analyzed factors only at a specific point in time, this study has novelty in that it divides the analysis into three periods: before and after COVID-19 (2019, 2020) and after the implementation of social distancing measures (2021) to understand the changes affecting crime according to social trends.  
Second, compared to prior research that primarily considered the physical aspect of commercial areas, this study examines the factors affecting the occurrence of crime by considering the green view index (GVI), representing the perception level of visitors, and the Mu-score, an alternative approach to show the degree of perceived urban decay.  
Third, because eight types of commercial crime reports (112 Emergency Number reported crime call data; illegal street vendors, dining and dashing, minor quarrels, theft, drunkenness, assault, vagrancy and disturbing the peace) were used rather than the five major violent crimes (murder, robbery, rape, assault and theft) used in most studies of crime factors, it was possible to analyze crime that is more specifically related to commercial areas, which is largely composed of misdemeanors and not included in the main datasets (i.e., five major violent crimes data). In addition, commercial crime report data may more specifically identify/count the location of crimes and reflect more detailed crime types. As such, although prior studies have focused on the borough and administrative district level, this study's data are better able to look at the commercial district level, giving a more granular view of trends.  
Finally, unlike previous studies that used basic regression analysis to identify influencing factors, this study adopts more advanced regression-based machine learning models to assist in identifying the importance of influencing factors. Simple regression analyses are limited in their ability to suggest the importance of influencers that should be prioritized for crime prevention. Thus, the importance of crime-influencing factors, as well as the non-linear relationships between diverse variables and crime, can be identified through this study.