# Location Optimization of Emergency Bell Based on Coverage Analysis for Crime Prevention
## 2 Related Work  
In this section, an analysis of previous studies related to principal component analysis (PCA), K-means, MCLP and LSCP was conducted.  
To consider the optimal placement of emergency bells, this study extracted ten significant variables from a pool of 27 variables, simplifying the complexity of the dataset. For the dimensionality reduction of the ten significant variables, PCA was conducted. PCA  
Figure 1: Emergency bell operation process.  
is a statistical methodology that has been actively employed in various fields for data analysis [10; 11; 12] since its inception by [PERSON] in 1901 [13]. The PCA simplifies the complexity of high-dimensional data while preserving trends and patterns. It accomplishes this by transforming data into a lower dimension, serving the purpose of summarizing features [14].  
K-means, introduced by [PERSON] in 1967 [15], is a widely used unsupervised clustering method. It partitions a set of objects into k clusters, aiming for low intercluster similarity and high intracluster similarity, typically represented by the mean values of objects within each cluster [16]. In this study, PCA and K-means were utilized for dimensionality reduction and clustering of the data [17], allowing for analyzation of the administrative districts corresponding to the high-level cluster.  
Meanwhile, unlike other types of sensors characterized by hierarchical interconnection [18] and data transmission techniques [19], CCTV cameras require a flat structure and do not employ data coding techniques. Interestingly, the optimization of coverage has remained the main concern in CCTV camera deployment. Optimal location problems determine the most efficient placement of facilities while considering budget constraints. For example, the LSCP, which was proposed by [PERSON] [20], and the MCLP, introduced by [PERSON] and [PERSON] [21], have received significant attention due to their wide application range.  
Although LSCP and MCLP models have existed for some time, recent advancements in computing power have enabled the application of these models to big data. Simultaneously, the availability of geographic visualization programs has facilitated their active and recent utilization.  
These methodologies have been applied in various fields, including schools, police stations, fire stations, ambulances, hospitals, post offices, and even advertisements [22; 23; 24; 25]. The objective of MCLP is to place a fixed number of facilities to maximize the coverage of demanded points. Meanwhile, LSCP aims to deploy the minimum number of facilities necessary to achieve the complete coverage of demanded points [25].  
There has been a growing interest in analyzing location-based problems by utilizing GIS. In particular, [PERSON] outlined the interconnections between location analysis and GIS, emphasizing how GIS has contributed to the field of location science in terms of data entry, visualization, problem-solving, and theoretical advancements [26]. In another study, [PERSON] and [PERSON] proposed the MCLP model, which incorporates the concept of complementary partial coverage [27]. Specifically, they utilized GIS to enhance the visual representation of locations. With the use of GIS, solutions for MCLP and LSCP can be displayed on a map, and various analyses can be conducted based on the visualizations.  
The study of optimal location has garnered significant attention in the fields of geography and has progressed through the application of mathematical models. However, only a few studies have focused on the issue of emergency bell location for crime prevention. This highlights the necessity of this study.