## 4 Empirical Analysis
### Model Validation  
The Fuzzy TOPSIS model does not have its own verification method such as AHP's consistency verification. Therefore, this study aims to compare the results of the classic AHP analysis most commonly used in MCDM for verification of the Fuzzy TOPSIS model. Like Fuzzy TOPSIS, the AHP technique is also capable of structuring alternative choices in complex decision-making situations based on the experience and knowledge of experts. Therefore, only the survey items were separately constructed and analyzed based on the surveys of the same 48 experts. AHP, developed by [PERSON] (1986), is a method of evaluating relative importance through pairwise comparison between decision-making hierarchical structure components ([PERSON] et al., 2018). In the case of the AHP evaluation criteria, it was hierarchically structured by reorganizing it into three upper items, namely conformity to crime prevention purpose, technology suitability, and technology feasibility, and based on this, the relative importance evaluation and final weight between the upper and lower items were calculated. In the case of alternative evaluation, the AHP technique was also finally calculated by applying the weight of each evaluation criterion to 12 intelligent CPTED service elements like Fuzzy TOPSIS. Model verification was largely composed of two types: AHP's consistency verification, which is the same survey basis, and AHP and Fuzzy TOPSIS techniques' methodological correlation verification. Since AHP's consistency verification was analyzed based on the same questionnaire, the reliability of Fuzzy TOPSIS' expert response can be indirectly verified. Verifying the correlation between AHP and Fuzzy TOPSIS techniques can verify the analytical feasibility with AHP, which is used a lot in the research and policy-making process and has public confidence. First, it is the consistency verification of AHP. Unlike the Fuzzy TOPSIS technique, AHP is based on relative evaluation, so consistency verification is possible to verify how consistent the analysis is. For consistency calculation, the consistency ratio (CR) was calculated using the dimension-wise average random index (RI) of the matrix obtained from empirical data. As a result, the CR of the total effective sample (n = 48) was 0.072, showing high consistency. Generally, if the CR value is within 0.1, it is consistent, and if it is within 0.2, it is accepted at an acceptable level [51]. Second, the methodological correlation verification between AHP and Fuzzy TOPSIS. In the case of the evaluation criteria, the correlation coefficient between AHP and Fuzzy TOPSIS was 0.782, indicating a positive correlation. The AHP evaluation criteria analyzed the relative importance based on pairwise comparison, and Fuzzy TOPSIS analyzed the absolute importance of the Likert scale evaluation basis, so it is judged to have an appropriate level of correlation. Looking at the differences between the items, it can be seen that the importance of AHP's policy conformity is much higher than that of Fuzzy TOPSIS. This is because the importance of crime prevention conformity', a major category to which policy conformity is dependent, was very high (0.490 major category crime prevention conformity, 0.264 minor category policy conformity). That is, since biased results may be produced due to the dependency problem between the major and minor categories, Fuzzy TOPSIS, which is more intuitive and easier to respond to the questionnaire, is judged as an appropriate methodology (Table 13). In the case of alternative evaluation, the correlation coefficient between AHP and Fuzzy TOPSIS is 0.976, which has a very strong positive correlation. This is based on the same questionnaire response based on absolute importance, and it can be interpreted that the Fuzzy TOPSIS model is methodologically suitable. One thing to note is that when calculating the optimized ratio for each technology element, the difference between the 1 st and 12 th Fuzzy TOPSIS normalized values was 10.4%, while AHP was only 4.6%.