## 4 Empirical Analysis
### Evaluation of Services
#### 4.3.1 Calculation Process and Analysis Results of All Respondents
\begin{table}
\begin{tabular}{l l l c c c c c} \hline \hline
**Major Classification** & **Medium Classification** & **Minor Classification (Service Model)** & **FPS** & **FNIS** & **CC\({}_{i}\)** & **Rank** \\ \hline  & A-1. Preventive detection of traffic accidents at night & & A-1-1. Pedestrian detection at night & 0.071 & 0.611 & 0.896 & 1 \\ \cline{2-7}  & A-1-2. Nighttime mobility detection & & 0.096 & 0.586 & 0.859 & 2 \\ \cline{2-7} A. & A-2-1. Violence-related incident detection & & 0.373 & 0.343 & 0.479 & 13 \\ \cline{2-7} Object anomaly detection & A-2. Detection of night security incidents & & A-2-2. Detection of crime fear & 0.473 & 0.260 & 0.355 & 18 \\ \cline{2-7}  & A-3. Intrusion status detection & & 0.349 & 0.380 & 0.521 & 11 \\ \cline{2-7}  & A-3. Pedestrian hazard detection & & 0.3-1. Detection of dense pedestrian situation & 0.257 & 0.457 & 0.640 & 7 \\ \cline{2-7}  & A-3.2: Calculation of cluster population & & 0.255 & 0.420 & 0.588 & 8 \\ \hline  & & B-1-1. Road surface temperature detection & & 0.393 & 0.334 & 0.459 & 14 \\ \cline{2-7} B. & B-1-2. Road weather hazard detection & & 0.334 & 0.375 & 0.529 & 10 \\ \cline{2-7} Road anomaly detection & & B-1-3. Road ice detection & & 0.241 & 0.464 & 0.658 & 6 \\ \cline{2-7}  & B-2. Road surface hazard detection & & B-2-1. Road pathologic detection & & 0.444 & 0.278 & 0.385 & 16 \\ \cline{2-7}  & B-2. Direction of false objects on road & & 0.532 & 0.166 & 0.238 & 21 \\ \hline  & C-1. Building status detection & C-1-1. Detection of building fade damage & & 0.528 & 0.201 & 0.276 & 19 \\ \cline{2-7} C. & C-1-2. Building structure diagnostics & & 0.522 & 0.193 & 0.270 & 20 \\ \cline{2-7} C. & C-2. Detection of precursor fire events in facility & & C-2-1. Detection of precursor fire events within buildings & 0.135 & 0.576 & 0.810 & 4 \\ \cline{2-7} Facility anomaly detection & C-2-2. Detection of precursor fire events in facilities & & 0.104 & 0.606 & 0.854 & 3 \\ \cline{2-7}  & C-3. Detection of facility accident symptoms & & C-3-1. Detection of emissions in business places & 0.354 & 0.379 & 0.517 & 12 \\ \cline{2-7}  & C-3-2. Detection of explosives at large events & & 0.453 & 0.266 & 0.370 & 17 \\ \hline D. & D-1. Natural disaster prevention detection & & D-1-1. Detection of precursor forest fire & 0.164 & 0.538 & 0.767 & 5 \\ \cline{2-7} disaster prevention anomaly detection & & D-1-2. River flooding detection & & 0.408 & 0.294 & 0.419 & 15 \\ \cline{2-7} Detection & D-2. Heat island detection & & 0.323 & 0.425 & 0.568 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Fuzzy TOPSIS analysis results for all respondents. Other similarly highly ranked services included precursor forest fire (D-1-1, CC\({}_{i}\): 0.767) and heat island (D-2-1, CC\({}_{i}\): 0.568) detection services ranked fifth and ninth, respectively. Clearly, the pre-fire phenomenon was ranked high, perhaps reflecting the fact that heat waves are occurring more frequently due to abnormal weather conditions in summer. Despite being related to disaster, river flooding detection (D-1-2, CC\({}_{i}\): 0.419) was ranked low (15 th). This may reflect the fact that it is possible to control flooding even with other measurement sensors like radars, even if a thermography camera is not used. Road ice (B-1-3, CC\({}_{i}\): 0.658) and road flooding (B-1-2, CC\({}_{i}\): 0.529) detection services were ranked sixth and 10 th, respectively. The relatively better ranking may reflect the importance of preventing road vehicle accidents in inclement weather. Detection service of dense pedestrian situations (A-3-1, CC\({}_{i}\): 0.640) and calculation service of cluster population (A-3-2, CC\({}_{i}\): 0.588) were also ranked high at seventh and eighth, respectively. This may have happened due to the Itaewon stampede in October 2022, in which 159 people died.