## 4 Empirical Analysis
### Evaluation of Services
#### 4.3.2 Comparison of Analysis Results between Developers and Local Governments
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline \multirow{2}{*}{**Major Classification**} & **Medium** & **Minor** & **Deviceper** & \multicolumn{2}{c}{**Local Government**} \\  & **Classification** & **Minsification (Service Model)** & **CC** & **Rank** & **CC** & **Rank** \\ \hline \multirow{4}{*}{A-1: Proventive detection of traffic accidents at night} & A-1: & Pedestrian detection at night & 0.806 & 3 & 0.872 & 1 \\ \cline{2-6}  & A-2: & Nighttime mobility detection & 0.805 & 4 & 0.821 & 2 \\ \cline{2-6}  & A-2: Detection of night security incidents & A-2: & Valence-related incident detection & 0.512 & 11 & 0.552 & 12 \\ \cline{2-6}  & A-2: Detection of crime fear & 0.463 & 14 & 0.425 & 19 \\ \cline{2-6}  & A-2: & Intrusion status detection & 0.367 & 20 & 0.799 & 3 \\ \cline{2-6}  & A-3: & Detection of dense pedestrian situation & 0.598 & 8 & 0.710 & 7 \\ \cline{2-6}  & A-3: & 2. Calculation of cluster population & 0.583 & 9 & 0.647 & 9 \\ \hline \multirow{4}{*}{B-Road anomaly detection} & B-1: & Road weather hazard detection & B-1: & Road temperature temperature & 0.500 & 12 & 0.499 & 18 \\ \cline{2-6}  & B-1: & Road weather hazard detection & B-1: & Road temperature & 0.531 & 10 & 0.581 & 11 \\ \cline{2-6}  & B-1: & Road ice detection & 0.636 & 5 & 0.597 & 10 \\ \cline{2-6}  & B-2: & Road surface hazard detection & B-2: & Road public detection & 0.435 & 16 & 0.518 & 16 \\ \cline{2-6}  & B-2: & Detection of false objects on road & 0.188 & 21 & 0.483 & 17 \\ \hline \multirow{4}{*}{C-1: Building status detection} & C-1: & Detection of building facade damage & 0.375 & 18 & 0.468 & 20 \\ \cline{2-6}  & C-1: & Building structure diagnostics & 0.439 & 15 & 0.322 & 21 \\ \cline{2-6}  & C-2: Detection of precursor fire events in facility & C-2: & Detection of precursor fire events with buildings & 0.892 & 1 & 0.706 & 8 \\ \cline{2-6}  & C-2: & Detection of precursor fire events in facilities & 0.836 & 2 & 0.743 & 5 \\ \cline{2-6}  & C-3: Detection of of emissions in business places & 0.465 & 13 & 0.711 & 6 \\ \cline{2-6}  & C-3: & 2. Detection of explosives at large events & 0.388 & 17 & 0.546 & 15 \\ \hline \multirow{4}{*}{D-1: Natural disaster prevention detection} & D-1: & Detection of precursor forest fire & 0.633 & 6 & 0.768 & 4 \\ \cline{2-6}  & D-1: & 2. River flooding detection & 0.396 & 19 & 0.547 & 14 \\ \cline{1-1} \cline{2-6}  & D-2: & Heat island detection & 0.601 & 7 & 0.549 & 13 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Comparison of analysis results between developers and local government operators. Third, local governments evaluated the importance of Natural disaster prevention detection services more highly than developers (e.g., detection of precursor forest fire (D-1-1) and river flooding detection (D-1-2)). This appears to be the result of local governments highlighting the importance of control in disaster prevention; poor response can lead to larger disasters. This is characterized by local or spatial control rather than the detection of specific objects. Heat island detection, which is in the same disaster prevention field but does not lead to an urgent disaster, was ranked higher by developers. The urgency of disasters appears to be an important factor in local governments' selected priorities. Meanwhile, developers put a high priority on analyzing differences in heat due to temperature changes; specifically, they seek to maximize the service that can only be provided by thermal cameras and is difficult to measure with other measurement sensors.