## 2 Optimization Siting Model
### Risk Classification Method  
The risk classification method basically relied on the scoring system proposed by [PERSON] et al. [27]. A two-level indicator system was constructed by using the fuzzy analytic hierarchy process [28]. Temporal, spatial, and personnel-related factors were considered. Six key factors were ultimately characterized, including spatial location (A\({}_{1}\)), land attributes (A\({}_{2}\)), population density (A\({}_{3}\)), building volume ratio (A\({}_{4}\)), GDP density (A\({}_{5}\)), and incident density (A\({}_{6}\)). Table 1 illustrates the detailed risk assessment indicator system including the calculation method and score counts. The scoring model depended on expert ratings, correlating with the probability of triggering risks. The most significant factors were population density and incident density, accounting for 0.27 and 0.21, while the impact  
Figure 2: (**a**) The distribution of jurisdictional areas and annual incidents for all fire stations in 2022 and (**b**) the counts of fire stations for different incident ranges.  
of GDP density was minimal, contributing only 0.1. The proportions of land attributes, building volume ratio, and spatial location were 0.15, 0.15, and 0.12, respectively. The model calculation method was scientifically formulated. Taking spatial location as an example, it assigned 100 to the central urban area, 60 to the main urban area, and 30 to other regions. For land attributes, commercial service land was assigned 100, office land 80, and residential land 60. Other factors were scored on a scale from 0 to 100 based on the proportion relative to the maximum value.  
The fire safety risk score (\(R\)) can be calculated by the factors (\(A_{i}\)) and score count (\(\alpha_{i}\)) as  
\[R=\sum\alpha_{i}A_{i} \tag{1}\]  
A comprehensive score was assigned to each region and the regions were classified into four risk classifications: A (extremely high risk), B (high risk), C (moderate risk), and D (low risk), according to their scores. The specified risk classifications of Shanghai will be illustrated in Section 3.1.  
According to the risk classification, different fire response and time requirements were reasonably proposed, as shown in Table 2. The time taken for vehicles to travel and reach  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factors** & **Indicator Name** & **Method of Calculation** & **Score Count** \\ (\(A_{i}\)) & & & **(\(\alpha_{i}\))** \\ \hline A\({}_{1}\) & Spatial location & Assigning a value of 100 to the central urban area, a value of 60 to the main urban area, and a value of 30 to other regions. & 0.12 \\ A\({}_{2}\) & Land attributes & Current land use attributes (residential land assigned a value of 80, \\ A\({}_{3}\) & Population density & Population within the jurisdiction divided by the total area of 100, etc.). & 0.15 \\ A\({}_{4}\) & Building volume ratio & Total area of above-ground buildings divided by total area of & 0.27 \\ A\({}_{5}\) & GDP density & Total GDP of the region (in hundred million CNY) divided by the total area of the region (in square kilometers). & 0.15 \\ A\({}_{6}\) & Incident density & Fire incident density within the region over 12 months. & 0.21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The risk assessment indicator system.  
Figure 3: The framework of the optimization siting model based on fire risk classification.  
a scene was considered as the fire response time [3]. For the A-level area, the travel and response time were required to be 4 and 5 min, respectively. For B- through D-level areas, the travel time gradually extended from 7 to 19 min, and the response time increased from 8 to 20 min.