# Beyond Static Estimates: Dynamic Simulation of Fire-Evacuation Interaction in Historical Districts
## 2 Methods
### Study Area  
The research area, comprising 89.15 hectares, is situated in Dukezong Ancient Town, a provincially designated historical district in southern Diqing, Yunnan Province. Characterized by narrow allevyways, predominantly less than three meters wide, and numerous T-junction intersections, the district exemplifies spatial configurations typical of historical urban centers in Southwest China. These features inherently complicate emergency evacuation and disaster management protocols. Situated approximately 260 m north of the district boundary, Diqing Square functions as the designated emergency shelter, encompassing 3.3 hectares and providing an effective evacuation space of approximately 10,128 m\({}^{2}\)[31]. The residential catchment area between the historical district and Diqing Square covers 64.45 hectares, characterized primarily by four-story residential buildings housing an estimated population of 4538 residents (Figure 1) [32]. Given Yunnan Province's susceptibility to seismic events--documented by 42 earthquakes with magnitudes exceeding 5.0 between 2008 and 2023--and historical vulnerabilities exemplified by the 2014 fire disaster affecting 6.6 hectares within the district, comprehensive planning for secondary fires has become critically important [33]. Although Dukezong Ancient Town currently employs fire-fighting technologies and proactive public education campaigns, persisting vulnerabilities such as dead-end roads and inadequate infrastructure still highlight ongoing gaps [34]. Thus, an index of the built environment conditions related to evacuation and disaster development according to current findings is listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline
**Built-Environment Indicators** & **Indicator Value** \\ \hline Average Building Height [3] & 3.34 m \\ Floor Area Ratio [12; 2; 3] & 0.85 \\ Timber Building Percentage [3] & 96.15\% \\ Average Plot Area [2; 3] & 7545.67 m\({}^{2}\) \\ Road Density [3] & 2.16\% \\ Max Street Width [4] & 5 m \\ Min Street Width [4] & 2 m \\ Median Street Width [4] & 3 m \\ Dead-end Road Proportion [3] & 28.07\% \\ Max Dead-end Road Length [5] & 104 m \\ Min Dead-end Road Length [5] & 19 m \\ Average Dead-end Road Length [5] & 57.2 m \\ T-junction Proportion [3] & 42.11\% \\ Four-way Intersection Proportion [1; 3] & 29.82\% \\ Number of Open Spaces \textgreater{} 100 m\({}^{2}\) [3] & 6 \\ \hline \end{tabular} \({}^{\dagger}\)The Floor Area Ratio indicator is sourced from Reference [35], and the Four-way Intersection Proportion indicator is sourced from Reference [35]. \({}^{2}\) The Floor Area Ratio indicator is sourced from Reference [36], and the Average Plot Area indicator is sourced from Reference [36]. \({}^{3}\) The indicator is sourced from Reference [37]. \({}^{4}\) Derived from the Road Density indicator in Reference [37] combined with the content of this research.  
\end{table}
Table 1: Indicators of built environment [35; 36; 37].  
Figure 1: Study area, POIs, and demographics: (**a**) The study area, (**b**) POI \({}^{1}\) density of the historical district, (**c**) Population of the study area by block. Base map: Baidu Maps (copyright Baidu). \({}^{1}\) “POI” is the abbreviation for Point of Interest, referring to geo-coded locations of various urban facilities extracted from Baidu Map (e.g., restaurants, shops, public services, cultural or recreational venues). In this study, we focus on restaurants and hotels within the historic district to investigate the areas where they are most densely distributed. Points A–D in figure (**b**) denote the four potential fire-ignition locations identified from the highest-density clusters of restaurant and hotel POIs.