Analysis of the Evolution and Influencing Factors of Emergency Response Times in Megacities: An Empirical Study of Beijing Using Kernel Density Estimation

Analysis of the Evolution and Influencing Factors of Emergency Response Times in Megacities: An Empirical Study of Beijing Using Kernel Density Estimation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Analysis of the Evolution and Influencing Factors of Emergency Response Times in Megacities: An Empirical Study of Beijing Using Kernel Density Estimation Pengda Han, Jianhai Long, Mengjie Guo, Li Qin, Xu Wang, Kai Deng, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6577341/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background This study examined the distribution, temporal trends, and factors influencing emergency response times (ERT) across Beijing’s functional zones and administrative districts, providing insights for optimizing the city’s emergency response system. Methods Data from 1,2255 million nonemergency ambulance dispatches (2016, 2021, and 2022) were analyzed. Kernel density estimation was used to explore the ERT distribution patterns across Beijing’s functional zones and districts, analyzing variations by year, month, shift, time of day, and disease type. Findings: Ambulance dispatches increased annually. The urban expansion zone had the highest proportion of dispatches (> 45% annually) at the functional zone level. Chaoyang district had the highest share of dispatches (> 15% annually) at the district level, with a decreasing trend. From 2016–2022, ERTs decreased, particularly in the capital functional core zone. However, ERTs increased in Chaoyang and Shunyi in 2022 compared with 2016, whereas Daxing, Xicheng, and Miyun experienced substantial reductions. The proportion of dispatches with ERTs > 2000 s significantly decreased, particularly in the capital functional core zone. At the district level, Xicheng and Daxing presented the greatest reductions in ERTs > 2000 s. Daytime shifts dominated citywide dispatches of ambulances > 2000 s; however, their proportions decreased in Chaoyang, Tongzhou, Mentougou, Miyun, and Pinggu. Dispatch analysis at > 2000 s revealed increased respiratory and circulatory system diseases, whereas the proportions of neurological, pain-related, and infectious diseases decreased. Interpretation: ERTs in Beijing improved significantly between 2016 and 2022, especially in the capital functional core zone and Xicheng. Daxing’s consistently low ERTs serve as models for improving response times. Emergency response times Emergency response system Kernel density estimation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction China’s vast geography is characterized by notable regional disparities in economic development, population density, and demographics. These factors contribute to variations in the delivery and quality of emergency medical services (EMSs), which are influenced by local policies, geography, and culture. 1 Ambulance services, which are critical components of EMSs, play a key role in safeguarding public health by providing immediate medical care and transporting patients to healthcare facilities. The emergency response time (ERT) is a vital performance indicator in EMSs that directly affects patient outcomes. 2 For example, a concept related to the time sensitivity of trauma is the ‘‘three-peak distribution’’ of trauma mortality, 3 which is referenced in the Advanced Trauma Life Support protocols and has influenced EMS systems globally. 4 Understanding the distribution and temporal evolution of ERTs is crucial for optimizing EMS strategies. However, research on the probabilistic distribution of prehospital emergency ERT in China remains limited. Beijing is the capital of the People's Republic of China. It is the national center for politics, culture, international exchanges, and scientific innovation. By the end of 2023, the population had reached 21.86 million. This population growth, as well as traffic congestion, climate change, and public health emergencies such as H1N1 and COVID-19, has affected Beijing's emergency care needs and posed significant challenges to its prehospital emergency services. Therefore, this study investigated ERT distribution patterns and influencing factors in Beijing, a megacity with 4 functional zones and 16 administrative districts, with the aim of providing evidence for enhancing prehospital EMS and improving patient care outcomes. Methods Research Design The healthcare system in Beijing includes primary healthcare institutions, secondary and tertiary general hospitals, and specialized public health institutions. Relying on medical and health institutions at all levels, Beijing has established a relatively complete emergency medical rescue network throughout the city. These networks can provide prehospital emergency medical services (EMSs) and are subject to the unified dispatching of the Beijing 120 Emergency Dispatch Center (EDC). The Beijing 120 EDC has established a prehospital emergency dispatch and command system that covers the entire city. This system integrates global positioning system (GPS) technology from the initial call to the completion of the task, which can accurately measure all time intervals involved in prehospital emergency medical services. This study examined routine emergency medical rescue services in Beijing, focusing on ERT distribution, temporal trends, and influencing factors across different functional and administrative districts. This study aimed to optimize Beijing’s prehospital emergency response system. The analysis targeted regular nonemergency ambulance incidents dispatches, excluding those influenced by sudden incidents, to focus on standard EMS operations. Data collection The Beijing 120 EDC provides all prehospital emergency ambulance dispatch (EAD) records, including information such as the time of call reception, dispatch time, arrival time, disease type, task type, dispatch type, and accident type. The study excluded the following EAD records: 1) accident types included road traffic accidents, production safety accidents, and other sudden incidents; 2) dispatch types included supplementary dispatch or changed dispatch; and 3) treatment types included transfer to another hospital or home, which are not needed for treatment. The time from receiving the patient’s call to arriving at the patient's location was referred to as ERT in this study. The dataset included ERT records from 2016, 2021, and 2022, corresponding to the start of China’s 13th (2016) 5-year plan, 14th (2021) 5-year plan, and 2022, respectively. This timeframe enabled us to explore how national health policies, which are updated every 5 years, influence EMS performance. Extreme values were excluded via a trimming approach on the basis of the skewed data distribution, with outliers beyond the median ± 3×interquartile range (IQR) removed. This process resulted in a final dataset of 1,225,539 valid nonemergency dispatches, with 50,637 dispatches (3·97%) excluded as outliers. ERTs were measured within a range of 60–3600 s. Data analysis methods Kernel density estimation (KDE) was employed to assess the spatial and temporal distributions of ERTs across Beijing zones and districts. This nonparametric method estimates the probability density function of a variable, enabling a detailed exploration of the distribution of ERTs over time. The analysis focused on several key aspects. Annual and monthly dispatch distributions Day and night shift distributions (day shifts: 8:00 AM to 8:00 PM; night shifts: 8:00 PM to 8:00 AM). Time of day of dispatches Disease types associated with dispatches “Tail data” refers to dispatches with ERTs >2000 s, representing extreme delays (twice the median ERT of 1046·39 s in 2016). These cases indicate systemic inefficiencies or specific challenges within certain regions. The tail data analysis focused on the following: Frequency and distribution: Identifying patterns of extreme delays across different zones and districts. Temporal trends: Analyzing fluctuations in tail data over time (annually, monthly, and by shift). Disease etiology: Investigating the types of diseases most commonly associated with prolonged response times. Statistical Techniques Used Categorical data are reported as frequencies (percentages), with chi-square tests and Cramer’s V coefficient used to assess the correlation between two qualitative variables. For continuous variables, skewness and kurtosis tests were performed to determine normality, with α = 0·10 used as the threshold. When the skewness was > 0, the ERT distribution was positively skewed, indicating more dispatches with shorter response times; when the skewness was 0, the distribution curve peaked more than did a normal distribution, reflecting the concentration of ERTs around certain values. A higher kurtosis implies that a larger proportion of dispatches occur at the peak ERT. When kurtosis was infinite, all the ERTs were the same, and the curve became a straight line representing the average response time. When kurtosis < 0, the curve was flatter, indicating more dispersion and larger differences in ERTs across time points. A kurtosis of 0 indicated a moderate distribution, similar to a normal distribution. KDE was used to analyze the uneven distribution of ERTs. This method estimates the probability density of random variables and converts the data into continuous density curves, reflecting characteristics such as distribution shape, spread, and polarization. Normally distributed data are presented as the means ± standard deviations, whereas nonnormally distributed data are presented as the medians (IQRs). Student’s t test and the Kruskal–Wallis test were applied to compare samples that were normally or nonnormally distributed, with p value adjustments for multiple pairwise comparisons (e.g., for 3 groups: p-adjusted = 0·05/6 = 0·0083). The data were analyzed via STATA 18·0, with statistical significance set at p < 0·05. Role of the Funding Source: This study was funded by Capital’s Funds for Health Improvement and Research (Grant No. 2024-2-3061 and Grant No. 2024-1G-4252). Results Distribution and trends of ERT in Beijing and its administrative districts (2016, 2021, and 2022) 1.1 Overall distribution and trends in Beijing The number of routine, nonemergency ambulance dispatches in Beijing increased from 242,254 in 2016 to 486,956 in 2021 and 496,329 in 2022. Over this period, the median ERT decreased significantly, from 1046·39 s (IQR: 752·88–1483·50) in 2016 to 877·54 s (IQR: 648·40–1177·97) in 2022 (p 0 throughout the study period. The kurtosis coefficients increased from 4·50 in 2016 to 6·41 in 2022, indicating a narrow range of response times and reduced disparities in emergency response performance (Supplementary Table 1). Table 1 Summary of Emergency Vehicle Dispatches and Response Times in Beijing Region Emergency vehicle dispatches, n(%) Emergency response times, s, median(IQR) P 2016 2021 2022 2016 2021 2022 Beijing 243341 496332 567865 1048.3(753.9-1487.5) 867.7(644.5-1164.2) 924.3(674.1-1279.5) < 0.001 Capital Function Core Area 31,280 (12.9) 55,539 (11.2) 60,238 (10.6) 1121.9(799.5-1694.7) 835.9(628.8-1096.5) 919.8(681.4-1235.8) < 0.001 Urban Function Expansion Area 112,754 (46.3) 235,693 (47.5) 264,481 (46.6) 998.0(727.3-1389.3) 861.3(646.0-1137.3) 940.5(694.1-1290.5) < 0.001 Urban Development New Area 74,121 (30.5) 157,267 (31.7) 186,995 (32.9) 1101.3(784.2-1538.2) 896.0(659.7-1223.3) 922.9(666.4-1291.8) < 0.001 Ecological Conservation and Development Area 25,186 (10.4) 47,833 (9.6) 56,151 (9.9) 1062.0(737.7–1539.0) 849.7(612.1-1221.2) 847.1(605.6-1229.8) < 0.001 Dongcheng District 14,433 (5.9) 22,720 (4.6) 24,203 (4.3) 1087.5(799.1-1505.8) 900.0(696.6-1171.1) 999.6(762.1–1326.0) < 0.001 Xicheng District 23,644 (9.7) 58,501 (11.8) 65,769 (11.6) 1087.5(776.0-1704.9) 797.9(600.1-1039.2) 873.4(658.1-1166.1) < 0.001 Chaoyang District 15,450 (6.3) 29,622 (6.0) 35,150 (6.2) 1202.7(884.2-1648.3) 786.9(595.8-1021.4) 748.6(557.2–1005.0) < 0.001 Haidian District 4,744 (1.9) 10,437 (2.1) 12,595 (2.2) 1167.4(864.1-1640.4) 919.7(664.7-1292.6) 881.2(620.3-1255.2) < 0.001 Fengtai District 6,924 (2.8) 10,977 (2.2) 12,024 (2.1) 968.6(675.6-1374.6) 918.8(640.9–1325.0) 851.8(606.5-1205.2) < 0.001 Shijingshan District 4,092 (1.7) 7,228 (1.5) 8,181 (1.4) 909.9(622.4-1411.6) 753.8(529.4-1106.1) 838.3(568.3-1263.5) < 0.001 Tongzhou District 4,395 (1.8) 9,235 (1.9) 11,439 (2.0) 1317.6(927.9-1831.5) 849.4(632.5-1200.1) 782.8(582.4-1133.1) < 0.001 Changping District 17,407 (7.2) 30,616 (6.2) 33,260 (5.9) 1177.1(817.5-1673.6) 860.6(638.7-1177.6) 947.7(688.4-1326.9) < 0.001 Daxing District 12,485 (5.1) 35,842 (7.2) 45,722 (8.1) 1141.7(762.2-1736.1) 909.1(661.5-1235.7) 941.4(688.4–1284.0) < 0.001 Fangshan District 54,145 (22.3) 96,595 (19.5) 101,948 (18.0) 972.3(714.7-1298.1) 902.0(675.0-1189.3) 1032.6(763.4-1405.9) < 0.001 Shunyi District 27,658 (11.4) 63,889 (12.9) 78,665 (13.9) 982.6(720.3-1382.8) 893.0(672.1-1175.9) 906.7(661.6-1259.7) < 0.001 Huairou District 7,307 (3.0) 16,708 (3.4) 18,099 (3.2) 1020.8(722.2-1660.1) 768.2(590.5-1003.6) 871.7(653.4-1169.4) < 0.001 Mentougou District 16,847 (6.9) 32,819 (6.6) 36,035 (6.3) 1166.6(799.8-1932.5) 791.0(586.9–1040.0) 864.3(633.4-1171.3) < 0.001 Miyun District 15,317 (6.3) 34,655 (7.0) 41,105 (7.2) 1047.0(784.4-1391.3) 949.6(705.9-1274.6) 1003.8(731.7-1389.6) < 0.001 Pinggu District 5,031 (2.1) 9,956 (2.0) 11,912 (2.1) 980.6(690.8-1424.2) 776.4(587.5-1117.9) 876.7(636.2-1304.6) < 0.001 Yanqing District 13,462 (5.5) 26,532 (5.3) 31,758 (5.6) 949.0(671.6-1288.4) 1018.5(727.1-1398.3) 1007.6(704.3-1415.8) < 0.001 The day and night shifts exhibited distinct patterns. The night shift had higher peaks in the ERT distribution, suggesting more concentrated response times at night. However, day shifts consistently accounted for > 55% of all dispatches (Fig. 1C and Supplementary Fig. 1). Kernel density analysis of ERT data over time revealed a notable shift in the peak distribution. In 2016, dual peaks were observed at 8:00–10:00 and 20:00–24:00, whereas by 2021 and 2022, these peaks merged into a single peak from 8:00 to 11:00 (Fig. 2), indicating a possible reallocation of emergency resources or a shift in demand patterns. 1.2 Overall distribution and trends in the distributions of the four functional zones ERT across Beijing’s four functional zones showed consistent patterns with varying degrees of improvement. The urban function extension zone had the highest proportion of routine emergency dispatches, accounting for 46·39%, 47·56%, and 46·77% of the total dispatches in 2016, 2021, and 2022, respectively. The most significant reductions in ERT were observed in the capital core functional zone, where the median ERT decreased from 1120·42 s (IQR: 798·53–1691·42) in 2016 to 865·80 s (IQR: 648·80–1127·45) in 2022 (p 0. The kurtosis coefficients increased over time, suggesting a concentration of dispatches around peak ERT values. The capital core functional zone had the most concentrated distribution, with kurtosis increasing from 3·39 in 2016 to 6·99 in 2022 (Supplementary Table 1). The day and night shifts in these zones exhibited distinct patterns. Except for those in the ecological conservation development zone, the night-shift ERTs in the other three zones presented relatively high peaks, reflecting relatively high response times at night. Day shifts consistently accounted for most dispatches in all zones (Fig. 1C, Supplementary Fig. 1). Kernel density analysis revealed temporal shifts in the peak times. From 2016 to 2022, the urban and ecological conservation development zones transitioned from a relatively uniform distribution throughout the day to a concentrated peak between 8:00 and 11:00. In the capital core functional and urban function extension zones, the dual peaks observed in 2016 (8:00–10:00 and 20:00–24:00) merged into a single peak from 8:00–11:00 in subsequent years (Supplementary Fig. 2). These shifts may reflect changes in resource allocation or emergency demand patterns. 1.3 Overall Distribution and Trends in the 16 Administrative Districts The ERT distribution patterns of the 16 administrative districts across Beijing were generally consistent, although the improvements varied. Chaoyang District consistently accounted for the highest proportion of routine dispatches, with 22·27%, 19·49%, and 17·90% of the total dispatches in 2016, 2021, and 2022, respectively, reflecting a declining share over time. With respect to ERT changes, those in the Chaoyang and Shunyi districts increased. In Chaoyang, the median ERT increased from 971·03 s (95% CI: 713·73–1295·45) in 2016 to 977·74 s (95% CI: 733·17–1287·71) in 2022 (p < 0·001), whereas in Shunyi, the ERT increased from 947·09 s (95% CI: 670·61–1284·76) to 977·41 s (95% CI: 685·94–1350·00) (p < 0·001). Conversely, most districts show significant reductions, with Daxing District showing the greatest improvement (from 1201·15 s in 2016 to 716·36 s in 2022, p < 0·001). Xicheng and Miyun districts followed, with reductions from 1164·95 s to 809·43 s (p < 0·001) and 1162·48 s to 842·07 s (p < 0·001), respectively (Table 1 , Fig. 1A). The monthly trends showed lower ERT peaks in December for the Xicheng, Haidian, and Shijingshan districts, whereas the Miyun district exhibited a lower peak in January with a shift to the right. The other districts presented minimal monthly variations (Fig. 1B). Across Beijing’s 16 administrative districts, ERT distributions were consistently right skewed. The kurtosis coefficients were positive, indicating a concentration of dispatches near the peak ERT values. The Shunyi, Tongzhou, and Chaoyang districts presented declining kurtosis trends, suggesting a wider dispersion of ERT values over time. Conversely, districts such as Xicheng, Fengtai, and Daxing experienced significant increases in kurtosis, reflecting a narrowing of response times and improved consistency in 2021 and 2022 (Supplementary Table 1). These changes indicated progress in standardizing response performance, particularly in districts such as Daxing, which showed substantial reductions in overall ERT. The day and night shifts followed consistent patterns across districts. The core districts displayed higher and leftward-shifted night-shift peaks, suggesting shorter ERTs at night. Day-shift dispatches accounted for a greater proportion of cases in all districts (Fig. 1C, Supplementary Fig. 1). Kernel density analysis of the ERT distribution over time revealed temporal shifts in peak times. From 2016–2022, several districts, including Xicheng, Chaoyang, Changping, Fangshan, Fengtai, and Pinggu, transitioned from nighttime peaks (21:00–24:00) to morning peaks (8:00–11:00), with the most pronounced shift occurring in Xicheng district. Other districts, such as Dongcheng, Haidian, Daxing, and Shijingshan, maintained stable peaks between 8:00 and 11:00, with Shijingshan showing the highest consistency (Supplementary Fig. 3). Analysis of the right-skewed tail structure of ERT in Beijing and its districts in 2016, 2021, and 2022 2.1 Trend Analysis of the Right-Skewed Tail Structure The proportion of dispatches with ERT > 2000 s in Beijing declined significantly, from 12·41% in 2016 to 3·72% in 2022, reflecting a clear downward trend over the years (r=-0·386, p = 0·003). Similar reductions were observed across the four functional zones, with the capital core functional zone demonstrating the most pronounced improvement. Here, the proportion of dispatches exceeding 2000 s decreased from 18·78% in 2016 to 2·50% in 2022 (r=-0·678, p = 0·007). At the district level, all 16 administrative districts showed a declining trend in ERT > 2000 s. Among them, Xicheng (r=-0·768, p = 0·008), Daxing (r=-0·729, p = 0·010), and Shijingshan (r=-0·639, p = 0·014) achieved the most substantial reductions (Table 2 ). Table 2 Emergency response times greater than 2000 s in various districts of Beijing < 2000s ≥ 2000s r p 2016 2021 2022 2016 2021 2022 Beijing 212183(87.59) 470587(96.64) 477843(96.28) 30071(12.41) 16369(3.36) 18486(3.72) -0.386 0.003 Capital Function Core Area 25303(81.22) 53590(98.15) 49268(97.50) 5852(18.78) 1008(1.85) 1262(2.50) -0.678 0.007 Urban Function Expansion Area 100082(89.06) 225300(97.28) 224095(96.53) 12294(10.94) 6304(2.72) 8048(3.47) -0.362 0.005 Urban Development New Area 64965(88.14) 147602(95.87) 157184(95.98) 8742(11.86) 6353(4.13) 6589(4.02) -0.338 0.006 Ecological Conservation and Development Area 21833(87.28) 44095(94.22) 47296(94.81) 3183(12.72) 2704(5.78) 2587(5.19) -0.291 0.009 Dongcheng District 12503(86.98) 21803(97.61) 19780(96.88) 1872(13.02) 534(2.39) 638(3.12) -0.512 0.014 Xicheng District 12800(76.28) 31787(98.53) 29488(97.93) 3980(23.72) 474(1.47) 624(2.07) -0.768 0.008 Chaoyang District 50898(94.36) 92224(97.17) 84800(95.43) 3045(5.64) 2685(2.83) 4062(4.57) -0.032 0.009 Haidian District 24340(88.26) 60695(96.92) 66000(96.92) 3239(11.74) 1929(3.08) 2097(3.08) -0.41 0.009 Fengtai District 18931(80.28) 56272(97.63) 57384(97.47) 4650(19.72) 1368(2.37) 1488(2.53) -0.63 0.008 Shijingshan District 5913(81.30) 16109(98.04) 15911(97.54) 1360(18.70) 322(1.96) 401(2.46) -0.639 0.014 Tongzhou District 14104(92.64) 32593(95.79) 33329(95.48) 1121(7.36) 1432(4.21) 1576(4.52) -0.129 0.014 Changping District 10150(81.71) 33699(95.57) 38732(95.90) 2272(18.29) 1561(4.43) 1654(4.10) -0.436 0.011 Daxing District 13056(85.11) 28506(98.19) 30755(98.69) 2285(14.89) 524(1.81) 407(1.31) -0.729 0.01 Fangshan District 14754(85.14) 28534(95.20) 28058(95.02) 2576(14.86) 1440(4.80) 1469(4.98) -0.374 0.011 Shunyi District 12901(96.36) 24270(94.56) 26310(94.66) 488(3.64) 1396(5.44) 1483(5.34) 0.087 0.014 Huairou District 3524(80.84) 8520(93.97) 9838(95.51) 835(19.16) 547(6.03) 462(4.49) -0.469 0.017 Mentougou District 4427(88.33) 9284(95.01) 9810(93.38) 585(11.67) 488(4.99) 695(6.62) -0.154 0.021 Miyun District 3979(84.66) 9519(93.84) 10646(95.21) 721(15.34) 625(6.16) 536(4.79) -0.371 0.018 Pinggu District 6285(91.35) 9972(93.19) 10309(96.69) 595(8.65) 729(6.81) 353(3.31) -0.31 0.018 Yanqing District 3618(89.00) 6800(95.57) 6693(92.52) 447(11.00) 315(4.43) 541(7.48) -0.091 0.026 These findings highlight a citywide effort to increase emergency response efficiency. The consistent decline in extreme delays suggests that targeted interventions, including infrastructure expansion and optimized resource allocation, have been effective in narrowing performance gaps and ensuring more equitable EMS delivery across Beijing. 2.2 Analysis of Day–Night Shift Composition Ratios Across Beijing, day shifts accounted for most dispatches with ERT > 2000 s, and their proportion increased over time, whereas night shifts showed a decreasing trend, with the trend most pronounced in the capital core functional zone. At the district level, most districts followed this trend, with the largest shifts observed in Yanqing, Xicheng, Fangshan, Shijingshan, and Daxing. However, Pinggu, Tongzhou, Miyun, Mentougou, and Chaoyang displayed the opposite pattern, with increasing night-shift proportions (Fig. 3). Logistic regression analysis revealed that night shifts generally reduced the likelihood of ERT > 2000 s (odds ratio [OR] = 0·87, p < 0·001), except in the ecological conservation development zone (OR = 0.98, p = 0·303). Notable exceptions included Shijingshan (OR = 1·83, p < 0·001), Pinggu (OR = 1·22, p < 0·001), Miyun (OR = 1·16, p < 0·001), and Shunyi (OR = 1·1, p 2000 s increased significantly in 2021 (OR = 1·52, p < 0·001) and 2022 (OR = 1·49, p < 0·001) (Supplementary Table 2), contrary to the trends observed in the other 15 districts. These findings suggest that while night shift dispatches generally reduce extreme delays across most regions, certain districts, such as Shunyi and Shijingshan, require targeted interventions to address localized challenges. 2.3 Disease composition analysis for ERT > 2000 s Dispatches with ERT > 2000 s in Beijing were attributed primarily to circulatory system diseases, traumatic injuries, neurological diseases, respiratory conditions, and digestive disorders. Over time, the number of respiratory and circulatory cases has increased citywide, whereas the number of neurological, pain-related, and infectious diseases has declined. Among the functional zones, circulatory diseases became more prominent in the capital core and ecological conservation zones, whereas traumatic injuries rose significantly in the urban development zone. At the district level, circulatory cases had the largest increases in Miyun, Mentougou, and Dongcheng, whereas Huairou experienced a notable increase in traumatic injuries (Fig. 4). Logistic regression revealed that circulatory diseases (OR = 0·95, p < 0·001) and traumatic injuries (OR = 0·97, p = 0·003) were generally associated with shorter delays. Circulatory diseases in urban development zones (OR = 0·90, p 2000 s, as did traumatic injuries in urban development zones (OR = 0·94, p 2000 s, including traumatic injuries in Tongzhou (OR = 1·25, p < 0·001) and Mentougou (OR = 1·32, p < 0·001), respiratory diseases in Changping (OR = 1·13, p = 0·012) and Pinggu (OR = 1·18, p = 0·040), digestive system diseases in Fangshan (OR = 1·20, p < 0·001), and neurological disorders in Huairou (OR = 1·15, p = 0·033). These findings highlight the need for targeted interventions addressing specific disease types and regional disparities to further improve emergency response efficiency. 2.4 Day–Night Shift Differences and Trends in Disease Composition for ERT > 2000 s The primary disease composition ratios (> 10%) during the day and night shifts aligned with overall city-wide patterns. From 2016 to 2022, the number of circulatory, respiratory, and traumatic cases increased, whereas the number of neurological diseases decreased (Supplementary Fig. 4). During day shifts, circulatory diseases rose in the capital core and urban function extension zones, whereas traumatic injuries increased in the ecological conservation zone. At night, circulatory diseases dominated across all zones. District-level trends revealed rising day-shift proportions of circulatory diseases in Dongcheng and Xicheng and traumatic injuries in Pinggu, Huairou, and Yanqing. At night, circulatory diseases increased in Chaoyang, Mentougou, and Fengtai, with minimal changes observed in other districts. These findings underscore distinct regional and shift-specific trends, highlighting the need for tailored strategies to improve emergency response outcomes. Discussion The distribution patterns and temporal trends of ERT provide critical guidance for the design and planning of EMSs. 4 This study analyzed the differences in ERT distributions across Beijing’s 4 functional zones and 16 administrative districts, focusing on different years, months, shifts, times of day, and causes. These findings offer valuable insights for improving Beijing’s emergency medical rescue planning and provide a foundation for future research. The key findings include the following: ERTs have gradually shortened, narrowing the differences between Beijing’s functional zones and administrative districts, and peak ERT times shifted from 8:00–10:00 and 20:00–24:00 to 8:00–11:00. The occurrence of ERTs > 2000 s steadily declined from 12·41% in 2016 to 3·72% in 2022. Night-shift dispatches, as well as those for circulatory systems and traumatic injuries, showed a decreasing probability of ERT > 2000 s. By excluding emergency tasks related to large-scale incidents, such as the coronavirus disease 2019 (COVID-19) pandemic, this study revealed significant reductions in Beijing’s daily ERTs, which contrasts with the findings of many international studies. Studies aggregating all emergency incidents, including major events such as the COVID-19 pandemic, may not accurately reflect the real state of routine EMSs. For example, Sabbaghi et al. reported longer response times during the pandemic, 5 whereas Eskol et al. reported no significant changes. 6 Similarly, Satty et al. reported no differences in nontraffic ERTs in Pennsylvania before and during the pandemic. 7 Conversely, Chocron et al. reported a negative correlation between ambulance density and EMS response times, 8 which aligns with the findings of our study. From 2020 to 2022, the number of emergency stations in Beijing increased, 9 likely contributing to the improvement in response times. For example, Daxing district expanded its emergency stations from 9 to 23, equipping 3·3 ambulances per 100,000 people, 10 similar to Paris’s 4·2 ambulances per 100,000 people. 4 Beijing’s shift from dual ERT peaks in 2016 to a single peak in 2021 and 2022 (8:00–11:00) contrasts with the patterns observed in other countries. In Iran, dispatch proportions were evenly distributed across morning, evening, and night shifts 5 ; in Ireland, peak dispatch proportions occurred between 12:00 and 18:00 11 ; and in Finland, 47.4% of dispatches occurred between 08:00 and 16:00. 12 These differences may arise from this study’s focus on routine emergency services, unlike broader international analyses. Additionally, strict pandemic lockdowns in many countries 13 have reduced nighttime activity and emergency needs; however, resource shortages have left daytime demands unmet. 5 In contrast, Beijing has expanded community healthcare 14 and emergency station capacity, 9 effectively managing daytime surges. Regional differences are key factors influencing the proportion of ERT > 2000 s. Economic factors have long been recognized as the primary drivers of prolonged ERT. 15 Across the four functional zones, the proportion of ERT > 2000 s increased from the capital core functional zone to the ecological conservation development zone, which was correlated with lower per capita GDP. 16 This pattern is consistent with that of prior studies. Verma et al. reported that counties with higher average household incomes had 12% faster EMS arrival times than lower-income countries did. 2 Heidet et al. reported that regional poverty was correlated with longer EMS response times. 17 The distribution of healthcare resources plays a crucial role in response time disparities. A study on Beijing’s healthcare resources revealed that the capital core functional zone outperformed other zones in terms of healthcare infrastructure, such as beds per 1,000 residents, practicing physicians, and financial expenditures on healthcare. 18 This concentration of resources likely explains the lower proportion of ERTs > 2000 s in the capital core functional zone. Government policies influence ERT outcomes alongside economic and resource-related factors. For example, Daxing District, despite its moderate per capita GDP and healthcare resource density within Beijing, 16 recorded the lowest proportion of ERT > 2000 s in 2022 (1·31%). This success can be attributed to local government initiatives, including the standardization of emergency station planning and the expansion of emergency infrastructure, with each street in the district having at least one standardized emergency station and 3·3 ambulances per 100,000 residents. 10 , 19 Additionally, the study revealed that the ERTs during night shifts were generally lower than those during day shifts, likely because of less daytime traffic congestion. 19 However, this trend was not observed in the ecological conservation development zone, which had a lower population density (43 km² per 10,000 residents) than the capital core functional zone (96 km² per 10,000 residents). 18 These findings highlight the substantial impact of population density and traffic on ERT distributions. Disease- and region-specific patterns emerged in ERTs > 2000 s. Ageta noted that internal medicine-related conditions, particularly infections, frequently require additional time for patient history inquiries (e.g., recent symptoms or travel history), especially during a pandemic. 20 However, logistic regression with time (COVID-19) as a covariate revealed that internal medicine diseases did not affect ERTs > 2000 s, whereas circulatory and traumatic diseases reduced their incidence. The reason lies in the heightened focus on cardiovascular diseases. Circulatory diseases continue to show a shortening trend due to sustained focus. Reducing ERTs to the 90th percentile of 8 min could increase survival rates by 8%, whereas reducing them to 5 min could increase survival rates by 10–11%. 21 Meanwhile, the 2018 release of the “Notice on Enhancing Trauma Treatment Capacity” 22 contributed to the decreasing trend of ERTs > 2000 s for traumatic cases. Specific diseases in regions, including Tongzhou, Mentougou (traumatic injuries), Pinggu, Changping (respiratory), Fangshan (digestive), and Huairou (neurological), significantly impact the incidence of ERTs > 2000 s, underscoring the need for targeted policy support. This study had several limitations. First, as an observational study, causality could not be established. Second, it focused solely on Beijing and analyzed data from 2016, 2021, and 2022, with 2021 and 2022 overlapping with the COVID-19 pandemic, which may limit generalizability. Finally, the study concentrated on the ERT distribution within the prehospital EMS system without analyzing the total response times or patient outcomes. Conclusion This study highlights the significant improvements in ERTs across Beijing’s functional zones and administrative districts, driven by factors such as infrastructure expansion, government policies, and population density. However, variations remain, particularly in disease-specific response times, and further research is needed to explore the broader implications for patient outcomes. These findings provide valuable insights for optimizing the EMS and guiding future improvements in response efficiency. Declarations Declaration of Interests The authors declare that they have no competing interests. Ethical Considerations The study did not involve any human participants or animals, so no ethics approval or informed consent was needed. Funding This study was funded by Capital’s Funds for Health Improvement and Research (Grant No. 2024-2-3061 and Grant No. 2024-1G-4252). Author Contribution Jianhai Long, PengDa Han and Mengjie Guo contributed equally to this work.• Data analysis: Jianhai Long, Mengjie Guo and Li Qin• Project administration: Yang Zheng• Data collection and processing: PengDa Han, Xu Wang and Jinjun Zhang• Supervision: Yang Zheng• Writing—original draft preparation: PengDa Han, Jianhai Long and Mengjie Guo• Writing—review & editing: Yang Zheng, PengDa Han, Jianhai Long and Mengjie Guo• All authors have read and approved the final version of the manuscript and agreed with the order of presentation of the authors. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Yu D. [Analysis of Influencing Factors on Prehospital Emergency Effectiveness in a Region of Zhejiang Province]. Health Care Today. 2020; 20 (6): 37-40.Chinese. Verma S, Wilson F, Wang H, Smith L, Tak HJ. Impact of Community Socioeconomic Characteristics on Emergency Medical Service Delays in Responding to Fatal Vehicle Crashes. AJPM Focus 2023; 2 (4): 100129. Baker CC, Oppenheimer L, Stephens B, Lewis FR, Trunkey DD. Epidemiology of trauma deaths. Am J Surg 1980; 140 (1): 144-50. Lam SS, Nguyen FN, Ng YY, et al. Factors affecting the ambulance response times of trauma incidents in Singapore. Accid Anal Prev 2015; 82 : 27-35. Sabbaghi M, Namazinia M, Miri K. Time indices of prehospital EMS missions before and during the COVID-19 pandemic: a cross-sectional study in Iran. BMC Emerg Med 2023; 23 (1): 9. Eskol JR, Zegers FD, Wittrock D, Lassen AT, Mikkelsen S. Increased ambulance on-scene times but unaffected response times during the first wave of the COVID-19 pandemic in Southern Denmark. BMC Emerg Med 2022; 22 (1): 61. Satty T, Ramgopal S, Elmer J, Mosesso VN, Martin-Gill C. EMS responses and nontransports during the COVID-19 pandemic. Am J Emerg Med 2021; 42 : 1-8. Chocron R, Cariou A, Dumas F. Response by Chocron et al to Letter Regarding Article, "Ambulance Density and Outcomes After Out-of-Hospital Cardiac Arrest: Insights From the Paris Sudden Death Expertise Center Registry". Circulation 2019; 140 (10): e549-e50. Beijing Municipal Health Commission, Beijing Municipal Commission of Planning and Natural Resources. Notice on the Joint Issuance of the "Beijing Municipal Prehospital Medical Emergency Facilities Spatial Layout Special Plan (2020-2022)"[EB/OL]. [0001-07-01]. https://wjw.beijing.gov.cn/zwgk_20040/wsyj/202007/t20200728_1963964.html.2020.Chinese. Office of Daxing District People's Goverment of Beijing Municipality.Notice on Issuing the "Daxing District Implementation Plan for Accelerating the Construction of Prehospital Medical Emergency System"[EB/OL]. https://www.bjdx.gov.cn/bjsdxqrmzf/zwfw/zfwj67/zfwj/1859366/index.html.2021. Chinese. Burton E, Quinn R, Crosbie-Staunton K, et al. Temporal trends of ambulance time intervals for suspected stroke/transient ischemic attack (TIA) before and during the COVID-19 pandemic in Ireland: a quasiexperimental study. BMJ Open 2024; 14 (3): e078168. Laukkanen L, Lahtinen S, Liisanantti J, Kaakinen T, Ehrola A, Raatiniemi L. Early impact of the COVID-19 pandemic and social restrictions on ambulance missions. Eur J Public Health 2021; 31 (5): 1090-5. James MM, Rodrigues J, Montoya M, et al. The Pandemic Experience Survey II: A Second Corpus of Subject Reports of Life Under Social Restrictions During COVID-19 in the UK, Japan, and Mexico. Front Public Health 2022; 10 : 913096. Beijing Municipal Health Commission. Notice from the Beijing Municipal Health Commission on Issuing the Key Points of Primary Health Care Work in 2020[EB/OL]. https://wjw.beijing.gov.cn/.2020. Chinese. Friedson AI. Income and Ambulance Response Time Inequality: No Simple Explanation, No Simple Fix. JAMA Netw Open 2018; 1 (7): e185201. Beijing Municipal Bureau of Statistics,Survey Office of the National Bureau of Statistics in Beijing. Beijing Statistical Yearbook. China Statistics Press 2022.Chinese Heidet M, Da Cunha T, Brami E, et al. EMS Access Constraints And Response Time Delays For Deprived Critically Ill Patients Near Paris, France. Health Aff (Millwood) 2020; 39 (7): 1175-84. WANG SP，HUANG ED. [Analysis on the Allocation Equality in Health Resources in Beijing Based on Theil Index and Agglomeration Degree]. Chinese Health Economics 2020; 39 (4): 44-8.Chinese Jafari M, Mahmoudian P, Ebrahimipour H, et al. Response Time and Causes of Delay in Prehospital Emergency Missions in Mashhad, 2015. Med J Islam Repub Iran 2021; 35 : 142. Ageta K, Naito H, Yorifuji T, et al. Delay in Emergency Medical Service Transportation Responsiveness during the COVID-19 Pandemic in a Minimally Affected Region. Acta Med Okayama 2020; 74 (6): 513-20. Pell JP, Sirel JM, Marsden AK, Ford I, Cobbe SM. Effect of reducing ambulance response times on deaths from out of hospital cardiac arrest: cohort study. BMJ 2001; 322 (7299): 1385-8. Office of the National Health and Family Planning Commission of the People's Republic of China. [Notice on Further Enhancing Trauma Treatment Capabilities][EB/OL].http://www.nhc.gov.cn/yzygj/s3594q/201807/79daad75e4c746118fb7d0237c7588bd.shtml; 2018.Chinese. Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigure1.pdf Supplemental Figure 1 Proportion of day and night shifts with emergency response times (ERT) in Beijing and its districts. CFCA: Capital Function Core Area; UFEA: Urban Function Expansion Area; UDNA: Urban Development New Area; ECDA: Ecological Conservation and Development Area. SupplenmentaryFigure2.pdf Supplemental Figure 2 Multidimensional kernel density plot of emergency response times (ERT) in Beijing. A, Capital function core area; B, Urban function expansion area; C, Urban development new area; D, Ecological conservation and development area. SupplenmentaryFigure3.pdf Supplemental Figure 3 Dimensional kernel density plot of emergency response times (ERT) in Shijingshan and Dongcheng district of Beijing. SupplementaryFigure4.pdf Supplemental Figure 4 The composition of causes for emergency response times exceeding 2000 seconds during the day-night shifts in Beijing and its districts. CFCA, Capital function core area; UFEA, Urban function expansion area; UDNA, Urban development new area; ECDA, Ecological conservation and development area. SupplementaryTables.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 30 May, 2025 Editor invited by journal 08 May, 2025 Editor assigned by journal 06 May, 2025 Submission checks completed at journal 06 May, 2025 First submitted to journal 02 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6577341","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464882650,"identity":"63bca0df-dfd0-4227-be61-c1e7d8f4c01c","order_by":0,"name":"Pengda Han","email":"","orcid":"","institution":"Beijing Emergency Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Pengda","middleName":"","lastName":"Han","suffix":""},{"id":464882651,"identity":"d92abd99-242c-4562-93da-38626480d0b8","order_by":1,"name":"Jianhai Long","email":"","orcid":"","institution":"Department of Pulmonary and Critical Care Medicine, Beijing Tiantan Hospital, Capital Medicine University","correspondingAuthor":false,"prefix":"","firstName":"Jianhai","middleName":"","lastName":"Long","suffix":""},{"id":464882652,"identity":"92d92e40-199e-4b32-a2d2-7b8364a15d44","order_by":2,"name":"Mengjie Guo","email":"","orcid":"","institution":"Beijing Center for Public Health Emergency Management","correspondingAuthor":false,"prefix":"","firstName":"Mengjie","middleName":"","lastName":"Guo","suffix":""},{"id":464882653,"identity":"d5f2a726-5c3d-4909-89af-fb38d0a7edad","order_by":3,"name":"Li Qin","email":"","orcid":"","institution":"Beijing Center for Public Health Emergency Management","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Qin","suffix":""},{"id":464882654,"identity":"cdc0cce8-29f3-4d9e-806a-39449f2e2669","order_by":4,"name":"Xu Wang","email":"","orcid":"","institution":"Beijing Emergency Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Xu","middleName":"","lastName":"Wang","suffix":""},{"id":464882655,"identity":"6a6a33ba-ccd7-43e9-9843-af95235cbdaf","order_by":5,"name":"Kai Deng","email":"","orcid":"","institution":"Beijing Center for Public Health Emergency Management","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Deng","suffix":""},{"id":464882656,"identity":"fcc724fe-9606-44d7-93f7-690147f75161","order_by":6,"name":"Jinjun Zhang","email":"","orcid":"","institution":"Beijing Emergency Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Jinjun","middleName":"","lastName":"Zhang","suffix":""},{"id":464882657,"identity":"8ae2288b-7d18-48a0-8bfd-95146162975f","order_by":7,"name":"Yang Zheng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYLACxgYgwcx88MGHCgk5eeK1sLMlG844Y2Fs2EC0Fn4eM2netopEhgMEVBsc7zF8+HOHTZ68M4+xAe88iQTGBuaHj27g03LmjLGB5Jm0YsPDbIUPJLdJ5LEzsBkb5+DTciPHTMKw7XDixmbmzQaG2ySKGRt42KQJakls+w/UwgBkzJFIbDhAjJaDbQcS5zOzABkNRGiRPHOs2LCxLTlxAzMwkBuOSRgbNhPwC9/x5o0Pf7bZJc7vP3zw8Z+aOjl59uaHj/FpUTjAYQBx4QGYEDMe5SAg38D+AMogoHIUjIJRMApGLgAArvpRGLMNz9kAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Center for Public Health Emergency Management","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zheng","suffix":""}],"badges":[],"createdAt":"2025-05-02 09:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6577341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6577341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83828060,"identity":"be87fec8-0b93-4523-8e47-d1a0df730f28","added_by":"auto","created_at":"2025-06-03 10:50:03","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":941516,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of emergency response times (ERT) across Beijing and its districts.\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/96c904709a8a22dfda760b51.jpg"},{"id":83828065,"identity":"6bc259aa-d457-4711-922f-33348dec418f","added_by":"auto","created_at":"2025-06-03 10:50:03","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":240888,"visible":true,"origin":"","legend":"\u003cp\u003eMultidimensional kernel density plot of emergency response times (ERT) in Beijing. A:2016; B:2021; C:2022.\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/1bff465aa6fa165936ad2371.jpg"},{"id":83828062,"identity":"b4c1e03a-af1c-4f91-a795-a3bd4c046cd9","added_by":"auto","created_at":"2025-06-03 10:50:03","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":590541,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of day and night shifts with emergency response times (ERT) exceeding 2000 seconds in Beijing and its districts.\u003c/p\u003e\n\u003cp\u003eCFCA: Capital Function Core Area; UFEA: Urban Function Expansion Area; UDNA: Urban Development New Area; ECDA: Ecological Conservation and Development Area.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/89a5ec0dc07b5747cdbb6908.jpg"},{"id":83828070,"identity":"d9d6949a-22ab-4a86-af93-b1c18e037a85","added_by":"auto","created_at":"2025-06-03 10:50:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":654574,"visible":true,"origin":"","legend":"\u003cp\u003eProportion of factors contributing to emergency response times (ERT) greater than 2000 seconds in Beijing and its districts.\u003c/p\u003e\n\u003cp\u003eCFCA: Capital Function Core Area; UFEA: Urban Function Expansion Area; UDNA: Urban Development New Area; ECDA: Ecological Conservation and Development Area.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/13aa474a99ccb712568f99e6.jpg"},{"id":83828778,"identity":"048be401-a04a-4bd7-86fd-953b18056155","added_by":"auto","created_at":"2025-06-03 11:06:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3662676,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/f6f8de98-1fc8-44a8-aa17-32a050258e97.pdf"},{"id":83828552,"identity":"7af27aa9-8210-42a7-8a8a-871802a59120","added_by":"auto","created_at":"2025-06-03 10:58:03","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":46741,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 1 \u003c/strong\u003eProportion of day and night shifts with emergency response times (ERT) in Beijing and its districts.\u003c/p\u003e\n\u003cp\u003eCFCA: Capital Function Core Area; UFEA: Urban Function Expansion Area; UDNA: Urban Development New Area; ECDA: Ecological Conservation and Development Area.\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/05f0ed5f1c7d6ebfce8ce85f.pdf"},{"id":83828553,"identity":"ad4e3cc1-ec01-41e7-9bd2-c0741a3c5d1f","added_by":"auto","created_at":"2025-06-03 10:58:03","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1086412,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 2 \u003c/strong\u003eMultidimensional kernel density plot of emergency response times (ERT) in Beijing. A, Capital function core area; B, Urban function expansion area; C, Urban development new area; D, Ecological conservation and development area.\u003c/p\u003e","description":"","filename":"SupplenmentaryFigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/02bb1c41c6211c6c133a899f.pdf"},{"id":83828554,"identity":"9d7621c7-9804-485c-9769-0004f2610ff9","added_by":"auto","created_at":"2025-06-03 10:58:03","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":545980,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 3 \u003c/strong\u003eDimensional kernel density plot of emergency response times (ERT) in Shijingshan and Dongcheng district of Beijing.\u003c/p\u003e","description":"","filename":"SupplenmentaryFigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/c0000efd6e3b19f6861c75c7.pdf"},{"id":83828071,"identity":"d90cc157-cbee-4d13-a8f8-9784e769a727","added_by":"auto","created_at":"2025-06-03 10:50:03","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":133316,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplemental Figure 4 \u003c/strong\u003eThe composition of causes for emergency response times exceeding 2000 seconds during the day-night shifts in Beijing and its districts.\u003c/p\u003e\n\u003cp\u003eCFCA, Capital function core area; UFEA, Urban function expansion area; UDNA, Urban development new area; ECDA, Ecological conservation and development area.\u003c/p\u003e","description":"","filename":"SupplementaryFigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/5c9fa11d02e89866c869c7d2.pdf"},{"id":83828067,"identity":"a90cc891-ff95-41ff-bbf8-9092274b8e2e","added_by":"auto","created_at":"2025-06-03 10:50:03","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":39324,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-6577341/v1/34a3725b79703dea3636a4d1.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Analysis of the Evolution and Influencing Factors of Emergency Response Times in Megacities: An Empirical Study of Beijing Using Kernel Density Estimation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChina\u0026rsquo;s vast geography is characterized by notable regional disparities in economic development, population density, and demographics. These factors contribute to variations in the delivery and quality of emergency medical services (EMSs), which are influenced by local policies, geography, and culture.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Ambulance services, which are critical components of EMSs, play a key role in safeguarding public health by providing immediate medical care and transporting patients to healthcare facilities. The emergency response time (ERT) is a vital performance indicator in EMSs that directly affects patient outcomes.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e For example, a concept related to the time sensitivity of trauma is the \u0026lsquo;\u0026lsquo;three-peak distribution\u0026rsquo;\u0026rsquo; of trauma mortality,\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e which is referenced in the Advanced Trauma Life Support protocols and has influenced EMS systems globally.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e Understanding the distribution and temporal evolution of ERTs is crucial for optimizing EMS strategies. However, research on the probabilistic distribution of prehospital emergency ERT in China remains limited. Beijing is the capital of the People's Republic of China. It is the national center for politics, culture, international exchanges, and scientific innovation. By the end of 2023, the population had reached 21.86\u0026nbsp;million. This population growth, as well as traffic congestion, climate change, and public health emergencies such as H1N1 and COVID-19, has affected Beijing's emergency care needs and posed significant challenges to its prehospital emergency services. Therefore, this study investigated ERT distribution patterns and influencing factors in Beijing, a megacity with 4 functional zones and 16 administrative districts, with the aim of providing evidence for enhancing prehospital EMS and improving patient care outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Design\u003c/h2\u003e \u003cp\u003eThe healthcare system in Beijing includes primary healthcare institutions, secondary and tertiary general hospitals, and specialized public health institutions. Relying on medical and health institutions at all levels, Beijing has established a relatively complete emergency medical rescue network throughout the city. These networks can provide prehospital emergency medical services (EMSs) and\u003c/p\u003e \u003cp\u003eare subject to the unified dispatching of the Beijing 120 Emergency Dispatch Center (EDC). The Beijing 120 EDC has established a prehospital emergency dispatch and command system that covers the entire city. This system integrates global positioning system (GPS) technology from the initial call to the completion of the task, which can accurately measure all time intervals involved in prehospital emergency medical services.\u003c/p\u003e \u003cp\u003eThis study examined routine emergency medical rescue services in Beijing, focusing on ERT distribution, temporal trends, and influencing factors across different functional and administrative districts. This study aimed to optimize Beijing\u0026rsquo;s prehospital emergency response system. The analysis targeted regular nonemergency ambulance incidents dispatches, excluding those influenced by sudden incidents, to focus on standard EMS operations.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData\u003c/b\u003e collection\u003c/p\u003e \u003cp\u003eThe Beijing 120 EDC provides all prehospital emergency ambulance dispatch (EAD) records, including information such as the time of call reception, dispatch time, arrival time, disease type, task type, dispatch type, and accident type. The study excluded the following EAD records: 1) accident types included road traffic accidents, production safety accidents, and other sudden incidents; 2) dispatch types included supplementary dispatch or changed dispatch; and 3) treatment types included transfer to another hospital or home, which are not needed for treatment. The time from receiving the patient\u0026rsquo;s call to arriving at the patient's location was referred to as ERT in this study.\u003c/p\u003e \u003cp\u003eThe dataset included ERT records from 2016, 2021, and 2022, corresponding to the start of China\u0026rsquo;s 13th (2016) 5-year plan, 14th (2021) 5-year plan, and 2022, respectively. This timeframe enabled us to explore how national health policies, which are updated every 5 years, influence EMS performance. Extreme values were excluded via a trimming approach on the basis of the skewed data distribution, with outliers beyond the median\u0026thinsp;\u0026plusmn;\u0026thinsp;3\u0026times;interquartile range (IQR) removed. This process resulted in a final dataset of 1,225,539 valid nonemergency dispatches, with 50,637 dispatches (3\u0026middot;97%) excluded as outliers. ERTs were measured within a range of 60\u0026ndash;3600 s.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData analysis methods\u003c/h3\u003e\n\u003cp\u003eKernel density estimation (KDE) was employed to assess the spatial and temporal distributions of ERTs across Beijing zones and districts. This nonparametric method estimates the probability density function of a variable, enabling a detailed exploration of the distribution of ERTs over time. The analysis focused on several key aspects.\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAnnual and monthly dispatch distributions\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDay and night shift distributions\u003c/strong\u003e (day shifts: 8:00 AM to 8:00 PM; night shifts: 8:00 PM to 8:00 AM).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTime of day of dispatches\u003c/strong\u003e\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDisease types associated with dispatches\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u0026ldquo;Tail data\u0026rdquo; refers to dispatches with ERTs \u0026gt;2000 s, representing extreme delays (twice the median ERT of 1046\u0026middot;39 s in 2016). These cases indicate systemic inefficiencies or specific challenges within certain regions. The tail data analysis focused on the following:\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eFrequency and distribution:\u003c/strong\u003e Identifying patterns of extreme delays across different zones and districts.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eTemporal trends:\u003c/strong\u003e Analyzing fluctuations in tail data over time (annually, monthly, and by shift).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDisease etiology:\u003c/strong\u003e Investigating the types of diseases most commonly associated with prolonged response times.\u003c/li\u003e\n\u003c/ul\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Techniques Used\u003c/h2\u003e \u003cp\u003eCategorical data are reported as frequencies (percentages), with chi-square tests and Cramer\u0026rsquo;s V coefficient used to assess the correlation between two qualitative variables. For continuous variables, skewness and kurtosis tests were performed to determine normality, with α\u0026thinsp;=\u0026thinsp;0\u0026middot;10 used as the threshold. When the skewness was \u0026gt;\u0026thinsp;0, the ERT distribution was positively skewed, indicating more dispatches with shorter response times; when the skewness was \u0026lt;\u0026thinsp;0, the distribution was negatively skewed, indicating the opposite. A skewness of 0 indicated symmetry. When kurtosis was \u0026gt;\u0026thinsp;0, the distribution curve peaked more than did a normal distribution, reflecting the concentration of ERTs around certain values. A higher kurtosis implies that a larger proportion of dispatches occur at the peak ERT. When kurtosis was infinite, all the ERTs were the same, and the curve became a straight line representing the average response time. When kurtosis\u0026thinsp;\u0026lt;\u0026thinsp;0, the curve was flatter, indicating more dispersion and larger differences in ERTs across time points. A kurtosis of 0 indicated a moderate distribution, similar to a normal distribution.\u003c/p\u003e \u003cp\u003eKDE was used to analyze the uneven distribution of ERTs. This method estimates the probability density of random variables and converts the data into continuous density curves, reflecting characteristics such as distribution shape, spread, and polarization. Normally distributed data are presented as the means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations, whereas nonnormally distributed data are presented as the medians (IQRs). Student\u0026rsquo;s t test and the Kruskal\u0026ndash;Wallis test were applied to compare samples that were normally or nonnormally distributed, with p value adjustments for multiple pairwise comparisons (e.g., for 3 groups: p-adjusted\u0026thinsp;=\u0026thinsp;0\u0026middot;05/6\u0026thinsp;=\u0026thinsp;0\u0026middot;0083). The data were analyzed via STATA 18\u0026middot;0, with statistical significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRole of the Funding Source:\u003c/h3\u003e\n\u003cp\u003eThis study was funded by Capital\u0026rsquo;s Funds for Health Improvement and Research (Grant No. 2024-2-3061 and Grant No. 2024-1G-4252).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDistribution and trends of ERT in Beijing and its administrative districts (2016, 2021, and 2022)\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e1.1 Overall distribution and trends in Beijing\u003c/h2\u003e \u003cp\u003eThe number of routine, nonemergency ambulance dispatches in Beijing increased from 242,254 in 2016 to 486,956 in 2021 and 496,329 in 2022. Over this period, the median ERT decreased significantly, from 1046\u0026middot;39 s (IQR: 752\u0026middot;88\u0026ndash;1483\u0026middot;50) in 2016 to 877\u0026middot;54 s (IQR: 648\u0026middot;40\u0026ndash;1177\u0026middot;97) in 2022 (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;1A). No significant monthly variations in ERT were observed (Fig.\u0026nbsp;1B). The ERT distribution showed right-skewed characteristics with skewness coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0 throughout the study period. The kurtosis coefficients increased from 4\u0026middot;50 in 2016 to 6\u0026middot;41 in 2022, indicating a narrow range of response times and reduced disparities in emergency response performance (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Emergency Vehicle Dispatches and Response Times in Beijing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eEmergency vehicle dispatches, n(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eEmergency response times, s, median(IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeijing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e243341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e496332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e567865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1048.3(753.9-1487.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e867.7(644.5-1164.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e924.3(674.1-1279.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital Function Core Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31,280 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55,539 (11.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60,238 (10.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1121.9(799.5-1694.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e835.9(628.8-1096.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e919.8(681.4-1235.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Function Expansion Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e112,754 (46.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e235,693 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264,481 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e998.0(727.3-1389.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e861.3(646.0-1137.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e940.5(694.1-1290.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Development New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74,121 (30.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e157,267 (31.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186,995 (32.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1101.3(784.2-1538.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e896.0(659.7-1223.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e922.9(666.4-1291.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcological Conservation and Development Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25,186 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47,833 (9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56,151 (9.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1062.0(737.7\u0026ndash;1539.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e849.7(612.1-1221.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e847.1(605.6-1229.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDongcheng District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14,433 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22,720 (4.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24,203 (4.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1087.5(799.1-1505.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e900.0(696.6-1171.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e999.6(762.1\u0026ndash;1326.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXicheng District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23,644 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58,501 (11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65,769 (11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1087.5(776.0-1704.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e797.9(600.1-1039.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e873.4(658.1-1166.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChaoyang District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,450 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29,622 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35,150 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1202.7(884.2-1648.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e786.9(595.8-1021.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e748.6(557.2\u0026ndash;1005.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaidian District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,744 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,437 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,595 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1167.4(864.1-1640.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e919.7(664.7-1292.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e881.2(620.3-1255.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFengtai District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,924 (2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10,977 (2.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12,024 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e968.6(675.6-1374.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e918.8(640.9\u0026ndash;1325.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e851.8(606.5-1205.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShijingshan District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,092 (1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7,228 (1.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8,181 (1.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e909.9(622.4-1411.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e753.8(529.4-1106.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e838.3(568.3-1263.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongzhou District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,395 (1.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,235 (1.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,439 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1317.6(927.9-1831.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e849.4(632.5-1200.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e782.8(582.4-1133.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChangping District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17,407 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30,616 (6.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33,260 (5.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1177.1(817.5-1673.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e860.6(638.7-1177.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e947.7(688.4-1326.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaxing District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12,485 (5.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35,842 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45,722 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1141.7(762.2-1736.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e909.1(661.5-1235.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e941.4(688.4\u0026ndash;1284.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFangshan District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54,145 (22.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96,595 (19.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e101,948 (18.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e972.3(714.7-1298.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e902.0(675.0-1189.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1032.6(763.4-1405.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShunyi District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27,658 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63,889 (12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78,665 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e982.6(720.3-1382.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e893.0(672.1-1175.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e906.7(661.6-1259.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuairou District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7,307 (3.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16,708 (3.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18,099 (3.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1020.8(722.2-1660.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e768.2(590.5-1003.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e871.7(653.4-1169.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMentougou District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16,847 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32,819 (6.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36,035 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1166.6(799.8-1932.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e791.0(586.9\u0026ndash;1040.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e864.3(633.4-1171.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiyun District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15,317 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34,655 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41,105 (7.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1047.0(784.4-1391.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e949.6(705.9-1274.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1003.8(731.7-1389.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinggu District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5,031 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9,956 (2.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11,912 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e980.6(690.8-1424.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e776.4(587.5-1117.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e876.7(636.2-1304.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYanqing District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,462 (5.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26,532 (5.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31,758 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e949.0(671.6-1288.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1018.5(727.1-1398.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1007.6(704.3-1415.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe day and night shifts exhibited distinct patterns. The night shift had higher peaks in the ERT distribution, suggesting more concentrated response times at night. However, day shifts consistently accounted for \u0026gt;\u0026thinsp;55% of all dispatches (Fig.\u0026nbsp;1C and Supplementary Fig.\u0026nbsp;1). Kernel density analysis of ERT data over time revealed a notable shift in the peak distribution. In 2016, dual peaks were observed at 8:00\u0026ndash;10:00 and 20:00\u0026ndash;24:00, whereas by 2021 and 2022, these peaks merged into a single peak from 8:00 to 11:00 (Fig.\u0026nbsp;2), indicating a possible reallocation of emergency resources or a shift in demand patterns.\u003c/p\u003e \u003cp\u003e \u003cb\u003e1.2 Overall distribution and trends in the\u003c/b\u003e distributions of the four functional zones\u003c/p\u003e \u003cp\u003eERT across Beijing\u0026rsquo;s four functional zones showed consistent patterns with varying degrees of improvement. The urban function extension zone had the highest proportion of routine emergency dispatches, accounting for 46\u0026middot;39%, 47\u0026middot;56%, and 46\u0026middot;77% of the total dispatches in 2016, 2021, and 2022, respectively. The most significant reductions in ERT were observed in the capital core functional zone, where the median ERT decreased from 1120\u0026middot;42 s (IQR: 798\u0026middot;53\u0026ndash;1691\u0026middot;42) in 2016 to 865\u0026middot;80 s (IQR: 648\u0026middot;80\u0026ndash;1127\u0026middot;45) in 2022 (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;1A). The monthly variations in ERT remained negligible across all four zones (Fig.\u0026nbsp;1B). All zones exhibited right-skewed ERT distributions with skewness coefficients\u0026thinsp;\u0026gt;\u0026thinsp;0. The kurtosis coefficients increased over time, suggesting a concentration of dispatches around peak ERT values. The capital core functional zone had the most concentrated distribution, with kurtosis increasing from 3\u0026middot;39 in 2016 to 6\u0026middot;99 in 2022 (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eThe day and night shifts in these zones exhibited distinct patterns. Except for those in the ecological conservation development zone, the night-shift ERTs in the other three zones presented relatively high peaks, reflecting relatively high response times at night. Day shifts consistently accounted for most dispatches in all zones (Fig.\u0026nbsp;1C, Supplementary Fig.\u0026nbsp;1). Kernel density analysis revealed temporal shifts in the peak times. From 2016 to 2022, the urban and ecological conservation development zones transitioned from a relatively uniform distribution throughout the day to a concentrated peak between 8:00 and 11:00. In the capital core functional and urban function extension zones, the dual peaks observed in 2016 (8:00\u0026ndash;10:00 and 20:00\u0026ndash;24:00) merged into a single peak from 8:00\u0026ndash;11:00 in subsequent years (Supplementary Fig.\u0026nbsp;2). These shifts may reflect changes in resource allocation or emergency demand patterns.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Overall Distribution and Trends in the 16 Administrative Districts\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe\u003c/b\u003e ERT distribution patterns of the 16 administrative districts across Beijing were generally consistent, although the improvements varied. Chaoyang District consistently accounted for the highest proportion of routine dispatches, with 22\u0026middot;27%, 19\u0026middot;49%, and 17\u0026middot;90% of the total dispatches in 2016, 2021, and 2022, respectively, reflecting a declining share over time. With respect to ERT changes, those in the Chaoyang and Shunyi districts increased. In Chaoyang, the median ERT increased from 971\u0026middot;03 s (95% CI: 713\u0026middot;73\u0026ndash;1295\u0026middot;45) in 2016 to 977\u0026middot;74 s (95% CI: 733\u0026middot;17\u0026ndash;1287\u0026middot;71) in 2022 (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), whereas in Shunyi, the ERT increased from 947\u0026middot;09 s (95% CI: 670\u0026middot;61\u0026ndash;1284\u0026middot;76) to 977\u0026middot;41 s (95% CI: 685\u0026middot;94\u0026ndash;1350\u0026middot;00) (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). Conversely, most districts show significant reductions, with Daxing District showing the greatest improvement (from 1201\u0026middot;15 s in 2016 to 716\u0026middot;36 s in 2022, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001). Xicheng and Miyun districts followed, with reductions from 1164\u0026middot;95 s to 809\u0026middot;43 s (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) and 1162\u0026middot;48 s to 842\u0026middot;07 s (p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;1A). The monthly trends showed lower ERT peaks in December for the Xicheng, Haidian, and Shijingshan districts, whereas the Miyun district exhibited a lower peak in January with a shift to the right. The other districts presented minimal monthly variations (Fig.\u0026nbsp;1B). Across Beijing\u0026rsquo;s 16 administrative districts, ERT distributions were consistently right skewed. The kurtosis coefficients were positive, indicating a concentration of dispatches near the peak ERT values. The Shunyi, Tongzhou, and Chaoyang districts presented declining kurtosis trends, suggesting a wider dispersion of ERT values over time. Conversely, districts such as Xicheng, Fengtai, and Daxing experienced significant increases in kurtosis, reflecting a narrowing of response times and improved consistency in 2021 and 2022 (Supplementary Table\u0026nbsp;1). These changes indicated progress in standardizing response performance, particularly in districts such as Daxing, which showed substantial reductions in overall ERT.\u003c/p\u003e \u003cp\u003eThe day and night shifts followed consistent patterns across districts. The core districts displayed higher and leftward-shifted night-shift peaks, suggesting shorter ERTs at night. Day-shift dispatches accounted for a greater proportion of cases in all districts (Fig.\u0026nbsp;1C, Supplementary Fig.\u0026nbsp;1). Kernel density analysis of the ERT distribution over time revealed temporal shifts in peak times. From 2016\u0026ndash;2022, several districts, including Xicheng, Chaoyang, Changping, Fangshan, Fengtai, and Pinggu, transitioned from nighttime peaks (21:00\u0026ndash;24:00) to morning peaks (8:00\u0026ndash;11:00), with the most pronounced shift occurring in Xicheng district. Other districts, such as Dongcheng, Haidian, Daxing, and Shijingshan, maintained stable peaks between 8:00 and 11:00, with Shijingshan showing the highest consistency (Supplementary Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalysis of the right-skewed tail structure of ERT in Beijing and its districts in 2016, 2021, and 2022\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Trend Analysis of the Right-Skewed Tail Structure\u003c/h2\u003e \u003cp\u003eThe proportion of dispatches with ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s in Beijing declined significantly, from 12\u0026middot;41% in 2016 to 3\u0026middot;72% in 2022, reflecting a clear downward trend over the years (r=-0\u0026middot;386, p\u0026thinsp;=\u0026thinsp;0\u0026middot;003). Similar reductions were observed across the four functional zones, with the capital core functional zone demonstrating the most pronounced improvement. Here, the proportion of dispatches exceeding 2000 s decreased from 18\u0026middot;78% in 2016 to 2\u0026middot;50% in 2022 (r=-0\u0026middot;678, p\u0026thinsp;=\u0026thinsp;0\u0026middot;007). At the district level, all 16 administrative districts showed a declining trend in ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s. Among them, Xicheng (r=-0\u0026middot;768, p\u0026thinsp;=\u0026thinsp;0\u0026middot;008), Daxing (r=-0\u0026middot;729, p\u0026thinsp;=\u0026thinsp;0\u0026middot;010), and Shijingshan (r=-0\u0026middot;639, p\u0026thinsp;=\u0026thinsp;0\u0026middot;014) achieved the most substantial reductions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEmergency response times greater than 2000 s in various districts of Beijing\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;2000s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2000s\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeijing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e212183(87.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e470587(96.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e477843(96.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e30071(12.41)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16369(3.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18486(3.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapital Function Core Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25303(81.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e53590(98.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e49268(97.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5852(18.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1008(1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1262(2.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.678\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Function Expansion Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e100082(89.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225300(97.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224095(96.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12294(10.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6304(2.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8048(3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban Development New Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64965(88.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147602(95.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e157184(95.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8742(11.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6353(4.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6589(4.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcological Conservation and Development Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21833(87.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44095(94.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47296(94.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3183(12.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2704(5.78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2587(5.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDongcheng District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12503(86.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21803(97.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19780(96.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1872(13.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e534(2.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e638(3.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.512\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXicheng District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12800(76.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31787(98.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29488(97.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3980(23.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e474(1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e624(2.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.768\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChaoyang District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50898(94.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92224(97.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84800(95.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3045(5.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2685(2.83)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4062(4.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaidian District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24340(88.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60695(96.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66000(96.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3239(11.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1929(3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2097(3.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFengtai District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18931(80.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56272(97.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57384(97.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4650(19.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1368(2.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1488(2.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShijingshan District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5913(81.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16109(98.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15911(97.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1360(18.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e322(1.96)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e401(2.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.639\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTongzhou District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14104(92.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32593(95.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33329(95.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1121(7.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1432(4.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1576(4.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChangping District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10150(81.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33699(95.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38732(95.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2272(18.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1561(4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1654(4.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaxing District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13056(85.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28506(98.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30755(98.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2285(14.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e524(1.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e407(1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e-0.729\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFangshan District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14754(85.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28534(95.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28058(95.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2576(14.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1440(4.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1469(4.98)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShunyi District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12901(96.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24270(94.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26310(94.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e488(3.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1396(5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1483(5.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHuairou District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3524(80.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8520(93.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9838(95.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e835(19.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e547(6.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e462(4.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMentougou District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4427(88.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9284(95.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9810(93.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e585(11.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e488(4.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e695(6.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiyun District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3979(84.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9519(93.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10646(95.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e721(15.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e625(6.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e536(4.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePinggu District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6285(91.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9972(93.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10309(96.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e595(8.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e729(6.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e353(3.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYanqing District\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3618(89.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6800(95.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6693(92.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e447(11.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e315(4.43)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e541(7.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e-0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese findings highlight a citywide effort to increase emergency response efficiency. The consistent decline in extreme delays suggests that targeted interventions, including infrastructure expansion and optimized resource allocation, have been effective in narrowing performance gaps and ensuring more equitable EMS delivery across Beijing.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Analysis of Day\u0026ndash;Night Shift Composition Ratios\u003c/h2\u003e \u003cp\u003eAcross Beijing, day shifts accounted for most dispatches with ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s, and their proportion increased over time, whereas night shifts showed a decreasing trend, with the trend most pronounced in the capital core functional zone. At the district level, most districts followed this trend, with the largest shifts observed in Yanqing, Xicheng, Fangshan, Shijingshan, and Daxing. However, Pinggu, Tongzhou, Miyun, Mentougou, and Chaoyang displayed the opposite pattern, with increasing night-shift proportions (Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eLogistic regression analysis revealed that night shifts generally reduced the likelihood of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s (odds ratio [OR]\u0026thinsp;=\u0026thinsp;0\u0026middot;87, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), except in the ecological conservation development zone (OR\u0026thinsp;=\u0026thinsp;0.98, p\u0026thinsp;=\u0026thinsp;0\u0026middot;303). Notable exceptions included Shijingshan (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;83, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), Pinggu (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;22, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), Miyun (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;16, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), and Shunyi (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), where night shifts increased delays. Year-to-year analysis revealed an exception in the Shunyi district, where the probability of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s increased significantly in 2021 (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;52, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) and 2022 (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;49, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) (Supplementary Table\u0026nbsp;2), contrary to the trends observed in the other 15 districts.\u003c/p\u003e \u003cp\u003eThese findings suggest that while night shift dispatches generally reduce extreme delays across most regions, certain districts, such as Shunyi and Shijingshan, require targeted interventions to address localized challenges.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Disease composition analysis for ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s\u003c/h2\u003e \u003cp\u003eDispatches with ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s in Beijing were attributed primarily to circulatory system diseases, traumatic injuries, neurological diseases, respiratory conditions, and digestive disorders. Over time, the number of respiratory and circulatory cases has increased citywide, whereas the number of neurological, pain-related, and infectious diseases has declined. Among the functional zones, circulatory diseases became more prominent in the capital core and ecological conservation zones, whereas traumatic injuries rose significantly in the urban development zone. At the district level, circulatory cases had the largest increases in Miyun, Mentougou, and Dongcheng, whereas Huairou experienced a notable increase in traumatic injuries (Fig.\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eLogistic regression revealed that circulatory diseases (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;95, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) and traumatic injuries (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;97, p\u0026thinsp;=\u0026thinsp;0\u0026middot;003) were generally associated with shorter delays. Circulatory diseases in urban development zones (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;90, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) and ecological conservation zones (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;94, p\u0026thinsp;=\u0026thinsp;0\u0026middot;043) significantly reduced the likelihood of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s, as did traumatic injuries in urban development zones (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;94, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) and urban function extension zones (OR\u0026thinsp;=\u0026thinsp;0\u0026middot;97, p\u0026thinsp;=\u0026thinsp;0\u0026middot;018). In contrast, certain conditions were linked to higher risks of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s, including traumatic injuries in Tongzhou (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;25, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001) and Mentougou (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;32, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), respiratory diseases in Changping (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;13, p\u0026thinsp;=\u0026thinsp;0\u0026middot;012) and Pinggu (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;18, p\u0026thinsp;=\u0026thinsp;0\u0026middot;040), digestive system diseases in Fangshan (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;20, p\u0026thinsp;\u0026lt;\u0026thinsp;0\u0026middot;001), and neurological disorders in Huairou (OR\u0026thinsp;=\u0026thinsp;1\u0026middot;15, p\u0026thinsp;=\u0026thinsp;0\u0026middot;033).\u003c/p\u003e \u003cp\u003eThese findings highlight the need for targeted interventions addressing specific disease types and regional disparities to further improve emergency response efficiency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Day\u0026ndash;Night Shift Differences and Trends in Disease Composition for ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s\u003c/h2\u003e \u003cp\u003eThe primary disease composition ratios (\u0026gt;\u0026thinsp;10%) during the day and night shifts aligned with overall city-wide patterns. From 2016 to 2022, the number of circulatory, respiratory, and traumatic cases increased, whereas the number of neurological diseases decreased (Supplementary Fig.\u0026nbsp;4). During day shifts, circulatory diseases rose in the capital core and urban function extension zones, whereas traumatic injuries increased in the ecological conservation zone. At night, circulatory diseases dominated across all zones. District-level trends revealed rising day-shift proportions of circulatory diseases in Dongcheng and Xicheng and traumatic injuries in Pinggu, Huairou, and Yanqing. At night, circulatory diseases increased in Chaoyang, Mentougou, and Fengtai, with minimal changes observed in other districts.\u003c/p\u003e \u003cp\u003eThese findings underscore distinct regional and shift-specific trends, highlighting the need for tailored strategies to improve emergency response outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe distribution patterns and temporal trends of ERT provide critical guidance for the design and planning of EMSs.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e This study analyzed the differences in ERT distributions across Beijing\u0026rsquo;s 4 functional zones and 16 administrative districts, focusing on different years, months, shifts, times of day, and causes. These findings offer valuable insights for improving Beijing\u0026rsquo;s emergency medical rescue planning and provide a foundation for future research. The key findings include the following: ERTs have gradually shortened, narrowing the differences between Beijing\u0026rsquo;s functional zones and administrative districts, and peak ERT times shifted from 8:00\u0026ndash;10:00 and 20:00\u0026ndash;24:00 to 8:00\u0026ndash;11:00. The occurrence of ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s steadily declined from 12\u0026middot;41% in 2016 to 3\u0026middot;72% in 2022. Night-shift dispatches, as well as those for circulatory systems and traumatic injuries, showed a decreasing probability of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s.\u003c/p\u003e \u003cp\u003eBy excluding emergency tasks related to large-scale incidents, such as the coronavirus disease 2019 (COVID-19) pandemic, this study revealed significant reductions in Beijing\u0026rsquo;s daily ERTs, which contrasts with the findings of many international studies. Studies aggregating all emergency incidents, including major events such as the COVID-19 pandemic, may not accurately reflect the real state of routine EMSs. For example, Sabbaghi et al. reported longer response times during the pandemic,\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e whereas Eskol et al. reported no significant changes.\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e Similarly, Satty et al. reported no differences in nontraffic ERTs in Pennsylvania before and during the pandemic.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Conversely, Chocron et al. reported a negative correlation between ambulance density and EMS response times,\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e which aligns with the findings of our study. From 2020 to 2022, the number of emergency stations in Beijing increased,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e likely contributing to the improvement in response times. For example, Daxing district expanded its emergency stations from 9 to 23, equipping 3\u0026middot;3 ambulances per 100,000 people,\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e similar to Paris\u0026rsquo;s 4\u0026middot;2 ambulances per 100,000 people.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eBeijing\u0026rsquo;s shift from dual ERT peaks in 2016 to a single peak in 2021 and 2022 (8:00\u0026ndash;11:00) contrasts with the patterns observed in other countries. In Iran, dispatch proportions were evenly distributed across morning, evening, and night shifts\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e; in Ireland, peak dispatch proportions occurred between 12:00 and 18:00\u003csup\u003e11\u003c/sup\u003e; and in Finland, 47.4% of dispatches occurred between 08:00 and 16:00.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e These differences may arise from this study\u0026rsquo;s focus on routine emergency services, unlike broader international analyses. Additionally, strict pandemic lockdowns in many countries\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e have reduced nighttime activity and emergency needs; however, resource shortages have left daytime demands unmet.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e In contrast, Beijing has expanded community healthcare\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e and emergency station capacity,\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e effectively managing daytime surges.\u003c/p\u003e \u003cp\u003eRegional differences are key factors influencing the proportion of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s. Economic factors have long been recognized as the primary drivers of prolonged ERT.\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e Across the four functional zones, the proportion of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s increased from the capital core functional zone to the ecological conservation development zone, which was correlated with lower per capita GDP.\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e This pattern is consistent with that of prior studies. Verma et al. reported that counties with higher average household incomes had 12% faster EMS arrival times than lower-income countries did.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e Heidet et al. reported that regional poverty was correlated with longer EMS response times.\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e The distribution of healthcare resources plays a crucial role in response time disparities. A study on Beijing\u0026rsquo;s healthcare resources revealed that the capital core functional zone outperformed other zones in terms of healthcare infrastructure, such as beds per 1,000 residents, practicing physicians, and financial expenditures on healthcare.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e This concentration of resources likely explains the lower proportion of ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s in the capital core functional zone. Government policies influence ERT outcomes alongside economic and resource-related factors. For example, Daxing District, despite its moderate per capita GDP and healthcare resource density within Beijing,\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e recorded the lowest proportion of ERT\u0026thinsp;\u0026gt;\u0026thinsp;2000 s in 2022 (1\u0026middot;31%). This success can be attributed to local government initiatives, including the standardization of emergency station planning and the expansion of emergency infrastructure, with each street in the district having at least one standardized emergency station and 3\u0026middot;3 ambulances per 100,000 residents.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e Additionally, the study revealed that the ERTs during night shifts were generally lower than those during day shifts, likely because of less daytime traffic congestion.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e However, this trend was not observed in the ecological conservation development zone, which had a lower population density (43 km\u0026sup2; per 10,000 residents) than the capital core functional zone (96 km\u0026sup2; per 10,000 residents).\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e These findings highlight the substantial impact of population density and traffic on ERT distributions.\u003c/p\u003e \u003cp\u003eDisease- and region-specific patterns emerged in ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s. Ageta noted that internal medicine-related conditions, particularly infections, frequently require additional time for patient history inquiries (e.g., recent symptoms or travel history), especially during a pandemic.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e However, logistic regression with time (COVID-19) as a covariate revealed that internal medicine diseases did not affect ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s, whereas circulatory and traumatic diseases reduced their incidence. The reason lies in the heightened focus on cardiovascular diseases. Circulatory diseases continue to show a shortening trend due to sustained focus. Reducing ERTs to the 90th percentile of 8 min could increase survival rates by 8%, whereas reducing them to 5 min could increase survival rates by 10\u0026ndash;11%.\u003csup\u003e21\u003c/sup\u003e Meanwhile, the 2018 release of the \u0026ldquo;Notice on Enhancing Trauma Treatment Capacity\u0026rdquo; \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e contributed to the decreasing trend of ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s for traumatic cases. Specific diseases in regions, including Tongzhou, Mentougou (traumatic injuries), Pinggu, Changping (respiratory), Fangshan (digestive), and Huairou (neurological), significantly impact the incidence of ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s, underscoring the need for targeted policy support.\u003c/p\u003e \u003cp\u003eThis study had several limitations. First, as an observational study, causality could not be established. Second, it focused solely on Beijing and analyzed data from 2016, 2021, and 2022, with 2021 and 2022 overlapping with the COVID-19 pandemic, which may limit generalizability. Finally, the study concentrated on the ERT distribution within the prehospital EMS system without analyzing the total response times or patient outcomes.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study highlights the significant improvements in ERTs across Beijing\u0026rsquo;s functional zones and administrative districts, driven by factors such as infrastructure expansion, government policies, and population density. However, variations remain, particularly in disease-specific response times, and further research is needed to explore the broader implications for patient outcomes. These findings provide valuable insights for optimizing the EMS and guiding future improvements in response efficiency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eDeclaration of Interests\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthical Considerations\u003c/h2\u003e \u003cp\u003eThe study did not involve any human participants or animals, so no ethics approval or informed consent was needed.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was funded by Capital\u0026rsquo;s Funds for Health Improvement and Research (Grant No. 2024-2-3061 and Grant No. 2024-1G-4252).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJianhai Long, PengDa Han and Mengjie Guo contributed equally to this work.\u0026bull; Data analysis: Jianhai Long, Mengjie Guo and Li Qin\u0026bull; Project administration: Yang Zheng\u0026bull; Data collection and processing: PengDa Han, Xu Wang and Jinjun Zhang\u0026bull; Supervision: Yang Zheng\u0026bull; Writing\u0026mdash;original draft preparation: PengDa Han, Jianhai Long and Mengjie Guo\u0026bull; Writing\u0026mdash;review \u0026amp; editing: Yang Zheng, PengDa Han, Jianhai Long and Mengjie Guo\u0026bull; All authors have read and approved the final version of the manuscript and agreed with the order of presentation of the authors.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eYu D. [Analysis of Influencing Factors on Prehospital Emergency Effectiveness in a Region of Zhejiang Province]. \u003cem\u003eHealth Care Today.\u003c/em\u003e2020; \u003cstrong\u003e20\u003c/strong\u003e(6): 37-40.Chinese.\u003c/li\u003e\n\u003cli\u003eVerma S, Wilson F, Wang H, Smith L, Tak HJ. Impact of Community Socioeconomic Characteristics on Emergency Medical Service Delays in Responding to Fatal Vehicle Crashes. \u003cem\u003eAJPM Focus\u003c/em\u003e 2023; \u003cstrong\u003e2\u003c/strong\u003e(4): 100129.\u003c/li\u003e\n\u003cli\u003eBaker CC, Oppenheimer L, Stephens B, Lewis FR, Trunkey DD. Epidemiology of trauma deaths. \u003cem\u003eAm J Surg\u003c/em\u003e 1980; \u003cstrong\u003e140\u003c/strong\u003e(1): 144-50.\u003c/li\u003e\n\u003cli\u003eLam SS, Nguyen FN, Ng YY, et al. Factors affecting the ambulance response times of trauma incidents in Singapore. \u003cem\u003eAccid Anal Prev\u003c/em\u003e 2015; \u003cstrong\u003e82\u003c/strong\u003e: 27-35.\u003c/li\u003e\n\u003cli\u003eSabbaghi M, Namazinia M, Miri K. Time indices of prehospital EMS missions before and during the COVID-19 pandemic: a cross-sectional study in Iran. \u003cem\u003eBMC Emerg Med\u003c/em\u003e 2023; \u003cstrong\u003e23\u003c/strong\u003e(1): 9.\u003c/li\u003e\n\u003cli\u003eEskol JR, Zegers FD, Wittrock D, Lassen AT, Mikkelsen S. Increased ambulance on-scene times but unaffected response times during the first wave of the COVID-19 pandemic in Southern Denmark. \u003cem\u003eBMC Emerg Med\u003c/em\u003e 2022; \u003cstrong\u003e22\u003c/strong\u003e(1): 61.\u003c/li\u003e\n\u003cli\u003eSatty T, Ramgopal S, Elmer J, Mosesso VN, Martin-Gill C. EMS responses and nontransports during the COVID-19 pandemic. \u003cem\u003eAm J Emerg Med\u003c/em\u003e 2021; \u003cstrong\u003e42\u003c/strong\u003e: 1-8.\u003c/li\u003e\n\u003cli\u003eChocron R, Cariou A, Dumas F. Response by Chocron et al to Letter Regarding Article, \u0026quot;Ambulance Density and Outcomes After Out-of-Hospital Cardiac Arrest: Insights From the Paris Sudden Death Expertise Center Registry\u0026quot;. \u003cem\u003eCirculation\u003c/em\u003e 2019; \u003cstrong\u003e140\u003c/strong\u003e(10): e549-e50.\u003c/li\u003e\n\u003cli\u003eBeijing Municipal Health Commission, Beijing Municipal Commission of Planning and Natural Resources. Notice on the Joint Issuance of the \u0026quot;Beijing Municipal Prehospital Medical Emergency Facilities Spatial Layout Special Plan (2020-2022)\u0026quot;[EB/OL]. [0001-07-01]. https://wjw.beijing.gov.cn/zwgk_20040/wsyj/202007/t20200728_1963964.html.2020.Chinese.\u003c/li\u003e\n\u003cli\u003eOffice of Daxing District People\u0026apos;s Goverment of Beijing Municipality.Notice on Issuing the \u0026quot;Daxing District Implementation Plan for Accelerating the Construction of Prehospital Medical Emergency System\u0026quot;[EB/OL]. https://www.bjdx.gov.cn/bjsdxqrmzf/zwfw/zfwj67/zfwj/1859366/index.html.2021. Chinese.\u003c/li\u003e\n\u003cli\u003eBurton E, Quinn R, Crosbie-Staunton K, et al. Temporal trends of ambulance time intervals for suspected stroke/transient ischemic attack (TIA) before and during the COVID-19 pandemic in Ireland: a quasiexperimental study. \u003cem\u003eBMJ Open\u003c/em\u003e 2024; \u003cstrong\u003e14\u003c/strong\u003e(3): e078168.\u003c/li\u003e\n\u003cli\u003eLaukkanen L, Lahtinen S, Liisanantti J, Kaakinen T, Ehrola A, Raatiniemi L. Early impact of the COVID-19 pandemic and social restrictions on ambulance missions. \u003cem\u003eEur J Public Health\u003c/em\u003e 2021; \u003cstrong\u003e31\u003c/strong\u003e(5): 1090-5.\u003c/li\u003e\n\u003cli\u003eJames MM, Rodrigues J, Montoya M, et al. The Pandemic Experience Survey II: A Second Corpus of Subject Reports of Life Under Social Restrictions During COVID-19 in the UK, Japan, and Mexico. \u003cem\u003eFront Public Health\u003c/em\u003e 2022; \u003cstrong\u003e10\u003c/strong\u003e: 913096.\u003c/li\u003e\n\u003cli\u003eBeijing Municipal Health Commission. Notice from the Beijing Municipal Health Commission on Issuing the Key Points of Primary Health Care Work in 2020[EB/OL]. https://wjw.beijing.gov.cn/.2020. Chinese.\u003c/li\u003e\n\u003cli\u003eFriedson AI. Income and Ambulance Response Time Inequality: No Simple Explanation, No Simple Fix. \u003cem\u003eJAMA Netw Open\u003c/em\u003e 2018; \u003cstrong\u003e1\u003c/strong\u003e(7): e185201.\u003c/li\u003e\n\u003cli\u003eBeijing Municipal Bureau of Statistics,Survey Office of the National Bureau of Statistics in Beijing. Beijing Statistical Yearbook. \u003cem\u003eChina Statistics Press\u003c/em\u003e 2022.Chinese\u003c/li\u003e\n\u003cli\u003eHeidet M, Da Cunha T, Brami E, et al. EMS Access Constraints And Response Time Delays For Deprived Critically Ill Patients Near Paris, France. \u003cem\u003eHealth Aff (Millwood)\u003c/em\u003e 2020; \u003cstrong\u003e39\u003c/strong\u003e(7): 1175-84.\u003c/li\u003e\n\u003cli\u003eWANG SP，HUANG ED. [Analysis on the Allocation Equality in Health Resources in Beijing Based on Theil Index and Agglomeration Degree]. \u003cem\u003eChinese Health Economics\u003c/em\u003e 2020; \u003cstrong\u003e39\u003c/strong\u003e(4): 44-8.Chinese\u003c/li\u003e\n\u003cli\u003eJafari M, Mahmoudian P, Ebrahimipour H, et al. Response Time and Causes of Delay in Prehospital Emergency Missions in Mashhad, 2015. \u003cem\u003eMed J Islam Repub Iran\u003c/em\u003e 2021; \u003cstrong\u003e35\u003c/strong\u003e: 142.\u003c/li\u003e\n\u003cli\u003eAgeta K, Naito H, Yorifuji T, et al. Delay in Emergency Medical Service Transportation Responsiveness during the COVID-19 Pandemic in a Minimally Affected Region. \u003cem\u003eActa Med Okayama\u003c/em\u003e 2020; \u003cstrong\u003e74\u003c/strong\u003e(6): 513-20.\u003c/li\u003e\n\u003cli\u003ePell JP, Sirel JM, Marsden AK, Ford I, Cobbe SM. Effect of reducing ambulance response times on deaths from out of hospital cardiac arrest: cohort study. \u003cem\u003eBMJ\u003c/em\u003e 2001; \u003cstrong\u003e322\u003c/strong\u003e(7299): 1385-8.\u003c/li\u003e\n\u003cli\u003eOffice of the National Health and Family Planning Commission of the People\u0026apos;s Republic of China. [Notice on Further Enhancing Trauma Treatment Capabilities][EB/OL].http://www.nhc.gov.cn/yzygj/s3594q/201807/79daad75e4c746118fb7d0237c7588bd.shtml; 2018.Chinese.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Emergency response times, Emergency response system, Kernel density estimation","lastPublishedDoi":"10.21203/rs.3.rs-6577341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6577341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study examined the distribution, temporal trends, and factors influencing emergency response times (ERT) across Beijing\u0026rsquo;s functional zones and administrative districts, providing insights for optimizing the city\u0026rsquo;s emergency response system.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData from 1,2255\u0026nbsp;million nonemergency ambulance dispatches (2016, 2021, and 2022) were analyzed. Kernel density estimation was used to explore the ERT distribution patterns across Beijing\u0026rsquo;s functional zones and districts, analyzing variations by year, month, shift, time of day, and disease type.\u003c/p\u003e\u003ch2\u003eFindings:\u003c/h2\u003e \u003cp\u003eAmbulance dispatches increased annually. The urban expansion zone had the highest proportion of dispatches (\u0026gt;\u0026thinsp;45% annually) at the functional zone level. Chaoyang district had the highest share of dispatches (\u0026gt;\u0026thinsp;15% annually) at the district level, with a decreasing trend. From 2016\u0026ndash;2022, ERTs decreased, particularly in the capital functional core zone. However, ERTs increased in Chaoyang and Shunyi in 2022 compared with 2016, whereas Daxing, Xicheng, and Miyun experienced substantial reductions. The proportion of dispatches with ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s significantly decreased, particularly in the capital functional core zone. At the district level, Xicheng and Daxing presented the greatest reductions in ERTs\u0026thinsp;\u0026gt;\u0026thinsp;2000 s. Daytime shifts dominated citywide dispatches of ambulances\u0026thinsp;\u0026gt;\u0026thinsp;2000 s; however, their proportions decreased in Chaoyang, Tongzhou, Mentougou, Miyun, and Pinggu. Dispatch analysis at \u0026gt;\u0026thinsp;2000 s revealed increased respiratory and circulatory system diseases, whereas the proportions of neurological, pain-related, and infectious diseases decreased.\u003c/p\u003e\u003ch2\u003eInterpretation:\u003c/h2\u003e \u003cp\u003eERTs in Beijing improved significantly between 2016 and 2022, especially in the capital functional core zone and Xicheng. Daxing\u0026rsquo;s consistently low ERTs serve as models for improving response times.\u003c/p\u003e","manuscriptTitle":"Analysis of the Evolution and Influencing Factors of Emergency Response Times in Megacities: An Empirical Study of Beijing Using Kernel Density Estimation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 10:49:58","doi":"10.21203/rs.3.rs-6577341/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-05-30T13:54:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-08T07:35:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-06T11:48:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-06T11:45:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Emergency Medicine","date":"2025-05-02T09:44:24+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-emergency-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emmd","sideBox":"Learn more about [BMC Emergency Medicine](http://bmcemergmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/emmd","title":"BMC Emergency Medicine","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"43ed7611-09b4-45a2-abc8-85a6386e228e","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-06-03T10:49:58+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 10:49:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6577341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6577341","identity":"rs-6577341","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}