基于火灾数据的消防时间关联分析与应急决策模型研究
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摘要
消防救援对火灾的控制作用显著,主要体现在消防出动时间和消防战斗时间,前者反映消防救援的及时性,后者反映消防救援的有效性。本文基于江西省2000--2010年火灾数据,在空间维度上分析了消防出动时间与消防战斗时间的内在联系,以及消防战斗时间与火灾损失之间的关联规律；在时间维度上分析了消防出动时间和消防战斗时间与火灾损失的时间依存关系；最后综合分析了空间和时间两个维度上消防出动时间和战斗时间与火灾损失之间的量化规律,并在此基础上发展了建筑火灾应急救援序贯决策模型。
在消防时间的空间维度统计规律方面,发现不同火灾场所消防出动时间存在差异,这种差异主要是其地理位置造成的。战斗时间受火灾场所和城市区域双重因素的影响,表现在市区和县城火灾的战斗时间显著小于集镇和郊区,厂房和仓库火灾的战斗时间大于其他场所,消防出动时间和战斗时间之间存在一定的相关性,出动时间15分钟内,平均战斗时间随出动时间线性增长,出动时间大于15分钟后,平均战斗时间随出动时间增长趋势逐渐变缓。
在消防时间与火灾损失的关系方面,发现不同战斗时间下城市建筑火灾“频率—过火面积”满足幂律分布,幂函数指数可表征控火能力,指数的绝对值越大则小火发生概率越大,大火发生概率越小。该值与战斗时间负相关,表明控火能力随着战斗时间的增长而降低。不同火灾场所幂函数指数存在差异,随着战斗时间的增大,住宅的控火能力衰减最快,公共娱乐场所、商业场所和厂房次之,仓库衰减最慢。不同城市区域火灾的“频率—过火面积”幂律分布存在差异,城市市区、县城城区和郊区农村满足“一段式”幂律分布,而在集镇镇区火灾满足“两段式”幂律分布。
在时间维度上,发现建筑火灾过火面积及消防时间均存在时间标度性特征,且随着阈值的变大,分形特征逐渐消失,时间序列过渡为泊松分布。根据艾伦因子,过火面积≥200m2的城市建筑火灾表现为泊松分布,当出动时间大于60分钟后,其时间标度性消失,当战斗时间大于110分钟时,其时间标度性消失,表现为泊松分布。
进一步进行地时间序列分析表明：周平均出动时间的滞后阶数为1阶时,是火灾的Granger原因,而周平均战斗时间在滞后4阶时是火灾的Granger原因；从长期来看,平均出动时间、平均战斗时间与平均过火面积变化之间存在均衡关系,且均为正稳定关系；平均出动时间和平均战斗时间的突变均会对平均过火面积产生正的影响,且影响持续时间约为1个月；相较于平均战斗时间,平均出动时间对平均火灾损失的贡献度更大。敏感性分析表明对于不同火灾场所,住宅的出动时间对过火面积最敏感,而战斗时间的敏感性最小；对于不同城市区域,城市市区的出动时间敏感性最大,同时城市市区和县城城区火灾的过火面积对战斗时间的敏感性要大于集镇镇区和郊区农村火灾。
基于上述消防时间关联分析,考虑了应急救援车辆的动态特征、火灾发展的动态特征及火灾发生的时序特征,建立了针对建筑火灾应急救援的动态序贯决策模型,模型引入火灾重要度的概念解决火灾场所和城市区域对消防时间和过火面积的影响,采用双层两目标规划方法,考虑当前并发火灾损失最小和并发火灾总体损失最小两个目标,实现了局部最优和整体最优。实证分析表明本文发展的建筑火灾应急救援决策模型适用于复杂应急救援条件下的救援决策,能够为决策者提供最优的资源调度决策方案。
Well performed fire rescue can significantly control fires. This is represented by fire attendance time and fire fighting time. The former reflects the timely ability of fire rescue, while the latter reflects the effectiveness of fire fighting. This paper, based on the urban fire data from2000to2010in Jiangxi province, on the spatial dimension, the inter connection between fire attendance time and fire fighting time and the correlation between fire fighting time and fire loss; on the time dimension the dependency relationship between fire fighting time and fire loss. Finally it summarizes the statistical law based on the two dimensions and developed a sequential decision model of building fire emergency rescue.
The differences of the first attendance time at difference places are caused by the different location. Fire fighting time is confined by both the fire location and the urban area, which is manifested in that fire fighting time in the city and county was significantly less than that in towns and suburbs, fire fighting time in workshop and warehouse is more than that in other places. There is a certain correlation between fire attendance time and fire fighting time. If the fire attendance time is within15minutes, the average fire fighting time grows linearly with it, while it expends more than15minutes; the increasing trend of fire fighting time becomes slow.
Under different fire fighting time, the city building fire "frequency-burned area" satisfies the power-law distribution. Power function index represents the fire control ability. If the absolute value of the index is large, the probability of small fires is large and the probability of large fires is small. Meanwhile, this value of index has a negative correlation with fire fighting time, that is to say the fire control ability decreased with the growth of fire fighting time. Power function index can be different at different locations. As the fire fighting time increases, the control ability in residence decreased fastest, which followed by the public entertainment places, commercial places and factories. The control ability in warehouse decreases the slowest."Frequency-burned area" power law distribution of regional fire in different cities act differently. Urban areas of cities and county and suburban rural area meet "the one part form" power law distribution, while that of township fire follows "the two part form" power law distribution.
Burned area and fire attendance time of building fire have time scale characteristics. With the increasing of the threshold, the fractal characteristic gradually disappears, and the time series transports to Poisson distribution. According to Allen factor, if the burned area is larger than200m2, city building fire performance follows Poisson distribution. When the fire attendance time is larger than60minutes, the time scale disappears and performed as Poisson distribution. The same presents when fighting time is more than110minutes.
Time sequence analysis showed that:the average weekly fire attendance time becomes the Granger effect of fire if it lags one order, the average weekly fire fighting time becomes the Granger effect of fire if it lags4orders. In the long run, there is an equilibrium relationship among the average fire attendance time, the average fire fight time and the average burned area and all of the relationships are stable. Average fire attendance time and average fire fighting time positively influence average burned area for about a month. Compared with the average fire fighting time, the average fire attendance time contributes more to the average fire loss. Sensitivity analysis shows that burned area is most sensitive to the attendance time in residential places. And the fire fighting time has the least effect on the burned area. For different city zone, the burned area is most sensitive to fire attendance time in city urban area. And the sensitivity of burned area in city and county urban area to fire fighting time is greater than that of fires in the rural town and suburban town.
Based on the correlation analysis of fire attendance time, this paper has set up a building dynamic sequential decision model of fire emergency rescue, considering the dynamic characteristics of the emergency rescue vehicle, fire development and the temporal characteristics of the fire presence. This model introduces the concept of fire important degree, which can solve the impact of fire location and urban areas on fire rescue time and burned area. And it adopted a double layer two goal programming method, considering the current minimum concurrent fire loss and minimum concurrent fire total loss as two targets to achieve local optimum and global optimum. Empirical analysis shows that the building fire emergency rescue decision model developed in this paper is suitable for rescue decision under complex emergency rescue condition, and can provide optimal resource scheduling decision scheme.
在消防时间的空间维度统计规律方面,发现不同火灾场所消防出动时间存在差异,这种差异主要是其地理位置造成的。战斗时间受火灾场所和城市区域双重因素的影响,表现在市区和县城火灾的战斗时间显著小于集镇和郊区,厂房和仓库火灾的战斗时间大于其他场所,消防出动时间和战斗时间之间存在一定的相关性,出动时间15分钟内,平均战斗时间随出动时间线性增长,出动时间大于15分钟后,平均战斗时间随出动时间增长趋势逐渐变缓。
在消防时间与火灾损失的关系方面,发现不同战斗时间下城市建筑火灾“频率—过火面积”满足幂律分布,幂函数指数可表征控火能力,指数的绝对值越大则小火发生概率越大,大火发生概率越小。该值与战斗时间负相关,表明控火能力随着战斗时间的增长而降低。不同火灾场所幂函数指数存在差异,随着战斗时间的增大,住宅的控火能力衰减最快,公共娱乐场所、商业场所和厂房次之,仓库衰减最慢。不同城市区域火灾的“频率—过火面积”幂律分布存在差异,城市市区、县城城区和郊区农村满足“一段式”幂律分布,而在集镇镇区火灾满足“两段式”幂律分布。
在时间维度上,发现建筑火灾过火面积及消防时间均存在时间标度性特征,且随着阈值的变大,分形特征逐渐消失,时间序列过渡为泊松分布。根据艾伦因子,过火面积≥200m2的城市建筑火灾表现为泊松分布,当出动时间大于60分钟后,其时间标度性消失,当战斗时间大于110分钟时,其时间标度性消失,表现为泊松分布。
进一步进行地时间序列分析表明：周平均出动时间的滞后阶数为1阶时,是火灾的Granger原因,而周平均战斗时间在滞后4阶时是火灾的Granger原因；从长期来看,平均出动时间、平均战斗时间与平均过火面积变化之间存在均衡关系,且均为正稳定关系；平均出动时间和平均战斗时间的突变均会对平均过火面积产生正的影响,且影响持续时间约为1个月；相较于平均战斗时间,平均出动时间对平均火灾损失的贡献度更大。敏感性分析表明对于不同火灾场所,住宅的出动时间对过火面积最敏感,而战斗时间的敏感性最小；对于不同城市区域,城市市区的出动时间敏感性最大,同时城市市区和县城城区火灾的过火面积对战斗时间的敏感性要大于集镇镇区和郊区农村火灾。
基于上述消防时间关联分析,考虑了应急救援车辆的动态特征、火灾发展的动态特征及火灾发生的时序特征,建立了针对建筑火灾应急救援的动态序贯决策模型,模型引入火灾重要度的概念解决火灾场所和城市区域对消防时间和过火面积的影响,采用双层两目标规划方法,考虑当前并发火灾损失最小和并发火灾总体损失最小两个目标,实现了局部最优和整体最优。实证分析表明本文发展的建筑火灾应急救援决策模型适用于复杂应急救援条件下的救援决策,能够为决策者提供最优的资源调度决策方案。
Well performed fire rescue can significantly control fires. This is represented by fire attendance time and fire fighting time. The former reflects the timely ability of fire rescue, while the latter reflects the effectiveness of fire fighting. This paper, based on the urban fire data from2000to2010in Jiangxi province, on the spatial dimension, the inter connection between fire attendance time and fire fighting time and the correlation between fire fighting time and fire loss; on the time dimension the dependency relationship between fire fighting time and fire loss. Finally it summarizes the statistical law based on the two dimensions and developed a sequential decision model of building fire emergency rescue.
The differences of the first attendance time at difference places are caused by the different location. Fire fighting time is confined by both the fire location and the urban area, which is manifested in that fire fighting time in the city and county was significantly less than that in towns and suburbs, fire fighting time in workshop and warehouse is more than that in other places. There is a certain correlation between fire attendance time and fire fighting time. If the fire attendance time is within15minutes, the average fire fighting time grows linearly with it, while it expends more than15minutes; the increasing trend of fire fighting time becomes slow.
Under different fire fighting time, the city building fire "frequency-burned area" satisfies the power-law distribution. Power function index represents the fire control ability. If the absolute value of the index is large, the probability of small fires is large and the probability of large fires is small. Meanwhile, this value of index has a negative correlation with fire fighting time, that is to say the fire control ability decreased with the growth of fire fighting time. Power function index can be different at different locations. As the fire fighting time increases, the control ability in residence decreased fastest, which followed by the public entertainment places, commercial places and factories. The control ability in warehouse decreases the slowest."Frequency-burned area" power law distribution of regional fire in different cities act differently. Urban areas of cities and county and suburban rural area meet "the one part form" power law distribution, while that of township fire follows "the two part form" power law distribution.
Burned area and fire attendance time of building fire have time scale characteristics. With the increasing of the threshold, the fractal characteristic gradually disappears, and the time series transports to Poisson distribution. According to Allen factor, if the burned area is larger than200m2, city building fire performance follows Poisson distribution. When the fire attendance time is larger than60minutes, the time scale disappears and performed as Poisson distribution. The same presents when fighting time is more than110minutes.
Time sequence analysis showed that:the average weekly fire attendance time becomes the Granger effect of fire if it lags one order, the average weekly fire fighting time becomes the Granger effect of fire if it lags4orders. In the long run, there is an equilibrium relationship among the average fire attendance time, the average fire fight time and the average burned area and all of the relationships are stable. Average fire attendance time and average fire fighting time positively influence average burned area for about a month. Compared with the average fire fighting time, the average fire attendance time contributes more to the average fire loss. Sensitivity analysis shows that burned area is most sensitive to the attendance time in residential places. And the fire fighting time has the least effect on the burned area. For different city zone, the burned area is most sensitive to fire attendance time in city urban area. And the sensitivity of burned area in city and county urban area to fire fighting time is greater than that of fires in the rural town and suburban town.
Based on the correlation analysis of fire attendance time, this paper has set up a building dynamic sequential decision model of fire emergency rescue, considering the dynamic characteristics of the emergency rescue vehicle, fire development and the temporal characteristics of the fire presence. This model introduces the concept of fire important degree, which can solve the impact of fire location and urban areas on fire rescue time and burned area. And it adopted a double layer two goal programming method, considering the current minimum concurrent fire loss and minimum concurrent fire total loss as two targets to achieve local optimum and global optimum. Empirical analysis shows that the building fire emergency rescue decision model developed in this paper is suitable for rescue decision under complex emergency rescue condition, and can provide optimal resource scheduling decision scheme.
引文
陈恒.2006.江苏省城市火灾的时间序列分析[D]：硕士.合肥：中国科学技术大学,31-47.
陈志宗,尤建新.2006.重大突发事件应急救援设施选址的多目标决策模型[J].管理科学,19(4)：10-14.
陈子锦,王福亮,陆守香,方兆本.2007.我国火灾统计数据的聚类分析[J].中国工程科学,9(1)：86-89.
冯海江.2010.地震灾害救援中的应急物资分配优化研究[D]：硕士.上海：上海交通大学船舶海洋与建筑工程学院.
付丽华,张瑞芳,陆松,龙胜刚.2008.火灾事故发展趋势预测方法的应用研究[J].火灾科学,17(2)：.
郭艳丽.2001.群死群伤恶性火灾的特点及预防[J].武警学院学报,17(6)：34-40.
贾传亮,池宏等.2005.基于多阶段灭火过程的消防资源布局模型[J].系统工程,23(9)：12-15.
贾水库,温晓虎,蒋仲安,林大建.2008.灰色系统理论在城市火灾事故预测中的应用[J],中国安全生产科学技术,4(6).
姜丽珍,刘茂.2008.森林火灾灭火物资优化配置模型研究[J].中国公共安全,3：97-100.
李海江.2010.2000-2008年全国重特大火灾统计分析[J].火灾科学,16(1)：
李杰,张靖岩,郭建中,姜亢.2012.基于聚类分析的我国火灾空间分布研究[J].中国安全生产科学技术,8(2).
李勇,胡双启.2009.灰色Elman神经网络在火灾事故预测中的应用研究[J].中国安全生产科学技术,19(3)：.
刘春林,盛昭翰,何建敏.1999.基于连续消耗应急系统的多出救点选择问题[J].管理工程学报,13(3)：13-16.
陆松.2012.中国群死群伤火灾时空分布规律及影响因素研究[D].博士论文. 中国科学技术大学.
马锐.2005.我国群死群伤特大火灾研究[D].重庆：重庆大学
聂高众,高建国,苏桂武,等.2001.地震应急救助需求的模型化处理-来自地震震例的经验分析[J].资源科学,23(1)：69-76.
彭晨.2010.消防响应时间统计规律及其与城市火灾规模相关性研究[D].硕士论文.中国科学技术大学.
吴赤蓬,等.2001.我国火灾发生情况的聚类分析[J],预防医学文献信息,7(1)：4-7.
徐波.2012.经济发展及气候变化对中国城市火灾时空变化的宏观影响[D].博士论文,南京大学.
杨继君,许维胜,冯云生,等.2009.基于多模式分层网络的应急资源调度模型[J].计算机工程,35(10)：21-24.
杨立中,江大白.2003.中国火灾与社会经济因素的关系[J].中国工程科学,5(2)：62-67.
张玲,黄钧.2008.基于场景分析的应急资源布局模型研究[J].中国管理学,16：164-167.
张毅,郭晓汾,王笑风.2006.应急救援物资车辆运输线路的选择[J].安全与环境学报,6(3)：51-53.
张毅.2007.基于自然灾害的救灾物资物流决策理论与方法研究[D].西安：长安大学汽车学院.
郑红阳.2010.受气象因子驱动的火灾系统标度性研究[D].博士论文.中国科学技术大学.
周晓猛,姜丽珍,张云龙.2007.突发事故下应急资源优化配置定量模型研究[J].安全与环境学报,7(6)：113-115.
AVIV Y.2003. A time-series framework for supply chain inventory management[J].Operations Research,51 (2):210-227
BADRI M A, MORTAGY A K, ALSAYED C A.1998. A multi-objective model for locating fire stations[J]. European Journal of Operational Research,110:243-260.
BOX G E P, JENKINS G M, REINSEL G C.1994. Time series analysis: forecasting and control [J]. Prentice Hall, Englewood Cliffs, NJ.
CHANG M S, TSENG Y L, CHEN J W.2007. A scenario planning approach for the flood emergency logistics preparation problem under uncertainty[J]. Transportation Research Part E,43,737-754.
DARBRA R M, ELJARRAT E, BARCEL D.2008. How to measure uncertainties in environmental risk assessment[J]. Trends in Analytical Chemistry, 27(4):377-385.
Duncanson M, Woodward A, Reid P.2002. Socioeconomic deprivation and fatal unintentional domestic fire incidents in New Zealand 1993-1998 [J]. Fire Safety Journal,37(2):165-179.
Gunther P.1981. Fire cause patterns for different neighborhoods in Toledo, OH[J] Fire Journal 75:52-58.
Jennirtgs C. R.1996. Urban residential fires:all empirical analysis of building stock and socioeconomic characteristics for Memphis[D].
JIA H Z, ORD EZ F, DESSOUKY M M.2007. A modeling framework for facility location of medical services for large-scale emergencies [J]. HE Transactions, 39(1):41-55.
Karter M. J. AlLan D.1978. The effect of demographics on fire rates[J] Fire Journal,72 (1):53-65.
Linet Ozdamar, Ediz Ekinci, Beste Ku ukyazici.2004. Emergency Logistics Planning in Natural Disasters [J]. Annals of Operations Research,129:218-219.
Malamud BD.1998. Forest fires:An example of self-organized critical behavior [J].Science.281(5384):1840-1842.
Schaeman P. et al.1977. Procedures for Improving The Measurement of Local Fire Protection Effectiveness [M]. Bost:National Fire Protection Association.53-71
SHEU J B.2010. Dynamic relief-demand management for emergency logistics operations under large-scale disasters [J]. Transportation Research Part E,46:1-17.
WEI W W S.1990. Time series analysis:univariate and multivariate methods [M]. New York:Addison-Wesley Publishing Company.
Yang Lizhong, Zhou Xiaodong, Deng Zhihua, et al.2002. Fire situation and fire characteristic analysis based on fire statistics of China [J]. Fire Safety J,37 (8):785-802.
Yi W, Kumar A.2007. An colony optimization for disaster relief operations[J]. Transportation Research PartE:Logistics and Transportation Review,43(6):660-672.
《国务院关于进一步加强消防工作的意见》(国发(2006)15号)
《中华人民共和国消防法》.2008.
2009中国消防年鉴.江西部分.
2011中国消防年鉴.江西部分.
陈驰,任爱珠.消防站布局优化的计算机方法[J].清华大学学报.2003,43(10)：1390-1393.
城市消防站建设标准.2006.
丰国炳等.用模糊数学建立消防灭火救援力量合理配置优选模型的研究[J].消防技术与产品信息,2002.
公安消防部队执勤战斗条令.
龚啸.城市消防规划关键技术研究[D].长沙：中南大学.2007：3-8.
胡传平.区域火灾风险评估与灭火救援力量布局规划研究[D].上海：同济大学.2006：95-101.
金磊,徐德蜀,罗云.中国21世纪安全减灾战略[M].郑州：河南大学出版社.1998.
潘京.我国城市消防安全存在的问题与对策研究[D].重庆重庆大学.2005.
彭晨,朱霁平,佐藤晃由.消防第一出动时间的频次分布规律研究.火灾科学.2010,19(1)：33-37.
彭晨.2010.消防响应时间统计规律及其与城市火灾规模相关性研究[D].硕士论文.中国科学技术大学.
深入贯彻“二十公”精神全面推进消防“161工程”工作会议讲话.
王荷兰,杨君涛,吴军,吴美文.基于案例的我国消防响应问题及对策消防科学与技术.2010,229(2)：163-165.
叶子才.城市消防设施建设研究[D].重庆：重庆大学.2005：4-5.
俞艳,郭庆胜,何建华.顾及地理网络特征的城市消防站布局渐进优化[J].武汉大学学报.2005,30(4)：333-336.
张锐.2011.基于火灾统计的灭火救援时效性分析[D].工程硕士论文.中国科学技术大学.
周志军.城市灭火救援首战力量时效性分析与研究[J].消防科学与技术,2009,28(5)：354-357.
Clausen S E.1998. Applied correspondence analysis:an introduction[M]. Sage University Papers Series on Quantitative Applications in the Socia 1 Science,07-121. Sage Publications, Thousand Oaks, CA.
David Torv, George Hadjisophocleous,2 Matthew B. Guenther,, Gordon Thomas. Estimating Water Requirementsfor Firefighting Operations Using FIER Asystem. Fire Technology.2001,37:235-262
Eric J. Beh, Rosaria Lombardo, Biagio Simonetti (2011) A European perception of food using two methods of correspondence analysis. Food Quality and Preference 22(2):226-231. doi:10.1016/j.foodqual.2010.10.001
Halil Ibrahim Cakir, Siamak Khorram, Stacy A.C. Nelson (2006) Correspondence analysis for detecting land cover change. Remote Sensing of Environment 102(3-4):306-317. doi:10.1016/j.rse.2006.02.023
Hoffman D L, Franke G R,1986. Correspondence analysis:graphical representation of categorical data in marketing research[J]. Journal of Marketing Research,23:213-217.
Holborn P G, Nolan P F, Golt J, Townsend N. (2002) Fires in workplace premise: risk data. Fire Safety Journal,37(3):303-327. doi:10.1016/S0379-7112(01)00055-8 Jacques Benasseni (1993) Perturbational aspects in correspondence analysis. Computational Statistics & Data Analysis,15(4):393-410. doi:10.1016/0167-9473(93)90172-P
Lu Lu, Chen Peng, Jiping Zhu, Kohyu Satoh, Deyong Wang and Yunlong Wang (2013) Correlation Between Fire Attendance Time and Burned Area Based on Fire Statistical Data of Japan and China. Fire Technology 19(2). doi:10.1007/s 10694-012-0306-5.
Marco Diana, Cristina Pronello (2010) Traveler segmentation strategy with nominal variables through correspondence analysis. Transport Policy 17 (3):183-190. doi:10.1016/j.tranpol.2010.01.005
Masood A. Badri, Amr K. Mortagy, Colonel Ali Alsayed. A Multi-Objective Model for Locating Fire Stations [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH,1998:243-260.
N. Challands (2010) The Relationships Between Fire Service Response Time and Fire Outcomes. Fire technology 46(3):665-676. doi:10.1007/s10694-009-0111-y Nadia Sourial, Christina Wolfson, Bin Zhu, Jacqueline Quail, John Fletcher, Sathya Karunananthan, Karen Bandeen-Roche, Francois Beland, Howard Bergman (2010) Correspondence analysis is a useful tool to uncover the relationships among categorical variables. Journal of Clinical Epidemiology 63(6):638-646. doi:10.1016/j.jclinepi.2009.08.008
P.G. Holborn, P.F. Nolan, J. Golt (2004) An analysis of fire sizes, fire growth rates and times between events using data from fire investigations. Fire Safety Journal 39(6):481-524. doi:10.1016/j.firesaf.2004.05.002
S. Sardqvist, G. Holmstedt (2000) Correlation between Fire fighting Operation and Fire Area:Analysis of Statistics. Fire Technology 36(2):109-130. doi:10.1023/A:1015450308130
高伟(2009).不同功能建筑火灾荷载分布规律分析[D].硕士学位论文,中国科学技术大学.
陆松.2012.中国群死群伤火灾时空分布规律及影响因素研究[D].博士学位论文.中国科学技术大学.
彭晨.2010.消防响应时间统计规律及其与城市火灾规模相关性研究[D].硕士论文.中国科学技术大学.
Barnett, C. R. (1984) Pilot Fire Load Survey Carried Out for the New Zealand Fire Protection Association. MacDonald Barnett Partners, Auckland.
Eric J. Beh, Rosaria Lombardo, Biagio Simonetti (2011) A European perception of food using two methods of correspondence analysis. Food Quality and Preference 22(2):226-231. doi:10.1016/j.foodqual.2010.10.001
Fontana M, Favre JP, Fetz C. A survey of 40,000 building fires in Switzerland. Fire Safety Journal 1999; 32(2):137-58. doi:10.1016/S0379-7112(98)00034-4
Halil Ibrahim Cakir, Siamak Khorram, Stacy A.C. Nelson (2006) Correspondence analysis for detecting land cover change. Remote Sensing of Environment 102(3-4):306-317. doi:10.1016/j.rse.2006.02.023
Holborn P G, Nolan P F, Golt J, Townsend N. (2002) Fires in workplace premise: risk data. Fire Safety Journal,37(3):303-327. doi:10.1016/S0379-7112(01)00055-8 http://baike.baidu.com/view/237460.htm
Jacques Benasseni (1993) Perturbational aspects in correspondence analysis. Computational Statistics & Data Analysis,15(4):393-410. doi:10.1016/0167-9473(93)90172-P
Lu Lu, Chen Peng, Jiping Zhu, Kohyu Satoh, Deyong Wang and Yunlong Wang (2013) Correlation Between Fire Attendance Time and Burned Area Based on Fire Statistical Data of Japan and China. Fire Technology 19(2). doi:10.1007/s 10694-012-0306-5.
Marco Diana, Cristina Pronello (2010) Traveler segmentation strategy with nominal variables through correspondence analysis. Transport Policy 17 (3):183-190. doi:10.1016/j.tranpol.2010.01.005
N. Challands (2010) The Relationships Between Fire Service Response Time and Fire Outcomes. Fire technology 46(3):665-676. doi:10.1007/s10694-009-0111-y
Nadia Sourial, Christina Wolfson, Bin Zhu, Jacqueline Quail, John Fletcher, Sathya Karunananthan, Karen Bandeen-Roche, Francois Beland, Howard Bergman (2010) Correspondence analysis is a useful tool to uncover the relationships among categorical variables. Journal of Clinical Epidemiology 63(6):638-646. doi:10.1016/j.jclinepi.2009.08.008
P.G. Holborn, P.F. Nolan, J. Golt (2004) An analysis of fire sizes, fire growth rates and times between events using data from fire investigations. Fire Safety Journal 39(6):481-524.doi:10.1016/j.firesaf.2004.05.002
Ramachandran G. (1972) Extreme value theory and fire losses:further results. Fire Research Note 910, Fire Research Station, Borehamwood.
Ramachandran G. (1975) Extreme order statistics in large samples from exponential type distributions and their application to fire loss. In:Patil GP, Kotz S, Ord JK, editors. Statistical distributions in scientific work, Dordrecht:D. Reidel 2:355-67.
Ramachandran, G. (1998) Economics of Fire Protection. Taylor & Francis.
Robertson, A. F., Gross, D. (1970) Fire Load, Fire Severity, and Fire Endurance. ASTM Special Tech. Pub.464:3-29.
S. Sardqvist, G. Holmstedt (2000) Correlation between Firefighting Operation and Fire Area:Analysis of Statistics. Fire Technology 36(2):109-130. doi:10.1023/A:1015450308130
Shpilberg DC (1977) The probability distribution of fire loss amount. Journal of Risk and Insurance 44(1):103-15. http://www.jstor.org/discover/10.2307/251860
Song Lu, Canjun Liang, Weiguo Song, Heping Zhang (2013) Frequency-size distribution and time-scaling property of high-casualty fires in China:Analysis and comparison. Safety Science 51(1):209-216. doi:10.1016/j.ssci.2012.07.001
SUNIL KUMAR, C.V.S.KAMESWARA RAO (1995) Fire Load in Residential Buildings. Building and Environment,30(2):299-305. doi:10.1016/0360-1323(94)00043-R
W.G. Song, H.P. Zhang, T. Chen, W.C. Fan (2003) Power-law distribution of city fires. Fire Safety Journal 38(5):453-465. doi:10.1016/S0379-7112(02)00084-X.
Wang J H, Xie S, Sun J H (2011) Self-organized criticality judegement and extreme statistics analysis of major urban fires[J]. Chinese Sci Bull,56:567-572.
包群,彭水军.经济增长与环境污染：基于面板数据的联立方程估计[J].世界经济,2006,(11)：48-58.
高铁梅.计量经济分析方法与建模[M].北京：清华大学出版社,2009年.
李敏,陈胜可.EViews统计分析与应用[M].北京：电子工业出版社.2011
史代敏,谢晓燕.应用时间序列分析[M].北京：高等教育出版社.2011
时少英,刘式达,付遵涛,刘式适,梁福明,辛国君,李百炼.天气和气候的时间序列特征分析[J].地球物理学报,2005,48(2)：259-264.
魏武维.时间序列分析—单变量和多变量方法(第二版)[M].北京：中国人民大学出版社.2009
徐波.2012.经济发展及气候变化对中国城市火灾时空变化的宏观影响[D].博士论文,南京大学.
易丹辉.时间序列分析：方法与应用[M].北京：中国人民大学出版社.2011
于俊年.计量经济学软件—EViews的使用(第二版)[M].北京：对外经济贸易大学出版社.2012
郑红阳.2010.受气象因子驱动的火灾系统标度性研究[D].博士论文.中国科学技术大学.
A. Ohgai.Y. Gohnai.S. Ikaruga.M. Murakami, K. Watanabe.Cellular automata modeling for fire spreading as a tool to aid community-based planning for disaster mitigation [J].2004:193-209
Baltagi, B. H. Econometric analysis of panel data.2nd [M]. Chichester, UK, Wiley.2001
Chang, H. S. Study of the exploration of fire occurrence spatial characteristics and impact fectors- a case study of Tainan city [A],14th International Conference on Urban Planning and Regional Development in the Information Society, Spain: Sitges.2009
Clar, S., Drossel, B., Schenk, K., Schwabl, F.,1999. Self-organized criticality in forest-fire models. Physica A 266,153-159.
Dmowska R, Saltzman B. Advance in Geophysics:Long- range Persistence in Geophysical Time Series. San Diego, London,Boston, New York, Sydney, Tokyo, Toronto:Academic Press,1999,114-16
Elhorst, J. P. Specification and estimation of spatial panel data models[J]. International regional science review,2003, (3):244-268.
Engle, Rober F., C. W. J. Granger. Co-integration and error corrction: repressenation, estimation and testing. Econimetrica,1987,55:251-276.
Hardi, Kaddour. Testing for stationarity in heterogeneous panel data[J]. Econometric Journal,2000, (3):148-161
Hongyang Zheng, Weiguo Song, Kohyu Satoh. Detecting long-range correlations in fires equences with Detrended fluctuationanalysis. Physica A,389 (2010),837-842. doi:10.1016/j.physa.2009.10.022
Im K. S., Pesaran M. H., Y.Shin. Testing for unit roots in heterogeneous panels [J].Journal of Econometrics,2003,115:53-74
Jonathan Corcoran, Gary Higgs, Chris Bainsdon, Andrew Ware.The use of comaps to explore the spatial and temporal dynamics of fire incidents:a case study in South Wales,United Kingdom[J].The Professional Geographer,2007,59(4):521-536.
Kao C. Spurious regression and residua based tests forcointegration in panel data [J]. Journal of Econometrics,1999,90:1-44.
Koop, G., Pesaran, M.H., Potter, S.M. Impulse response analysis in nonlinear ultivariate models [J]. Journal of Ecnometrics,1996,74:119-147
Levin A., Lin C. F, Chu. Unit root tests in panel data:asymptotic and finite-sample lewis, properties [J]. Journal of Econometrics,2002,108:1-24.
Luciana Ghermandi, Rosa Lasaponara, Luciano Telesca. Intra-annual time dynamical patterns of fire sequences observed in Patagonia (Argentina). Ecological Modelling,221(2010):94-97. doi:10.1016/j.ecolmodel.2008.11.011
Luciano Telesca, Weiguo Song. Time-scaling properties of city fires[J]. Chaos, Solitons & Fractals 44 (2011) 558-568. doi:10.1016/j.chaos.2011.05.001
Malamud, B.D., Millington, J.D.A., Perry, G.L.W.,2005. Characterizing wild fire regimes in the USA. PNAS 102,4694-4699.
Malamud, B.D., Morein, G., Turcotte, D.L.,1998. Forest fires:an example of self-organized critical behaviour. Science 281,1840-1842
Mauro Costantini, Luciano Gutierrez. Simple panel unit root tests to detect changes in persistence. Economics Letters,2007,96(3):363-368.
Song Lu, Canjun Liang, Weiguo Song, Heping Zhang. Frequency-size distribution and time-scaling property of high-casualty fires in China:Analysis and comparison. Safety Science.51(2013):209-216
Spyratos V, Bourgeron PS, Ghil M. Development at the wildland-urban interlace and the mitigation of forest-fire risk [J]. Proc Natl Acad Sci USA,2007,104(36): 14272-14276
Stem D. I. The rise and fall of the environmental Kuznets curve [J]. World Development,2004,32(8):1419-1439.
World Fire Statisties. Information bulletin of world fire statisties centre [M]. International Assoeiation for the Study of Insurance Eeonomics,2007-2010.
陈驰,任爱珠.消防站布局优化的计算机方法[J].清华大学学报.2003,43(10)：1390-1393.
何建敏.应急管理与应急系统—选址、调度与算法[M].北京：科学出版社,2005
姜丽珍,刘茂.森林火灾灭火物资优化配置模型研究[J].中国公共安全(学术版),2008,(1)：97-100.
王波.基于均衡选择的应急物资调度决策模型研究[J].学理论,2010,(17)：40-43.
王炜,刘茂,王丽.基于马尔科夫决策过程的应急资源调度方案的动态优化[J].南开大学学报(自然科学版),2010,43(3)：18-23.
武小悦.决策分析理论[M].北京：科学出版社,2010
杨继君,吴启迪,程艳,许维胜.面向非常规突发事件的应对方案序贯决策[J].同济大学学报(自然科学版).2010.38(4)：619-624.
张靖,申世飞,杨锐.基于偏好序的多事故应急资源调配博弈模型[J].清华大学学报(自然科学版),2007,47(12)：2172-2175.
中华人民共和国消防法,2008.
周晓猛,姜丽珍,张云龙.突发事故下应急资源优化配置定量模型研究[J].安全与环境学报,2007,7(6)：113-115.
BSI DD 240. Fire safety engineering in buildings. London:British Standards Institution; 1997.
Chen Peng.The Statistics Law of Fire Response Time and Its Correlation with the Scale of Urban Fire[D].University of Sci&Tec of China,2010.30,55-56.
Custer RLP. Fire and arson investigation. In:Cote AE, Linville JL, editors. Fire protection handbook.18th ed.. Quincy, MA:National Fire Protection Associatio; 1997.
Fiedrich F, GehbauerF,RickersU. Optimized resource allocation for emergency response after earthquake disasters[J]. Safety Science,2000, (25):41-57.
Fiorucci P, Gaetani F, Minciardi R, et a.l. Real time optimal resource allocation in natural hazard manage-ment[A]. Proceedings of IEMSS 2004-Complexity and Integrated Resource Management[C]. Osnabreuck,2004.14-17.
Han Xin,Li Jie, Shen Zuyan.Non-autonomous coloured Petri net-based methodology for the dispatching process of urban fire-fighting.Fire Safety Journal[J],35 (2000) 299-325.
Helly W. Urban System Models [M]. New York:Academic Press,1975.
N. Challands. The Relationships between Fire Service Response Time and Fire Outcomes, Fire technology 46(2010) 665-676.
Nelson HE. An engineering analysis of the early stages of fire development-the fire at the Du Pont Plaza hotel and casino December 31 1986. US National Bureau of Standards, NBSIR 87-3560.Washington DC,1987.
NFPA 92B. Guide for smoke management systems in malls, atria and large areas. Quincy, MA:National Fire Protection Association; 1991.
P. G. Holborn, P. F. Nolan, J. Golt, An Analysis of Fire Sizes, Fire Growth Rates and Times between Events using Data from Fire Investigations, Fire Safety Journal 39(2004)481-524.
Rashmi S. Shetty. An Event Driven Single Game Solution For Resource Allocation In A Multi-Crisis Environment [D].Tampa:Univ South Florida.2004.
Stefan Sardqvist, Goran Holmstedt.Correlations between firefighting operations and fire area:analysis of statistics. Fire Technology 2000;36(2):109-130.
Stefan Svensson.A study of tactical patterns during fire fighting operations.Fire Safety Journal[J].37 (2002) 673-695.
Upavan Gupta. Multi-Event Crisis Management Using Non-Cooperative Repeated Games [D]. Tampa:Univ South Florida,2004.
Wright MS, Archer KHL. Further Development of Risk Assessment Toolkits for the UK Fire Service:technical note—financial loss model. UK Home Office Technical Note C8155/D99052,March 1999.
陈志宗,尤建新.2006.重大突发事件应急救援设施选址的多目标决策模型[J].管理科学,19(4)：10-14.
陈子锦,王福亮,陆守香,方兆本.2007.我国火灾统计数据的聚类分析[J].中国工程科学,9(1)：86-89.
冯海江.2010.地震灾害救援中的应急物资分配优化研究[D]：硕士.上海：上海交通大学船舶海洋与建筑工程学院.
付丽华,张瑞芳,陆松,龙胜刚.2008.火灾事故发展趋势预测方法的应用研究[J].火灾科学,17(2)：.
郭艳丽.2001.群死群伤恶性火灾的特点及预防[J].武警学院学报,17(6)：34-40.
贾传亮,池宏等.2005.基于多阶段灭火过程的消防资源布局模型[J].系统工程,23(9)：12-15.
贾水库,温晓虎,蒋仲安,林大建.2008.灰色系统理论在城市火灾事故预测中的应用[J],中国安全生产科学技术,4(6).
姜丽珍,刘茂.2008.森林火灾灭火物资优化配置模型研究[J].中国公共安全,3：97-100.
李海江.2010.2000-2008年全国重特大火灾统计分析[J].火灾科学,16(1)：
李杰,张靖岩,郭建中,姜亢.2012.基于聚类分析的我国火灾空间分布研究[J].中国安全生产科学技术,8(2).
李勇,胡双启.2009.灰色Elman神经网络在火灾事故预测中的应用研究[J].中国安全生产科学技术,19(3)：.
刘春林,盛昭翰,何建敏.1999.基于连续消耗应急系统的多出救点选择问题[J].管理工程学报,13(3)：13-16.
陆松.2012.中国群死群伤火灾时空分布规律及影响因素研究[D].博士论文. 中国科学技术大学.
马锐.2005.我国群死群伤特大火灾研究[D].重庆：重庆大学
聂高众,高建国,苏桂武,等.2001.地震应急救助需求的模型化处理-来自地震震例的经验分析[J].资源科学,23(1)：69-76.
彭晨.2010.消防响应时间统计规律及其与城市火灾规模相关性研究[D].硕士论文.中国科学技术大学.
吴赤蓬,等.2001.我国火灾发生情况的聚类分析[J],预防医学文献信息,7(1)：4-7.
徐波.2012.经济发展及气候变化对中国城市火灾时空变化的宏观影响[D].博士论文,南京大学.
杨继君,许维胜,冯云生,等.2009.基于多模式分层网络的应急资源调度模型[J].计算机工程,35(10)：21-24.
杨立中,江大白.2003.中国火灾与社会经济因素的关系[J].中国工程科学,5(2)：62-67.
张玲,黄钧.2008.基于场景分析的应急资源布局模型研究[J].中国管理学,16：164-167.
张毅,郭晓汾,王笑风.2006.应急救援物资车辆运输线路的选择[J].安全与环境学报,6(3)：51-53.
张毅.2007.基于自然灾害的救灾物资物流决策理论与方法研究[D].西安：长安大学汽车学院.
郑红阳.2010.受气象因子驱动的火灾系统标度性研究[D].博士论文.中国科学技术大学.
周晓猛,姜丽珍,张云龙.2007.突发事故下应急资源优化配置定量模型研究[J].安全与环境学报,7(6)：113-115.
AVIV Y.2003. A time-series framework for supply chain inventory management[J].Operations Research,51 (2):210-227
BADRI M A, MORTAGY A K, ALSAYED C A.1998. A multi-objective model for locating fire stations[J]. European Journal of Operational Research,110:243-260.
BOX G E P, JENKINS G M, REINSEL G C.1994. Time series analysis: forecasting and control [J]. Prentice Hall, Englewood Cliffs, NJ.
CHANG M S, TSENG Y L, CHEN J W.2007. A scenario planning approach for the flood emergency logistics preparation problem under uncertainty[J]. Transportation Research Part E,43,737-754.
DARBRA R M, ELJARRAT E, BARCEL D.2008. How to measure uncertainties in environmental risk assessment[J]. Trends in Analytical Chemistry, 27(4):377-385.
Duncanson M, Woodward A, Reid P.2002. Socioeconomic deprivation and fatal unintentional domestic fire incidents in New Zealand 1993-1998 [J]. Fire Safety Journal,37(2):165-179.
Gunther P.1981. Fire cause patterns for different neighborhoods in Toledo, OH[J] Fire Journal 75:52-58.
Jennirtgs C. R.1996. Urban residential fires:all empirical analysis of building stock and socioeconomic characteristics for Memphis[D].
JIA H Z, ORD EZ F, DESSOUKY M M.2007. A modeling framework for facility location of medical services for large-scale emergencies [J]. HE Transactions, 39(1):41-55.
Karter M. J. AlLan D.1978. The effect of demographics on fire rates[J] Fire Journal,72 (1):53-65.
Linet Ozdamar, Ediz Ekinci, Beste Ku ukyazici.2004. Emergency Logistics Planning in Natural Disasters [J]. Annals of Operations Research,129:218-219.
Malamud BD.1998. Forest fires:An example of self-organized critical behavior [J].Science.281(5384):1840-1842.
Schaeman P. et al.1977. Procedures for Improving The Measurement of Local Fire Protection Effectiveness [M]. Bost:National Fire Protection Association.53-71
SHEU J B.2010. Dynamic relief-demand management for emergency logistics operations under large-scale disasters [J]. Transportation Research Part E,46:1-17.
WEI W W S.1990. Time series analysis:univariate and multivariate methods [M]. New York:Addison-Wesley Publishing Company.
Yang Lizhong, Zhou Xiaodong, Deng Zhihua, et al.2002. Fire situation and fire characteristic analysis based on fire statistics of China [J]. Fire Safety J,37 (8):785-802.
Yi W, Kumar A.2007. An colony optimization for disaster relief operations[J]. Transportation Research PartE:Logistics and Transportation Review,43(6):660-672.
《国务院关于进一步加强消防工作的意见》(国发(2006)15号)
《中华人民共和国消防法》.2008.
2009中国消防年鉴.江西部分.
2011中国消防年鉴.江西部分.
陈驰,任爱珠.消防站布局优化的计算机方法[J].清华大学学报.2003,43(10)：1390-1393.
城市消防站建设标准.2006.
丰国炳等.用模糊数学建立消防灭火救援力量合理配置优选模型的研究[J].消防技术与产品信息,2002.
公安消防部队执勤战斗条令.
龚啸.城市消防规划关键技术研究[D].长沙：中南大学.2007：3-8.
胡传平.区域火灾风险评估与灭火救援力量布局规划研究[D].上海：同济大学.2006：95-101.
金磊,徐德蜀,罗云.中国21世纪安全减灾战略[M].郑州：河南大学出版社.1998.
潘京.我国城市消防安全存在的问题与对策研究[D].重庆重庆大学.2005.
彭晨,朱霁平,佐藤晃由.消防第一出动时间的频次分布规律研究.火灾科学.2010,19(1)：33-37.
彭晨.2010.消防响应时间统计规律及其与城市火灾规模相关性研究[D].硕士论文.中国科学技术大学.
深入贯彻“二十公”精神全面推进消防“161工程”工作会议讲话.
王荷兰,杨君涛,吴军,吴美文.基于案例的我国消防响应问题及对策消防科学与技术.2010,229(2)：163-165.
叶子才.城市消防设施建设研究[D].重庆：重庆大学.2005：4-5.
俞艳,郭庆胜,何建华.顾及地理网络特征的城市消防站布局渐进优化[J].武汉大学学报.2005,30(4)：333-336.
张锐.2011.基于火灾统计的灭火救援时效性分析[D].工程硕士论文.中国科学技术大学.
周志军.城市灭火救援首战力量时效性分析与研究[J].消防科学与技术,2009,28(5)：354-357.
Clausen S E.1998. Applied correspondence analysis:an introduction[M]. Sage University Papers Series on Quantitative Applications in the Socia 1 Science,07-121. Sage Publications, Thousand Oaks, CA.
David Torv, George Hadjisophocleous,2 Matthew B. Guenther,, Gordon Thomas. Estimating Water Requirementsfor Firefighting Operations Using FIER Asystem. Fire Technology.2001,37:235-262
Eric J. Beh, Rosaria Lombardo, Biagio Simonetti (2011) A European perception of food using two methods of correspondence analysis. Food Quality and Preference 22(2):226-231. doi:10.1016/j.foodqual.2010.10.001
Halil Ibrahim Cakir, Siamak Khorram, Stacy A.C. Nelson (2006) Correspondence analysis for detecting land cover change. Remote Sensing of Environment 102(3-4):306-317. doi:10.1016/j.rse.2006.02.023
Hoffman D L, Franke G R,1986. Correspondence analysis:graphical representation of categorical data in marketing research[J]. Journal of Marketing Research,23:213-217.
Holborn P G, Nolan P F, Golt J, Townsend N. (2002) Fires in workplace premise: risk data. Fire Safety Journal,37(3):303-327. doi:10.1016/S0379-7112(01)00055-8 Jacques Benasseni (1993) Perturbational aspects in correspondence analysis. Computational Statistics & Data Analysis,15(4):393-410. doi:10.1016/0167-9473(93)90172-P
Lu Lu, Chen Peng, Jiping Zhu, Kohyu Satoh, Deyong Wang and Yunlong Wang (2013) Correlation Between Fire Attendance Time and Burned Area Based on Fire Statistical Data of Japan and China. Fire Technology 19(2). doi:10.1007/s 10694-012-0306-5.
Marco Diana, Cristina Pronello (2010) Traveler segmentation strategy with nominal variables through correspondence analysis. Transport Policy 17 (3):183-190. doi:10.1016/j.tranpol.2010.01.005
Masood A. Badri, Amr K. Mortagy, Colonel Ali Alsayed. A Multi-Objective Model for Locating Fire Stations [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH,1998:243-260.
N. Challands (2010) The Relationships Between Fire Service Response Time and Fire Outcomes. Fire technology 46(3):665-676. doi:10.1007/s10694-009-0111-y Nadia Sourial, Christina Wolfson, Bin Zhu, Jacqueline Quail, John Fletcher, Sathya Karunananthan, Karen Bandeen-Roche, Francois Beland, Howard Bergman (2010) Correspondence analysis is a useful tool to uncover the relationships among categorical variables. Journal of Clinical Epidemiology 63(6):638-646. doi:10.1016/j.jclinepi.2009.08.008
P.G. Holborn, P.F. Nolan, J. Golt (2004) An analysis of fire sizes, fire growth rates and times between events using data from fire investigations. Fire Safety Journal 39(6):481-524. doi:10.1016/j.firesaf.2004.05.002
S. Sardqvist, G. Holmstedt (2000) Correlation between Fire fighting Operation and Fire Area:Analysis of Statistics. Fire Technology 36(2):109-130. doi:10.1023/A:1015450308130
高伟(2009).不同功能建筑火灾荷载分布规律分析[D].硕士学位论文,中国科学技术大学.
陆松.2012.中国群死群伤火灾时空分布规律及影响因素研究[D].博士学位论文.中国科学技术大学.
彭晨.2010.消防响应时间统计规律及其与城市火灾规模相关性研究[D].硕士论文.中国科学技术大学.
Barnett, C. R. (1984) Pilot Fire Load Survey Carried Out for the New Zealand Fire Protection Association. MacDonald Barnett Partners, Auckland.
Eric J. Beh, Rosaria Lombardo, Biagio Simonetti (2011) A European perception of food using two methods of correspondence analysis. Food Quality and Preference 22(2):226-231. doi:10.1016/j.foodqual.2010.10.001
Fontana M, Favre JP, Fetz C. A survey of 40,000 building fires in Switzerland. Fire Safety Journal 1999; 32(2):137-58. doi:10.1016/S0379-7112(98)00034-4
Halil Ibrahim Cakir, Siamak Khorram, Stacy A.C. Nelson (2006) Correspondence analysis for detecting land cover change. Remote Sensing of Environment 102(3-4):306-317. doi:10.1016/j.rse.2006.02.023
Holborn P G, Nolan P F, Golt J, Townsend N. (2002) Fires in workplace premise: risk data. Fire Safety Journal,37(3):303-327. doi:10.1016/S0379-7112(01)00055-8 http://baike.baidu.com/view/237460.htm
Jacques Benasseni (1993) Perturbational aspects in correspondence analysis. Computational Statistics & Data Analysis,15(4):393-410. doi:10.1016/0167-9473(93)90172-P
Lu Lu, Chen Peng, Jiping Zhu, Kohyu Satoh, Deyong Wang and Yunlong Wang (2013) Correlation Between Fire Attendance Time and Burned Area Based on Fire Statistical Data of Japan and China. Fire Technology 19(2). doi:10.1007/s 10694-012-0306-5.
Marco Diana, Cristina Pronello (2010) Traveler segmentation strategy with nominal variables through correspondence analysis. Transport Policy 17 (3):183-190. doi:10.1016/j.tranpol.2010.01.005
N. Challands (2010) The Relationships Between Fire Service Response Time and Fire Outcomes. Fire technology 46(3):665-676. doi:10.1007/s10694-009-0111-y
Nadia Sourial, Christina Wolfson, Bin Zhu, Jacqueline Quail, John Fletcher, Sathya Karunananthan, Karen Bandeen-Roche, Francois Beland, Howard Bergman (2010) Correspondence analysis is a useful tool to uncover the relationships among categorical variables. Journal of Clinical Epidemiology 63(6):638-646. doi:10.1016/j.jclinepi.2009.08.008
P.G. Holborn, P.F. Nolan, J. Golt (2004) An analysis of fire sizes, fire growth rates and times between events using data from fire investigations. Fire Safety Journal 39(6):481-524.doi:10.1016/j.firesaf.2004.05.002
Ramachandran G. (1972) Extreme value theory and fire losses:further results. Fire Research Note 910, Fire Research Station, Borehamwood.
Ramachandran G. (1975) Extreme order statistics in large samples from exponential type distributions and their application to fire loss. In:Patil GP, Kotz S, Ord JK, editors. Statistical distributions in scientific work, Dordrecht:D. Reidel 2:355-67.
Ramachandran, G. (1998) Economics of Fire Protection. Taylor & Francis.
Robertson, A. F., Gross, D. (1970) Fire Load, Fire Severity, and Fire Endurance. ASTM Special Tech. Pub.464:3-29.
S. Sardqvist, G. Holmstedt (2000) Correlation between Firefighting Operation and Fire Area:Analysis of Statistics. Fire Technology 36(2):109-130. doi:10.1023/A:1015450308130
Shpilberg DC (1977) The probability distribution of fire loss amount. Journal of Risk and Insurance 44(1):103-15. http://www.jstor.org/discover/10.2307/251860
Song Lu, Canjun Liang, Weiguo Song, Heping Zhang (2013) Frequency-size distribution and time-scaling property of high-casualty fires in China:Analysis and comparison. Safety Science 51(1):209-216. doi:10.1016/j.ssci.2012.07.001
SUNIL KUMAR, C.V.S.KAMESWARA RAO (1995) Fire Load in Residential Buildings. Building and Environment,30(2):299-305. doi:10.1016/0360-1323(94)00043-R
W.G. Song, H.P. Zhang, T. Chen, W.C. Fan (2003) Power-law distribution of city fires. Fire Safety Journal 38(5):453-465. doi:10.1016/S0379-7112(02)00084-X.
Wang J H, Xie S, Sun J H (2011) Self-organized criticality judegement and extreme statistics analysis of major urban fires[J]. Chinese Sci Bull,56:567-572.
包群,彭水军.经济增长与环境污染：基于面板数据的联立方程估计[J].世界经济,2006,(11)：48-58.
高铁梅.计量经济分析方法与建模[M].北京：清华大学出版社,2009年.
李敏,陈胜可.EViews统计分析与应用[M].北京：电子工业出版社.2011
史代敏,谢晓燕.应用时间序列分析[M].北京：高等教育出版社.2011
时少英,刘式达,付遵涛,刘式适,梁福明,辛国君,李百炼.天气和气候的时间序列特征分析[J].地球物理学报,2005,48(2)：259-264.
魏武维.时间序列分析—单变量和多变量方法(第二版)[M].北京：中国人民大学出版社.2009
徐波.2012.经济发展及气候变化对中国城市火灾时空变化的宏观影响[D].博士论文,南京大学.
易丹辉.时间序列分析：方法与应用[M].北京：中国人民大学出版社.2011
于俊年.计量经济学软件—EViews的使用(第二版)[M].北京：对外经济贸易大学出版社.2012
郑红阳.2010.受气象因子驱动的火灾系统标度性研究[D].博士论文.中国科学技术大学.
A. Ohgai.Y. Gohnai.S. Ikaruga.M. Murakami, K. Watanabe.Cellular automata modeling for fire spreading as a tool to aid community-based planning for disaster mitigation [J].2004:193-209
Baltagi, B. H. Econometric analysis of panel data.2nd [M]. Chichester, UK, Wiley.2001
Chang, H. S. Study of the exploration of fire occurrence spatial characteristics and impact fectors- a case study of Tainan city [A],14th International Conference on Urban Planning and Regional Development in the Information Society, Spain: Sitges.2009
Clar, S., Drossel, B., Schenk, K., Schwabl, F.,1999. Self-organized criticality in forest-fire models. Physica A 266,153-159.
Dmowska R, Saltzman B. Advance in Geophysics:Long- range Persistence in Geophysical Time Series. San Diego, London,Boston, New York, Sydney, Tokyo, Toronto:Academic Press,1999,114-16
Elhorst, J. P. Specification and estimation of spatial panel data models[J]. International regional science review,2003, (3):244-268.
Engle, Rober F., C. W. J. Granger. Co-integration and error corrction: repressenation, estimation and testing. Econimetrica,1987,55:251-276.
Hardi, Kaddour. Testing for stationarity in heterogeneous panel data[J]. Econometric Journal,2000, (3):148-161
Hongyang Zheng, Weiguo Song, Kohyu Satoh. Detecting long-range correlations in fires equences with Detrended fluctuationanalysis. Physica A,389 (2010),837-842. doi:10.1016/j.physa.2009.10.022
Im K. S., Pesaran M. H., Y.Shin. Testing for unit roots in heterogeneous panels [J].Journal of Econometrics,2003,115:53-74
Jonathan Corcoran, Gary Higgs, Chris Bainsdon, Andrew Ware.The use of comaps to explore the spatial and temporal dynamics of fire incidents:a case study in South Wales,United Kingdom[J].The Professional Geographer,2007,59(4):521-536.
Kao C. Spurious regression and residua based tests forcointegration in panel data [J]. Journal of Econometrics,1999,90:1-44.
Koop, G., Pesaran, M.H., Potter, S.M. Impulse response analysis in nonlinear ultivariate models [J]. Journal of Ecnometrics,1996,74:119-147
Levin A., Lin C. F, Chu. Unit root tests in panel data:asymptotic and finite-sample lewis, properties [J]. Journal of Econometrics,2002,108:1-24.
Luciana Ghermandi, Rosa Lasaponara, Luciano Telesca. Intra-annual time dynamical patterns of fire sequences observed in Patagonia (Argentina). Ecological Modelling,221(2010):94-97. doi:10.1016/j.ecolmodel.2008.11.011
Luciano Telesca, Weiguo Song. Time-scaling properties of city fires[J]. Chaos, Solitons & Fractals 44 (2011) 558-568. doi:10.1016/j.chaos.2011.05.001
Malamud, B.D., Millington, J.D.A., Perry, G.L.W.,2005. Characterizing wild fire regimes in the USA. PNAS 102,4694-4699.
Malamud, B.D., Morein, G., Turcotte, D.L.,1998. Forest fires:an example of self-organized critical behaviour. Science 281,1840-1842
Mauro Costantini, Luciano Gutierrez. Simple panel unit root tests to detect changes in persistence. Economics Letters,2007,96(3):363-368.
Song Lu, Canjun Liang, Weiguo Song, Heping Zhang. Frequency-size distribution and time-scaling property of high-casualty fires in China:Analysis and comparison. Safety Science.51(2013):209-216
Spyratos V, Bourgeron PS, Ghil M. Development at the wildland-urban interlace and the mitigation of forest-fire risk [J]. Proc Natl Acad Sci USA,2007,104(36): 14272-14276
Stem D. I. The rise and fall of the environmental Kuznets curve [J]. World Development,2004,32(8):1419-1439.
World Fire Statisties. Information bulletin of world fire statisties centre [M]. International Assoeiation for the Study of Insurance Eeonomics,2007-2010.
陈驰,任爱珠.消防站布局优化的计算机方法[J].清华大学学报.2003,43(10)：1390-1393.
何建敏.应急管理与应急系统—选址、调度与算法[M].北京：科学出版社,2005
姜丽珍,刘茂.森林火灾灭火物资优化配置模型研究[J].中国公共安全(学术版),2008,(1)：97-100.
王波.基于均衡选择的应急物资调度决策模型研究[J].学理论,2010,(17)：40-43.
王炜,刘茂,王丽.基于马尔科夫决策过程的应急资源调度方案的动态优化[J].南开大学学报(自然科学版),2010,43(3)：18-23.
武小悦.决策分析理论[M].北京：科学出版社,2010
杨继君,吴启迪,程艳,许维胜.面向非常规突发事件的应对方案序贯决策[J].同济大学学报(自然科学版).2010.38(4)：619-624.
张靖,申世飞,杨锐.基于偏好序的多事故应急资源调配博弈模型[J].清华大学学报(自然科学版),2007,47(12)：2172-2175.
中华人民共和国消防法,2008.
周晓猛,姜丽珍,张云龙.突发事故下应急资源优化配置定量模型研究[J].安全与环境学报,2007,7(6)：113-115.
BSI DD 240. Fire safety engineering in buildings. London:British Standards Institution; 1997.
Chen Peng.The Statistics Law of Fire Response Time and Its Correlation with the Scale of Urban Fire[D].University of Sci&Tec of China,2010.30,55-56.
Custer RLP. Fire and arson investigation. In:Cote AE, Linville JL, editors. Fire protection handbook.18th ed.. Quincy, MA:National Fire Protection Associatio; 1997.
Fiedrich F, GehbauerF,RickersU. Optimized resource allocation for emergency response after earthquake disasters[J]. Safety Science,2000, (25):41-57.
Fiorucci P, Gaetani F, Minciardi R, et a.l. Real time optimal resource allocation in natural hazard manage-ment[A]. Proceedings of IEMSS 2004-Complexity and Integrated Resource Management[C]. Osnabreuck,2004.14-17.
Han Xin,Li Jie, Shen Zuyan.Non-autonomous coloured Petri net-based methodology for the dispatching process of urban fire-fighting.Fire Safety Journal[J],35 (2000) 299-325.
Helly W. Urban System Models [M]. New York:Academic Press,1975.
N. Challands. The Relationships between Fire Service Response Time and Fire Outcomes, Fire technology 46(2010) 665-676.
Nelson HE. An engineering analysis of the early stages of fire development-the fire at the Du Pont Plaza hotel and casino December 31 1986. US National Bureau of Standards, NBSIR 87-3560.Washington DC,1987.
NFPA 92B. Guide for smoke management systems in malls, atria and large areas. Quincy, MA:National Fire Protection Association; 1991.
P. G. Holborn, P. F. Nolan, J. Golt, An Analysis of Fire Sizes, Fire Growth Rates and Times between Events using Data from Fire Investigations, Fire Safety Journal 39(2004)481-524.
Rashmi S. Shetty. An Event Driven Single Game Solution For Resource Allocation In A Multi-Crisis Environment [D].Tampa:Univ South Florida.2004.
Stefan Sardqvist, Goran Holmstedt.Correlations between firefighting operations and fire area:analysis of statistics. Fire Technology 2000;36(2):109-130.
Stefan Svensson.A study of tactical patterns during fire fighting operations.Fire Safety Journal[J].37 (2002) 673-695.
Upavan Gupta. Multi-Event Crisis Management Using Non-Cooperative Repeated Games [D]. Tampa:Univ South Florida,2004.
Wright MS, Archer KHL. Further Development of Risk Assessment Toolkits for the UK Fire Service:technical note—financial loss model. UK Home Office Technical Note C8155/D99052,March 1999.