主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2023, Vol. 42 ›› Issue (4): 573-576.

• • 上一篇    下一篇

超大型城市火警次数月度分布影响因素分析研究

陈永胜1，钱顾荣1，施 楠2，钟兆宁1   

  1. （1.上海市消防救援总队，上海 200051； 2.上海社会科学院信息研究所，上海 200235）
  • 出版日期:2023-04-15 发布日期:2023-04-15
  • 作者简介:陈永胜（1970- ），男，上海市消防救援总队副总队长兼灭火救援指挥部部长，硕士，主要从事灭火救援方面的研究，上海市长宁区中山西路229号，200051。
  • 基金资助:
    ：上海市科学技术委员会科研计划项目（18JG0500500）

Analysis and research on influencing factors of monthly distribution of fire alarm frequency in super large cities

Chen Yongsheng1，Qian Gurong1，Shi Nan2，Zhong Zhaoning1   

  1. （1. Shanghai Fire and Rescue Brigade， Shanghai 200051，China； 2. Information Research Institute of Shanghai Academy of Social Sciences， Shanghai 200235， China）
  • Online:2023-04-15 Published:2023-04-15

摘要： 基于超大型城市月度火警数据，结合气候、经济等相关数据，构建火警次数月度分布影响因素模型，通过计数数据的负二项回归，分析气候、经济变量对火警发生的解释力。结果表明，在控制日历因素的前提下，降雨天数与工业总产值的自变量组合对月度火警次数有较好的解释力，气温、用电量等数据对火警次数重要性较低。在其他变量不变的条件下，降雨天数较月平均水平每增加1天，火警次数减少2.1%。工业总产值较月平均水平每增加1亿元，火警次数增加0.033%。节假日天数较月平均水平每增加1天，火警次数增加2.4%。

关键词: 火警次数, 月度分布, 经济因素, 气候因素, 负二项回归, 计数数据

Abstract: Based on the monthly fire alarm data of super large cities, combined with the relevant data of climate, economy and so on, this paper constructs the influencing factor model of the monthly distribution of the number of fires, and analyzes the explanatory power of climate and economic variables on the occurrence of fires through the negative binomial regression of counting data. The results show that on the premise of controlling calendar factors, the combination of independent variables of rainfall days and total industrial output value has a good explanatory power for the monthly fire alarm times, and the data of temperature, electricity consumption and so on are less important for the fire alarm number. Under the condition that other variables remain unchanged, the fire alarm decreases by 2.1% for each additional day of rainfall; For every 100 million yuan increase in the total industrial output value, the fire alarm will increase by 0.033%; For every increase in the number of holiday, the fire alarm will increase by 2.4%.

Key words: number of fire alarms, monthly distribution, economic factors, climatic factors, negative binomial regression, count data