地球信息科学学报 ›› 2017, Vol. 19 ›› Issue (7): 886-894.doi: 10.3724/SP.J.1047.2017.00886

• 地球信息科学理论与方法 • 上一篇    下一篇

失散人员时空信息模糊匹配模型

周文娟1(), 张明锋1,2,3,*(), 林广发1,2,3   

  1. 1. 福建师范大学地理研究所, 福州 350007
    2. 福建省陆地灾害监测评估工程技术研究中心, 福州 350007
    3. 海西地理国情动态监测与应急保障研究中心, 福州 350007
  • 收稿日期:2016-08-30 修回日期:2017-02-28 出版日期:2017-07-10 发布日期:2017-07-10
  • 通讯作者: 张明锋 E-mail:giszwjer@163.com;totofeng@163.com
  • 作者简介:

    作者简介:周文娟(1993-),女,福建永安人,硕士生,主要从事自然灾害GIS应用方面研究. E-mail:giszwjer@163.com

  • 基金资助:
    福建省公益类科研院所专项项目(2015R1034-1);福建省测绘地理信息局科技资助项目(2015JX03)

A Fuzzy Matching Model of Spatial-temporal Information of Dispersed Person

ZHOU Wenjuan1(), ZHANG Mingfeng1,2,3,*(), LIN Guangfa1,2,3   

  1. 1. Institute of Geography, Fujian Normal University, Fuzhou 350007, China
    2. Fujian Provincial Engineering Research Center for Monitoring and Assessing Terrestrial Disasters, Fuzhou 350007, China
    3. Research Center for National Geographical Condition Monitoring and Emergency Support in the Economic Zone on the West Side of the Taiwan Strait, Fuzhou 350007, China
  • Received:2016-08-30 Revised:2017-02-28 Online:2017-07-10 Published:2017-07-10
  • Contact: ZHANG Mingfeng E-mail:giszwjer@163.com;totofeng@163.com

摘要:

失散人员时空信息数量多、失散信息地点的收集和查询较复杂,现有的网络寻亲平台虽具有信息采集快,应用普及范围广的特点,但对于失散人员的信息管理较分散,缺乏结合时间范畴和空间范畴的分析。本文在失散人员属性信息查询的基础上,针对失散信息的不准确性和模糊性,对不同失踪年龄段人员进行记忆模糊度分析,并结合汉语言分区以及模糊时空范围设置阈值和权重,建立失散人员时空信息模糊匹配模型。该模型根据失散孩子姓名、性别、血型、出生时间、失踪时间、失踪地点、方言口音及失踪年龄段的模糊特征等影响因子,综合计算出失散人员之间的信息匹配指数;并利用时间地理学方法设计了模型的时空修正方法,对匹配结果的时空可达范围是否存在交集进行了检验。案例数据验证结果表明,该模型能综合考虑已知的失散人员匹配指标项,可筛选出匹配程度较高的信息。

关键词: 模糊匹配, 失散人员, 方言分区, 时空信息, 时间地理学

Abstract:

In recent years, a large number of lost persons have aroused the attention of all sectors of society because the collection and query of information is not easy. The network tracing platform is fast in information acquisition and has widely used in the application. However, the information management of lost persons are scattered, and it is insufficient in the spatial and temporal category analysis. To solve the problems of the inaccuracy and ambiguity of information, we made the memory fuzziness analysis of different age groups of lost persons based on the query of their attribute information. Then, combining with the partition of Chinese language and the fuzzy range of space and time, we set threshold and weight for matching algorithm. Finally, we set up the fuzzy matching model for spatial-temporal information of lost persons. Considering several characteristics of the lost people information such as names, gender, blood types, date of birth, missing time, missing place, dialect accent and missing age, we computed the information matching index among the lost persons. In addition, we used the time geography method to design the time correction method of the model and we also verified the intersection of spatiotemporal reachable range of matching results. The results of case verification indicated that the model can consider the known items of matching index and select the information that has higher matching degree.

Key words: fuzzy matching, lost persons, dialect division, spatiotemporal information, time-geography