2017
DOI: 10.4236/gep.2017.512009
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Analysis of Geographical Distribution of Missing Children Based on the Missing Children Mobile GIS Mutual Assistance System of China

Abstract: The Missing Children Mobile GIS Mutual Assistance System of China (MCMAS) is a mobile service software based on mobile GIS platform software, and it is committed to providing the most convenient and efficient system of personally mutual tracing services for missing children family and society. Relying on collaborative utilization of location-based service technology, face image intelligent recognition technology, cloud computing technology, public big data sharing technology, and mobile GIS technology, the MCM… Show more

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Cited by 4 publications
(2 citation statements)
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“…Over the last few years, there have been global improvements to the way in which missing persons are recorded, with a concomitant increase in the overall number of recorded missing persons (Ferguson & Soave, 2020;Gong et al, 2017;National Crime Agency, 2016;National Crime Information Center-FBI, 2017;Stevenson & Thomas, 2018;Todorovi c & Butorac, 2017). In Spain, the Spanish National Centre of Missing Persons (CNDES), which depends on the Spanish Ministry of Interior, is the central authority in charge of the effective and continuous coordination of the "Sistema de Personas Desaparecidas y Restos Sin Identificar" (PDyRH system).…”
Section: Introductionmentioning
confidence: 99%
“…Over the last few years, there have been global improvements to the way in which missing persons are recorded, with a concomitant increase in the overall number of recorded missing persons (Ferguson & Soave, 2020;Gong et al, 2017;National Crime Agency, 2016;National Crime Information Center-FBI, 2017;Stevenson & Thomas, 2018;Todorovi c & Butorac, 2017). In Spain, the Spanish National Centre of Missing Persons (CNDES), which depends on the Spanish Ministry of Interior, is the central authority in charge of the effective and continuous coordination of the "Sistema de Personas Desaparecidas y Restos Sin Identificar" (PDyRH system).…”
Section: Introductionmentioning
confidence: 99%
“…Because the kernel density estimation method does not use the prior knowledge of data distribution and does not attach any assumptions to the data distribution, it is a method to study the characteristics of data distribution from the data sample itself (Gong et al, 2017). So that the kernel density analysis algorithm (Fu, Li, Yang et al, 2015;Shen & Guo, 2014) is used to analyze the urban management cases data.…”
Section: Methodsmentioning
confidence: 99%