The plight of missing children is particularly strenuous and sensitive for society at local, national and European levels, cutting across class, race, and age. A quarter million cases of missing children is reported in the EU annually, which are either parental abductions, stranger abductions, runaways, missing unaccompanied migrant minors, and generally lost, injured, or otherwise missing children. The problem of missing children is a complex, multi-faceted phenomenon, comprising legal, psychological and sociological aspects, which are complicated further due to the strong emotions from the close environment of the missing child. This paper presents the challenges missing children investigation and rescue currently faces, and proposes a solution that uses ICT, advanced analytics and collective intelligence, to achieve more rapid and effective resolutions. The proposed methodology leverages the untapped potential of open, social, and linked data to augment the background information of missing children, through multi-layer-personal, psychological, social and activity-profiling and predictive analytics, respecting and protecting privacy, and personal data. Using location-based mobile notifications that spread using geo-fencing, citizens close to the place a missing child was last seen or is more probable to be found become "social sensors" for the investigation, contributing and validating potential pieces of evidence. Through the EU-funded project ChildRescue, the proposed solution is currently at the last phase of its development and aims to be adopted by different voluntary organisations, according to their needs and the readiness of their systems and processes. The project's results are now piloted in missing children cases by organisations responsible for the Amber Alert, and the 116 000 pan-European hotline, as well as unaccompanied minors' cases supported by the Hellenic Red Cross. The resulting collaboration platform and mobile applications will be publicly launched in 2020.
The general idea of the AMBER/Child Alert System is that by broadcasting and distributing information about a missing child to the community, the public’s involvement can trigger critical feedback that would have otherwise been ignored. This feedback, in several cases, can be proved decisive in finding the missing child. Despite the efforts at country and European level to effectively address the issue of missing children, a number of key challenges remain open, including lack of location-focused distribution of alerts, insufficient capture and diffusion of information, and lack of a mechanism that uses and merges all available sources of information. The aim of this paper is to present a novel approach for handling such challenges through a data analytics platform and a mobile application available to all citizens. Using the active research fields of human mobility pattern analysis and machine learning, we show that missing children investigations, as well as search and rescue operations, can be actively supported and enhanced when multiple data sources are combined and analyzed.
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