Journal article
Predicting the locations of missing persons in China by using NGO data and deep learning techniques
International journal of digital earth, Vol.17(1), 2304076
12/31/2024
DOI: 10.1080/17538947.2024.2304076
Abstract
Missing person crimes can seriously affect the well-being of Chinese families, and missing person destination prediction can help to solve this problem. Using nongovernmental organization (NGO) data to predict the locations of missing persons by random forest (RF) model has made progress. However, studies using these data have ignored the mass of oral information. Recent studies have demonstrated the effectiveness of oral information in detecting missing persons, but the impact on destination prediction remains unexplored. Therefore, this study proposes a missing person prediction (MP-Net) framework to incorporate oral information into missing person destination prediction and quantitatively describe the effect of different word properties on the prediction. The results show that compared to the baseline RF model, the proposed framework achieves a higher recall rate (87.18%) in the location prediction of missing persons. According to a quantitative word analysis, verbs and nouns in oral information significantly contributed to location prediction. After adjectives that might cause adverse effects were removed, the stability of the model was improved considerably. Overall, the findings of the proposed model and quantitative word analysis can help police or NGOs collect descriptive information in a targeted manner and make more accurate predictions about the whereabouts of missing persons.
Details
- Title: Subtitle
- Predicting the locations of missing persons in China by using NGO data and deep learning techniques
- Creators
- Anning Dong - China University of GeosciencesYatao Zhang - Singapore-ETH CentreZijin Guo - China University of GeosciencesPeng Luo - Technical University of MunichYao Yao - Tokyo University of Information SciencesJialyu He - Hong Kong Polytechnic UniversityQia Zhu - China University of GeosciencesYing Jiang - China University of GeosciencesKailu Xiong - China University of GeosciencesQingfeng Guan - China University of Geosciences
- Resource Type
- Journal article
- Publication Details
- International journal of digital earth, Vol.17(1), 2304076
- DOI
- 10.1080/17538947.2024.2304076
- ISSN
- 1753-8947
- eISSN
- 1753-8955
- Publisher
- Taylor & Francis
- Grant note
- 2020B1212030009 / Guangdong-Hong Kong-Macau Joint Laboratory Program LBXT2023YB03 / Key Laboratory of Earth Surface System and Human-Earth Relations, Ministry of Natural Resources of China 2023YFB3906803 / National Key Research and Development Program of China "CUG Scholar" Scientific Research Funds at China University of Geosciences (Wuhan 42171466 / National Natural Science Foundation of China 2022034 / )
- Language
- English
- Date published
- 12/31/2024
- Academic Unit
- School of Earth, Environment, and Sustainability
- Record Identifier
- 9985219848702771
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