Predicting the locations of missing persons in China by using NGO data and deep learning techniques
Creators: name: Yao, Yao
name: Dong, Anning
name: Zijin, Guo
name: Zhu, Qia
name: Jiang, Ying
name: Xiong, Kailu
Publisher: name: figshare
Year: 2023
Subjects: classificationCode: 440699
subject: Human geography not elsewhere classified

description: <b>The MP-Net Model</b>This project is linked to a paper, entitled “<b>Predicting the locations of missing persons in China based on NGO data and deep learning techniques</b>”, which propose a TextRNN-based Missing Person Prediction Network (MP-Net) framework that uses deep learning methods to extract valuable clues about missing persons from oral information and other basic attributesThe project includes both the data and codes that support the MP-Net model.<b>Content</b><b>In the folder ' baobeihuijia_dataset ', there are three files:</b><b>dataset_generate.ipynb</b>: This file is the script file that generates the datasets. The data permutation in paper 2.2.2 is executed by this file, and the final six datasets are generated and saved in the "<b>data</b>" file.<b>data</b>: This folder contains all the datasets generated by <b>dataset_generate.ipynb</b>. Each line of data consists of words ranging from 14 to 128 bits, where the first 9 words are time information, 10 to 11 bits are auxiliary information, 12 to 14 bits are location information, and 15 to 128 bits of information are all oral information. Four files are included under each folder, which are：<br><b>class-each.txt</b>：Correspondence between city names and numbers. is used in the softmax classification of the model. Where the row number indicates the number corresponding to the city.<b>dev-1.txt</b>：Validation set, occupying 1/10 of the total data.<b>test-1.txt</b>：Test set, occupying 2/10 of the total data<b>train-1.txt</b>：Training set, occupying 7/10 of the total datathe mapping between the folder names and the data types in paper 2.2.2 is<br>Data type A: folder <b>'raw/'</b>Data type B: folder <b>'hanlp/'</b>Data type C1: folder <b>'vv-mix/'</b>Data type C2: folder <b>'adj-mix/'</b>Data type C3: folder <b>'nn-mix/'</b>Data type D: floder <b>'location_vv_nn-mix/'</b><b>vocab.pkl</b>: Indicates the correspondence between the words and the look up table in embedding. The file is obtained by counting all words using the dictionary.<b>saved_dict</b>: This folder contains the checkpoint of MP-Net training results, which are all the optimal training results of above six datasets. The names of the files correspond to the names of the datasets one by one.<b>In the folder ' models ', there are two files:</b><b>TextRNN.py</b>：This python script is the code for the TextRNN model that will be applied of MP-Net in <b>run_model.py</b>.<b>TextRNN_visual.py</b>：This python script is the code of the improved TextRNN model, which will be applied in <b>weight_cal.py</b> to quantitatively calculate the weights of the effects of different words on the prediction results.<b>In the folder ' temporary_data ', there are two files:</b><b>baby_word_type_list_dic.pickle</b>: This file records the type of each vocabulary. The type include nouns (containing location nouns and other nouns), verbs and adjectives.<b>textrnn_weight.pickle</b>: This file records the values obtained in the textrnn model for each word. It created by <b>weight_cal.py</b>, and facilitates the calculation of weights for vocabulary analysis.<b>This project also contains 4 python scripts. The core of the program lies in two python scripts:</b><b>run_model.py</b>: The script is the main method of MP-Net method. By using TextRNN model, the missing population prediction model is implemented to oral information.<b>weight_cal.py</b>: The script calculates the weight of each word through the g(x) global gate of the TextRNN model. The weights are saved in the <b>textrnn_weight.pickle</b>.<b>cal_recall_in_micro_and_macro.py</b>: Dependencies of <b>run_model.py</b>. This script has built-in calculations for Top-10 Precision, Recall and F1 Score based on macro and micro methods.<b>utils.py</b>: Toolbox script. This script integrates some common functions such as the definition of dataset iterators, as well as data fetching and dataset building.The environment required for this project includes:Python 3.7; Pytorch; Pandas; Numpy; Matplotlib.<b>Contacts</b>We welcome your suggestions and feedback on the research!If you have any mild issues with the dataset and methods, please contact the project leader, Professor Yao Yao (yaoy@cug.edu.cn ) and graduate student Dong Anning (donganning@cug.edu.cn)