Back
 GEP  Vol.5 No.12 , December 2017
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 MCMAS has achieved prominent application effects since it was deployed. At present, the MCMAS is running soundly, and it has received and released the information about 1011 missing children from May 25, 2016 to May 25, 2017. In order to explore the geographical distribution features and the influencing factors of missing children, the data of missing children are used for spatial and visual analysis by the data mining and GIS technologies. At the same time, we have built the spatial thermodynamic diagram of the big data of China missing children. By comparing provinces and cities with a higher proportion of missing children, the results showed that: 1) The high proportion of missing children spatially concentrated in the eastern part of the China. 2) The number of missing children was significantly correlated with the population density and economic status of the city. Furthermore, the paper macro-levelly presents a basic basis for rescuing the missing children from two aspects: regionally spatial characteristics and influencing factors.
Cite this paper: Gong, X. , Cheng, H. , Yang, L. , Duan, Y. , Yao, Y. , Feng, Y. , Fu, L. , Liu, J. and Du, M. (2017) Analysis of Geographical Distribution of Missing Children Based on the Missing Children Mobile GIS Mutual Assistance System of China. Journal of Geoscience and Environment Protection, 5, 117-134. doi: 10.4236/gep.2017.512009.
References

[1]   Li, Z. (2017) “Reunion” System Went Online to Retrieve 1274 Missing Children One Year.
http://m.xinhuanet.com/2017-05/17/c_1120989846.htm

[2]   Zhang, H. (2016) Readily Take Pictures to Help Children Return Home. Beijing Evening News, 13, 26.

[3]   Hua, Z. (2016) China Child Missing Warning Platform Simplified User Identity Authentication.
http://news.xinhuanet.com/legal/2016-04/22/c_128921368.htm

[4]   Guo, S. (2015) The First Domestic Search for Lost Children APP ‘RuiJieXunZi’ All Chips Online Line.
http://news.xinhuanet.com/gongyi/2015-07/15/c_128018916.htm

[5]   Gao, H. (2016) The Baby Went Home to Help 1379 Children Home in 9 Years
http://news.xinhuanet.com/gongyi/2016-02/14/c_128716389.htm

[6]   Marjon, J., Stephens, C., Mather, R.D. and Braly, A.M. (2016) Testing the Effect of AMBER Alerts on Face Vigilance. Journal of Scientific Psychology, No. 6, 18-27.

[7]   Fogarty, K. (2017) Teen Safety in Cyberspace. Family Youth & Community Sciences.

[8]   Liu, J., Du, M. and Wen, Y. (2015) Mobile Geographic Information System Development and Application. Publishing House of Electronics Industry, Beijing, 1-5.

[9]   Liu, J. (2016) The Missing Children Mutual-Assistance System of China.
https://www.researchgate.net/publication/303552767_zhongguoshizongerton
ghuzhuxitongThe_Missing_Children_Mutual-Assistance_System_of_China


[10]   Parimala, M., Lopez, D. and Senthilkumar, N.C. (2011) A Survey on Density Based Clustering Algorithms for Mining Large Spatial Databases. International Journal of Advanced Science and Technology, No. 7, 59-66.

[11]   Dattalo, P. (2013) Multivariate Analysis of Variance. Analysis of Multiple Dependent Variables. Oxford University Press, Oxford.
https://doi.org/10.1093/acprof:oso/9780199773596.001.0001

[12]   Shiota, S., Okamoto, Y., Okada, G., Takagaki, K., Takamura, M., Mori, A., et al. (2017) The Neural Correlates of the Metacognitive Function of Other Perspective: A Multiple Regression Analysis Study. Neuroreport, 28, 671-676.
https://doi.org/10.1097/WNR.0000000000000818

[13]   D’Urso, P., Massari, R. and Santoro, A. (2011) Robust Fuzzy Regression Analysis. Information Sciences, 181, 4154-4174. https://doi.org/10.1016/j.ins.2011.04.031

[14]   Kaufman, L. and Rousseeuw, P.J. (2009) Finding Groups in Data. An Introduction to Cluster Analysis. Wiley, New York, 37-51.

[15]   Jain, A.K., Murty, M.N. and Flynn, P.J. (1999) Data Clustering: A Review. ACM Computing Surveys, 31, 264-323.
https://doi.org/10.1145/331499.331504

[16]   Macqueen, J. (1967) Some Methods for Classification and Analysis of Multi-Variate Observations. Proceedings of Berkeley Symposium on Mathematical Statistics and Probability, 1, 281-297.

[17]   Park, H.S. and Jun, C.H. (2009) A Simple and Fast Algorithm for k-Medoids Clustering. Expert Systems with Applications, 36, 3336-3341.
https://doi.org/10.1016/j.eswa.2008.01.039

[18]   Ng, R.T. and Han, J. (2002) Clarans: A Method for Clustering Objects for Spatial Data Mining. IEEE Transactions on Knowledge & Data Engineering, 14, 1003-1016.
https://doi.org/10.1109/TKDE.2002.1033770

[19]   Zhao, T., Hua, Y., Xiang, L.I., Xiang, L.I. and Yang, F. (2016) Research on Heat Map Visualization of Geotagged Data. Engineering of Surveying & Mapping, 25, 28-32.

[20]   Wen, Y., Wang, J., Liu, J. and Du, M. (2015) Research and Implementation of Mobile GIS Service System of Medical Resources in Beijing. Geo Spatial Information, 13, 48-51.

[21]   Liu, J., Yao, Y., Gong, X., Cheng, H., Feng, Y., Fu, L. and Du, M. (2016) The Design and Cloud Achievement of the Missing Children Mobile GIS Mutual Assistance System of China. In: International Conference on Cartographic Visualization of Big Data for Early Warning & Disaster/Crisis Management (EW&CM): Methodology, Techniques, and Applications, 26-30.

[22]   Berrahou, L., Lalande, N., Serrano, E., Molla, G., Bimonte, S., Bringay, S., et al. (2015) A Quality-Aware Spatial Data Warehouse for Querying Hydroecological Data. Computers & Geosciences, 85, 126-135.
https://doi.org/10.1016/j.cageo.2015.09.012

[23]   Llobera, M. (2003) Extending GIS-Based Visual Analysis: The Concept of Visualscapes. International Journal of Geographical Information Science, 17, 25-48.
https://doi.org/10.1080/713811741

[24]   Arthur, D. and Vassilvitskii, S. (2007) K-Means++: The Advantages of Careful Seeding. Proceedings of the 18th Annual ACM-SIAM Symposiumon Discrete algorithms, 11, 1027-1035.

[25]   Kanungo, T., Mount, D.M., Netanyahu, N.S., Piatko, C.D., Silverman, R. and Wu, A.Y. (2002) An Efficient k-Means Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis & Machine Intelligence, 24, 881-892.
https://doi.org/10.1109/TPAMI.2002.1017616

[26]   Shameem, M.U.S. and Ferdous, R. (2009) An Efficient k-Means Algorithm Integrated with Jaccard Distance Measure for Document Clustering. Asian Himalayas International Conference on Internet, Kathmandu, 3-5 November 2009, 1-6.
https://doi.org/10.1109/AHICI.2009.5340335

 
 
Top