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Sains Malaysiana 54(10)(2025): 2553-2566
http://doi.org/10.17576/jsm-2025-5410-17
Spatial
Cluster Analysis of Human Trafficking in Asia Over a Six-Year Period
(Analisis Kluster Reruang Pemerdagangan Manusia di Asia Sepanjang Tempoh Enam Tahun)
SAFAT MOHAMMAD SAFAT,
NUZLINDA ABDUL RAHMAN*, NURUL SYAFIAH ABD NAEEIM & FAUHATUZ
ZAHROH SHAIK ABDULLAH
School of Mathematical Sciences, Universiti Sains Malaysia, 11800 Pulau Pinang, Malaysia
Received: 24 March 2025/Accepted: 27 August
2025
Abstract
Human trafficking is an
important issue which affects many regions of the world. Understanding the
spatial distribution of detected trafficking victims is essential to combating
modern slavery. The main objective of this study was to determine the hotspot
regions of trafficking victims in Asia. The yearly number of detected human
trafficking victims for 49 countries, from the year 2016 to 2021, was analysed
in this study. The ‘hidden nature’ of human trafficking leads to a substantial
amount of missing data. To perform an in-depth spatial clustering analysis, the
missing values were first addressed using several imputation techniques. Spatial clustering techniques are then used
to locate the high and low-rate clusters of victims in Asian countries. The
results indicated that repeated high-rate clusters consist of countries known
for being travel hubs. These clusters also include the bordering and nearby
countries which are easily accessible by land transportation. Repeated low-rate
clusters do not yield conclusive results yet they infer that the countries
located in these clusters may require additional resources to accurately report
on the statistics of human trafficking. Spatial clustering analysis was also
conducted on the covariates of age, form of exploitation and the sex of the
victims. The findings show that the comparison of clusters for different
variables can help determine which specific populations are most susceptible to
human trafficking with their respective locations. Enforcement agencies and nonprofit organizations can utilize these findings to
strengthen their methods of combating human trafficking.
Keywords: Clustering; human trafficking; SaTScan; scan statistics; spatial analysis
Abstrak
Pemerdagangan manusia adalah isu penting yang memberi kesan kepada banyak rantau di dunia. Memahami taburan reruang bagi mangsa pemerdagangan manusia yang dikesan adalah penting dalam memerangi perhambaan moden. Objektif utama kajian ini adalah untuk menentukan kawasan panas bagi mangsa pemerdagangan manusia di Asia. Jumlah tahunan mangsa pemerdagangan manusia yang dikesan bagi 49 negara, dari tahun 2016 hingga 2021 telah dianalisis dalam kajian ini. ‘Sifat tersembunyi’ pemerdagangan manusia menyebabkan sejumlah besar data tidak direkodkan. Bagi menjalankan analisis pengelompokan reruang yang mendalam, nilai yang hilang telah ditangani terlebih dahulu menggunakan beberapa teknik imputasi. Seterusnya, teknik pengelompokan reruang digunakan untuk mengenal pasti pengelompokan kadar tinggi dan rendah mangsa di negara Asia. Keputusan menunjukkan bahawa kelompok kadar tinggi yang berulang terdiri daripada negara yang dikenali sebagai hab perjalanan. Kelompok ini juga merangkumi negara berjiran dan berhampiran yang mudah diakses melalui pengangkutan darat. Kelompok kadar rendah yang berulang tidak memberikan keputusan yang konklusif namun ia memberikan gambaran bahawa negara dalam kelompok ini mungkin memerlukan sumber tambahan untuk melaporkan statistik pemerdagangan manusia dengan lebih tepat. Analisis pengelompokan reruang turut dijalankan ke atas kovariat umur, bentuk eksploitasi dan jantina setiap mangsa. Penemuan menunjukkan bahawa perbandingan antara kelompok bagi pemboleh ubah yang berbeza dapat membantu menentukan populasi tertentu yang paling terdedah kepada pemerdagangan manusia berserta lokasi masing-masing. Agensi penguatkuasaan undang-undang dan organisasi bukan berasaskan keuntungan boleh menggunakan penemuan ini untuk memperkukuhkan kaedah mereka dalam memerangi pemerdagangan manusia.
Kata kunci: Analisis reruang; pemerdagangan manusia; pengelompokan; SaTScan; statistik imbasan
References
Abdurrob, A.
& Kulldorff, M. 2016. SaTScan Tutorial #3 Advanced Options. Accessed 2nd September 2024. https://www.satscan.org/tutorials/advancedoptions/SaTScanTutorialAdvancedOptions.pdf
AlQadi, H.,
Bani Yaghoub, M., Wu, S., Balakumar,
S. & Francisco, A. 2022. Prospective spatial-temporal clusters of COVID-19
in local communities: Case study of Kansas City, Missouri, United States. Epidemiology
and Infection 151: e178.
Andridge, R.R.
& Little, R.J.A. 2010. A review of hot deck imputation for survey
non-response. International Statistical Review 78(1): 40-64.
Boba, R. 2001. Introductory Guide to Crime
Analysis and Mapping. Washington: U.S. Dept. of Justice.
Burgess, M. 2011. Understanding crime hotspot
maps. Crime and Justice Statistics. Issue paper No. 60.
Burke, M.C. 2022. Human Trafficking
Interdisciplinary Perspectives. 3rd ed. New York: Routledge.
Curran, J.M. 2013. The frequentist approach to
forensic evidence interpretation. In Encyclopedia of Forensic Sciences, edited by Siegel, J., Saukko,
P. & Houck, M. London: Elsevier, Academic Press. pp. 286-291.
Dakua, T., Rahaman, M. & Das, K.C. 2023. An analysis of the
spatial and temporal variations of human trafficking in India. Cogent Social
Sciences 10(1): 2293315.
David, F., Latham-Sprinkle, J., Bryant, K.
& Larsen, J. 2019. Migrants and Their Vulnerability to Human
Trafficking, Modern Slavery and Forced Labour. London: International
Organization for Migration.
Edens, C.,
Alden, N.B., Danila, R.N., Fill, M.-M.A., Gacek, P.,
Muse, A., Parker, E., Poissant, T., Ryan, P.A.,
Smelser, C., Angelo, T. & Schrag, S.J. 2019.
Multistate analysis of prospective Legionnaires’ disease cluster detection
using SaTScan, 2011-2015. PLoS ONE 14(5): e0217632.
Fischer, M.M. & Getis,
A. 2010. Handbook of Applied Spatial Analysis Software Tools, Methods and
Applications. 1st ed. Berlin: Springer.
Francis, S.S., Enders, C., Hyde, R., Gao, X.,
Wang, R., Ma, X., Wiemels, J.L., Selvin, S. & Metayer, C. 2020. Spatial-temporal cluster analysis of
childhood cancer in California. Epidemiology 31(2): 214-223.
Global Report on Trafficking in Persons. 2022. Accessed 16th August 2024. https://www.unodc.org/documents/data-and-analysis/glotip/2022/GLOTiP_2022_web.pdf
Jung, I., Kulldorff,
M. & Richard, O.J. 2010. A spatial scan statistic for multinomial data. Statistics
in Medicine 29(18): 1910-1918.
Kangaspunta, K.
2003. Mapping the inhuman trade: Preliminary findings of the database on
trafficking in human beings. Forum on Crime and Society 3(1&2):
81-103.
Knox, E.G. 1989. Detection of clusters. In Methodology of Enquiries into Disease Clustering, edited by Elliot, P. London: Small Area Health Statistics Unit 17(1): 20.
Kulldorff, M.
2022. SaTScan™ User Guide for version 10.1.
(2022). Accessed 16th August 2024.
https://www.satscan.org/cgi-bin/satscan/register.pl/SaTScan_Users_Guide.pdf?todo=process_userguide_download
Leach, R. 2022. Ending demand for modern-day
slavery: An analysis of human trafficking in the global marketplace. Indiana
Journal of Global Legal Studies 29(1): 232-259.
Li, T., He, J.G., Yang, C.H., Li, J., Xiao, Y.,
Li, Y.K., Chen, C. & Wu, J.L. 2020. A comparative study on SaTScan and FleXScan software for
spatial clustering analysis regarding the incidence of pulmonary tuberculosis. Zhonghua Liu Xing Bing Xue Za Zhi41(2): 207-212.
Maimon, O.
& Rokach, L. 2010. Data Mining and Knowledge
Discovery Handbook. 2nd ed. Springer.
Mohd Izham bin Mohd Jaya. 2018. Cold
deck missing value imputation with a trust-based selection method of multiple
web donors. PhD Thesis. Serdang: Universiti Putra
Malaysia (Unpublished).
Naeeim, N.S.A.
& Rahman, N.A. 2022. Spatio-temporal clustering
analysis using two different scanning windows: A case study of dengue fever in
Peninsular Malaysia. Spatial and Spatio-Temporal
Epidemiology 41(4): 100496.
Purwaningsih, T.
& Cintami, A. 2019. Analysis of factors affecting
the area of forest and land fires in Indonesia uses spatial regression Geoda and SaTScan. Jurnal Informatika 12(2): 58.
Schabenberger, O.
& Gotway, C.A. 2005. Statistical Methods for
Spatial Data Analysis. 1st ed. New York: Chapman and Hall/CRC.
Smith, K. & Climer,
S. 2024. Improving Data Cleaning Using Discrete Optimization. https://arxiv.org/abs/2405.00764
Spies, L. 2017. Possible imputation procedures
for the Census 2021. In Work Session on Statistical Data Editing, 24-26
April 2017, Netherlands. The Hague: United Nations Economic Commission for
Europe.
Statistics Canada. 2024. 3.4.4 Imputation.
Accessed 16th August 2024.
https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch3/imputation/5214784-
eng.htm
Stimers, M., Lenagala, S., Haddock, B., Paul, B.K. & Mohler, R.
2022. Space-time clustering with the space-time permutation model in SaTScanTM applied to building permit data following the
2011 Joplin, Missouri Tornado. International Journal of Disaster Risk
Science 13(6): 962-973.
UNODC. 2022. UNODC Research - Data Portal –
Trafficking in Person. Accessed 16th August 2024.
https://dataunodc.un.org/sites/dataunodc.un.org/files/metadata_trafficking_in_
persons.pdf
Weisbird, D.L.
& McEwan, T. 2015. Introduction: Crime Mapping and Crime Prevention.
PhD Thesis. Hebrew University of Jerusalem (Unpublished).
Xia, H., Li, G., Zhou, J., Yu, Y., Du, M. &
Feng, C. 2022. Spatial and temporal patterns, influencing factors, and
prevention and control measures of human trafficking in southwest China. Tropical
Geography 42 (9): 1534-1546.
Ying, F.F. 2022. Spatio-temporal
clustering analysis of COVID-19 cases in Johor. Master’s thesis. Universiti Sains Malaysia
(Unpublished).
Zeoli, A.M.,
Pizarro, J.M., Grady, S.C. & Melde, C. 2012.
Homicide as infectious disease: Using public health methods to investigate the
diffusion of homicide. Justice Quarterly 31(3): 609-632.
Zhang, Z. 2016. Missing data imputation:
Focusing on single imputation. Ann. Transl. Med. 4(1): 9.
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