Turkiye Klinikleri Journal of Forensic Medicine and Forensic Sciences

.: ORIGINAL RESEARCH
Ekonomik Verilerden Makine Öğrenmesi Yöntemiyle Suç Oranı Tahminlemesi: Analitik Araştırma
Machine Learning-Based Prediction of Crime Rates from Economic Data: Analytical Research
Abdullah GENÇAYa , Recep ERYİĞİTb
aT.C. İçişleri Bakanlığı Emniyet Genel Müdürlüğü, Bilgi Teknolojileri ve Haberleşme Daire Başkanlığı, Ankara, Türkiye
bAnkara Üniversitesi Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Ankara, Türkiye
Turkiye Klinikleri J Foren Sci Leg Med. 2024;21(1):21-35
doi: 10.5336/forensic.2023-100450
Article Language: TR
Full Text
ÖZET
 Amaç: Suçla ilişkili göstergelerin tespit edilmesi, suçun nedenlerinin ortaya çıkarılması üzerine yapılan kriminolojik analizler suçla mücadele sürecinin önemli destek noktalarıdır. Suçun gelecekte hangi düzeyde gerçekleşeceği, hangi suç tipinin daha yoğun meydana geleceği üzerine birçok araştırma yapılmaktadır. Bu çalışmada ekonomik göstergelerle suç arasındaki ilişkinin yapay zekanın alt dallarından makine öğrenmesi yöntemleri ile ortaya konulması amaçlanmıştır. Gereç ve Yöntemler: Çalışmada; işsizlik, gayrisafi yurt içi hasıla, nüfus, 15 ayrı suç tipine ait hükümlü sayıları kullanılarak KEn Yakın Komşu, Rastgele Orman, Naive Bayes isimli makine öğrenmesi yöntemleri kullanılarak suç oranları tahmin edilmiştir. Ayrıca veri bölgesel açıdan Türkiye geneli ve İstatistiki Bölge Birimleri Sınıflandırması-2 (26 Bölge) coğrafi bölümleme seviyelerini içermektedir. Bulgular: Çalışmada suç oranı tahminiyle ilgili farklı eğitim ve test oranlarına ilişkin sonuçlar, aynı yıl, 1 yıl sonraki ve 2 yıl sonraki suç oranına ilişkin sonuçlar ve ayrıca her suç türünün hangi yöntemle daha başarılı tahmin edildiğine olduğuna ilişkin sonuçlar olmak üzere birçok bulgu edinilmiştir. Hırsızlık, Sahtecilik, Yaralama ve Uyuşturucu İmal ve Ticareti suçlarına ilişkin suç oranlarının tüm yöntemlerle başarılı bir şekilde tahmin edilebildiği diğer suçlarda ise başarılı olan yöntemin değiştiği hesaplanmıştır. Sonuç: Sosyal bilimler alanında yapılan çalışmalarla ortaya konulmuş olan ekonomik göstergelerle suç arasındaki ilişkinin makine öğrenmesi algoritmalarının katkısı da eklenerek ileri boyutlara taşınabilmesi suçla etkin mücadeleye önemli katkılar sağlayacaktır. Türkiye'deki bölgelerin ayrı ayrı suç oranının ilerleyen yıllar için doğru tahmin edilebilmesi; suçla ilgili öncesinde önlem alınabilmesi, kolluk birimlerinin karar mekanizmalarını güçlendirmesi, kamu kaynaklarının verimli kullanılabilmesi gibi birçok açıdan güvenlik hizmetlerini destekleyecektir.

Anahtar Kelimeler: Suç tahminleme; kriminolojik analiz; makine öğrenmesi; sınıflandırma
ABSTRACT
 Objective: Criminological analyses that identify crimerelated indicators and reveal the root causes of criminal activity are crucial instruments in combating against crime. Numerous studies have been conducted regarding the future crime rate and the kind of crimes that will occur more frequently. In this study, it is aimed to reveal the relationship between economic indicators and crime using machine learning methods, one of the sub-branches of artificial intelligence. Material and Methods: In this study, crime rates were predicted with three machine learning methods [K-Nearest Neighbor (KNN), Random Forest (RF), Naive Bayes (NB)] by using unemployment, gross domestic product, population, number of convicted criminals of 15 different crime types datas. Data is available both at the Turkey country level and at the Nomenclature of Territorial Units for Statistics-2 (NUTS-2) geographical grouping level (26 distinct regions). Results: In the study, many findings were obtained on crime rate prediction, including results for different training and test rates, results for crime rates in the same year, 1 year later and 2 years later, as well as results on which method predicts each type of crime more successfully. As a result of the prediction study; theft, counterfeiting, injury, production and trade of narcotics and psychotropic substanctes crime rates predicted more succesfully with all three methods. Conclusion: Which has been demonstrated by studies in the field of social sciences, the ability to carry the relationship between economic indicators and crime, to advanced dimensions by adding the contribution of machine learning algorithms will make significant contributions to the effective fight against crime. The ability to accurately predict the crime rates of individual regions in Turkey for the coming years will support security services in many ways, such as taking precautions against crime in advance, strengthening the decision-making mechanisms of law enforcement units, and using public resources efficiently.

Keywords: Crime prediction; criminological analysis; machine learning; classification
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