Crime Analysis and Prediction Using Data Mining and Machine Learning Techniques

With the increase in crime rates across the world, it has become important for the Government and crime handling agencies to control the situation as it has put every person in distress. This paper is an attempt to systematically analyze and identify the crime trends across the years, the inter-state relations based on crime rates and categories through the data available, which will help in predicting the crime trends in future and will be instrumental for the Government to take informed actions and improve the country’s situation. This paper applies various data mining techniques in order to analyze the crime records in India. The results of analysis have been compared for various algorithms in the domain of Association Rule Mining, Clustering, Outlier Analysis, Regression and Classification. The paper also attempts to predict the future occurrences of crimes using classification and regression algorithms which use data mining techniques . Keywords: Crime Analysis, Data Mining, Association Rule Mining, Clustering, outlier Analysis, Classification, Regression

A Pszichopátiás Önértékelő Skála rövid változatának (SRP-SF) magyar nyelvű adaptációja

Bevezetés: A pszichopátia kutatásának története hosszú múltra tekint vissza a mentális egészségtudományokban. Ez idő alatt több megközelítés is született, melyekhez kapcsolódóan kérdőíveket is kidolgoztak a kutatók. Cél: Jelen tanulmány a Pszichopátiás Önértékelő Skála rövid változatának (Self Report Psychopathy Short Form; SRP-SF) magyar nyelvű adaptálását tűzi ki célul. A tanulmányban elemezzük a kérdőív faktorszerkezetét, belső konzisztenciáját, valamint konvergens validitását a nárcizmussal, a machiavellizmussal, a szenzoros élménykereséssel, a neuroticizmussal, az agresszióval, valamint konkurens validitását a pszichopátiát mérő mérőeszközzel. Módszerek: Keresztmetszeti, kérdőíves vizsgálatunkat 605 fős heterogén mintán folytattuk le. A kapcsolatrendszer elemzésére az SRP-SF mellett a fent említett konstruktumok mérésére alkalmas mérőeszközöket is használtunk, úgymint a Zuckerman–Kuhlman–Aluja személyiség-kérdőív 80 itemes rövid változatát, a Rövid Sötét Triád tesztet, illetve a Patológiás Nárcizmus kérdőívet. Eredmények: A megerősítő faktorelemzés eredménye rámutatott arra, hogy az általunk alkalmazott mintán a kérdőív háromfaktoros modellje illeszkedik a legjobban (illeszkedési mutatók: χ 2(164) = 817,741, p < 0,001; CFI = 0,930; TLI = 0,919; RMSEA [90% CI] = 0,081 [0,075–0,087]), a bűnügyi tendenciák skála jelenléte a jelen mintán nem megerősíthető. Az SRP-SF belső konzisztenciája megfelelőnek bizonyult (Cronbach-α = 0,659– 0,774). A kérdőív más mérőeszközökkel mutatott kapcsolataiból pedig arra lehet következtetni, hogy az állítások inkább az elsődleges pszichopátiát mérik, és a kérdőívben elkülönülnek a pszichopátia interperszonális és életmódbeli aspektusai. Következtetés: az SRP-SF egy olyan könnyen és gyorsan felvehető kérdőív, amely megfelelő pszichometriai mutatókkal rendelkezik és a bűnügyi tendenciák skálától eltekintve alkalmazható nem klinikai mintán is. Introduction: The research history of psychopathy has a long history in the mental health sciences. During this time, several approaches were developed, and in connection with the theories the researchers also developed questionnaires. Aim: The aim of the study is the Hungarian adaptation of the Self-Reporting Psychopathy Short Form (SRP-SF). We have analyzed the factor structure of the questionnaire, its internal consistency, and convergent validity of the questionnaire with narcissism, Machiavellianism, sensation seeking, neuroticism, aggression, and other measure of psychopathy. Methods: The study included 605 individuals, who were heterogeneous by profession. In addition to SRP-SF, an 80-item short version of the Zuckerman–Kuhlman–Aluja Personality Questionnaire, the Short Dark Triad test, and the Pathological Narcissism questionnaire were also administered to analyze the relationships of psychopathy. Results: The results of the confirmatory factor analysis showed that the three-factor model of the questionnaire fits best in the sample we used (fit indicators: χ 2(164) = 817.741, p < 0.001; CFI = 0.930; TLI = 0.919; RMSEA [90% CI] = 0.081 [0.075–0.087]) , the presence of the scale of crime trends in the present sample cannot be confirmed. The internal consistency of SRP-SF was found to be adequate (Cronbach’s α = 0.659–0.774). And from the relationships of the questionnaire with other measures, it can be concluded that the statements tend to measure primary psychopathy, and the interpersonal and lifestyle aspects of psychopathy are separated in the questionnaire. Conclusion: Except for the Crime trends scale, SRP-SF is an easy and quick to take test that has appropriate psychometric indicators and can be applied to a non-clinical sample

THE CHARACTERISTICS AND COUNTERMEASURES FOR BIOSECURITY CRIMES UNDER THE NEW SITUATION

Today, China is facing many risk factors and great challenges in biosecurity crimes. Covered by non-traditional security, In terms of crime risk sources, crime threat forms, crime consequences, crime status, and crime trends，biosecurity crimes are different from traditional security crimes. Under the new situation, countermeasures to address biosecurity crimes include coordination of traditional and non-traditional security; construction of a prevention and control system based on big data; intensified personnel training; technology research and development; improvement of laws and regulations; combined governance of departments; effective international cooperation, and so on.

Six months in: pandemic crime trends in England and Wales

Governments around the world have enforced strict guidelines on social interaction and mobility to control the spread of the COVID-19 virus. Evidence has begun to emerge which suggests that such dramatic changes in people’s routine activities have yielded similarly dramatic changes in criminal behavior. This study represents the first ‘look back’ on six months of the nationwide lockdown in England and Wales. Using open police-recorded crime trends, we provide a comparison between expected and observed crime rates for fourteen different offence categories between March and August, 2020. We find that most crime types experienced sharp, short-term declines during the first full month of lockdown. This was followed by a gradual resurgence as restrictions were relaxed. Major exceptions include anti-social behavior and drug crimes. Findings shed light on the opportunity structures for crime and the nuances of using police records to study crime during the pandemic.

Anchored k-medoids: a novel adaptation of k-medoids further refined to measure long-term instability in the exposure to crime

Longitudinal clustering techniques are widely deployed in computational social science to delineate groupings of subjects characterized by meaningful developmental trends. In criminology, such methods have been utilized to examine the extent to which micro places (such as streets) experience macro-level police-recorded crime trends in unison. This has largely been driven by a theoretical interest in the longitudinal stability of crime concentrations, a topic that has become particularly pertinent amidst a widespread decline in recorded crime. Recent studies have tended to rely on a generic implementation k-means to unpick this stability, with little consideration for its theoretical suitability. This study makes two methodological contributions. First, it demonstrates the application of k-medoids to study longitudinal crime concentrations, and second, it develops a novel ‘anchored k-medoids’ (ak-medoids), a bespoke clustering method specifically designed to meet the theoretical requirements of micro-place investigations into long-term stability. Using both simulated data and 15-years of police-recorded crime data from Birmingham, England, we compare the performances of k-medoids against ak-medoids. We find that both methods highlight instability in the exposure to crime over time, but the consistency and contribution of cluster solutions determined by ak-medoids provide insight overlooked by k-medoids, which is sensitive to short-term fluctuations and subject starting points. This has important implications for the theories said to explain longitudinal crime concentrations, and the law enforcement agencies seeking to offer an effective and equitable service to the public.

Data mining to analyse recurrent crime in South Africa

When South Africa is compared to other countries, it has a notably high rate of crime. The country has seen a concomitantly high occurrence of murder, residential burglary, drug-related crime and carjacking (hijacking) crime. The government is desperately seeking solutions that can be implemented to reduce recurrent crime. Several reasons to explicate high crime trends in different areas include alcohol or drug abuse, low standards of education, poor parenting skills and a lack of social and vocational skills. This study aimed to gain better insight into crime trends in South Africa using data mining techniques. Decision-making linked to the data could help the government implement a coherent crime strategy to mitigate crime. The crime dataset chosen for this study was publicly available at kaggle.com. The dataset was prepared using Python programming code. The research design was utilised as an overall strategy to compile all different components of this study with an intention of answering the research questions and attaining the research objectives. To identify the significant changes, ChangePoint Analysis (CPA) was performed to pinpoint the abrupt change in the South African crime dataset. Two methods called Cumulative Sum (CUSUM) and Bootstrap were implemented in this study of CPA. To analyse the trend of data, CUSUM and Bootstrap were performed to measure the occurrence of change points based on the confidence levels. The CPA outcome depicted multiple significant changes and abrupt shifts in several provinces of South Africa. Linear regression (LR) was utilised to predict the future trends of crime in South Africa from 2016 – 2022 based on the erstwhile 2005 – 2015 crime statistics. The results showed that crime has been on the increase in South Africa with certain provinces such as Western Cape, Gauteng and KwaZulu-Natal being identified as crime hotspots. Future studies on crime should focus only on one province to gain insight into the dominating crimes and hotspots within that particular province, with a view to developing highly specific crime-reduction interventions.