A Systematic Review of Artificial Intelligence in Crime Prediction by IRJET Journal

A Systematic Review of Artificial Intelligence in Crime Prediction

AISWARYA NS

MSc Computer Science Student, St. Thomas (Autonomous) College, Thrissur 680001, Kerala, India

Abstract - The escalating complexity of criminal activities necessitates the use of advanced analytical tools for effective crime prevention and mitigation. This systematic review synthesizesrecentadvancementsinartificialintelligence(AI), machine learning (ML), and deep learning (DL) techniques thathavebeenappliedtocrimeprediction.Byanalyzingover 150 scholarly articles, this study elucidates prominent methodologies, including Geographic Information System (GIS)-based, statistical, ML-based, andDL-basedapproaches, and examines the diverse datasets utilized, particularly emphasizing the increasing availability and utility of public crime records. Key findings reveal a predominant focus on spatio-temporal crime hotspot prediction and crime type classification, with Artificial Neural Networks and Random Forest emerging as frequently employed algorithms. Despite significant progress in predictive accuracy, substantial challenges persist, notably concerning data quality, accessibility, and the interpretability and explainability of AI models.Consequently,thisreviewunderscorestheimperative for the development of transparent and trustworthy AI systems,whichis deemedessentialtofosterpublicconfidence and ensure their ethical deployment. Future research trajectories are proposed to address extant deficiencies through the integration of explainable AI (XAI) techniques, exploration of hitherto underutilized ML categories, and systematic development of more robust, ethically sound, and inherently interpretable crime prediction models.

Keywords: Crimeprediction,ArtificialIntelligence,Machine Learning, Deep Learning, Explainable AI, Spatio-temporal analysis,Datamining,SystematicReview.

I. INTRODUCTION

Thepervasivenatureofcriminalactivityposessignificant societal and economic challenges worldwide. Traditional crime analysis methods, often reliant on manual investigation and reactive measures, frequently prove insufficient in adapting to evolving crime patterns and trends.Theadventofartificialintelligence(AI),particularly machine learning (ML) and deep learning (DL), has revolutionized criminology by offering sophisticated tools capable of analyzing vast datasets to identify intricate patternsandanticipatefuturecriminaloccurrences.These technologies empower law enforcement agencies to transition from reactive responses to proactive crime prevention strategies, thereby optimizing resource allocationandenhancingpublicsafety.

Despite the promising potential of AI-driven crime predictionsystems,theirwidespreadadoptioniscontingent uponaddressingtheirinherentlimitations.Acriticalconcern isthetrustworthinessandinterpretabilityofthesecomplex models. Opaque "black-box" AI systems, while potentially highly accurate, can obscure the rationale behind their predictions, raising ethical questions regarding fairness, accountability,andpotentialbiases,especiallywhenapplied to sensitive domains such as the criminal justice system. Moreover, challenges related to the availability of highquality, comprehensive, and ethically sourced crime data persist,hinderingthedevelopmentofuniversallyapplicable androbustpredictivemodelsforcrime.

Thissystematicreviewaimstoprovideacomprehensive synthesis of the current landscape of AI-based crimeprediction research. It meticulously examines the diverse methodologies employed, characteristics of the datasets utilized,andperformanceofvariousalgorithms.Particular emphasisisplacedontheemergingfieldofexplainableAI (XAI) and its role in enhancing the transparency and trustworthiness of crime prediction models. By critically analyzing the existing literature, this study identifies key advancements and persistent challenges and outlines promising future research directions necessary for the responsible and effective integration of AI in crime preventionefforts.

II. LITERATURE SURVEY

The domain of crime prediction has witnessed a significant evolution, transitioning from traditional statisticalmethodstoadvancedAI-drivenapproaches.Early methodologiesprimarilyleveragedGeographicInformation Systems(GIS)andhotspotanalysistovisualizecrimedata andidentifyareasofconcentratedcriminal activity.While instrumentalinstrategicplanningandresourceallocation, these methods were often criticized for being more descriptivethantrulypredictive,primarilyidentifyingareas wherecrime has occurred ratherthanforecastingwhereit will occur. Statistical techniques, such as AutoRegressive Integrated Moving Average (ARIMA) models, have historicallyprovidedvaluableinsightsintotemporalcrime trends due to their inherent interpretabilityand ability to modeltime-seriesdata.However,theircapacitytoaccount for the complex spatial dependencies and dynamic interactions inherent in crime data remains significantly limited,oftentreatinglocationsasindependententities.To address these limitations, hybrid models, integrating the

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strengthsofstatisticalmethodswiththeadvancedpattern recognitioncapabilitiesofMLandDL,haveemerged.These integratedapproachesdemonstrateenhancedaccuracyby effectivelycapturingbothcomplexspatiotemporalpatterns and underlying temporal trends, thereby offering a more holisticpredictiveframework.

The proliferation of machine learning (ML) in crime analysis since the early 2000s has marked a pivotal shift, fundamentallyalteringthelandscapeofpredictivepolicing. This paradigm shift has enabled the generation of more timelyandaccurateforecastsconcerningcrimelocationsand timings, moving beyond mere descriptive analysis to actionable predictive intelligence. A diverse array of ML algorithms has been commonly employed in this context, including Linear Regression (LR) and Logistic Regression (LogReg) for modeling linear relationships and binary outcomes, respectively. Decision Trees (DT) offer transparent, rule-based classification, while ensemble modelssuchasRandomForest(RF)andGradientBoosting variants (e.g., XGBoost, AdaBoost) aggregate predictions from multiple trees to enhance robustness and accuracy. SupportVectorMachines(SVM)areutilizedforclassification by finding optimal hyperplanes, and K-Nearest Neighbors (KNN) classify data points based on the majority class of their closest neighbors. These techniques offer varying trade-offs between predictive accuracy, model interpretability,andcomputationalefficiency.Notably,DTs andvariousregressionalgorithmsareoftenfavoredfortheir relativesimplicityandclarity,whichcanbeadvantageousin contextsrequiringdirectunderstandingofpredictivefactors. For instance, Sridharan et al. (2024) applied Linear RegressionandRandomForestalgorithmstopredictcrime typesinIndia,utilizingadatasetspanning2001to2016to forecast crime cases from 2017 to 2020. Their work emphasizedtheidentificationof"trend-changingyears"asa critical feature for enhancing prediction accuracy, demonstratingthepracticalutilityoftheseMLmethodsina nationalcontext.

DeepLearning(DL)representsaspecializedandpowerful branchofML,distinguishedbyitsutilizationofmulti-layered artificial neural networks designed to process vast and complex datasets. These networks excel in tasks such as intricate pattern recognition, feature extraction, and sophisticatedpredictivemodeling,particularlywhendealing withhigh-dimensionalorunstructureddata.ProminentDL models applied in crime prediction include Convolutional Neural Networks (CNNs), which are adept at processing spatialdataandidentifyinglocalizedpatterns,makingthem suitable for crime hotspot analysis. Recurrent Neural Networks(RNNs)andtheirmoreadvancedvariants,suchas Long Short-Term Memory Networks (LSTMs), are particularlywellsuitedformodelingsequentialandtemporal data,thusprovingeffectiveinforecastingcrimetrendsover time.MultilayerPerceptrons(MLPs),asfoundationalneural networks,alsofindapplicationinvariouscrimeprediction

tasks. While DL models have consistently demonstrated remarkableaccuracyinpredictingcrimehotspotsandrates, their intricate, multi-layered "opaque box" nature often impedes direct interpretability. This inherent lack of transparencynecessitatestheapplicationofExplainableAI (XAI) techniques to provide insights into their decisionmakingprocesses.Furthermore,hybridapproaches,which judiciouslycombinethestrengthsofDLarchitectureswith traditionalstatisticalmethodslikeARIMA,areincreasingly being explored to further bolster prediction accuracy, enhance model robustness, and potentially improve the interpretabilityofcomplexspatio-temporalpredictions.

Mandalapu et al. (2023) conducted a systematic review thatmeticulouslyexaminedover150articlestoexplorethe variousmachinelearninganddeeplearningalgorithmsthat havebeenappliedtocrimeprediction.Theircomprehensive analysisfocusedonidentifyingpatternsandtrendsincrime occurrences, providingvaluableinsights into thedifferent trendsandfactorsrelatedtocriminalactivities.Thisreview alsocriticallyassessedthedatasetsusedbyresearchersfor crime prediction, offering an overview of their characteristicsandaccessibility.TheworkofMandalapuet al. is particularly significant as it synthesizes a broad spectrum of research, highlighting the diverse methodological landscape and the evolving challenges, especially concerning the interpretability of advanced modelswithinthecrimepredictiondomain.Theirfindings contributetoaclearerunderstandingofthestate-of-the-art andfutureresearchdirections,particularlyinthecontextof balancingpredictiveperformanceandmodeltransparency.

Similarly, Jenga et al. (2023) presented a systematic literature review focused on machine learning for crime prediction,evaluatingstate-ofthe-arttechniquesdeveloped overtheprecedingdecade.Theirstudy,whichencompassed 68 selected machine learning papers, aimed to synthesize knowledge regarding ML-based crime prediction to assist lawenforcementauthoritiesandscientistsinmitigatingand preventing future crime occurrences. Jenga et al. meticulouslydiscussedthepossiblechallengesinherentin thefieldandprovidedaforward-lookingdiscussionoffuture work.Akeyobservationfromtheirreviewwasthatmostof theanalyzedpapersutilizedasupervisedmachinelearning approach, predicated on the assumption of prior labeled data. This study underscores methodological preferences withinthefieldandreinforcestheongoingneedtoaddress practicalchallenges,suchasdataavailabilityandtheethical implicationsofpredictivepolicing.

III. METHODOLOGY

This systematic review was meticulously conducted to ensure comprehensive coverage and rigorous analysis of scholarlyworkspertainingtotheuseofartificialintelligence incrimeprediction.Theresearchmethodologycompriseda two-stage approach, primarily leveraging the Scopus

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database,whichisrecognizedforitsextensiverepositoryof scientific literature abstracts and citations, offering broad globalandregionalcoverage.

Search Strategy and Data Collection: The initial stage involveddefiningaprecisesearchquerytoidentifyrelevant publications. The query encompassed key terms such as 'crime prediction' AND 'artificial intelligence' OR 'crime prediction' AND 'machine learning' OR 'crime prediction' AND'deeplearning'OR'crimeprediction'AND'explainable AI'OR'crimeprediction'AND'interpretability.’Thesearch waslimitedtorecentpaperspublishedfromJanuary1,2015, toMay1,2024,yielding660publications.

InclusionandExclusionCriteria: Torefinetheselectionof primary studies,stringent inclusionand exclusioncriteria wereapplied.Paperswereconsideredforinclusionifthey focused on crime categories identified by the National IncidentBasedReportingSystem(NIBRS),specifically:

•Crimes against persons (e.g., burglary, homicide, humantrafficking,kidnapping,andsexoffenses).

•Crimes against property (e.g., burglary, motor vehicletheft,fraud,androbbery).

• Crimesagainstsociety(e.g.,drug/narcoticoffenses).

Conversely, papers were excluded if they were not writteninEnglishordidnotdirectlyaddressAI-based crimeprediction.Afterthisrigorousselectionprocess, 142articlesremainedforadetailedanalysis.

Data Extraction and Synthesis: The second stage of the methodology focused on extracting and synthesizing valuable information from the selected articles. For each paper, data pertaining to the following aspects were recorded:

• TypeofCrimeAddressed: Categorizationbasedon NIBRSsystem.

• Dataset Description: Details regarding the dataset(s) used, including their source, size, and intrinsiccharacteristics.

• DatasetAccessibility: Classificationofdatasetsas eitherpublicorprivate,notingavailablelinks.

• AIApproachType: IdentificationoftheprimaryAI methodologiesemployed(e.g.,GIS-based,statistical, ML-based,DLbased,hybrid).

IV. RESULTS AND DISCUSSION

The systematic review of 142 scholarly articles published between2015and2024revealssignificantinsightsintothe applicationofAItechnologiesforcrimeprediction.

A. Analysis of

Datasets Used in Crime

Prediction The analysis of datasets revealed severalkeyaspects.Theft,robbery,andassault emergedasthemostfrequentlystudiedcrime types,collectivelyaccountingforasubstantial portionoftheresearchfocus.Geographically, crimedatafromtheUnitedStates,particularly frommajorcitieslikeChicago(33.70%),NewYork City (13.04%), San Francisco (11.96%), and Los Angeles(7.61%),dominatedthefield.Datasetsfrom India(7.78%),China,andBrazil(3.33%each)also featuredprominently.Asignificantfindingwasthe high proportion of public datasets utilized (77.55%),indicatingagrowingtrendtowardsdata transparencyandaccessibility,largelyfacilitatedby policedepartmentdataportals(e.g.,Chicago,NYC, SanFrancisco)andplatformslikeUCI,Kaggle,and GitHub. However, a notable limitation is the restrictednumberofavailabledatasets,withmany lacking crucial time and location information, underscoring a pressing need for broader public accessibilityofcomprehensivecrimedata.

B. Analysis of Approach Types and Prediction Objectives Traditional ML techniques were the most commonly employed approaches in crime prediction studies (42%), followed by DL techniques (26%), GIS-based hotspot prediction (20%), and statistical methods (13%). Hybrid approaches, integrating these diverse methodologies, demonstrated significant advancementsinpredictiveefficiency,particularly forpre-crimepredictionsbycorrelatingspatialand temporalpatterns.

Regardingpredictionobjectives,themostfrequently pursuedgoalwasthepredictionofspatio-temporal crime hotspots (36.62%), emphasizing the integrationofbothtemporalandspatialdimensions for a comprehensive understanding of crime dynamics. This was followed by crime type prediction (31.69%) and crime occurrence prediction (23.24%). A noticeable shift towards combining crime mapping with advanced visualization support (e.g., heat maps, 3D visualizations) has been observed since 2020, reflectingademandforintuitivetoolsforanalysts anddecision-makers.

C. Analysis of Prediction Techniques and Explainability

A wide array of 20 prediction techniques were identified, with classical ML techniques dominating. Random Forest was the mostcommonlyused(16.05%ofstudies),followed by Decision Trees (11.15%), SVM (9.07%), Naïve Bayes (7.91%), and KNN (7.91%). Among the DL

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techniques,LSTM(4.42%)wasthemostprevalent, followed by MLP, NN, and CNN. Hybrid models, integrating multiple techniques, consistently demonstratedenhancedperformance.

A critical observation was the limited explicit consideration of explainability aspects in the reviewed papers. While some studies employed interpretable-by-design models (ante-hoc explainability) like Decision Trees, a dedicated discussionontheexplainabilityofmodeldecisions wasoftenabsent.Foropaque"black-box"models, post-hocexplainabilitymethods,particularlySHAP andLIME,wereidentifiedasthemostfrequently employed techniques when explainability was addressed. This highlights a significant research gap: despite the recognized importance of transparencyforbuildingtrustinAIsystems,the integrationandexplicitdiscussionofXAIincrime predictionmodelsremainunderdeveloped.

V. CHALLENGES & FUTURE DIRECTIONS

Future techniques for improving the accuracy of crime predictionwillmovebeyondtraditionalmethodstoembrace advanceddeeplearningarchitectures,sophisticatedhybrid models,andnoveldatasources.

GraphNeuralNetworks(GNNs)arepoisedtobecomeakey tool,astheycanmodelthecomplexnetworkofrelationships betweendifferentcrimelocations,individuals,andevents, offeringamorenuancedunderstandingthanisolateddata points.Anotherpowerfuldeeplearningapproachinvolves

Transformermodels,whichwereoriginallyusedinnatural languageprocessing,areexcellentatidentifyinglong-range patternsintime-seriesdata,makingthemhighlyeffectivefor forecastingcrimetrendsoverextendedperiods.

Furthermore, hybrid models will play a crucial role in combining the strengths of different techniques. For example,integratingdeeplearningmodelslike

LSTMs (for temporal patterns) combined with statistical methods, such as ARIMA, can create a more robust and accuratepredictiveframework.Thisfusionofmethodologies is part of a broader trend toward multimodal data fusion, wherefuturemodelswillcombinetraditionalcrimerecords withawidervarietyofunstructureddata,includingurban planning information, public transit usage, and real-time sensor data,toprovide a more comprehensive picture for analysis.Theseadvancedtechniques,whencombined,are expectedtosignificantlyenhancethepredictivepowerand accuracyofcrimepredictionsystems

VI. CONCLUSION

learning on crime prediction. These advanced analytical tools have significantly enhanced the capacity of law enforcement agencies to identify crime patterns, forecast future occurrences, and optimize resource allocation, therebycontributingtoproactiveprevention.Theincreasing availabilityofpublicdatasets,particularlyfrommajorurban centers, has been instrumental in driving innovation and fosteringresearchinthisfield.

However, this review also highlights several critical challenges that necessitate concerted future efforts. Paramount among these is the imperative to develop AI modelsthatarenotonlyhighlyaccuratebutalsoinherently transparent, interpretable, and trustworthy. The current literature reveals a notable gap in the explicit integration and discussion of explainable AI (XAI) techniques, particularly for complex "black-box" models. Addressing issues of fairness, accountability, privacy, and potential biasesembeddedwithinAIsystemsiscrucialforensuring theirethicalandsociallyresponsibledeploymentinsensitive contexts,suchascriminaljustice.Furthermore,enhancing dataquality,diversity,andaccessibility,especiallyforless frequentlystudiedcrimetypesandregions,remainsvitalfor improvement.

Futureresearchshouldprioritizethedevelopmentofnovel XAImethodstailoredforcrimepredictiontoenableclearer insightsintomodeldecisionmakingprocesses.Thisincludes exploringbothante-hoc(interpretable-by-design)andposthoc explanation techniques, rigorously evaluating their effectiveness, and integrating them into practical applications in real-world scenarios. Additionally, efforts shouldfocusoncreatingrobust,scalable,andethicallysound AI frameworks that can effectively leverage diverse data sourceswhilemitigatingtheinherentbiases.Bybridgingthe gapbetweenpredictivepowerandinterpretability,thefield canadvancetowardsmoreeffective,equitable,andpublicly trustedAI-drivencrimepreventionstrategies.

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