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Regression trees, random forests, and evolutionary random forest models are trained using data from nine counties to generate county-level forecasts. The findings show that lagged crime rates deliver the strongest predictive performance, while economic variables improve accuracy especially for property crime. The study also outlines directions for extension using more detailed levels and larger datasets.","Bowling Green State University  \nScholarWorks@BGSU  \n\n| Honors Projects | Honors College |\n| --- | --- |\n| Winter 12-9-2024\u003Cbr>Forecasting Crime Rates Utilizing Machine Learning and Economic Indicators\u003Cbr>Owen Miller\u003Cbr>[owemill@bgsu.edu](owemill@bgsu.edu)\u003Cbr>Follow this and additional works at: [https://scholarworks.bgsu.edu/honorsprojects](https://scholarworks.bgsu.edu/honorsprojects)\u003Cbr> Part of the Criminology Commons, and the Econometrics Commons How does access to this work benefit you? Let us know! |  |\n\nRepository Citation  \nMiller, Owen, \"Forecasting Crime Rates Utilizing Machine Learning and Economic Indicators\" (2024) . Honors Projects. 1011.  \n[https://scholarworks.bgsu.edu/honorsprojects/101](https://scholarworks.bgsu.edu/honorsprojects/101)1  \nThis work is brought to you for free and open access by the Honors College at ScholarWorks@BGSU. It has been accepted for inclusion in Honors Projects by an authorized administrator of ScholarWorks@BGSU.  \nForecasting Crime Rates Utilizing Machine Learning and Economic Indicators  \nAuthor:  \nOwen Miller  \nEconomics Major, Bowling Green State University, Bowling Green Ohio  \nAdvisors:  \nDr. Walt Ryley  \nAssistant Teaching Professor, Bowling Green State University, Schmidthorst, Economics Dr. Adam Watkins  \nProfessor, Bowling Green State University, College of Health and Human Services, Criminal Justice  \nAbstract: The following project utilized the correlation between economic indicators and crime rates to build tree-based machine learning algorithms that predict monthly crime rates in Ohio. The goal of the study was to answer how economic variables could be used in forecasting county level crime rates. A sample of 9 counties were utilized in building regression trees, random forests and evolutionary random forest models. The study concluded that lagged crime rates provided the best predictive ability in forecasting models, while economic variables provided more accuracy to property crime. The study could be furthered by creating models atthe department level and expanding the amount of data utilized.  \nKeyWords: Machine Learning, Random Forests, Evolutionary Random Forests, Rational Choice Theory.  \nIntroduction  \nCrime forecasting began to fall out of favor in the 1990s, as crime rates began to fall, and forecasts began to fail (Rosenfield, Berg 2024). For example, the controversial “super predators”theory forecasted a continued increase in the crime waves of the 1980s. But this never happened (DeLisi et al, 2007) . The decline in crime, and the failure of crime forecasts to come to fruition, led to a shift of focus in literature in which forecasting was replaced with a greater emphasis on analyzing the underlying factors that influenced the drastic decline in crime (Levitt, 2004) .  \nThe success ofa forecast is dependent upon using the right models for the data. For instance, analyzing simple data structures allows for the use of simpler models. Meanwhile, more complex data requires models that can account for multiple variables and adapt to changes in trends. Crime is a complex socioeconomic phenomenon, which requires predictive tools that account for its underlying causes.  \nAs forecasting technology improved, crime forecasting experienced a resurgence in the literature (Rosenfield, Berg 2024). Recently, the forecasting industry has embraced machine learning models in place of traditional time series techniques. This is because machine learning methods often provide better accuracy, allow models to adapt to changes in the data-generating process, and can more adeptly accommodate non-linearity.  \nTraditionally, crime forecasting models use special data to determine hotspots through geo-spatial means (Nath, 2006). However, researchers have been increasingly utilizing demographic and economic data to predict crime rates as well. In this study, we investigate the capacity of tree-based machine learning models to forecast county crime rates in Ohio. Specifically, we used cou","cbCaioSyLreQ8gZd","https://ap.wps.com/l/cbCaioSyLreQ8gZd","pdf",440047,1,19,"English","en",105,"# Introduction\n## Research Question\n# Literature Review\n## Economic Indicators and Crime\n## Forecasting Methods and Machine Learning","[{\"question\":\"What is the main research goal of this project?\",\"answer\":\"To determine how economic variables can be used to forecast county-level crime rates in Ohio using machine learning models.\"},{\"question\":\"Which models are used to predict monthly crime rates?\",\"answer\":\"The project builds regression trees, random forests, and evolutionary random forest models.\"},{\"question\":\"What factors were found to be most predictive in the forecasting models?\",\"answer\":\"Lagged crime rates provided the best predictive ability, while economic variables added more accuracy for property crime.\"}]","Forecasting Crime Rates Utilizing Machine Learning and Economic Indicators | PDF",1785734304,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"forecasting-crime-rates-utilizing-machine-learning-and-economic-indicators","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/forecasting-crime-rates-utilizing-machine-learning-and-economic-indicators/121196/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main research goal of this project?","Question",{"text":76,"@type":77},"To determine how economic variables can be used to forecast county-level crime rates in Ohio using machine learning models.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which models are used to predict monthly crime rates?",{"text":81,"@type":77},"The project builds regression trees, random forests, and evolutionary random forest models.",{"name":83,"@type":74,"acceptedAnswer":84},"What factors were found to be most predictive in the forecasting models?",{"text":85,"@type":77},"Lagged crime rates provided the best predictive ability, while economic variables added more accuracy for property crime.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},"General","general"]