[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127572-en":3,"doc-seo-127572-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127572,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Crime Inference Using Machine Learning and Geographical Data","Crimes are influenced by environmental and socio-demographic conditions rather than occurring randomly. By characterizing local contexts, the study builds algorithms to predict criminal activity in specific places and time periods, enabling earlier anticipation and prevention through public-policy decisions. The work identifies the most informative feature types and the most predictive modeling approaches, and evaluates them using data from Philadelphia, Pennsylvania, across multiple years.","Crime Inference Using Machine Learning and Geographical Data  \nMiguel Roque  \nDissertation written under the supervision of professor Nicolò  \nBertani.  \nDissertation submitted in partial fulfilment of requirements for the MSc in Business Analytics, at the Universidade Católica Portuguesa,  \nJanuary 4th, 2023.  \nABSTRACT  \nCrimes are not random events in society, and eventually something must influence their occurrence. It is by characterizing the environment that it is possible to create algorithms that predict the criminal activity in a certain place and at some point in time, which allows its anticipation and prevention through decision-making in public policy.  \nThis study focusses on finding the best way to predict crimes, that is, which types of features are the most important to consider while predicting crimes, and which methods are the most predictive.  \nAn analysis of the city of Philadelphia, in the state of Pennsylvania (USA), is made, taking into account the urban, racial, demographic and socioeconomic characteristics of its different geographical blocks, and the number of criminal occurrences in each of them, over multiple years. The methods used are both linear and non-linear.  \nWhen non-linear methods are used, via machine learning techniques, it is evident that the prediction of the number of crimes is much more assertive for any type of variable, leading to the conclusion that the relationships studied here are not linear in nature, and therefore treebased models (especially gradient boosting and random forest) represent the most suitable approach for this data. In this perspective, the models that consider only the socio-demographic characteristics of the neighborhoods are significantly more effective in forecasting than the entirely urban ones.  \n▪ Title: Crime Inference Using Machine Learning and Geographical Data  \n▪ Author: Miguel Francisco Frade Roque  \n▪ Keywords: crimes; socio-demographic; urban; linear; non-linear.  \nABSTRATO  \nOs crimes não são eventos aleatórios na sociedade e, eventualmente, algo deve influenciar asua ocorrência. É pela caracterização do ambiente que é possível criar algoritmos que preveema atividade criminosa num determinado local e em algum momento no tempo, o que permite asua antecipação e prevenção por meio das tomadas de decisão na política pública.  \nEste estudo foca-se em encontrar a melhor forma de prever crimes, ou seja, que tipos de características são as mais importantes a considerar na previsão de crimes, e que métodos são os mais preditivos.  \nÉ feita uma análise da cidade de Filadélfia, no estado da Pensilvânia (EUA), tendo em consideração as características urbanas, raciais, demográficas e socioeconómicas dos seus diferentes quarteirões geográficos, e o número de ocorrências criminais em cada um deles, ao longo de vários anos. Os métodos utilizados são lineares e não lineares.  \nQuando são utilizados métodos não lineares, através de técnicas de machine learning, fica evidente que a previsão do número de crimes é muito mais assertiva para qualquer tipo devariável, levando à conclusão de que as relações aqui estudadas não são de natureza linear e, portanto, modelos baseados em árvores de decisão (especialmente gradient boosting e random forest) representam a abordagem mais adequada para estes dados. Nessa perspetiva, os modelos que consideram apenas as características sociodemográficas dos bairros são significativamentemais eficazes na previsão do que os inteiramente urbanos.  \n▪ Título: Crime Inference Using Machine Learning and Geographical Data  \n▪ Autor: Miguel Francisco Frade Roque  \n▪ Palavras-chave: crimes; socio-demographic; urban; linear; non-linear.  \nCONTENTS  \n1. INTRODUCTION.............................................................................................................. 1  \n2. BACKGROUND................................................................................................................ 4  \n2.1. CONTEXT OF CRIME IN THE USA.........","cbCaigflQoDJinLO","https://ap.wps.com/l/cbCaigflQoDJinLO","pdf",1341028,1,37,"English","en",105,"# Introduction\n# Background\n## Context of Crime in the USA\n## Literature Review on Forecasting Crime\n# Data\n# Analysis\n## Descriptive Statistics\n### Correlation\n### Variables Distributions\n## Metrics\n## Models\n### Linear Regression\n### Decision Tree\n### Gradient Boosting\n### Random Forest\n## Feature Selection\n### Socio-Demographic Features\n### Urban Features and Mixed Models\n## Results\n### Linear Models","[{\"question\":\"What problem does the study address in crime forecasting?\",\"answer\":\"It investigates how to predict criminal activity in a specific place and time by identifying influential environmental conditions and suitable modeling methods.\"},{\"question\":\"Which types of models are found to perform best?\",\"answer\":\"Non-linear machine learning approaches outperform linear ones; tree-based models, especially gradient boosting and random forest, are the most suitable for the data.\"},{\"question\":\"What variables are used in the Philadelphia analysis?\",\"answer\":\"The study uses geographical blocks’ urban, racial, demographic, and socioeconomic characteristics, along with the number of criminal occurrences over multiple years.\"}]","Crime Inference Using Machine Learning and Geographical Data | PDF",1785940040,93,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"crime-inference-using-machine-learning-and-geographical-data","",{"@graph":36,"@context":85},[37,54,68],{"@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/crime-inference-using-machine-learning-and-geographical-data/127572/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in crime forecasting?","Question",{"text":75,"@type":76},"It investigates how to predict criminal activity in a specific place and time by identifying influential environmental conditions and suitable modeling methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which types of models are found to perform best?",{"text":80,"@type":76},"Non-linear machine learning approaches outperform linear ones; tree-based models, especially gradient boosting and random forest, are the most suitable for the data.",{"name":82,"@type":73,"acceptedAnswer":83},"What variables are used in the Philadelphia analysis?",{"text":84,"@type":76},"The study uses geographical blocks’ urban, racial, demographic, and socioeconomic characteristics, along with the number of criminal occurrences over multiple years.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]