[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118377-en":3,"doc-seo-118377-105":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},118377,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine learning prediction of quality of life - Insight from property crime and tropical climate analysis","The study addresses predicting quality of life by leveraging machine learning models using property crime and tropical climate indicators, especially temperature. It contrasts five algorithms—GLM, random forest, decision tree, gradient boosted tree, and support vector machine—based on how each performs with these predictors. Although quality-of-life relationships may start with low correlation, temperature can substantially improve specific models and predictive accuracy, revealing the complexity of feature interactions. Results show SVM as the top performer, followed by RF and DT, supporting an evidence-based research framework.","Machine learning prediction of quality of life: Insight from property crime and tropical climate analysis  \nAnis Zulaikha Mohd Zukri1, Siti Rasidah Md Sakip1,2, Suraya Masrom3  \n1Department of Built Environment and Technology, Universiti Teknologi MARA, Seri Iskandar Campus , Perak, Malaysia 2Green Safe Cities Research Group, Department of Built Environment Studies and Technology, College of Built Environment,  \nUniversiti Teknologi MARA, Perak, Malaysia  \n3Computing Sciences Studies, College of Computing, Informatics and Media, Universiti Teknologi MARA, Perak, Malaysia  \nArticle history:  \nReceived Oct 30, 2023 Revised Jun 21, 2024 Accepted Jun 28, 2024  \nKeywords:  \nMachine learning Prediction  \nProperty crime Quality of life Tropical climate  \nCorresponding Author:  \nThe study addresses the prediction of quality of life, leveraging machine learning models with a focus on health, socioeconomics, subjective well-being, and environmental indicators. Thus, this study aims to evaluate the efficacy of machine learning in quality-of-life prediction based on property crime and temperature. Five machine learning algorithms were used to be empirically compared namely generalized linear model (GLM), random forest (RF), decision tree (DT), gradient boosted tree (GBT) and support vector machine (SVM) are compared empirically. The performance of each machine learning algorithm in predicting the quality of life has been observed based on the attributes of property crime and tropical climate (temperature) . Despite initial low correlation with quality of life, temperature significantly contributes to specific algorithms, enhancing predictive accuracy. This shows the complexity of machine learning impacts. SVM emerges as the best-performing algorithm, followed by RF and DT. The findings highlight the importance of seemingly unrelated factors in prediction outcomes. This paper presents a fundamental research framework useful for helping educatorsand researchers to explore in depth quality of life prediction with using property crime and temperature as a factor.  \nThis is an open access article under the CC BY-SA license.  \nSiti Rasidah Md Sakip  \nDepartment of Built Environment and Technology, Universiti Teknologi MARA Perak Branch, Seri Iskandar Campus, Malaysia  \nEmail: [sitir704@uitm.edu.my](sitir704@uitm.edu.my)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe concept of predicting quality of life has been extensively explored in various studies and papers, utilizing different methods and indicators. Common strategies involve evaluating health status [1]–[3], socioeconomic elements [2], subjective well-being [4], [5], and environmental conditions [6], [7] to forecast and assess quality of life. However, in the realm of urban geography and urban studies, there has been limited attention given to the connection between crime and quality of life, as noted by [8] . Notably, investigations into the intersection of quality of life and crime have been conducted by several researchers including [8]–[15] .  \nFurthermore, numerous studies, including Ranson [16] affirm a correlation between weather and crime. Weather ’s pivotal role in crime prediction is underlined, with hypotheses proposing that weather can impact social interaction-related crime rates. Anderson [17] connect weather with crime production and Becker’s model, while external factors like heat can influence aggression. Research by Ranson [16] reinforce the link between temperature and aggression. Cohn [18] acknowledges weather ’s role in crime theories and it  \nis behavioral effects. The influence of weather on behavior has garnered attention [18], [19], highlighting its relevance in crime dynamics.  \nThe weather-crime relationship received limited attention prior to the 1960s. Several researchers highlighted later studies focusing on weather’s impact on criminal behavior and psychology [18], [20] . Research suggests a direct link between weather and psychological triggers for violen","cbCailK0etqR96kO","https://ap.wps.com/l/cbCailK0etqR96kO","pdf",320436,1,7,"English","en",105,"# Introduction\n## Weather and crime literature\n## Property crime and quality of life focus\n## Machine learning approach","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To evaluate how effectively machine learning can predict quality of life using property crime and tropical climate (temperature) indicators.\"},{\"question\":\"Which machine learning algorithms were compared?\",\"answer\":\"The study empirically compares GLM, random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM).\"},{\"question\":\"Which algorithm performed best for quality-of-life prediction?\",\"answer\":\"SVM performed best overall, followed by RF and DT.\"}]","Machine learning prediction of quality of life - Insight from property crime and tropical climate analysis | PDF",1785683339,18,{"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},"machine-learning-prediction-of-quality-of-life-insight-from-property-crime-and-tropical-climate-analysis","",{"@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/machine-learning-prediction-of-quality-of-life-insight-from-property-crime-and-tropical-climate-analysis/118377/",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","2026-08-02",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 is the main goal of the study?","Question",{"text":75,"@type":76},"To evaluate how effectively machine learning can predict quality of life using property crime and tropical climate (temperature) indicators.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning algorithms were compared?",{"text":80,"@type":76},"The study empirically compares GLM, random forest (RF), decision tree (DT), gradient boosted tree (GBT), and support vector machine (SVM).",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performed best for quality-of-life prediction?",{"text":84,"@type":76},"SVM performed best overall, followed by RF and DT.","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},[93,97,101,105,110,115,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]