[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117493-en":3,"doc-seo-117493-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},117493,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","Spati​ally Explicit Machine Learning Approaches for House Price Models - Doctoral Dissertation","Spatial data carry geographic coordinates that enable identification of spatial patterns, relationships, and trends among spatial objects. Because spatial objects often exhibit spatial autocorrelation, spatial statistical techniques like clustering, interpolation, regression, and simulation are widely used. Mainstream machine learning typically lacks explicit spatial effects or context, motivating spatially explicit machine learning that integrates spatial effects into model design. Using Franklin County, OH residential transaction data, this work compares three data-driven approaches for submarket delineation, spatially weighted supervision, and spatial feature engineering for multiscale effects.","SPATIALLY EXPLICIT MACHINE LEARNING APPROACHES  \nFOR HOUSE PRICE MODELS  \nby  \nMeifang Chen  \nAPPROVED BY SUPERVISORY COMMITTEE:  \n\n| Yongwan Chun, Chair |\n| --- |\n| Daniel A. Griffith |\n| Fang Qiu |\n\nDohyeong Kim  \nCopyright 2023 Meifang Chen All Rights Reserved  \nTo my husband and my church, Thank you for your full support and encouragement, I love you.  \nSPATIALLY EXPLICIT MACHINE LEARNING APPROACHES  \nFOR HOUSE PRICE MODELS  \nby  \nMEIFANG CHEN, BS, MS  \nDISSERTATION  \nPresented to the Faculty of The University of Texas at Dallas in Partial Fulfillment  \nof the Requirements  \nfor the Degree of  \nDOCTOR OF PHILOSOPHY IN  \nGEOSPATIAL INFORMATION SCIENCES  \nTHE UNIVERSITY OF TEXAS AT DALLAS  \nACKNOWLEDGEMENTS  \nI am deeply grateful to my advisor, Dr. Yongwan Chun, whose guidance, encouragement, and expertise were instrumental in the completion of this dissertation. Your knowledge, insights, and patience have been invaluable throughout my doctoral studies. Many thanks to Dr. Daniel Griffith who provides valuable feedback, insights, and suggestions for my research.  \nI would also like to thank all my committee members for being collaborative and patient with me throughout this process. Your feedback, constructive criticism, and recommendations helped in refining the research and making it more comprehensive.  \nSpecial thanks to my husband for giving me full support in every aspect to ensure my success. Pursuing a PhD is never an easy task. Without you, I could never achieve this. Your love, understanding, encouragement, and unwavering belief in me have sustained me through all the challenges in this journey.  \nFinally, my gratitude extends to all the participants who generously gave their time and insights to this research.  \nApril 2023  \nSPATIALLY EXPLICIT MACHINE LEARNING APPROACHES  \nFOR HOUSE PRICE MODELS  \nMeifang Chen, PhD  \nThe University of Texas at Dallas, 2023  \nSupervising Professor: Yongwan Chun, Chair  \nSpatial data or georeferenced data are special in that it has spatial reference, meaning that it is linked with geographic coordinates on Earth. The spatial component allows for the identification of spatial patterns, relationships and trends among spatial objects. Spatial objects are usually not randomly or independently distributed, but spatially autocorrelated. In spatial data analysis, spatial autocorrelation has been well recognized with the advocate of spatial statistical techniques, such as spatial clustering, spatial interpolation, spatial regression, and spatial simulation. However, spatial effects or spatial context is largely absent in mainstream machine learning methods. With the popularity of machine learning in various applications in both industry and academia, a new research area has emerged in the spatial community: spatial explicit machine learning. It refers to the use of machine learning algorithms to analyze and predict spatial data with the explicit integration of spatial effects or patterns. It is expected to improve the model accuracy and prediction by incorporating spatial relationships or patterns in the data that have not been captured by traditional machine learning models and, subsequently, to gain better understanding of the data generation mechanism.  \nThis research utilizes Franklin County, OH residential house transaction data to explore three different data-driven approaches to integrate spatial perspectives into traditional machine learning algorithms: 1) imposing spatial constraints on unsupervised learning to delineate spatially constrained housing submarkets ; 2) integrating spatial weights into the cost function of supervised learning to improve house price prediction accuracy; and 3) enhancing data input using spatial feature engineering in tree-based ensemble learning for modeling multiscale spatial effects. It intends to contribute new insights for spatially explicit machine learning to the literature.  \nOverall, three studies explore spatially explicit machine learning metho","cbCaimfhlvcpivoL","https://ap.wps.com/l/cbCaimfhlvcpivoL","pdf",2490540,1,117,"English","en",105,"# Acknowledgements\n# Abstract\n# List of Figures\n# List of Tables\n# Chapter 1 Introduction\n# Chapter 2 Space-Time Housing Submarket Delineation Using Spatially Constrained Data-Driven Approaches\n## 2.1 Introduction\n## 2.2 Literature review\n## 2.3 Methodology","[{\"question\":\"What is spatially explicit machine learning and why is it needed for house price modeling?\",\"answer\":\"It integrates spatial effects or patterns directly into machine learning algorithms. It is intended to improve accuracy and help explain data generation mechanisms that traditional machine learning may miss.\"},{\"question\":\"What dataset and study area are used in this dissertation?\",\"answer\":\"The dissertation uses Franklin County, OH residential house transaction data to explore ways to incorporate spatial perspectives into traditional machine learning.\"},{\"question\":\"What are the three approaches proposed to incorporate spatial perspectives?\",\"answer\":\"The work uses: (1) spatial constraints for unsupervised learning to delineate housing submarkets, (2) spatial weights added to the cost function for supervised learning to enhance prediction, and (3) spatial feature engineering in tree-based ensemble learning to model multiscale spatial effects.\"}]","Spati​ally Explicit Machine Learning Approaches for House Price Models - Doctoral Dissertation | PDF",1785676215,295,{"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},"spatially-explicit-machine-learning-approaches-for-house-price-models-doctoral-dissertation","",{"@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/spatially-explicit-machine-learning-approaches-for-house-price-models-doctoral-dissertation/117493/",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","2026-08-05","2026-08-02",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 spatially explicit machine learning and why is it needed for house price modeling?","Question",{"text":76,"@type":77},"It integrates spatial effects or patterns directly into machine learning algorithms. It is intended to improve accuracy and help explain data generation mechanisms that traditional machine learning may miss.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What dataset and study area are used in this dissertation?",{"text":81,"@type":77},"The dissertation uses Franklin County, OH residential house transaction data to explore ways to incorporate spatial perspectives into traditional machine learning.",{"name":83,"@type":74,"acceptedAnswer":84},"What are the three approaches proposed to incorporate spatial perspectives?",{"text":85,"@type":77},"The work uses: (1) spatial constraints for unsupervised learning to delineate housing submarkets, (2) spatial weights added to the cost function for supervised learning to enhance prediction, and (3) spatial feature engineering in tree-based ensemble learning to model multiscale spatial effects.","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":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]