{"title":"提高人口数据粒度:利用激光雷达、POI 和二次编程的综合方法","authors":"Xinyue Ye , Weishan Bai , Wenyu Wang , Xiao Huang","doi":"10.1016/j.cities.2024.105223","DOIUrl":null,"url":null,"abstract":"<div><p>This research presents a sophisticated framework for the precise downscaling of population data from census blocks to individual residential units, employing an integration of housing unit characteristics. The aim was to devise and substantiate a thorough methodology for the distribution of households within specific residential buildings. Utilizing the Microsoft Building Footprint dataset, LiDAR remote sensing, and Point of Interest (POI) data, a detailed inventory of residential structures was compiled. A quadratic programming model and Monte Carlo Simulation techniques were applied independently for the strategic allocation of households to these buildings. For validation, this study conducted a comparative analysis between the two methods. The outcomes revealed that the quadratic programming model provided superior precision and detail in population data compared to the Monte Carlo Simulation technique. Consequently, the quadratic programming model significantly enhances the granularity of population distribution data, offering a valuable tool for more informed decision-making.</p></div>","PeriodicalId":48405,"journal":{"name":"Cities","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing population data granularity: A comprehensive approach using LiDAR, POI, and quadratic programming\",\"authors\":\"Xinyue Ye , Weishan Bai , Wenyu Wang , Xiao Huang\",\"doi\":\"10.1016/j.cities.2024.105223\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This research presents a sophisticated framework for the precise downscaling of population data from census blocks to individual residential units, employing an integration of housing unit characteristics. The aim was to devise and substantiate a thorough methodology for the distribution of households within specific residential buildings. Utilizing the Microsoft Building Footprint dataset, LiDAR remote sensing, and Point of Interest (POI) data, a detailed inventory of residential structures was compiled. A quadratic programming model and Monte Carlo Simulation techniques were applied independently for the strategic allocation of households to these buildings. For validation, this study conducted a comparative analysis between the two methods. The outcomes revealed that the quadratic programming model provided superior precision and detail in population data compared to the Monte Carlo Simulation technique. Consequently, the quadratic programming model significantly enhances the granularity of population distribution data, offering a valuable tool for more informed decision-making.</p></div>\",\"PeriodicalId\":48405,\"journal\":{\"name\":\"Cities\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.0000,\"publicationDate\":\"2024-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Cities\",\"FirstCategoryId\":\"96\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0264275124004372\",\"RegionNum\":1,\"RegionCategory\":\"经济学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"URBAN STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cities","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0264275124004372","RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"URBAN STUDIES","Score":null,"Total":0}
Enhancing population data granularity: A comprehensive approach using LiDAR, POI, and quadratic programming
This research presents a sophisticated framework for the precise downscaling of population data from census blocks to individual residential units, employing an integration of housing unit characteristics. The aim was to devise and substantiate a thorough methodology for the distribution of households within specific residential buildings. Utilizing the Microsoft Building Footprint dataset, LiDAR remote sensing, and Point of Interest (POI) data, a detailed inventory of residential structures was compiled. A quadratic programming model and Monte Carlo Simulation techniques were applied independently for the strategic allocation of households to these buildings. For validation, this study conducted a comparative analysis between the two methods. The outcomes revealed that the quadratic programming model provided superior precision and detail in population data compared to the Monte Carlo Simulation technique. Consequently, the quadratic programming model significantly enhances the granularity of population distribution data, offering a valuable tool for more informed decision-making.
期刊介绍:
Cities offers a comprehensive range of articles on all aspects of urban policy. It provides an international and interdisciplinary platform for the exchange of ideas and information between urban planners and policy makers from national and local government, non-government organizations, academia and consultancy. The primary aims of the journal are to analyse and assess past and present urban development and management as a reflection of effective, ineffective and non-existent planning policies; and the promotion of the implementation of appropriate urban policies in both the developed and the developing world.