Andreas Braun;Christian Khouri;Oliver Assmann;Gebhard Warth;Michael Schultz;Volker Hochschild
{"title":"Urban Morphologic Structures Retrieved by Satellite Imagery Correlate With Socioeconomic Household Data—Insights From the City of Kigali, Rwanda","authors":"Andreas Braun;Christian Khouri;Oliver Assmann;Gebhard Warth;Michael Schultz;Volker Hochschild","doi":"10.1109/JSTARS.2024.3466298","DOIUrl":null,"url":null,"abstract":"A substantial body of research exists on the use of remote sensing in urban contexts. However, only a limited number of studies have contributed to our understanding of the socioeconomic conditions of different urban areas. This research aims to demonstrate the potential of very high-resolution images and geospatial data by examining the interrelations between socioeconomic data retrieved from household surveys in the city of Kigali and spatial data on urban morphology retrieved by satellite imagery. As the surveys yielded large amounts of data of varying levels of measurement (categorical and numeric), we present different methods of statistical correlation, data mining, and machine learning to highlight socioeconomic patterns within the spatial data. The results demonstrate a significant correlation between the share of different building types, building density, average building heights, and distances to public infrastructure with a range of surveyed data, including building properties, household members, financial resources, and overall lifestyle habits. This highlights the potential of remote sensing and geospatial data to provide valuable insights into the socioeconomic conditions of urban areas. It also underscores the importance of using advanced statistical methods, data mining, and machine learning to enhance our understanding of urban morphology and its socioeconomic implications. However, it is important to acknowledge the limitations of such approaches, including the lack of information on ownership, potential for false inference and the direction of causation, which require further investigation.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10690174","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10690174/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
A substantial body of research exists on the use of remote sensing in urban contexts. However, only a limited number of studies have contributed to our understanding of the socioeconomic conditions of different urban areas. This research aims to demonstrate the potential of very high-resolution images and geospatial data by examining the interrelations between socioeconomic data retrieved from household surveys in the city of Kigali and spatial data on urban morphology retrieved by satellite imagery. As the surveys yielded large amounts of data of varying levels of measurement (categorical and numeric), we present different methods of statistical correlation, data mining, and machine learning to highlight socioeconomic patterns within the spatial data. The results demonstrate a significant correlation between the share of different building types, building density, average building heights, and distances to public infrastructure with a range of surveyed data, including building properties, household members, financial resources, and overall lifestyle habits. This highlights the potential of remote sensing and geospatial data to provide valuable insights into the socioeconomic conditions of urban areas. It also underscores the importance of using advanced statistical methods, data mining, and machine learning to enhance our understanding of urban morphology and its socioeconomic implications. However, it is important to acknowledge the limitations of such approaches, including the lack of information on ownership, potential for false inference and the direction of causation, which require further investigation.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.