{"title":"珞珈 1-01 夜光成像与城市社区平均居住价格之间的时空非稳态鲁棒建模","authors":"Chang Li;Linqing Zou;Yinfei He;Bo Huang;Yan Zhao","doi":"10.1109/JSTARS.2024.3456376","DOIUrl":null,"url":null,"abstract":"This article is the first to propose a novel spatiotemporal nonstationary robust modeling between high spatial resolution Luojia1-01 night-time light intensity (NTLI) and urban community average residence price (UCARP), which encodes the spatiotemporal independent variable NTLI based on a new proposed geographical coding (GeoCode) to enhance the explanatory power of NTLI and leverages geographically and temporally weighted regression (GTWR) based on a new proposed spatiotemporal anomaly detection (STAD) to remove spatiotemporal outliers and then to robustly estimate modeling result. UCARP data and Luojia1-01 NTL imagery obtained from Wuhan, China, in June, September and October 2018 were crawled and downloaded for the experiment, whose results show that GTWR performs better than geographically weighted regression and temporally weighted regression. The comparisons of GTWR with 1) original data; 2) GeoCode (GC); 3) STAD; 4) first STAD last GeoCode (STAD_GC), and 5) first GeoCode last STAD (GC_STAD) show that 1) the \n<italic>q</i>\n values of geographical detector corresponding to the above methods are 0.055, 0.407, 0.126, 0.666, and 0.671, respectively, during September; 2) the adjusted \n<italic>R</i>\n<sup>2</sup>\n values of GTWR are 0.460, 0.488, 0.683, 0.693, and 0.697, respectively; and 3) the proposed spatiotemporal data processing scheme, i.e., GC_STAD, has the most robust and best precision. This article not only proposes a new spatiotemporal nonstationary robust modeling method between small-scale NTL and UCARP but also reveals its underlying mechanism.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":null,"pages":null},"PeriodicalIF":4.7000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681033","citationCount":"0","resultStr":"{\"title\":\"Spatiotemporal Nonstationary Robust Modeling Between Luojia1-01 Night-Time Light Imagery and Urban Community Average Residence Price\",\"authors\":\"Chang Li;Linqing Zou;Yinfei He;Bo Huang;Yan Zhao\",\"doi\":\"10.1109/JSTARS.2024.3456376\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article is the first to propose a novel spatiotemporal nonstationary robust modeling between high spatial resolution Luojia1-01 night-time light intensity (NTLI) and urban community average residence price (UCARP), which encodes the spatiotemporal independent variable NTLI based on a new proposed geographical coding (GeoCode) to enhance the explanatory power of NTLI and leverages geographically and temporally weighted regression (GTWR) based on a new proposed spatiotemporal anomaly detection (STAD) to remove spatiotemporal outliers and then to robustly estimate modeling result. UCARP data and Luojia1-01 NTL imagery obtained from Wuhan, China, in June, September and October 2018 were crawled and downloaded for the experiment, whose results show that GTWR performs better than geographically weighted regression and temporally weighted regression. The comparisons of GTWR with 1) original data; 2) GeoCode (GC); 3) STAD; 4) first STAD last GeoCode (STAD_GC), and 5) first GeoCode last STAD (GC_STAD) show that 1) the \\n<italic>q</i>\\n values of geographical detector corresponding to the above methods are 0.055, 0.407, 0.126, 0.666, and 0.671, respectively, during September; 2) the adjusted \\n<italic>R</i>\\n<sup>2</sup>\\n values of GTWR are 0.460, 0.488, 0.683, 0.693, and 0.697, respectively; and 3) the proposed spatiotemporal data processing scheme, i.e., GC_STAD, has the most robust and best precision. This article not only proposes a new spatiotemporal nonstationary robust modeling method between small-scale NTL and UCARP but also reveals its underlying mechanism.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10681033\",\"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/10681033/\",\"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}","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/10681033/","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}
Spatiotemporal Nonstationary Robust Modeling Between Luojia1-01 Night-Time Light Imagery and Urban Community Average Residence Price
This article is the first to propose a novel spatiotemporal nonstationary robust modeling between high spatial resolution Luojia1-01 night-time light intensity (NTLI) and urban community average residence price (UCARP), which encodes the spatiotemporal independent variable NTLI based on a new proposed geographical coding (GeoCode) to enhance the explanatory power of NTLI and leverages geographically and temporally weighted regression (GTWR) based on a new proposed spatiotemporal anomaly detection (STAD) to remove spatiotemporal outliers and then to robustly estimate modeling result. UCARP data and Luojia1-01 NTL imagery obtained from Wuhan, China, in June, September and October 2018 were crawled and downloaded for the experiment, whose results show that GTWR performs better than geographically weighted regression and temporally weighted regression. The comparisons of GTWR with 1) original data; 2) GeoCode (GC); 3) STAD; 4) first STAD last GeoCode (STAD_GC), and 5) first GeoCode last STAD (GC_STAD) show that 1) the
q
values of geographical detector corresponding to the above methods are 0.055, 0.407, 0.126, 0.666, and 0.671, respectively, during September; 2) the adjusted
R
2
values of GTWR are 0.460, 0.488, 0.683, 0.693, and 0.697, respectively; and 3) the proposed spatiotemporal data processing scheme, i.e., GC_STAD, has the most robust and best precision. This article not only proposes a new spatiotemporal nonstationary robust modeling method between small-scale NTL and UCARP but also reveals its underlying mechanism.
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.