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作 者:赵子辉[1,2,3] 王延仓 魏艳娜 孙红艳[1,2] Zhao Zihui;Wang Yancang;Wei Yanna;Sun Hongyan(School of Remote Sensing and Information Engineering,North China Institute of Aerospace Engineering,Langfang 065000,China;Collaborative Innovation Center of Aerospace Remote Sensing Information Processing and Application of Hebei Province,Langfang 065000,China;Hebei Normal University,Shijiazhuang 050000,China)
机构地区:[1]北华航天工业学院遥感信息工程学院,河北廊坊065000 [2]河北省航天遥感信息处理与应用协同创新中心,河北廊坊065000 [3]河北师范大学,河北石家庄050000
出 处:《北华航天工业学院学报》2022年第4期21-23,50,共4页Journal of North China Institute of Aerospace Engineering
基 金:河北省高等学校人文社会科学研究重点项目(SD191048);河北省社会科学基金项目(HB19JL016);廊坊市科技支撑计划项目(2019011027)
摘 要:针对以往研究对住宅价格时空演变规律挖掘方面的不足,本文采用时空立方体模型对2013-2018年京津冀一体化影响下的廊坊市住宅价格进行空间依赖、时空格局与演变规律的研究结果表明:(1)廊坊市住宅价格发展经历三个阶段:①低低聚类的平稳增长期(2012.07-2015.07),②高低聚类的暴涨演化期(2015.07-2017.01),③高高聚类的稳高趋缓期(2017.01-2018.07);(2)研究时段住宅价格中81.3%由冷点转变为热点,表现为震荡的热点,不同区域冷热点转变时间节点不同;(3)采用时空聚类将住宅价格分为4类:极高值敏感区、环京高值区、环京次高值区、边缘低值区,与环京程度呈现正相关的圈层结构。本文方法论证了在住宅价格时空演变研究中,时空模型可以更有效地揭示价格在时空域的变化过程及演变规律。In view of the shortcomings of previous studies on the mining of the spatial-temporal evolution law of housing price,this paper studies the spatial-temporal pattern and evolution law of housing price in Langfang under the influence of Beijing-Tianjin-Hebei integration from 2013 to 2018 through spatial auto-correlation and space-time cube.The results show that:(1)The development of housing price in Langfang has experienced three stages;(2)In the study period,81.3%of the housing prices changed from cold spot to hot spot.The hot spot is relatively volatile,and the transition time nodes of cold and hot spots are different in different regions;(3)Through spatial-temporal clustering,housing prices can be divided into four categories:high value sensitive area,high value area around Beijing,sub high value area around Beijing and low value area on the edge,which are positively correlated with the degree of surrounding Beijing.this paper demonstrates that the spatial-temporal model can play a positive role in revealing the change process and evolution law of housing price in spatial-temporal domain.
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