摘要
【目的】准确把握测绘地理要素的变化特征及驱动机理,提高基础测绘效益。【方法】以浙江省居民地为例,综合运用GIS叠置分析、集聚分析和相关性分析方法,系统剖析居民地变化的社会经济驱动力。【结果】研究表明,居民地变化集中分布在浙江省的北部、中部和东南部;第二产业发展与居民地变化数目的相关系数为0.336,是居民地变化的主要驱动力;第三产业发展、政府公共投入与居民地变化数目的相关系数分别为-0.054和-0.100,对居民地变化有负驱动作用。【局限】制图综合等人为因素导致居民地存在"伪变化",因此变化统计数据的精确性有待进一步提升。【结论】浙江省居民地变化呈现明显的区域差异,且不同经济指标对居民地变化驱动作用的程度、方向各异。
[Objective]This paper aims to identify the changes of geographic elements in surveying and mapping,as well as their driving mechanism.[Methods]We collected the changes of residential areas from Zhejiang Province.With the help of GIS overlay and correlation analysis,we analyzed the socio-economic driving forces behind these changes.[Results]We found that the changes were concentrated in the north,central and southeast parts of Zhejiang Province.The development of industry was the main positive driving force(correlation coefficient:0.336).The development of the service or retail sectors and government public investments were negative driving forces for the changes(correlation coefficients:-0.054 and-0.100).[Limitations]The accuracy of statistical data needs to be further improved to reduce the"false changes"from cartographic synthesis.[Conclusions]The changes in residential areas were different and their economic driving factors were also different.
作者
周衡
陈张建
李爱勤
成晓强
吴华意
Zhou Heng;Chen Zhangjian;Li Aiqin;Cheng Xiaoqiang;Wu Huayi(State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University,Wuhan 430079,China;Zhejiang Academy of Surveying&Mapping,Hangzhou 311100,China;Faculty of Resources and Environmental Science,Hubei University,Wuhan 430062,China;Key Laboratory of Regional Development and Environmental Response,Hubei University,Wuhan 430062,China;Collaborative Innovation Center of Geospatial Technology,Wuhan 430079,China)
出处
《数据分析与知识发现》
CSSCI
CSCD
北大核心
2020年第9期81-90,共10页
Data Analysis and Knowledge Discovery
基金
国家重点研发计划项目“城市群经济区域建设与管理空间信息重点服务及应用示范”(项目编号:2017YFB0503802)
国家自然科学基金项目“基于视觉认知的网络混搭地图易读性评价与优化方法研究”(项目编号:41501443)的研究成果之一。
关键词
居民地变化
驱动力
社会经济因子
偏最小二乘回归
Changes in Residential Elements
Driving Force
Socio-economic Factors
Partial Least Squares Regression