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数据分析与知识发现  2020, Vol. 4 Issue (9): 81-90     https://doi.org/10.11925/infotech.2096-3467.2020.0156
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
居民地变化的空间分布及社会经济驱动力分析——以浙江省为例*
周衡1,陈张建2,李爱勤2,成晓强3,4(),吴华意1,5
1武汉大学测绘遥感信息工程国家重点实验室 武汉 430079
2浙江省测绘科学技术研究院 杭州 311100
3湖北大学资源环境学院 武汉 430062
4湖北大学区域开发与环境响应湖北省重点实验室 武汉 430062
5地球空间信息技术协同创新中心 武汉 430079
Spatial Distribution and Socio-economic Driving Forces of Residential Changes: Case Study of Zhejiang Province
Zhou Heng1,Chen Zhangjian2,Li Aiqin2,Cheng Xiaoqiang3,4(),Wu Huayi1,5
1State Key Laboratory of Information Engineering in Surveying,Mapping and Remote Sensing,Wuhan University, Wuhan 430079, China
2Zhejiang Academy of Surveying & Mapping, Hangzhou 311100, China
3Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China
4Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China
5Collaborative Innovation Center of Geospatial Technology, Wuhan 430079, China
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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。

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周衡
陈张建
李爱勤
成晓强
吴华意
关键词 居民地变化驱动力社会经济因子偏最小二乘回归    
Abstract

[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.

Key wordsChanges in Residential Elements    Driving Force    Socio-economic Factors    Partial Least Squares Regression
收稿日期: 2020-03-03      出版日期: 2020-10-14
ZTFLH:  P283  
基金资助:*本文系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”(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”的研究成果之一(41501443)
通讯作者: 成晓强     E-mail: carto@hubu.edu.cn
引用本文:   
周衡,陈张建,李爱勤,成晓强,吴华意. 居民地变化的空间分布及社会经济驱动力分析——以浙江省为例*[J]. 数据分析与知识发现, 2020, 4(9): 81-90.
Zhou Heng,Chen Zhangjian,Li Aiqin,Cheng Xiaoqiang,Wu Huayi. Spatial Distribution and Socio-economic Driving Forces of Residential Changes: Case Study of Zhejiang Province. Data Analysis and Knowledge Discovery, 2020, 4(9): 81-90.
链接本文:  
http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2020.0156      或      http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I9/81
Fig.1  未筛选的居民地变化结果
Fig.2  图斑紧凑度频数分布直方图
Fig.3  筛选后居民地变化结果
居民地变化指标 总计 均值 标准差 最小值 最大值
变化数目 1 905 691 21 412 32 380 2 189 791
变化面积(km2 221.01 2.48 2.65 0.05 14.74
Table 1  居民地变化指标统计特征
Fig.4  浙江省居民地变化的空间分布
Fig.5  浙江省居民地变化的空间集聚特征图
探测领域 探测因子 指标
经济发展 X1经济规模 人均GDP(元)
产业结构 X2第一产业发展 第一产业占比(%)
X3第二产业发展 第二产业占比(%)
X4第三产业发展 第三产业占比(%)
政府公共投入 X5政府公共投入程度 人均一般公共预算支出(元)
地方规模 X6人口规模 区县常驻人口数(人)
X7建筑规模 建筑物总面积(km2)
X8建筑物密度 建筑物面积占行政区划面积的比(%)
社会群体收支 X9消费水平 人均社会消费品零售额(元)
X10可支配收入水平 人均住户存款年末余额(元)
Table 2  社会经济驱动因子探测指标体系
Fig.6  居民地变化与社会经济因子的偏最小二乘回归模型预测图
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10
Y1 -0.047 0.050 0.336 -0.054 -0.100 0.146 0.042 0.088 0.081 0.294
Y2 0.017 0.079 0.241 -0.065 -0.051 0.096 0.015 0.100 0.098 0.217
Table 3  偏最小二乘回归模型回归系数
Fig.7  偏最小二乘回归模型回归系数直方图
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[1] 周衡, 陈张建, 李爱勤, 成晓强, 吴华意. 居民地变化的空间分布及社会经济驱动力分析—以浙江省为例 [J]. 数据分析与知识发现, 0, (): 1-.
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