[1]梁娟珠,徐泽潭.长三角城市群空间分区与城市边缘区的扩展特征分析[J].华侨大学学报(自然科学版),2022,43(1):102-110.[doi:10.11830/ISSN.1000-5013.202012049]
　LIANG Juanzhu,XU Zetan.Spatial Division in Yangtze River Delta Urban Agglomerations and Expansion Feature Analysis of Rural-Urban Fringe Area[J].Journal of Huaqiao University(Natural Science),2022,43(1):102-110.[doi:10.11830/ISSN.1000-5013.202012049]
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长三角城市群空间分区与城市边缘区的扩展特征分析()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第43卷
期数:
2022年第1期
页码:
102-110
栏目:
出版日期:
2022-01-09

文章信息/Info

Title:
Spatial Division in Yangtze River Delta Urban Agglomerations and Expansion Feature Analysis of Rural-Urban Fringe Area
文章编号:
1000-5013(2022)01-0102-09
作者:
梁娟珠;  徐泽潭
福州大学 数字中国研究院(福建), 福建 福州 350108
Author(s):
LIANG Juanzhu;  XU Zetan
Digital China Research Institute(Fujian), Fuzhou University, Fuzhou 350108, China
关键词:
空间分区;  自组织特征映射模型;  城市边缘区;  扩展特征;  长三角城市群
Keywords:
spatial division;  self-organizing feature mapping model;  rural-urban fringe area;  expansion feature;  Yangtze River Delta urban agglomerations
分类号:
P208
DOI:
10.11830/ISSN.1000-5013.202012049
文献标志码:
A
摘要:
以中国长江三角洲城市群为研究区,选取全球人造不透水面、可见光红外成像辐射仪(VIIRS)夜间灯光、道路、人口等多源数据,从土地利用、人类活动、道路设施3个视角出发构建分区指标,基于人工神经网络对长三角城市群地域空间进行划分,并探讨城市边缘区的时空分异与扩展特征.结果表明:使用多源数据与人工神经网络方法识别城市边缘区具有可行性,空间分区的3个指标较为合理;使用自组织特征映射模型将长三角城市群分为城市核心区、城市边缘区、乡村地区3类;2012-2018年间城市边缘区占长三角城市群总面积的比例由7.82%增长至11.27%,年均空间扩展强度指数为7.35%,城市边缘区面积扩展呈现集聚特征,热点区主要位于江苏省大部及浙江省北部,冷点区则分布于安徽省大部和浙江省南部.
Abstract:
Takes Yangtze River Delta urban agglomerations in China as research area, multi-source datas such as global artificial impervious surface, visible infrared imaging radiometer(VIIRS)night light, road and population are selected to construct zoning indicators from three perspectives of land use, human activities, and road facilities. Division of the regional space of Yangtze River Delta urban agglomerations based on artificial neural network, and the spatial and temporal differentiation and expansion characteristics of rural-urban fringe area are discussed. The results show that it is feasible to use multi-source datas and artificial neural network methods to identify rural-urban fringe area, and three indicators of spatial division are reasonable; using self-organizing feature mapping model to divide Yangtze River Delta urban agglomerations into three categories of the core area, the rural-urban fringe area, rural area; from 2012 to 2018, the proportion of rural-urban fringe area in the total area of the Yangtze River Delta urban agglomerations increase from 7.82% to 11.27%, with an average annual expansion intensity index of 7.35%, and the rural-urban fringe area show agglomeration characteristics. Hot spots are mainly located in most parts of Jiangsu Province and northern Zhejiang Province, while cold spots are located in most parts of Anhui Province and southern Zhejiang Province.

参考文献/References:

[1] 顾朝林,熊江波.简论城市边缘区研究[J].地理研究,1989,8(3):95-101.DOI:10.11821/yj1989030012.
[2] PRYOR R J.Defining the rural-urban fringe[J].Social Forces,1968,47(2):202-215.DOI:10.1093/sf/47.2.202.
[3] 彭建,马晶,袁媛.城市边缘带识别研究进展与展望[J].地理科学进展,2014,33(8):1068-1077.DOI:10.11820/dlkxjz.2014.08.007.
[4] FRIEDBERGER M.The rural-urban fringe in the late twentieth century[J].Agricultural History,2000,74(2):502-514.DOI:10.2307/3744868.
[5] ZHAO Pengjun.Too complex to be managed? New trends in peri-urbanisation and its planning in Beijing[J].Cities,2013,30:68-76.DOI:10.1016/j.cities.2011.12.008.
[6] MA Ting,ZHOU Yuke,WANG Yingjie,et al.Diverse relationships between Suomi-NPP VIIRS night-time light and multi-scale socioeconomic activity[J].Remote Sensing Letters,2014,5(7):652-661.DOI:10.1080/2150704X.2014.953263.
[7] SMALL C,ELVIDGE C D,BALK D,et al.Spatial scaling of stable night lights[J].Remote Sensing of Environment,2011,115:269-280.DOI:10.1016/j.rse.2010.08.021.
[8] 王秀兰,李雪瑞,冯仲科.基于TM 影像的北京城市边缘带范围界定方法研究[J].遥感信息,2010(4):100-104.DOI:10.3969/j.issn.1000-3177.2010.04.019.
[9] 钱建平,周勇,杨信廷.基于遥感和信息熵的城乡结合部范围界定: 以荆州市为例[J].长江流域资源与环境,2007,16(4):451-455.DOI:10.3969/j.issn.1004-8227.2007.04.010.
[10] PENG Jian,ZHAO Shiquan,LIU Yanxu,et al.Identifying the urban-rural fringe using wavelet transform and kernel density estimation: A case study in Beijing City, China[J].Environmental Modelling and Software,2016,83:286-302.DOI:10.1016/j.envsoft.2016.06.007.
[11] 马晶,李全,应玮.基于小波变换的武汉市城乡边缘带识别[J].武汉大学学报(信息科学版),2016,41(2):235-241.DOI:10.13203/j.whugis20140053.
[12] 张志刚,张安明,郭欢欢.基于DMSP/OLS夜间灯光数据的城乡结合部空间识别研究: 以重庆市主城区为例[J].地理与地理信息科学,2016,32(6):37-42.DOI:10.3969/j.issn.1672-0504.2016.06.007.
[13] 刘星南,吴志峰,骆仁波,等.基于多源数据和深度学习的城市边缘区判定[J].地理研究,2020,39(2):243-256.DOI:10.11821/dlyj020181085.
[14] 王海鹰,张新长,康停军,等.基于多准则判断的城市边缘区界定及其特征[J].自然资源学报,2011,26(4):703-714.DOI:10.11849/zrzyxb.2011.04.016.
[15] FENG Zhao,PENG Jian,WU Jiansheng.Using DMSP/OLS nighttime light data and K-means method to identify urban-rural fringe of megacities[J].Habitat International,2020,103:102227.DOI:10.1016/j.habitatint.2020.102227.
[16] GAO Yang,FENG Zhe,WANG Yang,et al.Clustering urban multifunctional landscapes using the self-organizing feature map neural network model[J].Journal of Urban Planning and Development,2014,140(2):29-37.DOI:10.1061/(ASCE)UP.1943-5444.0000170.
[17] 胡秋凤,陈娟,戴文远,等.快速城镇化下旅游海岛景观格局梯度分析: 以福建省平潭岛为例[J].福建师范大学学报(自然科学版),2019,35(2):109-116.DOI:10.12046/j.issn.1000-5277.2019.02.016.
[18] 龚亚西,程珊珊,季翔.生态安全格局视角下的徐州海绵城市建设[J].福建师范大学学报(自然科学版),2020,36(3):79-89.DOI:10.12046/j.issn.1000-5277.2020.03.010.
[19] GONG Peng,LI Xuecao,WANG Jie,et al.Annual maps of global artificial impervious area(GAIA)between 1985 and 2018[J].Remote Sensing of Environment,2020,236:111510.DOI:10.1016/j.rse.2019.111510.
[20] LEYK S,UHL J H,BALK D,et al.Assessing the accuracy of multi-temporal built-up land layers across rural-urban trajectories in the United States[J].Remote Sensing of Environment,2018,204:898-917.DOI:10.1016/j.rse.2017.08.035.
[21] XU Tao,MA Ting,ZHOU Chenghu,et al.Characterizing spatio-temporal dynamics of urbanization in China using time series of DMSP/OLS night light data[J].Remote Sensing,2014,6(8):7708-7731.DOI:10.3390/rs6087708.
[22] DING Shuo,CHANG Xiaoheng,WU Qinghui.Approximation performance of BP neural networks improved by heuristic approach[J].Applied Mechanics and Materials,2013,411/412/413/414:1952-1955.DOI:10.4028/www.scientific.net/AMM.411-414.1952.
[23] FOODY G M.Applications of the self-organizing featuremap neural network in community data analysis[J].Ecological Modelling,1999,120(2/3):97-107.DOI:10.1016/S0304-3800(99)00094-0.
[24] KOHONEN T.Essentials of the self-organizing map[J].Neural Networks,2013,37:52-65.DOI:10.1016/j.neunet.2012.09.018.

备注/Memo

备注/Memo:
收稿日期: 2020-12-24
通信作者: 梁娟珠(1978-),女,副研究员,博士,主要从事地理信息工程的研究.E-mail:liangjuanzhu@163.com.
基金项目: 国家自然科学基金资助项目(41771423); 福建省科技重点资助项目(2018Y0054)http://www.hdxb.hqu.edu.cn
更新日期/Last Update: 2022-01-20