文章以武汉市为例,利用手机信令数据、土地利用数据及遥感影像数据等多源时序大数据,识别近郊区主要就业中心及其主导产业,并提出从就业人口流动的数量、强度和方向3个方面构建就业人口流动评价指标体系。首先通过净活跃度与总活跃度测度就业人口流动的数量,并结合全局莫兰指数、Getis-ord Gi*指数识别就业人口流动的空间特征,然后采用就业人口流动强度识别不同产业类型对就业人口流动强度影响的差异,最后通过标准差椭圆识别不同产业类型对就业人口流动方向及范围影响的差异。研究表明：武汉市近郊区整体呈现就业人口向近郊区各就业中心流动的趋势,但各就业中心间就业人口活跃度差异较为显著。光芯屏端网、生物医药及信息技术产业是武汉市近郊区就业人口流动强度最大的产业,吸引范围主要涵盖高等院校密集的地区;汽车制造和服务业就业人口流动强度较大,但其吸引范围相对较小;航运物流与航空物流产业发展迅捷,其就业人口流动强度正逐步增强,受地理区位制约,其就业吸引范围最广;建筑业、装备制造业、智能制造业就业人口流动强度则呈现逐年减弱的趋势,部分地区甚至出现人口净流出的现象,其就业吸引范围也较小。
Taking Wuhan City as an example,this paper uses multi-source time-series big data such as cellular signaling data,land use data,and remote sensing data to identify major employment centers and their leading industries in inner suburbs,and constructs an evaluation index system of employment population mobility from three aspects: the quantity,intensity and direction of employment population mobility. Firstly, it uses net activeness and total activeness to measure the number of employment population mobility,and identifies the spatial characteristics of employment population mobility using the global Moran's index and the Getis-ord Gi* index,then uses the mobility intensity of employment population to identify the different effects of different industrial types on the mobility intensity of employment population. It identifies the different effects of different industry types on the direction and scope of employment population mobility by the means of the standard deviation ellipse. The study shows that: 1) The employment population in inner suburbs of Wuhan has a whole trend of migrating to employment centers in inner suburbs,but there is a significant difference in activeness of employment population among employment centers. 2) Optoelectronic-chip-screen-terminal-network,biomedicine and information technology industries are the industries with the highest intensity of employment population mobility in the suburbs of Wuhan, and the attraction scope mainly covers areas with high-dense colleges and universities. 3) The intensity of employment population mobility in automobile manufacturing and service industries is relatively high,but its attraction scope is relatively small. 4) The shipping and aviation logistics industry is developing rapidly,and the intensity of the employment population mobility is gradually increasing,and restricted by geographic location,its employment attraction scope is the widest. 5) The mobility intensity of the employment population in construction industry,equipment manufacturing industry and intelligent manufacturing industry shows a trend of weakening year by year,and in some areas,there is even a net outflow of population and their employment attraction scope is also relatively small.
[1] 国家统计局. 王萍萍:人口总量略有下降城镇化水平继续提高[EB/OL].http://www.stats.gov.cn/sj/sjjd/202302/t20230202_1896742.html,2023-02-02.
[2] 张艳,柴彦威. 基于居住区比较的北京城市通勤研究[J]. 地理研究,2009,28(5):1327-1340.
[3] 刘艳军,汤爽爽,吴康,等. 经济地理学视角下中国人口研究热点与展望[J]. 经济地理,2021,41(10):97-105.
[4] 章征涛,刘勇,王一波,等. 资本积累视角下城市郊区化机制及效应探析[J]. 城市发展研究,2016,23(6):59-65.
[5] 杨亮,杨胜利. 上海市流动人口特征与区域经济发展关系研究[J]. 人口与社会,2014,30(1):22-26.
[6] 鲁奇,吴佩林,鲁礼新,等. 北京流动人口特征与经济发展关系的区域差异[J]. 地理学报,2005(5):851-862.
[7] Wang Y,Li X,Yao X,et al.Intercity population migration conditioned by city industry structures[J]. Annals of the American Association of Geographers,2022,112(5):1441-1460.
[8] 王慧,吴晓,郑浩. 流动人口就业空间的集聚性和城市就业空间的关联研究——以南京市主城区为例[J]. 人文地理,2014,29(5):31-39.
[9] 叶昌东,代丹丹,刘海东. 2000年以来广州市中心城区职住空间演变及其行业差异研究[J]. 城市发展研究,2018,25(9):75-81.
[10] 孙铁山. 郊区化进程中的就业分散化及其空间结构演化——以北京都市区为例[J]. 城市规划,2015,39(10):9-15.
[11] Wang J,Dong L,Cheng X,et al.An extended exploration and preferential return model for human mobility simulation at individual and collective levels[J]. Physica A:Statistical Mechanics and Its Applications,2019,https://doi.org/10.1016/j.physa.2019.121921.
[12] 贺传皎,王旭,邹兵. 由“产城互促”到“产城融合”——深圳市产业布局规划的思路与方法[J]. 城市规划学刊,2012(5):30-36.
[13] 黄建中,黄亮,周有军. 价值链空间关联视角下的产城融合规划研究——以西宁市南川片区整合规划为例[J]. 城市规划,2017,41(10):9-16.
[14] 赵民,陈晨,郁海文. “人口流动”视角的城镇化及政策议题[J]. 城市规划学刊,2013(2):1-9.
[15] Zheng L.Job creation or job relocation? Identifying the impact of China's special economic zones on local employment and industrial agglomeration[J]. China Economic Review,2021,https://doi.org/10.1016/j.chieco.2021.101651.
[16] 武玉. 城市化进程中首都流动人口变动的新特点与问题[J]. 人口与社会,2015(2):84-94.
[17] 刘玉,张雪. 基于行业特征的流动人口就业选择偏好分析——以北京为例[J]. 城市发展研究,2021,28(3):115-122.
[18] Cheong T S,Wu Y.The impacts of structural transformation and industrial upgrading on regional inequality in China[J]. China Economic Review,2014,31:339-350.
[19] 许园园,塔娜,李响. 基于地铁刷卡数据的城市通勤与就业中心吸引范围研究[J]. 人文地理,2017,32(3):93-101.
[20] 刘耀林,方飞国,王一恒. 基于手机数据的城市内部就业人口流动特征及形成机制分析——以武汉市为例[J]. 武汉大学学报:信息科学版,2018,43(12):2212-2224.
[21] 谢智敏,甄峰,张姗琪. 基于大数据的城市就业空间特征与影响因素研究——以南京市中心城区为例[J]. 城市发展研究,2021,28(10):48-57.
[22] 牛强,盛富斌,刘晓阳,等. 基于手机信令数据的城内迁居活跃度识别方法研究——以武汉市为例[J]. 地理研究,2022,41(8):2142-2154.
[23] Pan Y,He S Y.Analyzing COVID-19's impact on the travel mobility of various social groups in China's Greater Bay Area via mobile phone big data[J]. Transportation Research Part A:Policy and Practice,2022,159:263-281.
[24] 梁向东,魏逸玭. 产业结构升级对中国人口流动的影响——基于255个城市的面板数据分析[J]. 财经理论与实践,2017,38(5):93-98.
[25] Yan X,Zhou J,Sheng F,et al.Influences of built environment at residential and work locations on commuting distance:Evidence from Wuhan,China[J]. ISPRS International Journal of Geo-Information,2022,11(2):124.
[26] 王德,李丹,傅英姿. 基于手机信令数据的上海市不同住宅区居民就业空间研究[J]. 地理学报,2020,75(8):1585-1602.
[27] 何娟娟. 城镇化进程中农民进城定居意愿与迁移强度影响因素研究[D]. 兰州:西北师范大学,2014.
[28] 赵永春. 关于“人口迁移”、“移民”及其相关概念[J]. 史学集刊,2012(2):24-28.
[29] 康维娜,邵景安,郭跃. 重庆典型山区县人口迁移的时空分布特征及其影响因素[J]. 热带地理,2016,36(1):132-141.
[30] Hu L,Sun T,Wang L.Evolving urban spatial structure and commuting patterns:A case study of Beijing,China[J]. Transportation Research Part D:Transport and Environment,2018,59:11-22.
[31] 符婷婷,张艳,柴彦威. 大城市郊区居民通勤模式对健康的影响研究——以北京天通苑为例[J]. 地理科学进展,2018,37(4):547-555.
[32] 孙铁山. 北京市居住与就业空间错位的行业差异和影响因素[J]. 地理研究,2015,34(2):351-363.
[33] 薛峰,李苗裔,党安荣. 中心性与对称性:多空间尺度下长三角城市群人口流动网络结构特征[J]. 经济地理,2020,40(8):49-58.
[34] 陈艳如,谷跃,宋伟轩,等. 长三角流动人口居留意愿差异及其影响因素[J]. 经济地理,2022,42(9):59-67.
[35] Li Q,Zeng F,Liu S,et al.The effects of China's sustainable development policy for resource-based cities on local industrial transformation[J]. Resources Policy,2021,https://doi.org/10.1016/j.resourpol.2020.101940.
