热带地理 ›› 2020, Vol. 40 ›› Issue (2): 254-265.doi: 10.13284/j.cnki.rddl.003218

• “地理空间智能技术及应用”专题 • 上一篇    下一篇

基于开放大数据的广州市中心城区职住平衡特征研究

林勋媛1, 王广兴1, 胡月明1,2,3()   

  1. 1.华南农业大学 资源环境学院//国土资源部建设用地再开发重点实验室//广东省土地利用与整治重点实验室//广东省土地信息工程技术研究中心,广州 510642
    2.青海大学 农牧学院,西宁 810016
    3.电子科技大学 资源与环境学院,成都 610054
  • 收稿日期:2019-07-15 修回日期:2020-01-06 出版日期:2020-03-10 发布日期:2020-05-15
  • 通讯作者: 胡月明 E-mail:yueminghugis@163.com
  • 作者简介:林勋媛(1994—),女,广东人,硕士研究生,主要研究方向为GIS在城乡规划中的应用,(E-mail) LINXunyuan1994@163.com。
  • 基金资助:
    国家重点研发计划(2018YFD1100801);广州市科技计划项目(201807010048)

Characteristics of the Jobs-Housing Balance in Central Guangzhou Based on Open Big Data

Lin Xunyuan1, Wang Guangxing1, Hu Yueming1,2,3()   

  1. 1.College of Natural Resources and Environment, South China Agricultural University//Key Laboratory for Construction Land Transformation, Ministry of Land and Resources//Guangdong Province Key Laboratory for Land Use and Consolidation//Guangdong Province Engineering Research Center for Land Information Technology, Guangzhou 510642, China
    2.College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China
    3.College of Natural Resources and Environment, University of Electronic Science and Technology, Chengdu 610054, China
  • Received:2019-07-15 Revised:2020-01-06 Online:2020-03-10 Published:2020-05-15
  • Contact: Hu Yueming E-mail:yueminghugis@163.com

摘要:

以广州市中心城区为例,借助百度热力图、百度实时路况和百度地图POI数据,从中观层面多角度综合分析广州市中心城区就业与居住的空间分布关系。结果显示:1)工作时间段人口聚集的高值区整体呈带状分布,斑块较为细碎,但绝大部分集中在核心地带;人口主要高度集中于各区的商业繁华地段与交通线路周围。而休息时间段的高值区则相对集中分布,用地效率较高,表现出多中心的圈层结构;人口主要高度集中分布在传统的老城居住区和新开发的商业住宅区,与核心商圈相对错开。2)不管是在上班时段还是休息时段,人口聚集程度越高的地区,POI设施密度表现越显著;这意味着人口的聚集具有一定的选择性,主要集中在城市基础设施发展较完备的区域。3)广州市中心城区各街道的职住比介于0.73~1.54,职住相对平衡,区域之间差异较小。其中,分值较高的街道多分布于核心地带(主要集中在越秀区、荔湾区北部和天河区南部),分值较低的街道多分布于核心地带的外围或边缘地区(主要在海珠区、荔湾区和白云区零散分布)。4)从城市交通响应上看,广州市中心城区工作日内早高峰的拥堵度大于晚高峰,但总体路况变化跨度不大,区内并没有出现特别严重的“潮汐通勤”现象。

关键词: 开放大数据, 人口聚集, 职住平衡, 交通响应, 广州市

Abstract:

This paper comprehensively analyzes the spatial distribution relationship between employment and residence in central Guangzhou on a meso level with the help of the Baidu Thermal Map, Baidu Real-time Road Conditions, and Baidu POI data. The results show that: 1) high-value areas where the population gathers during working hours are decently marked with plaques, but most of these are concentrated in the core area. The population is primarily concentrated around the commercial areas and transportation lines of various districts because of the close connection between employment activities and transportation. Examining the main functions of specific areas revealed that they are dominated by employment centers, business centers, and transportation hubs with a strong orientation towards work. High-value leisurely areas are relatively concentrated, have higher land use efficiency, and show a polycentric circle structure. The population is principally concentrated in the traditional local and newly developed commercial residential areas, which are relatively staggered from the core business district. There is a distinct difference between the working areas and places of gathering during non-work hours. The high-value places of gathering during non-work hours are mostly residential land; 2) from the correlation between the degree of population aggregation and the density of POI facilities, the density of POI facilities is more significant in areas with higher population aggregation, during both the work and non-work hours. Therefore, population aggregation has certain selectivity and is primarily concentrated in the area where urban infrastructure development is relatively complete; 3) according to Jobs-housing balance index measurements at the meso level, the ratio of occupation and housing (indicates the relative balance of occupation and housing) of each block in central Guangzhou is between 0.73 and 1.54 with little difference between regions. From a distribution perspective, the blocks with higher scores are mostly distributed in the core area (primarily concentrated in the Yuexiu District, northern Liwan District, and southern Tianhe District) and the blocks with lower scores are mostly distributed in the peripheral or marginal zones of the core area (primarily scattered in the Haizhu, Liwan, and Baiyun Districts); 4) from the perspective of the city’s response to traffic, peak morning congestion in central Guangzhou is greater than peak evening traffic. However, the overall variation in road conditions is minimal, and there is no particularly serious commuting phenomenon in the area.

Key words: open big data, population aggregation, jobs-housing balance, traffic response, Guangzhou

中图分类号: 

  • F299.27