地理研究

地理研究 ›› 2016, Vol. 35 ›› Issue (7): 1370-1382.doi: 10.11821/dlyj201607012

• 国外城市地理动态 • 上一篇    下一篇

圣保罗大都市区社会空间分异研究——多元统计方法在城市连绵区的应用

ReinaldoPaulPérezMachado1(), ViolêtaSaldanhaKubrusly2, Ligia Vizeu Barrozo1, 陈明玉3, 顾朝林3()   

  1. 1. 巴西圣保罗大学地理系,圣保罗 05508-000,巴西
    2. 圣保罗市住房秘书处,圣保罗 01008-906,巴西
    3. 清华大学建筑学院,北京 100084
  • 收稿日期:2016-03-05 修回日期:2016-05-23 出版日期:2016-07-30 发布日期:2016-07-26
  • 作者简介:

    作者简介：Reinaldo Paul Pérez Machado（1955-）,男,副教授,研究方向为制图学、地理信息学和遥感。E-mail: rpmgis@usp.br

  • 基金资助:
    国家社会科学基金重大项目（14ZDA026）

Social cartography of São Paulo Metropolitan Area: A multivariate analysis applied to the urban continuum

Paul Pérez Machado Reinaldo1(), Saldanha Kubrusly Violêta2, Vizeu Barrozo Ligia1, Mingyu CHEN3, Chaolin GU3()   

  1. 1. Department of Geography, University of S?o Paulo, S?o Paulo 05508-000, Brazil
    2. Municipality of S?o Paulo Housing Secretariat, S?o Paulo 01008-906, Brazil
    3. School of Architecture, Tsinghua University, Beijing 100084, China
  • Received:2016-03-05 Revised:2016-05-23 Online:2016-07-30 Published:2016-07-26

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摘要：

空间数据和地理信息系统在城市规划和决策中应用的重要性日见凸显。主要原因在于：重要的人口数据和社会变动经常表现出一定的空间特性,这种特性可以通过空间分析和统计方法被认识和解释。应用多元分析的空间分类方法编制圣保罗大都市区社会分异地图并进行相关分析。研究的主要数据来自2000年巴西全国人口普查,其中包括了圣保罗大都市的所有行政区和39个自治市的21774个人口普查区。为了把都市连绵区从数据全集中分离出来,我们采用混合技术进行互补分析,即在2000年4月30日的陆地卫星7号图像中绘制一个个多边形,这些被识别出来的多边形就是人口普查区。然后,通过目视解译出假彩色多边形集合。应用空间分类评分程序将这些多边形分成五类,并建立人口普查区的数目、覆盖的面积和都市连绵区之间的关系。这种多元分析方法是基于变量的均衡化来生成易于用分级统计图描述平均值,以促进可视化和后续的空间分布分析。基于多元分析的空间分类方法研究,清楚地展现了圣保罗大都市最重要的社会特征,也说明城市社会地图方法和多元分析的空间分类方法在大都市区的管理、公共政策规划和复杂决策中具有重要的应用价值。

关键词: 人口普查图, 定量分析, 城市群, 彩色合成图像, 分级统计图

Abstract:

The use of spatial data and Geographic Information Systems is becoming more and more important to urban planning and decision-making. This occurs because important demographic data and social variation often show a spatial expression which can be understood through spatial analysis and statistical methods. Thus, the purpose of this study was to create and analyze the social map of Sao Paulo Metropolitan Area (SPMR) by applying the methodology of spatial classification by multivariate analysis. The primary data source employed in this work was the Brazilian National Census from the year 2000. It contained 21774 census tracts, which were aggregated into districts and 39 municipalities. Next, aiming to separate the urban continuum from the data universe we used a mixed technique to yield a complementary analysis: the drawing of a polygon through visual interpretation over a false color composite from the Landsat 7 image (ETM+), from April 30, 2000. The census tracts that were totally contained on the above-mentioned polygon were then identified and selected for visualization. For the analysis, the procedure went through Spatial Classification Scores-SCSs, in five categories, establishing a relationship between the number of census tracts, occupied surface and urban continuum. This simple multivariate analysis is based on the equalization of the variables, leading to the creation of an average score that is easily depicted with choropletic maps, thus facilitating visualization and consequent analysis of its spatial distribution. Finally, the Unified Spatial Classification Score (USCS) was created to integrate all the variables. Due to the results obtained in this research it is concluded that the method of spatial classification by multivariate analysis used here, clearly represent the peculiarities of the most important social variables on the urban agglomerate of the city of S?o Paulo. Finally, it is possible to highlight that urban social cartography, and the methodology of spatial classification by multivariate analysis are tools of undeniable value for managing and planning public policies and complex decision making in big cities and metropolitan areas.

Key words: census tract maps, qualitative analysis, urban agglomeration, color composite images, choropleth maps