地理科学进展 ›› 2013, Vol. 32 ›› Issue (9): 1352-1361.doi: 10.11820/dlkxjz.2013.09.005

所属专题: 地理大数据

• “时空间行为与地理学”专栏 • 上一篇    下一篇

大数据时代城市时空间行为研究方法

秦萧1, 甄峰2, 熊丽芳1, 朱寿佳1   

  1. 1. 南京大学地理与海洋科学学院, 南京210093;
    2. 南京大学建筑与城市规划学院, 南京210093
  • 收稿日期:2013-02-01 修回日期:2013-08-01 出版日期:2013-09-25 发布日期:2013-09-25
  • 作者简介:秦萧(1987- ), 男, 江苏盐城人, 博士研究生, 主要研究方向为城市地理与区域规划。E-mail:qinxiao1070@126.com
  • 基金资助:
    国家自然科学基金项目(40971094);中央高校基本科研业务费专项资金项目(1115090201)

Methods in urban temporal and spatial behavior research in the Big Data Era

QIN Xiao1, ZHEN Feng2, XIONG Lifang1, ZHU Shoujia1   

  1. 1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China;
    2. School of Architecture and Urban Planning, Nanjing University, Nanjing 210093, China
  • Received:2013-02-01 Revised:2013-08-01 Online:2013-09-25 Published:2013-09-25

摘要: 信息技术的快速发展带来了“大数据”时代的到来,改变了城市的空间组织和居民行为,并使得城市时空间行为研究方法面临变革。本文在总结传统城市时空间行为研究方法存在问题的基础上,对影响其变革的数据获取与处理技术进行梳理,重点从居民时空行为、城市空间及城市等级体系3个方面综述了国内外应用大数据进行城市时空间行为研究的最新进展,构建了基于大数据应用的城市时空间行为研究方法框架。本文认为,大数据时代城市时空间行为研究方法的变革主要取决于对反映居民时空行为的网络或移动信息设备数据的挖掘、处理及应用,但是还需要进一步推动相关学科间的交叉与融合,加强社交网站等网络数据在居民时空行为和城市空间研究中的应用,并指导城市规划编制与管理方法的创新。

关键词: 变革, 城市时空间行为研究方法, 大数据时代, 网络数据, 移动信息设备数据

Abstract: The rapid development of information technology has taken us into the "Big Data Era", changed the organization and structure of urban space and residents' behavior, and also caused transformation of the methods in urban temporal and spatial behavior research. On the basis of summarizing the problems of traditional methods such as poor data accuracy, small sample size, weak continuity, and higher costs, this paper first combs through the data acquisition and processing technology for web data mining, residents' behavior data collection and analysis, and network map integration and visual development, which can affect the transformation of the research methods. Then it reviews the latest progress in applying big data to urban temporal and spatial behavior research at home and abroad from the perspectives of residents' behavior, urban space, and urban hierarchy, and builds up a method framework for urban temporal and spatial behavior research based on big data application. The methods in urban temporal and spatial behavior research are going through a great transformation because of the emergence of massive and various information data. Data collection methods have changed from yearbook statistics, social questionnaire survey, in-depth interview to mining of network data (social network data) and application of new spatial position technology (GPS, smart mobile phone, LBS, etc.), and the data shows obviously new characteristics such as large sample size, real-time dynamic, micro and detail, with more attention paid to the extraction of residents' geographic position information. However, as to specific research methods, the traditional ones are still widely used, such as descriptive statistical analysis, cluster analysis, factor analysis, gravity model, network analysis, space-time prism, etc. Generally speaking, the researches of urban temporal and spatial behavior have obvious characteristics of using "new" data and "old" methods to study "newer" and "older" problems at the present stage, and their research scope has also expanded from residential scale to urban space and regional range. However, problems still exist with the current research, such as how to eliminate fictitious data, how to learn and innovate analytical methods, how to expand research field and embody characteristics of the era. Therefore, it is necessary to promote the cross and integration of related disciplines such as sociology, economic geography, cultural geography, tourism geography, computer science, mathematics and geographic information science, in order to find new analysis methods, and also reinforce the research of residents' behavior and urban space by using social network (Twitter, Flikr, Facebook, Sina Microblog, etc.) data or other web (SouFun.com, Dianping.com, Zhaopin.com, Taobao.com, etc.) data, and guide innovation of urban planning methods.

Key words: Big Data Era, mobile information device data, research methods of urban temporal and spatial behavior, transformation, web data