大数据与小数据结合：信息时代城市研究方法探讨

doi:10.13249/j.cnki.sgs.2017.03.001

摘要/Abstract

摘要：

信息技术的快速发展引起了城市研究领域的“大数据”热潮,并带来了传统城市研究方法的变革。但是,其自身存在的诸多缺陷使得学者不得不重新考虑传统小数据的应用角色。但是,传统小数据并没有失去其应用价值,相反,以城市与居民行为活动关系研究为主体的信息时代的城市研究必要充分结合大数据与小数据,并探讨适宜的方法论与方法框架,从而应对日益复杂的城市问题和居民需求。提出“物质空间与活动空间结合”、“相关关系与因果关系结合”、“宏观分析与微观挖掘结合”的3个方法论,并在此基础上构建了“大样本空间发展评价+空间差异与联系发现+小样本影响因素探究”、“小样本模型构建+影响因素发现+大样本验证及挖掘”、“微观活动分析+活动空间界定+影响因素探究”3种方法框架,且分析了这些框架的具体应用,以期为未来的城市研究提供思路和方法借鉴。

关键词: 大数据, 小数据, EDCF, MFVE, AADSF, 城市研究, 信息时代

Abstract:

Appearance of Information and communication technology has set off a new wave of big data to promote a transformation of the traditional methods in urban studies. However, types of limitations of big data also make scholars rethink the role of small data in specific applications for research. We believe that the small data will not lose its value, instead, it can be combined with big data in urban study, which is needed to focus on relationship between urban and resident activity in the information era. Therefore, we should discuss a new framework for such combination on complicated urban problems and diversified resident demands. Firstly, we put forward to three methodologies including combination between physical space and activity space, combination between correlativity and causality, and combination between macro-scale analysis and micro-scale analysis. Secondly, based on above methodologies, we build three method frameworks for urban studies in the information era, namely ‘Spatial development evaluations for big samples+Spatial difference and connection discovery+Factors discussions for small samples’, ‘Model building for small samples+Factors discussions+Verifications and explorations for big samples’, and ‘Micro-analysis of activities+Delineations of activity space+Factors discussions’. Finally, we discuss applications of above three method frameworks.

Key words: big data, small data, EDCF, MFVE, AADSF, urban studies, information era

中图分类号:

F219

秦萧, 甄峰. 大数据与小数据结合：信息时代城市研究方法探讨[J]. 地理科学, 2017, 37(3): 321-330.

Xiao Qin, Feng Zhen. Combination Between Big Data And Small Data: New Methods of Urban Studies in The Information Era[J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(3): 321-330.

图/表 4

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图3

表1

信息时代城市研究中大数据与小数据结合方法对比分析"

组合类型	样本特征	优势	劣势	应用领域
EDCF	不同样本（小样本来源于大样本）	不仅可以掌握城市不同空间（或设施）总体分布,还可以了解居民真实评价或发展水平;居民活动的宏观把握,可以科学准确辨别空间实际利用情况和不同空间内居民活动联系;通过大数据分析,准确找出城市问题空间,并针对性进行调研或访谈,探究内在影响因素	大样本并非全样本的大数据分析,例如,网络数据具有年轻群体倾向;居民活动位置和文本信息并不能完全反映居民的真实活动内容;大数据分析样本和问卷调查样本并非为同一群体,抽样的代表性很难完全科学	居民行为活动对商业空间、职住空间、交通空间的影响......
MFVE	不同样本（小样本不来源于大样本）	大数据分析可以验证小数据模型指标及结果的科学性,找出真正显著影响因子;可以找出影响居民行为活动建成环境的具体分布和人文环境偏好,有利于城市优化或调整	跟居民活动相关的、具有显著性影响的建成环境因子并不可能全部都被大数据分析所验证（较难获取相关大数据）;调查样本与大数据验证分析样本并非同一群体,其对城市实际影响状态可能存在误差	住房环境对远程办公影响商业环境对居民网络购物影响;交通环境对居民出行影响......
AADSF	同一样本	大数据分析与小数据分析为同一样本群体,可以准确把握居民活动规律、日常需求偏好及其影响因子;利用GPS定位技术等捕获的居民行为活动数据较传统活动日志更为精确	样本数量一般较小、调查周期较短、成本较高,且具有中低收入群体为主的样本倾向（高收入群体一般不太愿意透露个人位置信息）	城市环境对低收入群体、消费者群体、大学生群体、老年群体行为影响......

表1

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