基于大数据的城市人口社会经济特征精细时空尺度估计——数据、方法与应用

Fine Spatio-temporal Scale Estimation of Urban Population’s Socio-economic Characteristics Based on Big Data:Data,Methals and Applications

随着时空大数据的出现和数据挖掘技术的发展,学界在精细时空尺度上对城市人口社会经济特征分布的研究中取得了长足进展,有效弥补了传统数据时效性差、空间精细度低等不足。但这些研究集中在城市信息学、计算社会科学、地理学等领域,其成果尚未广泛应用于以城市人口时空分布为决策依据的资源配置研究中,以解决制约城市精细化管理实践深入的科学问题。为促进信息沟通和学科间对话,就城市人口社会经济特征精细时空尺度估计的数据源、估计方法与模型、结果验证与模型评估、较成熟的人口特征分布数据集、精细尺度人口特征数据的应用背景和主题等展开分析,并展望了相关研究的前景与面临的挑战。分析发现:城市人口特征的精细时空尺度估计所运用的方法包括空间回归、机器学习以及神经网络等。在诸多社会经济特征中,人口数量的时空分布研究最为成熟,经历了从单一数据源到多种数据源、从一般格网化到带有空间信息的格网化、从低时空分辨率到高时空分辨率的发展。城市人口的性别、年龄、种族和教育程度等特征可基于社交媒体数据预测,但尚不成熟。人口特征的精细尺度数据应用主题日趋丰富,包括疾病防控、贫困识别、气候变化应对、城市风险识别、城市发展历程回溯、城市社会空间结构分析等。

With the emergence of spatio-temporal big data and the development of data mining technology,scholars have made great progress in estimation of urban population’s socio-economic characteristics on fine spatio-temporal scale,which makes up for the shortcomings of the traditional data with poor timeliness and low spatial precision effectively.However,related studies are restricted in urban informatics,computational social science,geography and other fields,which have not been widely applied to the resource allocation based on spatio-temporal distriution of urban population.This problem restricts the in-depth practice of urban fine management.In order to promote information communication and interdisciplinary dialogue,this paper prospected the research prospects and challenges according to the analysis of data source,estimation methodology,result verification and model evaluation,mature fine scale population characteristic distribution data set.The results found that spatial regression,machine learning and neural network can be used in the fine spatio-temporal scale estimation of urban population characteristics.Among many socio-economic characteristics,research on fine scale population distribution is the most developed,which has transformed from single data source to multiple data sources,from general grid to grid with spatial information,and from low spatio-temporal resolution to high spatio-temporal resolution.The gender,age,race and education of urban population can also be predicted based on social media data,but it is not mature.The application of fine scale urban population characteristics data is rich,which includes disease prevention and control,poverty identification,climate change mitigation,urban risk management,urban development backtracking,urban social spatial structure analysis,etc.