位置大数据的价值提取与协同挖掘方法

随着位置服务和车联网应用的不断普及，由地理数据、车辆轨迹和应用记录等所构成的位置大数据已成为当前用来感知人类社群活动规律、分析地理国情和构建智慧城市的重要战略性资源，是大数据科学研究极其重要的一部分.与传统小样统计不同，大规模位置数据存在明显的混杂性、复杂性和稀疏性，需要对其进行价值提取和协同挖掘，才能获得更为准确的移动行为模式和区域局部特征，从而还原和生成满足关联应用分析的整体数据模型.因此，着重从以下3个方面系统综述了针对位置大数据的分析方法，包括：（1）针对数据混杂性，如何先从局部提取出移动对象的二阶行为模式和区域交通动力学特征；（2）针对数据复杂性，如何从时间和空间尺度上分别对位置复杂网络进行降维分析，从而建立有关社群整体移动性的学习和推测方法；（3）针对数据的稀疏性，如何通过协同过滤、概率图分析等方法构建位置大数据全局模型.最后，从软件工程角度提出了位置大数据分析的整体框架.在这一框架下，位置数据将不仅被用来进行交通问题的分析，还能够提升人们对更为广泛的人类社会经济活动和自然环境的认识，从而体现位置大数据的真正价值.

Value Extraction and Collaborative Mining Methods for Location Big Data

Uncountable geographical location information, vehicle trajectories and users' application location records have been recorded from different location-based service (LBS) applications. These records are forming to a location big data resource which facilitates mining human migrating patterns, analyzing geographic conditions and building smart cities. Comparing with traditional data mining, location big data has its own characteristics, including the variety of resources, the complexity of data and the sparsity in its data space. To restore and recreate data analysis network model from location big data, this study applies data value extraction and cooperative mining on location big data to create trajectories behavior pattern and local geographical feature. In this paper, three major aspects of analysis methods on location big data are systematically explained follows: (1) For the variety of resources, how to extract potential contents, generate behavior patterns and discover transferring features of moving objects in a partial region; (2) For complexity of data, how to conduct dimension reduction analysis on complex location networks in temporal and spatial scale, and thus to construct learning and inferential methods for mobility behavior of individuals in communities; (3) For sparsity, how to construct the global model of location big data by using collaborative filtering and probabilistic graphical model. Finally, an integral framework is provided to analyze location big data using software engineering approach. Under this framework, location data is used not only for analyzing traffic problems, but also for promoting cognition on a much wider-range of human social economic activities and mastering a better knowledge of nature. This study incarnates the practical value of location big data.