中文摘要：

随着定位技术尤其是室外定位技术的发展，产生了大量的位置数据．这些数据在一定程度上能够反映出丰富的社会信息，从而使得位置服务向着社会化计算的方向发展．因此位置社会感知计算方法尤为重要．位置服务中的社会感知计算是指通过人类社会生活空间部署的大规模位置传感设备，感知识别社会个体的行为、分析挖掘群体社会交互特征和规律、引导个体社会行为、支持社群的互动、沟通和协作的一种计算技术，是位置服务从单纯的定位服务转变成为具有社会化计算形态的关键．围绕基于位置的社会感知计算相关方法，分别从计算模型和评估手段两方面进行了系统的分类和归纳，重点阐述了3个问题：1）什么是基于位置的社会感知及其计算框架；2）位置的社会性与人类行为的关联关系是什么样的．主要将其划分为感知位置的社会语义、感知人类移动与其社交活动的关系、感知和预测用户的移动行为和感知用户的社会属性4个方面来展开论述；3）在实际分析及系统应用中尤其是面对位置大数据分析时，常用的感知和数据挖掘方法有哪些．

英文摘要：

Positioning technologies, especially outdoor positioning, have been extensively developed recently. There has been massive location and user data which can reflect abundant social information. With the information, location based services can be intelligent and personalized. Thus the technology for location based awareness computation is needed urgently. Social awareness computation in location based services is a kind of computational technology. This technology utilizes the mass position devices deplo~ed in human social life space to achieve following goals： 1） Analyze and recognize the behaviors of social individuals; 2） Analyze the characteristics and laws of social community interaction; 3） Guide individual social behavior; and ~.） Support community interaction, communication, and collaboration. Social awareness computation is the key of transferring location based service from simple positioning service to social computation pattern. This paper focuses on these methods, and thus addresses three issues： First of all, what is location based social awareness and its framework; Secondly, what is the relationship between social properties of locations and human behaviors; And at last, what are the common awareness and data mining methods in real analysis and system application, especially in big data analysis of locations. This paper gives details from four parts： social semantic awareness of locations, location based relationship awareness of users, mobility awareness of users, and location based social characteristic awareness of users. This paper systematically classifies and generalizes those methods from the two aspects： computation models and evaluation methods.