关键词: 信息通信技术, 城市活动空间, 活动空间区划, 大数据

Abstract:

Since 1990s, information and communication technologies (ICTs) have experiecned a rapid development over the world. Information and communication devices have almost penetrated into each aspect of people’s daily life, and thus become the necessities in the modern world. Under this advancement, the inner relationship among activities, time and location has been theoretically changed because of a serious of the responding tele-activities. However, we still have little knowledge from the empirical studies due to the lack of data. Besides, as a part of urban social space analysis, a dynamic analysis of urban activity space is even scarce, albeit with its importance in understanding the undergoing and future changes in the E-society. Especially in China where is under the socio-economic transition, understanding residents’ own need and their real-time activity as well as the influence of ICTs on activity space are quite necessary for the future urban management and planning. With the aid of Location-based service (LBS), Global Positioning System (GPS) and other applications, ICTs helps to record people’s real space-time activity, which is one of the main sources of big data in recent research. Among them, given by the soaring popularity of online social network such as Sina micro-blog (the most influential social networking platform in China) and the LBS check-in application, the check-in data undoubtedly provide a real-time big data for the study of urban activity space. Based on this understanding, this article tries to analyze the dynamic changes of urban space-time activity focused on activity, time, and location, using the LBS checking-in data from Sina micro-blog. The results show that: 1) although some research argued that residents’ activity schedule would be more flexible by the help of ICTs, in this study we find that the traditional routine still dominate in residents’ check-in activities, and thus the rhythm of check-in activities could be used to reflect the corresponding relationship between time and activity; 2) there are differences of residents’ check-in activity between on working days, weekends, and holidays, as well as between in downtown and in outskirts, which reveals the difference of daily activities in reality; 3) in a day urban activity space experience dynamic changes, specifically, varied from relative disperse to agglomeration in the morning (6-12 O’clock) and keeps further agglomeration till the afternoon (12-18 O’clock); while encounters dispersion in the evening (18-24 O’clock), though with a relative agglomeration in the night (0-6 O’clock); 4) according to the dynamic changes, activity zones are subdivided into office area, bedroom area, leisure area, nightlife area, and multifunctional area; 5) these activity zones could be generally characterized as hybrid, within a blurred boundary.

Key words: ICTs, urban activity space, activity zonning, big data