The legacy of socialist urban planning and recently established market economy in Beijing, China often create a mismatch between urban land use plans and actual land use: Before China’s opening and reforming in 1978, Beijing’s landscape reflects egalitarian premise and is dominated by large parcels and single-purpose zoning. Yet the city’s recent economic success, as evidenced by mushrooming financial districts, retail malls, and technology centers, has surely spurred up mixed development (Yang et al. 2013).

In order to capture this mismatch, we compute a mixed land use index (Frank et al. 2004) and ask “will the zoning plan and the actual land use reveal different levels of mixed development in Beijing?” Furthermore, such index has often been used to understand evolving travel mode choice, public health outcomes, as well as the sense of community (Manaugh and Kreider 2013).

Nevertheless, quantifying the degree of mixed-use has proven difficult. Conventional methods (e.g., remote sensing and survey) have focused on the proportion of different land use types at (aggregated) parcel level, rather than different land use of individual establishments (e.g., residential building, restaurants, and retail stores) within parcels.

Our study therefore employs land use data at multiple spatial scales. More specifically, city-wide planned land use for 21,922 parcels is obtained from Beijing Institute of City Planning (BICP), while the actual land use is measured by three datasets: (1) 22,027 current land parcels identified from remote sensing images; (2) 84,541 Point-of-Interests (PoIs) crawled from Sina Weibo (the Chinese equivalent of Twitter) which catalogues business establishments and housing options throughout the city; and (3) 6,555,529 check-ins for all PoIs in (2), reflecting land-use intensity. All parcels, check-in points, and PoIs are associated with one of the eight commercial and residential land use types (Long et al. 2012)

As our data sources reveal – planned as well as actual – mixed land use in Beijing at various geographical scales, a consistent geographic framework is constructed for comparison: central Beijing (Figure 1) is divided into 2,272 square km grids, and for each grid the mixed land use index is computed following Frank et al. (2004).

Figure 1 reveals a consistent pattern of mixed land use in Beijing computed from different data sources, and reflects the effectiveness of urban planning implementation (Long et al. 2012): the mixing of land use is higher in the city center and much lower in the periphery; the spatial extents of planned and actual urban activities largely overlap; and there exists a lack of residential and commercial activities (i.e., blank grids) along several axes in the city periphery. Moreover, central-city land use captured by check-in and PoI data are more mixed, as check-ins and PoIs entail additional information about heterogeneous land use within parcels. We therefore suggest that, check-in and PoI data – with fine-grained locational information – would be supplement to conventional ways of measuring urban land use.

Chinese cities are proposing increasing number of urban redevelopment projects. The local communities, in this process, vary from each other by their activity patterns and ways of life. Under this condition, social media data could provide a point of view that local activities are respected and recognized. Using such data, this project is to explore a dynamic tool to catalog urban redevelopment projects, and help planners/decision makers to better engage the communities virtually and physically*.

*Sina Weibo check-in data and Douban Event data are employed to measure virtual and physical activities respectively

http://web.mit.edu/qqzhang/www/crowdup/

See BCL working paper 2: Planning community evaluation for three planning institutes in China using Sina Weibo

Exploring virtual human activities in Beijing using micro-blog data: A preliminary exploration using big data

Micro-blogs are a form of volunteered geographic information (VGI). Each post contains time, space, and topic tags and provides us with a better understanding of the urban space and its evolution processes. These social media platforms also provide information on the urban structure and dynamics, as well as on individual and collective human activities. In this chapter, we use Sina Weibo (WEIBO in Figure 1), the largest micro-blog in China with over 300 million registered users, to evaluate the urban structure of Beijing from the point of view of human activities. Using the API provided by Sina Weibo, we captured over 100 million posts of 1 million bloggers registered in Beijing by reading Sina Weibo’s public timeline posts. In addition, the check-in information was crawled to reveal human activities in Beijing. Compared to a conventional urban GIS, we expect that this on-going research involving urban activity analysis could reveal interesting sub-layers of results related to the urban structure of Beijing.