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After twenty years development of urban informatization and digital city, the concept of Smart city is becoming the next stage of urbanization on a global scale. The data science and technologies, such as big data, data vitalization, and data mining, play pivotal roles in building of Smart City. In this paper, we present a survey about data-centric Smart City technologies from an informatics perspective. This paper first summarizes the variety of urban data used in existing Smart City applications, and analyzes their features. Then, the state-of-the-art progresses in the research of data-centric Smart City are surveyed from three aspects: general architectures, application solutions, and core technologies. Finally, the paper concludes the characteristics of the research on data-centric Smart City and raises some directions for future works.

CA (cellular automata) models have achieved much progress in urban simulation since the 1980s. However, most relevant researches are from the field of geography and thence are not ready for application in urban planning, especially as a support tool for planning policymaking. Therefore this research explores this application of CA by proposing Peixian Urban Spatial Development Model as a planning support tool for a mining city Peixian in East China. The model is based on Beijing Urban Spatial Development Model (BUDEM) developed by Long (2008) and is focused on the specific influencing factors of Peixian’s urban growth. The simulation result indicates that the urban spatial development in BAU scenario is quite different the master plan. Further simulation in three policy scenarios, which are industrial development scenario, urban development scenario and environmental protection scenario, shows that implementation of the master plan could be improved by setting development promotion zones, strengthening certain policies, government-led development, setting no-development zones, etc. Then the value of urban models to practical urban planning work is discussed.

In the past couple of post-reform decades of rapid urban growth of Chinese cities, Beijing experienced dramatic spatial and socioeconomic transformation. This study aims to examine the evolution of Beijing’s social-spatial structure in the transforming process over the past 20 years. We first collect and process Beijing’s socio-demographic variables at the level of sub-district in three years between 1990 and 2009. Then a two-step spatial clustering approach is taken for spatial data mining of the spatial structures of social areas in the three time periods. Results are visualized in GIS. Finally, the interpretation of the analysis result is provided, followed by a discussion of policy implications for the development of a socially sustainable municipality.

Fine-scale simulation, in which the parcel is the basic spatial unit and urban activity body is the simulation object, is an important research direction for the urban modeling in the future, and the arrival of big data era also provides an important development opportunity for it. In the paper, the mainstream modeling methods for fine-scale urban modeling are introduced mainly, including cellular automata (CA), agent-based modeling(ABM) and traditional Microsimulation (MSM) ,all of which are microscopic simulation from the bottom up. Then, according with the high-standard data requirements for the fine-scale urban modeling, the paper sums up the internationally acceptable methods for the fine-scale simulation data synthesis (population synthesis), and also gives a number of practical cases about the fine-scale urban modeling in recent years. Finally, the paper puts forward the framework and key technology, based on GIS platform and combined with CA / ABM / MSM method, to construct fine-scale urban modeling, to support the development and assessment of spatial policy in the metropolitan area.

Aiming at the paucity of urban parcels in developing countries in general and China in particular, this paper proposes a method to automatically identify and characterize parcels (AICP) with ubiquitous available OpenStreetMap (OSM) and Points of Interest (POIs). Parcels are the basic spatial units for fine-scale urban modeling, urban studies, as well as spatial planning. Conventional ways of identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource-consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in developing countries. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based CA model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82,645 urban parcels in 297 cities.

Affordable housing has significant social benefits for improving its target group’s quality of life. However, it also results in the loss of land transfer revenues (a lot of opportunity costs) for urban governments. The reasonable site selection of affordable housing projects depends on a rational trade-off between these social benefits and opportunity cost of land supply. This requires deep insights into the preferences of residential location choice for both low-income and high-income residents, looking for the locations low-income residents are more preferred relatively, where means higher social benefits and lower opportunity cost.In this paper, we quantify their differences in residential location choice preferences on the basis of a large sample micro data from 2010 Beijing Urban Household Survey, using revealed preference method (Hedonic model). Our analysis unit is the 1911 traffic analysis zones (TAZ) of Beijing, by comparing these two groups of people’s overall willingness to pay for each TAZ, we construct a feasibility index for affordable housing site selection at different locations.This research can provide technical support for the affordable housing site selection decisions, and contribute to the balance of both social benefits from affordable housing and the financial constraints of urban governments.

Since an interactive relation exists in transportation and land use, the planning and evaluation of major policies, measures and infrastructures having a long-lasting influence shall focus on the research of the advantages, disadvantages and influence degrees of transportation and land use. Meanwhile, land-use and transportation integrated model is required to complete the research and analysis of urban real estate market, evaluation of planned land-use structure and layout, and the assessment of the effectiveness of planning scheme of urban transportation system. Based on the abovementioned demand, this paper takes Beijing as an example. Beijing land-use model is established and calibrated on the basis of the transportation model of Beijing. As a result, the construction of transportation and land-use integrated model has been realized. Also, typical model application analysis has been carried out in order to evaluate the harmonious of urban land-use planning and transportation system planning.

This working paper briefly reviewed applied urban modelling, followed by elaborating opportunities facing by AUM in the era of "big data". I here also introduced our experience on mining bus smart card data in Beijing using rules identified from the conventional household travel survey. I highlighted that it would be promising to combine both "new" data and "old" data.

This article reviewed the development history, classification and typical models of urban spatial development models (hereafter urban models). The results indicate that fine-scale urban models in the parcel and urban actor (e.g. a household and firm) scale will be the future direction of urban models. We further reviewed dominating modelling approaches for find-scale urban models, including bottom-up approaches like cellular automata (CA), agent-based modelling (ABM) and microsimulation (MSM). We also reviewed worldwide micro-data synthetic approaches, which are essential for building fine-scale urban models. Lastly, we proposed the framework and key techniques based on GIS incorporating CA/ABM/MSM approaches for establishing a fine-scale urban model in Beijing, China. The model is expected to promote both the theory of planning support systems and the quantitative analysis level in planning practices in China.

Cities comprise various functional zones, including residential, educational, commercial zones etc... It is important for urban planners to identify different functional zones and understand their spatial structure within the city in order to make better urban plans. In this research, we used 77976010 bus smart card records of Beijing City in one week in April 2008 and converted them into two-dimensional time series data of each bus platform, Then, through data mining in the big database system and previous studies on citizens’ trip behavior, we established the DZoF (Discovering Zones of different Functions) model based on SCD (Smart Card Data) and POIs (Points of Interest), and pooled the results at the TAZ (traffic analysis zone) level. The results suggested that DzoF model and cluster analysis based on dimensionality reduction and EM (expectation-maximization) algorithm can identify functional zones that well match the actual land uses in Beijing. The methodology in the present research can help urban planners and the public understand the complex urban spatial structure and contribute to the academia of urban geography and urban planning.

Planning support systems (PSSs) have attracted extensive attention from scholars and decision makers for decades. Most of the existing research on PSSs is related to system design, implementation, application as well as evaluation of a standalone system in one area, e.g. What if?, CommunityViz and INDEX. There is no existing research on an entire framework of PSSs for various types of plans. In this paper, we propose a PSS framework for various types of plans in China, e.g. master plan, detailed plan, municipal infrastructure plan and transport plan. Based on an extensive literature review and multiple rounds of planner and decision maker surveys, the framework focuses on two aspects. On one hand, we itemize plan contents (termed as “plan elements”) into various steps for each type of plan, e.g. population forecasting and establishing urban growth boundaries in a master plan. On the other hand, we list related PSSs for each plan element. In our research, PSSs embody three forms, which are existing PSS software (e.g. What if? and INDEX), planning support models to be developed or already developed as well as quantitative methods (e.g. scenario analysis, systems analysis, and logistic regression). The two dimensional framework provides a full picture of PSS applications in various types of plans. The framework has been applied in the Beijing Institute of City Planning (BICP) for several months, and has attracted hundreds of application requests from planners.

Existing jobs-housing studies have rarely (a) used smart card data provided by the public transportation agency and (b) focused on commuters of bus mode. In this study, massive smart card data were used to estimate 216,844 bus commuters‟ workplace and residence locations in Beijing. This enables a jobs housing study of bus commuters in the metropolis with a much larger sample size than most existing studies. This study indicates that Beijing's bus commuters (a) have a shorter actual required commuting (ARC) and minimum required commuting (MRC) than commuters in four other auto-dependent western cities with comparable population and/or land use sizes; (b) have a longer ARC and MRC than commuters of all modes in Guangzhou, another metropolis in South China that is half of the size of Beijing. Using local expert consultations, field surveys and information provided by on-line housing search engines to supplement smart card data, this study has established five land-use prototypes of jobs housing imbalance and has proposed countermeasures to address the imbalance.

Under the perspective of space of flows, the passenger flow of High-speed rail has become an important representation of functional linkage between the city-regions. Based on the interviews and questionnaires from the passengers of high-speed rail in Beijing and Tianjin, this paper analyzes the intercity space of flows and the spatial integration indicated by the individual micro behavior choice. The findings mainly include: (1) Both of the metropolitan areas of Beijing and Tianjin are the dense areas of intercity passenger flows while suburban counties and districts are the sparse areas, which indicates the spatial polarization of HSR in the aspect of passengers’ characteristics; The central city of Beijing-Tianjin is the dominant spatial association, while Beijing-Tanggu, Beijing-Wuqing and Tianjin-Wuqing corridors are the secondary spatial association axes, which presents a hub-and-spoke pattern. (2) Leisure activities, such as tourism, shopping, enhance the cross-city flows, though intercity high-speed rail reduces the temporal and spatial distance to a certain extent, the effects on changing place of housing or work to another city are not obvious. (3) The frequency of cross-city activities is not very high, and commuters who across cities generally consider 7 days as a cycle; Currently, passenger flows of intercity by HSR are mainly business travel and leisure tourism, which reflects HSR as the material foundation for the spaces of flows; the respondents who take the HSR are mostly male, business people with high education and prospective occupation, and those business travelers who have a higher cross-city frequency are more sensitive to travel time, which demonstrates the intercity space of flow has represented some of the elite space characteristics. (4) It shows spatial asymmetry in the cross-city space of flow between Beijing and Tianjin, which could be found from the uneven distribution of O-D passenger flows, the differences on the proportion of the business travel flows and the unbalanced function linkage directions.

The Beijing urban development model (BUDEM), based on prevalent urban growth theory and constrained cellular automatic, has been developed in 2008 for analyzing and simulating urban growth for the Beijing Metropolitan Area（BMA）. It is proved that the model is capable of analyzing historical urban growth mechanisms and predicting future urban growth for metropolitan areas in China. In this chapter, we extend the study of BUDEM from the BMA to Beijing-Tianjin-Hebei Area (JJJ), via replacing the datasets of the model and adjusting necessary parameters. In BUDEM-JJJ, the parameters include the minimum distance to the center of Beijing (f_ctr_bj), the minimum distance to the center of Tianjin and Shijiazhuang (f_ctr_tjsjz), the minimum distance to the center of prefecture-level city (f_ctr_other), the minimum distance to the center of town (f_ctr_cty),the minimum distance to the railway (f_rail), the minimum distance to the highway (f_r_hig), the minimum distance to the national road(f_r_nat), the minimum distance to the provincial road (f_r_pro), whether in the forbidden zone (constrain), neighborhood development intensity (neighbor). The model BUDEM-JJJ is used to identify urban growth mechanisms in two historical phases from 2000 to 2005 and from 2005 to 2010 , to retrieve urban growth policies needed to implement the desired (planned) urban form in 2020,and to simulate urban growth scenarios for 2049 based on the urban form and parameter set in 2020. Seven urban growth scenarios are put forward, such as the trend scenario, high-speed growth scenario, low-speed growth scenario，highway finger growth scenario, urban promoting growth scenario, developing forbidden area growth scenario, and traffic leading growth scenario. BUDEM-JJJ considers the heterogeneity of driving force and model parameters, and fulfill accurate simulation in large-scale. In addition, BUDEM-JJJ is the first applicable urban growth model in the Beijing-Tianjin-Hebei Area and has been applied in several plan projects.

The growth of the main city of Beijing is characterised by a pancake like expansion, from 100 km2 in 1950 to 1210 km2 in 2005 in successive waves. The approach to future urban expansion will require careful consideration, as economic, environmental and social conflicts at the urban fringe have intensified. Two successive greenbelts have been designated to contain expansion and engender more compact growth. However, the first greenbelt has not been achieved successfully and many areas designated as the second greenbelt is facing implementation challenges. This paper builds on existing research into greenbelt policy implementation and investigates the impacts of alternative urban growth boundary proposals under a systematic modelling framework. It reviews the theoretical insights into growth at the urban fringe, and puts forward a methodology that links development at the urban fringe to the functioning of the entire metropolitan area. It outlines six alternative development scenarios that encompass the existing planning proposals for the urban fringe: trend growth, densification, stringent greenbelt, loose greenbelt, skewed and green wedge. We use a prototype spatial equilibrium model which simulates the interactions among households, businesses, urban land use and transport to quantify the performance of the development scenarios in terms of production costs, consumer welfare, wages, floorspace rents, and commuting times. The analyses suggest that the physical forms of fringe area development do significantly affect the economic performance of the whole municipality. Alternative proposals, including those that have rarely considered in the past, should be investigated carefully in this light, in conjunction with related studies on social and environmental impacts.

Location Based Services (LBS) provide a new perspective for spatiotemporally analyzing dynamic urban systems. Research has investigated urban dynamics using GSM (Global System for Mobile Communications), GPS (Global Positioning System), SNS (Social Networking Services) and Wi-Fi techniques. However, less attention has been paid to the analysis of urban structure (especially commuting pattern) using smart card data (SCD), which are widely available in most cities. Additionally, ubiquitous LBS data, although providing rich spatial and temporal information, lacks rich information on the social dimension, which limits its in-depth application. To bridge this gap, this paper combines bus SCD for a one-week period with a one-day household travel survey, as well as a parcel-level land use map to identify job-housing locations and commuting trip routes in Beijing. Two data forms (TRIP and PTD) are proposed, with PTD used for jobs-housing identification and TRIP used for commuting trip route identification. The results of the identification are aggregated in the bus stop and traffic analysis zone (TAZ) scales, respectively. Particularly, commuting trips from three typical residential communities to six main business zones are mapped and compared to analyze commuting patterns in Beijing. The identified commuting trips are validated on three levels by comparison with those from the survey in terms of commuting time and distance, and the positive validation results prove the applicability of our approach. Our experiment, as a first step toward enriching LBS data using conventional survey and urban GIS data, can obtain solid identification results based on rules extracted from existing surveys or censuses.

This article examines the effectiveness of Beijing’s driving restrictions, which were implemented to reduce the severe congestion and air pollution that the city has been experiencing. One day each week, a car may not be used within the 5th Ring Road from 7 am to 8 pm, with the restricted day depending on the license plate number. Using 2010 Beijing Household Travel Survey data, we find that the driving restrictions decrease the probability of auto use about 10%, much lower than what was expected. The policy does reduce car travel, but some households find other ways to use cars during the restricted period. Men, workers with fixed work schedules, and low-income drivers are more likely to seek out ways to use cars. This suggests differential demand for auto use and willingness to pay for it, which is not addressed by the current policy. On the other hand, the analysis finds that congestion hurts even non-drivers, who reduce their trip-making on days when congestion is higher (i.e., on days that restrict plates ending in “4”, since relatively few plates display this “unlucky” number.) The findings show that while it is important to reduce auto impacts in Beijing, car restrictions may not be the most efficient way to do so, considering variation in need for a car, willingness to pay, and cultural issues.

This paper attempts to identify city planners from CAUPD, THUPDI and BMICPD on Sina micro-blog, and to crawl their user information, Based on which, this study creates a social graph of all identified members from these 3 institutes, and summarizes the social relationship characteristics of each institute, such as morphological feature of social graph, stability of the internal social relationship, the density of relationship and influence power. As a part of a series of studies on big data analysis in city planning, this paper shows the value of social media data for city planning.

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).

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