Ying Long

Automated identification and characterization of parcels with OpenStreetMap and points of interest

Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for 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 China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata 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. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.

Beijing urban development model: Urban growth analysis and simulation

Abstract Urban growth analysis and simulation have been recently conducted by cellular automata (CA) models based on self-organizing theory which differs from system dynamics models. This paper describes the Beijing urban development model (BUDEM) which adopts the CA approach to support urban planning and policy evaluation. BUDEM, as a spatio-temporal dynamic model for simulating urban growth in the Beijing metropolitan area, is based on the urban growth theory and integrates logistic regression and MonoLoop to obtain the weights for the transition rule with multi-criteria evaluation configuration. Local sensitivity analysis for all the parameters of BUDEM is also carried out to assess the models performances. The model is used to identify urban growth mechanisms in the various historical phases since 1986, to retrieve urban growth policies needed to implement the desired (planned) urban form in 2020, and to simulate urban growth scenarios until 2049 based on the urban form and parameter set in 2020. The model has been proved to be capable of analyzing historical urban growth mechanisms and predicting future urban growth for metropolitan areas in China.

An urban containment planning support system for Beijing

Abstract Urban containment policies, including urban growth boundaries, urban service boundaries and greenbelts, have been extensively discussed worldwide for managing urban growth. This paper focuses on the issues associated with supporting an urban containment plan and its application in China using a planning support system. The background is that the urban containment plan has been enacted as a new component of the urban plan under the City Planning Law of the People’s Republic of China. In China, the accommodating or restrictive features are integrated as control factors (CFs), which include control indicators for land-use type control, urban activity control, building height control, as well as underground development control. This paper proposes an urban containment planning support system (UC-PSS) based on ArcGIS for automatically compiling the Beijing urban containment plan considering 60 control factors with various control indicators. The compiled plan was also applied for reviewing urban master and district detail plans in Beijing supported by the UC-PSS. The effectiveness of UC-PSS was comprehensively evaluated from the perspectives of planning compilation and planning review via interviewing urban containment planners (main users of the UC-PSS) in Beijing.

Retrieving Spatial Policy Parameters from an Alternative Plan Using Constrained Cellular Automata and Regionalized Sensitivity Analysis

In this paper we propose an approach to identify the spatial policy parameters (termed the implementation intensity reflecting planning controls on corresponding spatial constraint) associated with a predefined alternative plan, namely, a predefined-binary urban form. During plan implementation, the alternative plan cannot be fully realized in some cases due to practical urban growth driven by both institutional forces and market incentives, which are comprehensive and complex. Few researchers have investigated spatial policies appropriate for an alternative plan. We aim to propose a novel approach incorporating constrained cellular automata and regionalized sensitivity analysis, a method for global sensitivity analysis to calculate the realization possibility and identify the spatial policy parameters for an alternative plan. This approach is first tested in a virtual space with four predefined urban forms and various point, line, and polygon spatial constraints, with both positive and negative impacts on urban growth. Finally, the approach is also tested in the Beijing Metropolitan Area to identify the required spatial policy parameters for four alternative plans with seven spatial constraints.

How green are the streets? An analysis for central areas of Chinese cities using Tencent Street View

Extensive evidence has revealed that street greenery, as a quality-of-life component, is important for oxygen production, pollutant absorption, and urban heat island effect mitigation. Determining how green our streets are has always been difficult given the time and money consumed using conventional methods. This study proposes an automatic method using an emerging online street-view service to address this issue. This method was used to analyze street greenery in the central areas (28.3 km2 each) of 245 major Chinese cities; this differs from previous studies, which have investigated small areas in a given city. Such a city-system-level study enabled us to detect potential universal laws governing street greenery as well as the impact factors. We collected over one million Tencent Street View pictures and calculated the green view index for each picture. We found the following rules: (1) longer streets in more economically developed and highly administrated cities tended to be greener; (2) cities in western China tend to have greener streets; and (3) the aggregated green view indices at the municipal level match with the approved National Garden Cities of China. These findings can prove useful for drafting more appropriate policies regarding planning and engineering practices for street greenery.

Mapping Block-Level Urban Areas for All Chinese Cities

As a vital indicator for measuring urban development, urban areas are expected to be identified explicitly and conveniently with widely available dataset thereby benefiting the planning decisions and relevant urban studies. Existing approaches to identify urban areas normally based on midresolution sensing dataset, socioeconomic information (e.g. population density) generally associate with low-resolution in space, e.g. cells with several square kilometers or even larger towns/wards. Yet, few of them pay attention to defining urban areas with micro data in a fine-scaled manner with large extend scale by incorporating the morphological and functional characteristics. This paper investigates an automated framework to delineate urban areas in the parcel level, using increasingly available ordnance surveys for generating all parcels (or geo-units) and ubiquitous points of interest (POIs) for inferring density of each parcel. A vector cellular automata model was adopted for identifying urban parcels from all generated parcels, taking into account density, neighborhood condition, and other spatial variables of each parcel. We applied this approach for mapping urban areas of all 654 Chinese cities and compared them with those interpreted from mid-resolution remote sensing images and inferred by population density and road intersections. Our proposed framework is proved to be more straight-forward, time-saving and fine-scaled, compared with other existing ones, and reclaim the need for consistency, efficiency and availability in defining urban areas with well-consideration of omnipresent spatial and functional factors across cities.As a vital indicator for measuring urban development, urban areas are expected to be identified explicitly and conveniently with widely available data sets, thereby benefiting planning decisions and relevant urban studies. Existing approaches to identifying urban areas are normally based on midresolution sensing data sets, low-resolution socioeconomic information (e.g., population density) in space (e.g., cells with several square kilometers or even larger towns or wards). Yet, few of these approaches pay attention to defining urban areas with high-resolution microdata for large areas by incorporating morphological and functional characteristics. This article investigates an automated framework to delineate urban areas at the block level, using increasingly available ordnance surveys for generating all blocks (or geounits) and ubiquitous points of interest (POIs) for inferring density of each block. A vector cellular automata model was adopted for identifying urban blocks from all generated blocks, taking into account density, neighborhood condition, and other spatial variables of each block. We applied this approach for mapping urban areas of all 654 Chinese cities and compared them with those interpreted from midresolution remote sensing images and inferred by population density and road intersections. Our proposed framework is proven to be more straightforward, time-saving, and fine-scaled compared with other existing ones. It asserts the need for consistency, efficiency, and availability in defining urban areas with consideration of omnipresent spatial and functional factors across cities.

Jobs-Housing Balance of Bus Commuters in Beijing Exploration with Large-Scale Synthesized Smart Card Data

Jobs-housing studies have rarely used smart card data provided by public transportation agencies or focused on bus commuters. In this study, massive smart card data were used to estimate 216,844 bus commuters’ workplace and residence locations in Beijing. These data enabled a jobs-housing study of bus commuters in the metropolis with a much larger sample size than in most other studies. The study found that Beijings bus commuters had a shorter actual required commute (ARC) and a shorter minimum required commute (MRC) than commuters in four other auto-dependent Western cities with comparable population and land use size. The study also indicated that Beijings bus commuters had a longer ARC and a longer MRC than commuters of all modes in Guangzhou, a metropolis in southern China half the size of Beijing. Consultations with local experts, field surveys, and information provided by online housing search engines were used to supplement the smart card data. The study established five land use prototypes of jobs-housing imbalance and proposed countermeasures to address the imbalance.

BUDEM: An urban growth simulation model using CA for Beijing metropolitan area

It is in great need of identifying the future urban form of Beijing, which faces challenges of rapid growth in urban development projects implemented in Beijing. We develop Beijing Urban Developing Model (BUDEM in short) to support urban planning and corresponding policies evaluation. BUDEM is the spatio-temporal dynamic model for simulating urban growth in Beijing metropolitan area, using cellular automata (CA) and Multi-agent system (MAS) approaches. In this phase, the computer simulation using CA in Beijing metropolitan area is conducted, which attempts to provide a premise of urban activities including different kinds of urban development projects for industrial plants, shopping facilities, houses. In the paper, concept model of BUDEM is introduced, which is established basing on prevalent urban growth theories. The method integrating logistic regression and MonoLoop is used to retrieve weights in the transition rule by MCE. After model sensibility analysis, we apply BUDEM into three aspects of urban planning practices: (1) Identifying urban growth mechanism in various historical phases since 1986; (2) Identifying urban growth policies needed to implement desired urban form (BEIJING2020), namely planned urban form; (3) Simulating urban growth scenarios of 2049 (BEIJING2049) basing on the urban form and parameter set of BEIJING2020.

Shrinking cities in a rapidly urbanizing China

Many cities across particular areas in Europe and North America have a dwindling population, emerging vacant spaces, and the underuse of existing urban infrastructure (Haase et al., 2014). As one of the more prosperous urbanized countries in the world, China has witnessed an unprecedented active stage of urban expansion (see the Beijing City Lab Ranking 8 for details, http://www.beijingcitylab.com/ranking/), which also attracted extensive attention from academics (Deng et al., 2010). Our previous study on mushingrooming Jiedaos (the basic administrative unit of a city proper) indicates that urbanization in China often involves a significant political dimension. Largely rural settlements (e.g., Zhen) could be accorded with the city status (e.g., Jiedao) overnight by administrative power, which further accelerates the urban process (Wu et al., 2015). Meanwhile, some large cities and inshore developed cities in East China have attracted huge numbers of migrants from rural areas and small cities during the last ten years. Vacant villages have been widely reported in the context of China (Long et al., 2012), while we observe shrinkage at township and city levels. For all the townships in Mainland China, we estimated their population (residents not Hukou) based on the Population Censuses of China in 2000 and 2010, respectively. We found that 19 882 among all 39 007 townships were losing their population during 2000–10, and the total area was 3.24 million km, which covered almost about one third of the territories of China (Figure 1). Those shrinking townships are distributed in both rural and urban areas. Among them are 1147 urban townships with a total area of 47 420 km in 367 cities. Besides the shrinking townships observed, we further identify 180 shrinking cities in China including one provincial capital city, Urumqi—40 prefectural-level cities and 139 county-level cities (Figure 1). In addition, we use a cartogram to reveal population density in 2010 at the prefectural level, based on which shrinking prefectures are mapped (Figure 2). More work is needed to understand these shrinking localities, the reasons behind the population falls, and possible policy tools. Both decision makers and city planners are accustomed to the urban growth and population increasing in China. We hope that these featured graphics will inform them of our findings. In addition, we have established the

Does block size matter? The impact of urban design on economic vitality for Chinese cities

The influence of urban design on economic vitality has been analyzed by a number of researchers and is also a key focus of many planning/design theories. However, most quantitative studies are based on just one city or a small set of cities, rather than a large number of cities that are representative of an entire country. With the increasing availability of new data, we aim to alleviate this gap by examining the impact of urban design upon economic vitality for the 286 largest cities in China by looking at a grid of geographical units that are 1 km by 1 km. We use these units and a set of new data (emerging big data and new data that reflecting urban developments and human mobility) to look at the impact of urban form indicators, such as intersection density (urban design), level of mixed use, and access to amenities and transportation, on economic vitality represented by activities using social media data. Our results show that these urban design indicators have a significant and positive relationship with levels of economic vitality for cities at every administrative level. The results contribute to a holistic understanding of how to improve economic vitality in cities across China at a detailed level, particularly at a time when China’s economic growth will depend largely on growth of the service sector in urban areas. We think these results can help decision makers, developers, and planners/designers to improve economic vitality in cities across China.