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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 1km by 1km. We use these units and a set of new data 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 has a significant and positive impact on 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.
The paper provides an overview on the transformation of Chinese urban study driven by the emergence of new data environment in China in recent years. We first give a brief introduction to the new data environment, which has been made possible by the availability of big data and open data in recent years, as well as a review on the research progress both in China and abroad. It is followed by an analysis on the four major transformations in quantitative urban study, supported by typical research cases. The four transformations are (1) transformation in spatial scale from high resolution but small coverage or wide coverage but low resolution to wide coverage with high resolution, (2) transformation in temporal scale from static cross-sectional to dynamic consistent, (3) transformation in granularity from land-oriented to human-oriented, (4) transformation in methodology from conventional research group to crowd-sourcing. The paper also points out that quantitative urban research is faced with problems like data bias, lack of long term analysis, lack of linkage to planning practice, etc.
In the past decade, data explosion has taken place in Chinese cities, due to the wide spread of mobile Internet, Web2.0 applications and the development of ‘Wired City’. With advanced techniques in data storage and high-performance computing, big/open urban data has opened up important avenues for urban studies, planning practice and commercial consultancy. Urban researchers and planners are eager to make use of these abundant, sophisticated and dynamic data to deepen their understanding on urban form and functions. However, in practice, limited urban data is freely accessible and usable in China, due to institutional constraints on data distribution and data holders’ hesitations on data opening. Consequently, lack of proper data is an often-met dilemma in urban analytics. Efficient and effective interoperation of multi-source urban datasets is a must to draw reliable conclusions on the complex urban system, but dealing with the heterogeneity between datasets is another critical challenge, especially for urban planners or government officers. They need the support of data analytics, but have little data processing experience. In such context, we initiated SinoGrids (Plan Xu Xiake), a crowdsourcing platform for encouraging the standardization (downscaling), sharing and interoperation of micro-scale urban data in China. A human participated test was proposed for user performance evaluation of SinoGrids.
City is a complex self organized system, in which there are all kinds of sub-systems interacting with each other, and eventually affects the urban order which emerged during different times. Due
to the lack of knowledge about the system, for a long time, urban planners and designers had to work under a simplified concept framework. This over simplified methodology has been affecting the
debate about the sustainability of city. Even the new data environment now offers possibility to acknowledge this kind of complex relationship, theories and methodologies of urban planning and
design that response to the future still missing. From the perspective of the quantitative understanding of urban order, this paper try to explain how to understand the relationship between the
urban orders and the meaning of sustainability in the context of the new data environment, thus further constructing a methodology framework of data augmented sustainable planning, and
reasserting how to achieve the value rationality in the new era through the instrumental rationality.
In this study, we presented a conceptual model of the city and explained how it could be sustainable, then comparatively evaluated three labels, namely BREEM Communities, LEED-ND and CASBEE City,
based on our explanation. The three labels were compared from three aspects: dimension coverage, process coverage, and weighting and scoring. CASBEE City is concluded to be a relative better
label to evaluate the sustainable performance for cities; this is reasonable since it’s a city-level label, while other twos are actually neighborhood-level labels. In the next stage of our
study, a new sustainability assessment label at the city level would be developed, aiming to build socioeconomic, input- and output-related indicators so as to evaluate urban sustainability more
Aimed at the challenges faced by the current urban development and urban planning, along with the research opportunities brought by “big data,” this paper proposes an analytical framework based
on crowd-sourced data for urban planning by reviewing related literature and practice. The framework is mainly oriented towards three major requirements of analysis in urban planning: the
physical spaces, the user communities, and the social relationships. This analytical framework can be regarded as a preliminary attempt for future data-intensive applications in urban planning
In the process of developing urban public transport system, the coverage of bus station is an essential indicator in evaluating the service level of public transportation. Based on the meticulous data of bus stations, this study calculates the bus station coverage ratio of urban built-up area of 313 major cities, among which there are 281 prefecture-level or above prefecture-level cities whose average coverage ratio of bus station is 64.4%. Meanwhile this study reveals significant correlation of two group variables; one is between bus station coverage ratio and population density and bus station density, and the other is between used times of public transportation service per ten thousand people and ownership of public bus per ten thousand people and per capita GDP of urban district. According to the spatial feature of bus station coverage ratio, this study divides 313 cities into 5 categories, and tries to find out the general patterns and rules of Chinese public transportation service system. And the further trial analyses human activities and facilities condition within 500m service scope of bus station based on Flickr photos, Weibo position and POIs data, indicating that 94.4% facilities and more than 92% human activities are included in this service scope, which demonstrates that the majority demand of human activities and facilities can be fulfilled in view of bus station layout of Chinese cities. While most present studies on bus station coverage are focus on certain single city, and researches about general pattern of the majority cities of China are rare. On the one hand, study up to the whole country is subject to mass base data, on the other hand, studying most cities in microcosmic scale is held back by the transformation between different scale. This study is an experiment in dissolving micro-scale problems, considering both macro-scale and microcosmic analysis units, and taking advantage of meticulous data and analysis. Therefore, the results of this study will be able to provide evidence in the process of optimizing urban public transportation service and propelling urban public transportation planning at the same time.
The new data environment composed by big data and open data has descripted urban physical and social space in a more detailed way. Currently, numerous quantitative urban studies have been conducted under new data environment. However, most studies concentrated on status quo evaluation and problem identification of urban system, and few of them have a perspective into future-oriented urban planning and design. A new planning and design methodology termed Data Augmented Design (DAD) is presented in this paper. Empowered by quantitative urban analysis, utilizing approaches such as data analyzing, modeling and forecasting, DAD provides supporting tools covering the whole planning and design process from investigation, analysis, project design, evaluation, to feedbacks. Empirical data analysis in DAD improves the scientific level of planning and design, and inspires the creativity of planners and designers. This paper illustrates our knowledge and understanding of DAD from the following aspects: its definition, method, procedure, theory and practice, features and conceptual distinctions, frequently used approaches and tools, as well as its expected applicable situations. Case studies both in research and design aspects of DAD are presented at the last section of the paper.
The Liang-Chen scenario for Beijing Urban Planning, though failed to put into practice, is an important milestone in the history of urban planning in China. However, planners hold different views on this scenario: Some think that, if the scenario had been adopted, the development pattern of single center would not appear; while some others point out that this scenario is just another form of single- center-spreading. These arguments are derived from perceptual knowledge, but not rational analysis. Based on constrained cellular automatic and the theory of urban space development, the study simulates the possible urban forms that might be constructed under the assumption that Liang-Chen Scenario was adopted. Compared with the real urban forms in 1976, 1981, 1991, 2004, and 2008 of Beijing, the results show that the Liang-Chen Scenario cannot avoid Beijing urban form from single-center-spreading, if the impact of the scenario is limited to the migration of administrative center excluding subsequent influence.
Climate change, as a serious environmental problem contemporary society faces, has led to an international debate over what should be done to reduce energy consumption and corresponding negative environmental impact. Extensive research has found that a dominant share of urban energy consumption belongs to transport sector (e.g. commuting, shopping travel, and school travel etc.), which has a strong relationship with urban form. However, little attention has been paid to the relationship between urban form, transport energy consumption, and its environmental impact in the inner-city level. This paper aims to propose the LCF-PSS: an integrated planning support system for supporting the achievement of the low carbon form in cities. After proposing the whole framework, we tested it in a simplified virtual space to demonstrate its workability. In this test: 1) three land use types (R Residential, C Commercial, and O Others) were considered; 2) Planner Agents (PAs) established four urban forms (including land use allocation and urban density distribution); 3) 2000 agents, with various socio-economic attributes, found a R parcel to live, and a C parcel to work; 4) each agent chose a travel mode to commute be-tween living and working parcels; 5) we calculated the amount of energy consumption and environmental impact for the commuting of each agent using provided indicators for each travel mode; 6) finally, we found a low carbon form (LCF) by comparing the total amounts of energy consumption for four established forms. Results show the framework has the potential to support the achievement of low carbon forms in cities.
The wide application of smart card-based automated fare collection systems for public transportation has produced large quantities of spatial-temporal data at an individual level. Such data not only records mobility behavior of cardholders, but also reveals the usage patterns of cities. Owing to its high spatial-temporal resolution, low cost and large quantity, transit smart card data attracts more and more attention from urban/transport planners, playing an increasingly important role in urban studies. This article presents a comprehensive review of latest development on quantitative urban studies empowered by smart card data, from both China and overseas. The review covers the following four aspects: (1) data processing and origin-destination inference, (2) transit operation and management, (3) spatial structure of cities, and (4) mobility behavior and social networks. Finally, the review summarizes existing studies and gives a brief introduction to privacy protection and information extraction, and present potential future avenues of research.
Much like other post-socialist cities, Chinese cities experienced dramatic changes after economic reform. The danwei, or state-owned work unit, was once a fundamental building block of Chinese cities. In addition to being the basic form of economic and social organization, danwei communities defined Chinese urban development before reform, taking the form of gated, walled-off combined factory and residential areas. This paper focuses on spatial changes at the neighborhood scale in danwei, by selecting the Tongrentang pharmaceutical factory in Beijing, China during the time period between 1973 and 2006 as a case study. Through archival material and interviews, this paper describes how the community changed from gated, boring, solidified and strictly constrained work units to un-gated, vibrant, mixed-use and flexible urban neighborhoods. This case study in urban China provides implications for planning professionals and policy makers. By properly redeveloping these brownfields, traditional danwei communities may change to become un-gated, livable, accessible, integrated, and sustainable in the post-socialist era. The implication for current transition theory is that despite the similarity to Central and East European countries, urban China has a local context and unique spatial changes that should be embraced in future transition studies.
In line with the human-oriented focus and the need for new urbanization, big data has become a new paradigm in the field of urban planning and computer science. It brings great opportunities for research, planning practice, and business consulting. This article aims to provide an overview of urban big data studies from the institutional, academic, and practical perspectives, respectively. Particularly, it elaborates a number of research projects carried out by Beijing City Lab, a virtual research community dedicated to urban dynamics studies. Four major transformations of BCL urban studies have been highlighted in the paper.
This paper analyzes the relationship between the spatial parameters calculated by space syntax and the data from dazhongdianping Beijing, a popular website for choosing and reviewing restaurants by Chinese people. The result suggests that the use of informational technology is strengthening the spatial distribution logic of these restaurants instead of weakening the role of physical urban space. Furthermore, this paper focuses on the actual uses of shopping and catering spaces in the Wangfujing area and three major shopping malls. This study explores the potential and limitation of “big data” from Dazhongdianping in customer behavior research. It also illustrates the spatial logic for the distribution and use of catering spaces in architecture scale.