New Data Environment

This paper is a systematic review of the application of new data in the field of urban planning. Firstly, the acquisition, management and platform of new data are summarized. Secondly, it illustrates the application of new data in urban studies including the revolution of methodology and the relevant academic achievements. In this part, ‘big model’, a new research paradigm, is specifically introduced. The third part is about the revolution of urban planning practices, and emphasizes an important method called DAD (Data Augmented Design). Then, from the perspective of urban planning application in the transition period, the paper introduces a brand new platform called ‘Urban Planning Cloud Platform’. In the final part, the paper gives a summary on the experience and lessons of the application of new data in urban planning so as to bring new inspiration and reference for the future development.

Journal of Urban Management

(Open Access by Elsevier)

Guest editor: Dr Ying Long, Tsinghua University

ylong@tsinghua.edu.cn

Editorial: Big/open data for urban management

Big data generated by the growing use of information and communication technologies (ICT) and open data generated by open government initiatives are providing more and more opportunities for researchers to better understand and design cities around the world. Urban management efforts aimed at solving the problems of cities and managing city systems also benefit from the explosion of new data environment formed by big/open urban data, which can serve as an important complement to conventional survey data and data collected by various administrative departments.

The availability of big/open data to researchers has led to major transformations in the nature of urban studies, and these changes range from transformations in the spatial and temporal scales used to transformations in the levels of granularity and the research methods employed (see Long and Liu (2015) for more details). These transformations indicate that paradigms themselves may be in transition as well, thus suggesting possible new avenues for urban management issues.

Given this background, I organized this special issue with the generous support of Editor-in-Chief Shih-Kung Lai and have accepted four papers to address the state-of-the-art in using big/open data for urban management. It is worth noting that big data sources are not always “open” and open data sources are not always “big”, and most of these papers are based on open data rather big data. This is also the situation for most of the emerging big/open data-based urban studies.

This special issue is composed of four articles from the USA, China, Japan, and Germany, respectively. Chakraborty et al. develop a framework using open data and apply it to Mumbai, India. Their case study shows how open data information can be useful for understanding urbanization and for better integrating informal settlements into formal urban management and planning processes. Hao et al. propose a critical review of urban studies and planning practices in China using big data (as well as open data, although its use is not indicated in the title of their paper), and illustrate an overall picture of the studies and practices in this field. Yang et al. analyze and investigate the morphological features of multi-scale interactions between function and spatial configurations using points of interest in Beijing, an exploration which ends with the identification of four types of centers within the city. Zhang evaluates the density and diversity of OpenStreetMap road networks in China, an investigation which should be helpful for those who are interested in conducting urban studies in China using the OpenStreetMap open data.

While we are celebrating the many benefits that big/open data sources have provided to us, we as researchers should be cautious with regard to their potential biases. For instance, the studies on urban residents’ happiness using geotagged Weibo posts suffer from data bias with regard to several aspects, including the duplicity of Weibo senders, the limitations of natural language processing technology, the representativeness of Weibo senders, and the black box of Weibo’s API, all of which raise doubts about the reliability of such Weibo-based studies. Long and Liu (2015) have discussed possible strategies for combating these potential biases.

Reference:

Long, Y., & Liu, L. (2015). Big/open data in Chinese urban studies and planning: A review. ISOCARP Review 11.

All full papers in this issue are available for downloading at http://www.sciencedirect.com/science/journal/22265856/4

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.

A solid understanding of urbanizing China – the world’s largest and most rapidly transforming urban society – calls for improved urban data provision and analysis. This paper therefore looks at major technological, social-cultural, and institutional challenges of understanding urban China with open data, and showcases our attempt at understanding Chinese cities with open urban data. Through our showcases, we hope to demonstrate the usefulness of open urban data in (1) mapping urbanization in China with a finer spatiotemporal scales; (2) reflecting social and environmental dimensions of urbanization; and (3) visualizing urban China at multiple scales.

The smart city represents a perspective on the way urban living is being transformed by the widespread introduction of new information technologies in public spaces, collective institutions, and common municipal activities that deliver services for the public good. In the last 50 years, computers have become all pervasive in contemporary society in both the private and public realms but it is only recently that very wide areas in cities are being wired in such a way that computation can be used to deliver various forms of services to very large numbers of the population, thus enabling these services to be massively improved in terms of their efficiency and equity.

新信息技术在公共空间、集体体系以及为公益事业提供服务的公众参与活动中的广泛应用正在改变城市生活，智慧城市正是描绘了这样一种愿景。在过去的50年里，计算机全面渗透到当代社会的公共与私人领域，但直到最近，城市的各大领域才广泛互联，使得计算科学能够为大众提供多样化的服务，并极大提高这些服务的效率与公平性。

These developments have proceeded very rapidly during the last 10 years, particularly since sensing technologies have massively improved to the point where services that cover wide areas of cities such as transportation, are being revolutionised. These technologies produce very large amounts of data on their functioning and thus offer new ways of managing and controlling such services to further the pubic good. Moreover the fact that many such services can be monitored now in real time, changes the focus in their planning from the longer term to the shorter term. These expanded time horizons have elevated questions of urban change onto the planning agenda in ways that mean that our models and theories must respond much more effectively to questions of urban dynamics. Space-time studies in geography and transport are increasingly relevant in this new context, while theories that deal with the dynamics of cities and the way cities evolve from the bottom up as reflected in complexity science, are becoming much more central to our understanding.

这些发展在过去十年里进展非常迅速，特别是感知技术的极大进步，带来了城市交通等覆盖大范围服务的革命性变化。这些技术在运行过程中产生了大量数据，并为提升公众利益提供了管理和控制这些服务的新方法。此外，由于许多服务已实现实时监测，其规划焦点也逐渐由长期转为短期。时间维度上的拓展使得城市变化被纳入到城市规划议程中来，这也意味着我们的模型与理论必须更高效地应对这些城市动态议题。在这一背景下，地理与交通领域时空研究的相互关联度将日益增加，同时，复杂科学所反映出的自下而上的城市演进途径以及城市动态性相关理论，正愈加成为我们理解当今城市的核心。

‘Big data’ is a consequence of these new technologies. The data sets that are being generated from the real-time city have the potential for developing a new understanding of how people move and locate and for the first time are providing new insights into patterns of communication and interaction. Cities, as Jane Jacobs and more recently Ed Glaeser have argued before, are all about bringing people together to engage in social and economic production, and the smart city is providing new social capital for enabling such interactions. This is particularly significant in transport. For example, from call data records, mobile phone usage is beginning to provide us with new data on spatial interactions which is enriching our knowledge of how people travel.

“大数据”是这些新技术催生的产物。城市所产生的实时数据集有望发展对人们移动与定位方式的新理解，并提供前所未有的研究通讯与互动模式的新视角。正如简.雅各布斯以及近期的Ed Glaeser所指出的那样，城市就是人们聚集到一起从事社会与经济生产，而智慧城市则为这些互动提供新的社会资本。这在交通领域表现的尤为明显。例如，移动电话使用所产生的通话记录数据正为我们提供新的空间互动数据，并不断丰富我们对于人们出行的理解。

Social media – ranging from social networks sites such as Facebook, Sina Weibo, text messaging such as Twitter and photo archives such as Flickr are providing new perspectives on how people move and locate while a variety of smart card usage is providing us with data ranging from detailed profiling of retailing and online commerce to the use of transport and energy across the city. Geo-demographics is being transformed by big data and is providing us with enriched data sets that are key to better geographic information science and systems.

社交媒体——包括Facebook、新浪微博等社交网站，Twitter等短信推送，Flickr等照片存档，都在为我们提供研究人们移动与定位方式的新视角。同时，各类智能卡的使用也为我们提供了从详细零售记录、网上购物到交通与能源使用等城市中的各类数据。地域人口统计学因大数据而转变，并为我们提供了更加丰富的数据集，而这些数据正是提升地理信息科学与系统的关键。

There is little doubt that technology changes urban behaviour if only because it provides new ways of communication. By mining big data associated with such behaviour, new insights can be derived as new patterns are discovered. We need powerful new techniques to explore such data but we need even more powerful ways of using these insights to restructure the urban planning process.

毫无疑问，技术带来的新通讯方式正在改变城市居民的行为。通过对这些行为相关的大数据进行挖掘，我们将发现并建立新的行为模式和研究视角。在此，我们不仅需要强大的新技术来探索这些数据，还需要更强大的方法以利用这些视角重塑城市规划流程。

We need new ways of interacting with such data so that we can develop new forms of urban analytics to explore them and to fashion them into models that enable us to make much better predictions of the near future then anything we have been able to develop hitherto. A new era of urban modelling is in prospect as we get ever more detailed data sets in space and time, as data becomes open, and as more routine functions become automated. In this way, we will be able to extend our portfolio of plans and policies to deal with change over a wide variety of time scales and spatial scales, thus enriching the process of thinking about our urban future in ways that will improve the liveability and prosperity of our cities.

我们需要借助新的数据处理方式，以开发新的城市分析形式，实现数据挖掘与数据模型的匹配，比以往更好地对近期未来进行预测。借由更详细的时空数据集，数据开放以及常规功能的自动化，新城市模型时代即将到来。在此基础上，我们将进一步拓展现有的规划与政策，以应对更加多样时空尺度下内的城市变化，并不断丰富我们对于未来更加宜居而繁荣城市的设想。

We need a new science to tackle all this. The papers in this volume give an insight into how these ideas are being explored and being developed in China. There are some dramatic developments afoot in national urbanisation policy to use smart technologies to improve and control the future city in ways that will provide new experiences. Planning, in an international context, will be able to draw upon these new experiences and the articles in this issue point the way.

我们需要新的科学来处理所有这些问题。这一期的论文将详细介绍中国对于这些理念的探索和发展。中国的智能技术运用，不仅为提高和管理未来城市积攒经验，同时也为中国的全国城镇化政策正带来了巨大的发展和变化。从国际视角来看，规划将积极运用这些新的经验，而这一期的文章正聚焦于这一方向。

All papers in this special issue are available at http://pan.baidu.com/s/1jHcsm1K