Journal of Urban Management
(Open Access by Elsevier)
Guest editor: Dr Ying Long, Tsinghua University
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.
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.
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.
‘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.
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.
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.
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.
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.
All papers in this special issue are available at http://pan.baidu.com/s/1jHcsm1K