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Defining Urban Big Data in Urban Planning: Literature Review
Abstract
Despite the unprecedentedly growing discussion of big data generated in urban environments and the widespread use of so-called urban big data (UBD) in recent years, there has been no consensus or widely accepted definition of UBD. Existing UBD studies have been either case-specific or applied in specific planning domains, such as transportation or tourism planning. A comprehensive exploration of the definitions of UBD in urban planning and related fields is timely and vital. This study is a systematic review of recent literature, consolidating 49 UBD definitions from 48 published articles in 39 journals, and classifying them into four themes: characteristics, sources, analytics, and impact. We found that most definitions are not given in an urban context and do not differentiate UBD from big data in a general sense. It is difficult to arrive at a one-size-fits-all definition of what constitutes UBD. Instead, the fourfold classification of UBD definitions allows us to identify three essential qualities of UBD that differentiate UBD from general big data and benefit urban studies: refinement of both spatiotemporal features and individual attributes at the microlevel, and the capacity and impact to depict, predict, and manage cities. We also identified three categories of challenges imposed on urban planning. This study serves as a starting point for a comprehensive understanding of UBD and contributes to expanding the discussion of UBD definitions and opportunities that UBD opens up in urban planning, facilitating better city management in the future.
... Unsurprisingly, this is consistent with the results of prior research [77,78]. Urban analytics and urban big data [79] can be viewed as the core of the "datafication" and "dataveillance" concepts that are central to such SC initiatives [80]. This also raises challenges of data access, ownership, and privacy. ...
... Third, it is easy to see a growing awareness of the collection of urban big data (e.g., traffic data, transit data, or probe data) [79] and the applying of urban analytics during smart cities development in the U.S. Almost all SC proposals mentioned data infrastructure and analytic platforms as the basis of their smart transportation system. As concluded by Kandt and Batty [77], integrating various forms of urban data and analyses will be an essential step in promising SC initiatives in urban policy and planning. ...
Smart City (SC) strategies developed by local governments reflect how governments and planners envision SC and apply smart technologies, and what challenges they face and try to address. Little attention, however, has been given to investigating SC strategies or applications, especially in the U.S. context. Moreover, there is insufficient attention paid to whether SC strategies address social issues such as equity and public participatory opportunities. Based on the documentation from the U.S. Department of Transportation 2015 Smart City Challenge, we developed a framework to evaluate SC strategies on urban transportation systems using six standards: Safety, Mobility, Sustainability, Opportunity, Efficiency, and Equity. In addition, we synthesized the challenges U.S. smart cities encounter, and SC strategies proposed by local municipal governments to tackle them. Our findings show that most SC strategies aimed to improve Efficiency (78%) and Mobility (57%), while less attention has been given to providing Equity (8%) or Opportunity (7%). The most well-acknowledged challenge that the local governments face is the limited data and tools for decision-making, with 416 SC strategies (27%) proposed to address related issues. Our framework and results contribute to the future SC strategy evaluation and inclusive smart city development. Our study also identified a broad spectrum of available SC strategies planners and policymakers can refer to when designing an SC or overcoming SC challenges.
... Through a meta-analysis of 48 urban big data studies, Wang and Yin [306] identified the essential qualities of urban big data. In a nutshell, urban big data focuses on refined spatiotemporal features and individual attributes at very fine levels (a street block, a building, etc.), and also has the capacity and impact to depict, predict, and manage cities through the complex interactions among individual data points and the collective trend such interactions demonstrate. ...
During the past decades, multiple remote sensing data sources, including nighttime light images, high spatial resolution multispectral satellite images, unmanned drone images, and hyperspectral images, among many others, have provided fresh opportunities to examine the dynamics of urban landscapes. In the meantime, the rapid development of telecommunications and mobile technology, alongside the emergence of online search engines and social media platforms with geotagging technology, has fundamentally changed how human activities and the urban landscape are recorded and depicted. The combination of these two types of data sources results in explosive and mind-blowing discoveries in contemporary urban studies, especially for the purposes of sustainable urban planning and development. Urban scholars are now equipped with abundant data to examine many theoretical arguments that often result from limited and indirect observations and less-than-ideal controlled experiments. For the first time, urban scholars can model, simulate, and predict changes in the urban landscape using real-time data to produce the most realistic results, providing invaluable information for urban planners and governments to aim for a sustainable and healthy urban future. This current study reviews the development, current status, and future trajectory of urban studies facilitated by the advancement of remote sensing and spatial big data analytical technologies. The review attempts to serve as a bridge between the growing “big data” and modern urban study communities.
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
Urban big data often contain spatial and temporal elements that have increasingly become an integral part of various applications and projects such as smart mobility, smart city, and other digitally enhanced urban infrastructure. It is critical to develop an open and collaborative environment so that these data can be used by a wide range of users. This chapter first discusses some characteristics and sources of urban big data. Three hypothetical user stories are described to highlight the potential of these data. After describing the internal data structure of these data and techniques that can be used to retrieve the data, we discuss the difficulty in making the data useful for the general public and elaborate on a self-organizing agile approach to developing an urban big data infrastructure.
Urban big data include various types of datasets, such as air quality data, meteorological data, and weather forecast data. Air quality index is broadly used in many countries as an indicator to measure the air pollution status. This indicator has a great impact on outdoor activities of urban residents, such as long-distance cycling, running, jogging, and walking. However, for routes planning for outdoor activities, there is still a lack of comprehensive consideration of air quality. In this paper, an air quality index prediction model (namely airQP-DNN) and its application are proposed to address the issue. This paper primarily consists of two components. The first component is to predict the future air quality index based on a deep neural network, using historical air quality datasets, current meteorological datasets, and weather forecasting datasets. The second component refers to a case study of outdoor activities routes planning in Beijing, which can help plan the routes for outdoor activities based on the airQP-DNN model, and allow users to enter the origin and destination of the route for the optimized path with the minimum accumulated air quality index. The air quality monitoring datasets of Beijing and surrounding cities from April 2014 to April 2015 (over 758,000 records) are used to verify the proposed airQP-DNN model. The experimental results explicitly demonstrate that our proposed model outperforms other commonly used methods in terms of prediction accuracy, including autoregressive integrated moving average model, gradient boosted decision tree, and long short-term memory. Based on the airQP-DNN model, the case study of outdoor activities routes planning is implemented. When the origin and destination are specified, the optimized paths with the minimum accumulated air quality index would be provided, instead of the standard static Dijkstra shortest path. In addition, a Web-GIS-based prototype has also been successfully developed to support the implementation of our proposed model in this research. The success of our study not only demonstrates the value of the proposed airQP-DNN model, but also shows the potential of our model in other possible extended applications.
Vast amounts of data are produced with the development of smart cities and urban computing technologies. The data is often captured from multiple sensors, with heterogeneous structures and highly decentralized connections. Integrated data representation and smart computational models are required for more complex tasks in urban computing. We dwell deeply on two fundamental questions — can we provide an integrated data representation for the whole cyber–physical–social system? And, can we provide an integrated framework to choose the appropriate data for understanding a specific urban event? A holography data representation and the quasi-holography computational model have been proposed to address these problems. We describe case studies using the quasi-holography computational model, and discuss further problems to solve regarding our model.
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This research applies a unique conceptual model and methodology incorporating popularity, commitment, and virality to measure the social media engagement with residents and visitors of smart cities and how they communicate ‘smart’ elements and their brands. Digital content analysis was applied to a sample of ten Spanish smart cities (including Barcelona, Bilbao, Madrid, Seville and Valencia, among others), with measurable and quantifiable elements of engagement (e.g., likes, shares and comments). The smart cities analysed achieved acceptable, but rudimentary, levels of engagement via social media using Facebook, Twitter and Instagram. However, they displayed weaknesses related to their image and branding as well as the effectiveness with which they communicated their smart characteristics. The main implication of this research is that these Spanish smart cities have considerable scope to improve their use of social media to enhance their communications and branding. Greater emphasis is required on delivering emotional (affective) messages and a higher priority needs to be given to business and business event travellers and those visiting friends and relatives.
The increasing availability of tremendous amounts of data generated by people, vehicles, and things have provided unprecedented opportunities for understanding human behavior in the urban environment. At the same time, crowd management systems can benefit city planning, emergency control, and mobile network design. In this work, we exploit urban data as a way of analyzing crowd behavior. We analyze the types of crowd situations, describe the major types of urban data, and highlight their strengths and weaknesses. We then discuss the key research challenges and opportunities in the analysis of urban environments. Moreover, through case studies, we explain how to apply urban data for spatial, temporal, and semantic observations of crowd situations.
The perceptions of people toward neighborhoods reveal their satisfaction with their living environments and their perceived quality of life. Recently, there is an emergence of Web sites designed for helping people to find suitable places to live. On these Web sites, current and previous residents can review their neighborhoods by providing numeric ratings and textual comments. Such online neighborhood review data provide novel opportunities for studying the perceptions of people toward their neighborhoods. In this article, we analyze such online neighborhood review data. Specifically, we extract two types of knowledge from the data: (1) semantics, or the semantic topics (or aspects) that people talk about regarding their neighborhoods, and (2) sentiments, or the emotions that people express toward the different aspects of their neighborhoods. We experiment with a number of different computational models in extracting these two types of knowledge and compare their performances. The experiments are based on a data set of online reviews about the neighborhoods in New York City, which were contributed by 7,673 distinct Web users. We also conduct correlation analyses between the subjective perceptions extracted from this data set and the objective socioeconomic attributes of New York City neighborhoods and find similarities and differences. The effective models identified in this research can be applied to neighborhood reviews in other cities for supporting urban planning and quality of life studies.
The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.
The emergence of smart cities aims at mitigating the challenges raised due to the continuous urbanization development and increasing population density in cities. To face these challenges, governments and decision makers undertake smart city projects targeting sustainable economic growth and better quality of life for both inhabitants and visitors. Information and Communication Technology (ICT) is a key enabling technology for city smartening. However, ICT artifacts and applications yield massive volumes of data known as big data. Extracting insights and hidden correlations from big data is a growing trend in information systems to provide better services to citizens and support the decision making processes. However, to extract valuable insights for developing city level smart information services, the generated datasets from various city domains need to be integrated and analyzed. This process usually referred to as big data analytics or big data value chain. Surveying the literature reveals an increasing interest in harnessing big data analytics applications in general and in the area of smart cities in particular. Yet, comprehensive discussions on the essential characteristics of big data analytics frameworks fitting smart cities requirements are still needed. This paper presents a novel big data analytics framework for smart cities called “Smart City Data Analytics Panel — SCDAP”. The design of SCDAP is based on answering the following research questions: what are the characteristics of big data analytics frameworks applied in smart cities in literature and what are the essential design principles that should guide the design of big data analytics frameworks have to serve smart cities purposes? In answering these questions, we adopted a systematic literature review on big data analytics frameworks in smart cities. The proposed framework introduces new functionalities to big data analytics frameworks represented in data model management and aggregation. The value of the proposed framework is discussed in comparison to traditional knowledge discovery approaches.
Cities worldwide are attempting to transform themselves into smart cities. Recent cases and studies show that a key factor in this transformation is the use of urban big data from stakeholders and physical objects in cities. However, the knowledge and framework for data use for smart cities remain relatively unknown. This paper reports findings from an analysis of various use cases of big data in cities worldwide and the authors' four projects with government organizations toward developing smart cities. Specifically, this paper classifies the urban data use cases into four reference models and identifies six challenges in transforming data into information for smart cities. Furthermore, building upon the relevant literature, this paper proposes five considerations for addressing the challenges in implementing the reference models in real-world applications. The reference models, challenges, and considerations collectively form a framework for data use for smart cities. This paper will contribute to urban planning and policy development in the modern data-rich economy.
In an era of smart cities, planning support systems (PSS) offer the potential to harness the power of urban big data and support land-use and transport planning. PSS encapsulate data-driven modelling approaches for envisioning alternative future cities scenarios. They are widely available but have limited adoption in the planning profession (Russo, Lanzilotti, Costabile, & Pettit, 2017). Research has identified issues preventing their mainstream adoption to be, among others, the gap between PSS supply and demand (Geertman, 2016), their difficulty of use, a need for greater understanding of PSS capabilities and a lack of awareness of their applications (Russo et al., 2017; Vonk, Geertman, & Schot, 2005). To address this, a review of five PSS is conducted in the context of four vignettes applied in Australia and applicable internationally. A critical review has been undertaken, demonstrating how these PSS provide an evidence basis to understand, model and manage growing cities. The results suggest that PSS can assist in undertaking key tasks associated with the planning process. In addition to supporting planning and decision making, PSS can potentially enable better co-ordination between city, state and federal planning and infrastructure agencies, thus promoting a multi-scaled approach that improves local and national data sharing, modelling, reporting and scenario planning. The research demonstrates that PSS can assist in navigating the complexities of rapid multi-faceted urban growth to achieve better-informed planning outcomes. The paper concludes by outlining ways PSS address limitations of the past and can begin to address anticipated future challenges.
Urbanization and modernization accelerate the evolution of urban morphology with the formation of different functional regions. To develop a smart city, how to efficiently identify the functional regions is crucial for future urban planning. Differed from the existing works, we mainly focus on how to identify the latent functions of subway stations. In this paper, we propose a semantic framework (IS2Fun) to identify spatiotemporal functions of stations in a city. We apply the semantic model Doc2vec to mine the semantic distribution of subway stations based on human mobility patterns and points of interest (POIs), which sense the dynamic (people’s social activities) and static characteristics (POI categories) of each station. We examine the correlation between mobility patterns of commuters and travellers and the spatio-temporal functions of stations. In addition, we develop the POI feature vectors to jointly explore the functions of stations from a perspective of static geographic location. Subsequently, we leverage Affinity Propagation (AP) algorithm to cluster all the stations into 10 functional clusters and obtain the latent spatio-temporal functions. We conduct extensive experiments based on the massive urban data including subway smart card transaction data and POIs to verify that the proposed framework IS2Fun outperforms existing benchmark methods in terms of identifying the functions of subway stations.
The integration of things’ data on the Web and Web linking for things’ description and discovery is leading the way towards smart Cyber–Physical Systems (CPS). The data generated in CPS represents observations gathered by sensor devices about the ambient environment that can be manipulated by computational processes of the cyber world. Alongside this, the growing use of social networks offers near real-time citizen sensing capabilities as a complementary information source. The resulting Cyber–Physical–Social System (CPSS) can help to understand the real world and provide proactive services to users. The nature of CPSS data brings new requirements and challenges to different stages of data manipulation, including identification of data sources, processing and fusion of different types and scales of data. To gain an understanding of the existing methods and techniques which can be useful for a data-oriented CPSS implementation, this paper presents a survey of the existing research and commercial solutions. We define a conceptual framework for a data-oriented CPSS and detail the various solutions for building human–machine intelligence.
Social media has revolutionized our societies. It has made fundamental impact on the way we work and live. More importantly, social media is gradually becoming a key pulse of smart societies by sensing the information about the people and their spatio-temporal experiences around the living spaces. Big data and computational intelligence technologies are helping us to manage and analyze large amounts of data generated by the social media, such as twitter, and make informed decisions about us and the living spaces. This paper reports our preliminary work on the use of social media for the detection of spatio-temporal events related to logistics and planning. Specifically, we use big data and AI platforms including Hadoop, Spark, and Tableau, to study twitter data about London. Moreover, we use the Google Maps Geocoding API to locate the tweeters and make additional analysis. We find and locate congestion around the London city. We also discover that, during a certain period, top third tweeted words were about job and hiring, leading us to locate the source of the tweets which happened to be originating from around the Canary Wharf area, UK's major financial center. The results presented in the paper have been obtained using 500,000 tweets.
Cities are perhaps one of the most challenging and yet enabling arenas for sustainable development goals. The Sustainable Development Goals (SDGs) emphasize the need to monitor each goal through objective targets and indicators based on common denominators in the ability of countries to collect and maintain relevant standardized data. While this approach is aimed at harmonizing the SDGs at the national level, it presents unique challenges and opportunities for the development of innovative urban-level metrics through big data innovations. In this article, we make the case for advancing more innovative targets and indicators relevant to the SDGs through the emergence of urban big data. We believe that urban policy-makers are faced with unique opportunities to develop, experiment, and advance big data practices relevant to sustainable development. This can be achieved by situating the application of big data innovations through developing mayoral institutions for the governance of urban big data, advancing the culture and common skill sets for applying urban big data, and investing in specialized research and education programs.
Big Data is the term being used to describe a wide spectrum of observational or " naturally-occurring " data generated through transactional, operational, planning and social activities that are not specifically designed for research. Due to the structure and access conditions associated with such data, their use for research and analysis becomes significantly complicated. New sources of Big Data are rapidly emerging as a result of technological, institutional, social, and business innovations. The objective of this background paper is to describe emerging sources of Big Data, their use in urban research, and the challenges that arise with their use. To a certain extent, Big Data in the urban context has become narrowly associated with sensor (e.g., Internet of Things) or socially generated (e.g., social media or citizen science) data. However, there are many other sources of observational data that are meaningful to different groups of urban researchers and user communities. Examples include privately held transactions data, confidential administrative micro-data, data from arts and humanities collections, and hybrid data consisting of synthetic or linked data. The emerging area of Urban Informatics focuses on the exploration and understanding of urban systems by leveraging novel sources of data. The major potential of Urban Informatics research and applications is in four areas: (1) improved strategies for dynamic urban resource management, (2) theoretical insights and knowledge discovery of urban patterns and processes, (3) strategies for urban engagement and civic participation, and (4) innovations in urban management, and planning and policy analysis. Urban Informatics utilizes Big Data in innovative ways by retrofitting or repurposing existing urban models and simulations that are underpinned by a wide range of theoretical traditions, as well as through data-driven modeling approaches that are largely theory agnostic, although these divergent research approaches are starting to converge in some ways. The paper surveys the kinds of urban problems being considered by going from a data-poor environment to a data-rich world and the ways in which such enquiries have the potential to enhance our understanding, not only of urban systems and processes overall, but also contextual peculiarities and local experiences. The paper concludes by commenting on challenges that are likely to arise in varying degrees when using Big Data for Urban Informatics: technological, methodological, theoretical/epistemological, and the emerging political economy of Big Data.
Software-enabled technologies and urban big data have become essential to the functioning of cities. Consequently, urban operational governance and city services are becoming highly responsive to a form of data-driven urbanism that is the key mode of production for smart cities. At the heart of data-driven urbanism is a computational understanding of city systems that reduces urban life to logic and calculative rules and procedures, which is underpinned by an instrumental rationality and realist epistemology. This rationality and epistemology are informed by and sustains urban science and urban informatics, which seek to make cities more knowable and controllable. This paper examines the forms, practices and ethics of smart cities and urban science, paying particular attention to: instrumental rationality and realist epistemology; privacy, datafication, dataveillance and geosurveillance; and data uses, such as social sorting and anticipatory governance. It argues that smart city initiatives and urban science need to be re-cast in three ways: a re-orientation in how cities are conceived; a reconfiguring of the underlying epistemology to openly recognize the contingent and relational nature of urban systems, processes and science; and the adoption of ethical principles designed to realize benefits of smart cities and urban science while reducing pernicious effects.
This article is part of the themed issue ‘The ethical impact of data science’.
The current interest in big data, machine learning and data analytics has generated the widespread impression that such methods are capable of solving most problems without the need for conventional scientific methods of inquiry. Interest in these methods is intensifying, accelerated by the ease with which digitized data can be acquired in virtually all fields of endeavour, from science, healthcare and cybersecurity to economics, social sciences and the humanities. In multiscale modelling, machine learning appears to provide a shortcut to reveal correlations of arbitrary complexity between processes at the atomic, molecular, meso- and macroscales. Here, we point out the weaknesses of pure big data approaches with particular focus on biology and medicine, which fail to provide conceptual accounts for the processes to which they are applied. No matter their ‘depth’ and the sophistication of data-driven methods, such as artificial neural nets, in the end they merely fit curves to existing data. Not only do these methods invariably require far larger quantities of data than anticipated by big data aficionados in order to produce statistically reliable results, but they can also fail in circumstances beyond the range of the data used to train them because they are not designed to model the structural characteristics of the underlying system. We argue that it is vital to use theory as a guide to experimental design for maximal efficiency of data collection and to produce reliable predictive models and conceptual knowledge. Rather than continuing to fund, pursue and promote ‘blind’ big data projects with massive budgets, we call for more funding to be allocated to the elucidation of the multiscale and stochastic processes controlling the behaviour of complex systems, including those of life, medicine and healthcare.
This article is part of the themed issue ‘Multiscale modelling at the physics–chemistry–biology interface’.
In this first paper, we introduce the reader to many salient features of big data and develop these in relation to data pertaining to cities, particularly through the current wave of interest in making cities smarter using new technologies. We begin by defining what big means in terms of size and then introduce the 'three Vs' of big data – volume, velocity and variety – which relate to various other characteristics of such data sets. Big data, in fact, is changing our focus on what is important in cities and focusing our attention on much smaller time spans over which cities change. We describe how such data is captured and generated and extend our argument to traditional data which can also become big under certain conditions were it to be disaggregated or combined with other data. We illustrate diff erent types of big data and then illustrate its features using three examples involving movement and social media. We use these arguments as a prelude to the substance and the method of the papers included in this issue which focus on diff erent aspects of big data and the wider context in which the data and models are developed as part of the smart cities movement.
Cities are restless systems. Increasing urbanisation and the cumulative growth of urban management issues are pressing local governments to provide valuable services. Information and Communication Technologies (ICTs) are established as drivers of this new wave of change in the redefinition of the relationship between the city and its citizens. Smart Cities rely strongly on strategies and solutions enabled by ICTs involving directly local governments, citizens and communities. Open data alone, though a relevant component of these strategies, cannot provide enough incentives for the engagement of citizens necessary to establish a collaborative and open governance system; data need to be appropriately elaborated, communicated and used. The main objective of this paper is to investigate how open data together with simple and standardised elaborations and innovative visualisation techniques may be used to provide new and updated services to citizens and communities: free and readily available services based on the wealth of information ‘owned’ by local governments. Adopting a design science research methodology we develop and test a collaborative ICT-based tool called UrbanSense. We use it to highlight how systemic connections between citizens and city-government may be devised. On this point, the focus is on the feedforwarding of open data integrated with basic elaborations and visualisations as a means for the local government to create new and open services for citizens and communities. The use of the services prompts citizens to feedback new information in real-time to the city government. These interactions may be used to foster an open innovation ecosystem.
This study provides a definition for urban big data while exploring its features and applications of China's city intelligence. The differences between city intelligence in China and the “smart city” concept in other countries are compared to highlight and contrast the unique definition and model for China's city intelligence in this paper. Furthermore, this paper examines the role of urban big data in city intelligence by showing that it not only serves as the cornerstone of this trend as it also plays a core role in the diffusion of city intelligence technology and serves as an inexhaustible resource for the sustained development of city intelligence. This study also points out the challenges of shaping and developing of China's urban big data. Considering the supporting and core role that urban big data plays in city intelligence, the study then expounds on the key points of urban big data, including infrastructure support, urban governance, public services, and economic and industrial development. Finally, this study points out that the utility of city intelligence as an ideal policy tool for advancing the goals of China's urban development. In conclusion, it is imperative that China make full use of its unique advantages—including using the nation's current state of development and resources, geographical advantages, and good human relations—in subjective and objective conditions to promote the development of city intelligence through the proper application of urban big data.
In this chapter we discuss recent trends in the application of urban big data and their impact on real estate markets. We expect such technologies to improve quality of life and the productivity of cities over the long run.
Population migration, social check-in, vehicle navigation, and other spatial behavior big data have become vital carriers characterizing users' spatial behavior. “Tencent Migration” big data can real-timely, dynamically, completely and systematically record population flow routs using LBS device. Through gathering residents daily mobility among 299 cities in China during the period of “National Day–Mid-Autumn Festival” (NDMAF) vacation (from September 30 to October 8) in 2017 in “Tencent Migration” and defining three periods with “travel period, journey period, return period”, this paper is designed to analyze and explore the characteristics and spatial patterns of daily flow mobility cities from the perspective of population daily mobility distribution levels, flow distribution layers network aggregation, spatial patterns and characteristics of the complex structure of the flow network. Results show that “Tencent migration” big data clearly discovers the temporal-spatial pattern of population mobility in China during the period of NDMAF. The net inflow of population showed a diamond shaped with cross frame support in each period, the four nodes of the diamond contain Beijing, Shanghai, Guangzhou and Xi'an. Main mobility assembling centers are distributed in the urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta and Chengdu-Chongqing, and those centers have strong coherence with those urban hierarchies. Most cities are in a state of “relative equilibrium” in the population flow, and clear hierarchical structure and level distinction can be identified. Spatial patterns of population mobility present obvious core-periphery structures. The Dali-Hegang line exhibits a significant network of spatial differences in terms of boundary divisions. In this context, the spatial distribution of urban network could be summarized as “dense in the East and sparse in the West”, and the core linkages of urban network could be characterized as “parallel in the East and series in the West”. The whole network exhibits a typical “small world” network characteristic, which shows that China's urban population flow network has high connectivity and accessibility during the period of NDMAF. The network has a distinct “community” structure in the local area, including 2 national communities, 2 regional communities and 3 local-level communities.
Social inequalities induced by education accessibility are widespread and concern land use policy makers globally. Yet much of existing research heavily emphasizes on uncovering the spatial patterns of housing price in relation to education resources. This paper aims to renew our understanding of this classical topic by bringing the importance of temporal and institutional dynamics to the fore. Specifically, this research presents a detailed examination of the relationship between school quality and housing rental dynamics under the intervention of equitable housing policy, with special reference to Shanghai, the most developed megacity in China, also one of the first Chinese cities granting equal rights for renters to access schools within the catchment area. Based on time-series housing rental data collected from social media, the t-test discovers that school quality has a significant impact on housing rental prices after the introduction of this new housing policy. Moreover, housing rental prices within high-quality school districts are significantly higher than those within ordinary school districts. The hedonic model and variance partitioning further confirm that school quality is capitalized into housing rental prices, and its relative contribution varies with temporal periods. More specifically, how the relative contribution of school quality changes over time is consistent with the schedule of primary school enrollment and sensitive to policy intervention. The difference in difference model foregrounds that the equitable housing policy does result in rental prices hike in high-quality school districts, i.e., a 13.5 % rental premium of housings within high-quality school districts compared with those within ordinary ones. We conclude that the equitable housing policy fails to achieve the expected goal and propose alternative suggestions for mitigating social inequalities in education accessibility. This study demonstrates a novel methodological framework for evaluating the social consequence of equitable housing policy based on social media data. It unravels how housing rental changes with school district division and the capitalization effect of school quality in housing rental prices.
Understanding subjective aspects of urban life can be a powerful tool for policymakers to improve public well-being by focusing on the peculiarities their metropolis. Cities are as diverse as the humans living in it. Just as any other complex system, a city changes and evolves with time, accumulating experiences and features that are particular to their history. Unmatched memories are what makes people feel connected to a place. This uniqueness of characteristics is what we call the city identity. This paper proposes a method to detect and compare identities of cities using news articles. Revealing the most unique topics for each city can uncover their utmost interests and points of view. We exemplified its use with a set of thirty-six big cities from the United States of America creating a footprint of components containing the city's identity profiles. This method allows the comparison of cities at scale without compromising the quality of the results.
Scene categorization is an indispensable technique in intelligent systems, such as scene parsing, video surveillance or autonomous driving. Considering traffic analysis under big data, in this paper, we propose scene categorization towards urban tunnel traffic based on image quality assessment. Specifically, the dataset is obtained through analyzing urban tunnel traffic incidents from 2016 to 2018. And we classify the traffic accidents in the big data environment. Then, the vehicles in the surveillance videos are extracted using conventional detector. The spatial information of vehicles in the image reflects the traffic situation. In order to encode such important information, we leverage the information clustering algorithm based on information entropy for image classification. Afterward, we establish a quality evaluation model based on each clustered images. The trained image quality assessment model will guide tunnel traffic classification and event analysis. The experimental results show the correct rate is more than 90%, and the overall detection effect is better than the k-modes algorithm and the Ng’k-modes algorithm.
Urban big data fusion creates huge values for urban computing in solving urban problems. In recent years, various models and algorithms based on deep learning have been proposed to unlock the power of knowledge from urban big data. To clarify the methodologies of urban big data fusion based on deep learning (DL), this paper classifies them into three categories: DL-output-based fusion, DL-input-based fusion and DL-double-stage-based fusion. These methods use deep learning to learn feature representation from multi-source big data. Then each category of fusion methods is introduced and some examples are shown. The difficulties and ideas of dealing with urban big data will also be discussed.
Smart Cities make use of ICT technology to address the challenges of modern urban management. The cloud provides an efficient and cost-effective platform on which they can manage, store and process data, as well as build applications performing complex computations and analyses. The quickly changing requirements in a Smart City require flexible software architectures that let these applications scale in a distributed environment such as the cloud. Smart Cities have to deal with huge amounts of data including sensitive information about infrastructure and citizens. In order to leverage the benefits of the cloud, in particular in terms of scalability and cost-effectiveness, this data should be stored in a public cloud. However, in such an environment, sensitive data needs to be encrypted to prevent unauthorized access. In this paper, we present a software architecture design that can be used as a template for the implementation of Smart City applications. The design is based on the microservice architectural style, which provides properties that help make Smart City applications scalable and flexible. In addition, we present a hybrid approach to securing sensitive data in the cloud. Our architecture design combines a public cloud with a trusted private environment. To store data in a cost-effective manner in the public cloud, we encrypt metadata items with CP-ABE (Ciphertext-Policy Attribute-Based Encryption) and actual Smart City data with symmetric encryption. This approach allows data to be shared across multiple administrations and makes efficient use of cloud resources. We show the applicability of our design by implementing a web-based application for urban risk management. We evaluate our architecture based on qualitative criteria, benchmark the performance of our security approach, and discuss it regarding honest-but-curious cloud providers as well as attackers trying to access user data through eavesdropping. Our findings indicate that the microservice architectural style fits the requirements of scalable Smart City applications while the proposed security approach helps prevent unauthorized access.
In recent years, the widespread use of Social media has generated new and big datasets and provided new platforms for Urban planning. However, existing studies have often been case-specific or focused on a specific planning domain, leaving the role of Social media in Urban planning generally questioned. This study conducts a systematic review of to which extent Social media can be used in Urban planning. There are two main findings. On the one hand, Social media data have been increasingly used for urban analysis and Modeling/Modelling, often combined with conventional and new datasets. The domains of application include Individual activity patterns, Urban land use, transportation behavior, and Landscape. On the other hand, Social media have provided a new platform for Participation, Communication and Collaboration. They provide new opportunities for Cities to hear the voices of distinctive social groups, even those who do not formally participate in planning processes. In recent years, citizens have used Social media to initiate and organize themselves collective actions in planning practices. Issues of using Social media data in Urban planning include Population and spatial biases, Privacy issues, and difficulties in extracting useful information out of the Social media data. It is necessary to pay more attention to the proper dealing with these issues during the collection and methodological handling of Social media data.
Research in Big Data and analytics offers tremendous opportunities to utilize evidence in making decisions in many application domains. To what extent can the paradigms of Big Data and analytics be used in the domain of transport? This article reports on an outcome of a systematic review of published articles in the last five years that discuss Big Data concepts and applications in the transportation domain. The goal is to explore and understand the current research, opportunities, and challenges relating to the utilization of Big Data and analytics in transportation. The review shows the potential of Big Data and analytics to garner insights and improve transportation systems through the analysis of various forms of data obtained from traffic monitoring systems, connected vehicles, crowdsourcing, and social media. We discuss some platforms and software architecture for the transport domain, along with a wide array of storage, processing, and analytical techniques, and describe challenges associated with the implementation of Big Data and analytics. This review contributes broadly to the various ways in which cities can utilize Big Data in transportation to guide the creation of sustainable and safer traffic systems. Since research in Big Data and transportation is, by and large, at infancy, this article does not prescribe recommendations to the various challenges identified, which also constitutes the limitation of the article.
Cities are increasingly turning towards specialized technologies to address issues related to society, ecology, morphology and many others. The emerging concept of Smart Cities highly encourages this prospect by promoting the incorporation of sensors and Big Data through the Internet of Things (IoT). This surge of data brings new possibilities in the design and management of cities just as much as economic prospects. While Big Data processing through Artificial Intelligence (AI) can greatly contribute to the urban fabric, sustainability and liveability dimensions however must not be overlooked in favour of technological ones. This paper reviews the urban potential of AI and proposes a new framework binding AI technology and cities while ensuring the integration of key dimensions of Culture, Metabolism and Governance; which are known to be primordial in the successful integration of Smart Cities for the compliance to the Sustainable Development Goal 11 and the New Urban Agenda. This paper is aimed towards Policy Makers, Data Scientists and Engineers who are looking at enhancing the integration of Artificial Intelligence and Big Data in Smart Cities with an aim to increase the liveability of the urban fabric while boosting economic growth and opportunities.
Urban planning and its relevant transportation deploying have a particularly profound influence on the sustainability and livability of a city, and which also be crucial to the quality of life to urban residents at the same time. It was also suggested that the conception of livability should be extended to embrace the concerns associated with the sustainability. However, planning frameworks or assessment patterns that address the dynamics of urban planning and demand for transportation deploying are relatively rare; there also few public policies in related research fields have discussed the effects of the changes in various assessment indicators over time. Furthermore, following the rising advancements in social communication and computer technologies in modern society, the data collection, storage, and processing capabilities of people have improved substantially. And, the emergence of big data or extendible open data facilitates analysis and prediction availability, and enabled people to find immediate solutions to numerous dilemmas encountered. Therefore, based on the aforementioned intention, treating the city as a dynamic process with the trying of introducing the big data or extendible open data for facilitating urban sustainability and livability is undoubtedly worth to explore in further.
The present study intends to initially examine the application of big data in sustainable and livable transportation strategies in Taipei City, Taiwan. Firstly, we investigate previous research on transportation sustainability in various countries to generalize our preliminary list of transportation sustainability indices that satisfy the principles of livable cities. And, key indices were then selected through the Fuzzy Delphi Method by administering a questionnaire to six experts from industrial, governmental, and academic sectors respectively. The research results were applied to develop decision-making strategies for responding to the environmental dynamics of Taipei City's transportation infrastructure system by using the analytic network process combined with a data-mining technique. Thus, big data pertaining to urban transportation were analyzed to predict the future dynamic trends of the key indices and prioritize the sustainable transportation strategies for a livable city under dynamic temporal and spatial changes. Ultimately, the policy implications of this study can not only offer a solution for current needs related to urban planning but also serve as a more transparent decision-making or well selection basis for developing sustainable and livable urban life in near future.
The rapid process of urbanization aggravates the imbalance between the supply and demand of urban public services. Urban parks are among the most important urban public services, and their use efficiency has been an important index for urban planning. Therefore, it is essential to estimate well their service area and influencing factors. Traditional survey data used to analyze the characteristics of urban park services are limited by small samples and high cost. Owing to thriving information communication technologies, vast amounts of human activity data have become available that enable understanding of human travel behavior. In this study, we analyzed a park service area, which is defined as the zone of influence of individual parks, in Beijing, and the factors that influence the service area. First, the service area was estimated using 1-SDE based on mobile phone signaling data. A multiple linear regression model was then used to analyze the influence of factors on the park service area. The results show that (1) external factors including population density, the number of commercial facilities, and traffic convenience have significant influences on the park service area; (2) employment places positively influence the park service area on the weekday; and (3) other factors such as park design and park reputation had inconsistent effects on the park service area, in either sign or significance, regarding the weekday and the weekend. The findings of this study will be of practical value when designing parks or undertaking city planning in the future.
Even at an early stage, diverse big data have been applied to tourism research and made an amazing improvement. This paper might be the first attempt to present a comprehensive literature review on different types of big data in tourism research. By data sources, the tourism-related big data fall into three primary categories: UGC data (generated by users), including online textual data and online photo data; device data (by devices), including GPS data, mobile roaming data, Bluetooth data, etc.; transaction data (by operations), including web search data, webpage visiting data, online booking data, etc. Carrying different information, different data types address different tourism issues. For each type, a systematical analysis is conducted from the perspectives of research focuses, data characteristics, analytic techniques, major challenges and further directions. This survey facilitates a thorough understanding of this sunrise research and offers valuable insights into its future prospects.
Cities can be observed through a broad set of sensing technologies, spanning from physical sensors in the streets, to socio-economic reports, to other kinds of sources that are able to represent the behaviour of the citizens and visitors, such as mobile phone records, social media posts, and other digital traces.
In this paper, we propose a conceptual framework for putting at use this variety of Big Data sources, with a unified approach that applies spatial and temporal analysis over heterogeneous streams of data. We define spatial analysis based on conceptual grids (made of cells) over the city space, and then we study: the time series of signals both at grid and cell level; the correlation across signals and across cells; the prediction of city dynamics based on multiple signals; and the identifications of anomalies based on the difference between the observed dynamics and their prediction.
To implement this model we propose a general architectural framework that uses Big Data technologies (such as HDFS, YARN, HIVE, PIG, Cascalog, Spark, Spark SQL, Spark Streaming and SparkR) and can be deployed in different configurations based on different needs. By taking an inherent data science approach to the problem we are able to address at scale: technical problems such as heterogeneous time and space granularity of the data, as well as appropriate interpretation of the results through tools that enable intuitive and immediate visual perception of emerging patterns and dynamics.
We demonstrate feasibility, generality and effectiveness of our Urban Data Science at scale approach through multiple use cases and examples taken from real-world requirements collected in various cities and accounting for diverse business and city needs.
Big Data is an emerging paradigm and has currently become a strong attractor of global interest, specially within the transportation industry. The combination of disruptive technologies and new concepts such as the Smart City upgrades the transport data life cycle. In this context, Big Data is considered as a new pledge for the transportation industry to effectively manage all data this sector required for providing safer, cleaner and more efficient transport means, as well as for users to personalize their transport experience. However, Big Data comes along with its own set of technological challenges, stemming from the multiple and heterogeneous transportation/mobility application scenarios. In this survey we analyze the latest research efforts revolving on Big Data for the transportation and mobility industry, its applications, baselines scenarios, fields and use case such as routing, planning, infrastructure monitoring, network design, among others. This analysis will be done strictly from the Big Data perspective, focusing on those contributions gravitating on techniques, tools and methods for modeling, processing, analyzing and visualizing transport and mobility Big Data. From the literature review a set of trends and challenges is extracted so as to provide researchers with an insightful outlook on the field of transport and mobility.
This study applies big data mining, machine learning analysis technique and uses the Waikato Environment for Knowledge Analysis (WEKA) as a tool to discuss the convenience stores energy consumption performance in Taiwan which consists of (a). Influential factors of architectural space environment and geographical conditions; (b). Influential factors of management type; (c). Influential factors of business equipment; (d). Influential factors of local climatic conditions; (e). Influential factors of service area socioeconomic conditions. The survey data of 1,052 chain convenience stores belong to 7-Eleven, Family Mart and Hi-Life groups by Taiwan Architecture and Building Center (TABC) in 2014. The implicit knowledge will be explored in order to improve the traditional analysis technique which is unlikely to build a model for complex, inexact and uncertain dynamic energy consumption system for convenience stores. The analysis process comprises of (a). Problem definition and objective setting; (b). Data source selection; (c). Data collection; (d). Data preprocessing/preparation; (e). Data attributes selection; (f). Data mining and model construction; (g). Results analysis and evaluation; (h). Knowledge discovery and dissemination. The key factors influencing the convenience stores energy consumption and the influence intensity order can be explored by data attributes selection. The numerical prediction model for energy consumption is built by applying regression analysis and classification techniques. The optimization thresholds of various influential factors are obtained. The different cluster data are compared by using clustering analysis to verify the correlation between the factors influencing the convenience stores energy consumption characteristic. The implicit knowledge of energy consumption characteristic obtained by the aforesaid analysis can be used to (a). Provide the owners with accurate predicted energy consumption performance to optimize architectural space, business equipment and operations management mode; (b). The design planners can obtain the optimum design proposal of Cost Performance Ratio (C/P) by planning the thresholds of various key factors and the validation of prediction model; (c). Provide decision support for government energy and environment departments, to make energy saving and carbon emission reduction policies, in order to estimate and set the energy consumption scenarios of convenience store industry.
The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart sustainable cities as an emerging urban development approach due to its great potential to advance environmental sustainability. As one of the prevalent ICT visions or computing paradigms, the IoT is associated with big data analytics, which is clearly on a penetrative path across many urban domains for optimizing energy efficiency and mitigating environmental effects. This pertains mainly to the effective utilization of natural resources, the intelligent management of infrastructures and facilities, and the enhanced delivery of services in support of the environment. As such, the IoT and related big data applications can play a key role in catalyzing and improving the process of environmentally sustainable development. However, topical studies tend to deal largely with the IoT and related big data applications in connection with economic growth and the quality of life in the realm of smart cities, and largely ignore their role in improving environmental sustainability in the context of smart sustainable cities of the future. In addition, several advanced technologies are being used in smart cities without making any contribution to environmental sustainability, and the strategies through which sustainable cities can be achieved fall short in considering advanced technologies. Therefore, the aim of this paper is to review and synthesize the relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data applications enabled by the IoT for environmental sustainability and related data processing platforms and computing models in the context of smart sustainable cities of the future. Also, this paper identifies the key challenges pertaining to the IoT and big data analytics, as well as discusses some of the associated open issues. Furthermore, it explores the opportunity of augmenting the informational landscape of smart sustainable cities with big data applications to achieve the required level of environmental sustainability. In doing so, it proposes a framework which brings together a large number of previous studies on smart cities and sustainable cities, including research directed at a more conceptual, analytical, and overarching level, as well as research on specific technologies and their novel applications. The goal of this study suits a mix of two research approaches: topical literature review and thematic analysis. In terms of originality, no study has been conducted on the IoT and related big data applications in the context of smart sustainable cities, and this paper provides a basis for urban researchers to draw on this analytical framework in future research. The proposed framework, which can be replicated, tested, and evaluated in empirical research, will add additional depth to studies in the field of smart sustainable cities. This paper serves to inform urban planners, scholars, ICT experts, and other city stakeholders about the environmental benefits that can be gained from implementing smart sustainable city initiatives and projects on the basis of the IoT and related big data applications.
Chinese officials are increasingly turning to a policy known as Informatisation, connecting industry online, to utilise technology to improve efficiency and tackle economic developmental problems in China. However, various recent laws have made foreign technology firms uneasy about perceptions of Rule of Law in China. Will these new laws, under China's stated policy of “Network Sovereignty” (“网络主权” “wangluo zhuquan”) affect China's ability to attract foreign technology firms, talent and importantly technology transfers? Will they slow China's technology and Smart City drive? This paper focuses on the question of whether international fears of China's new Cyber Security Law are justified. In Parts I and II, the paper analyses why China needs a cyber security regime. In Parts III and IV it examines the law itself.
The last several decades have witnessed a rapid yet uneven urban expansion in developing countries. The existing studies rely heavily on official statistical yearbooks and remote sensing images. However, the former data sources have been criticized due to its non-objectivity and low quality, while the latter is labor and cost consuming in most cases. Recent efforts made by fractal analyses provide alternatives to scrutinize the corresponding “natural urban area”. In our proposed framework, the dynamics of internal urban contexts is reflected in a quasi-real-time manner using emerging new data and the expansion is a fractal concept instead of an absolute one based on the conventional Euclidean method. We then evaluate the magnitude and pattern of natural cities and their expansion in size and space. It turns out that the spatial expansion rate of official cities (OCs) in our study area China has been largely underestimated when compared with the results of natural cities (NCs). The perspective of NCs also provides a novel way to understanding the quality of uneven urban expansion. We detail our analysis for the 23 urban agglomerations in China, especially paying more attention to the three most dominating urban agglomerations of China: Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD). The findings from the OC method are not consistent with the NC method. The distinctions may arise from the definition of a city, and the bottom-up NC method contributes to our comprehensive understanding of uneven urban expansion.
Undoubtedly, sustainable development has inspired a generation of scholars and practitioners in different disciplines into a quest for the immense opportunities created by the development of sustainable urban forms for human settlements that will enable built environments to function in a more constructive and efficient way. However, there are still significant challenges that need to be addressed and overcome. The issue of such forms has been problematic and difficult to deal with, particularly in relation to the evaluation and improvement of their contribution to the goals of sustainable development. As it is an urban world where the informational and physical landscapes are increasingly being merged, sustainable urban forms need to embrace and leverage what current and future ICT has to offer as innovative solutions and sophisticated methods so as to thrive—i.e. advance their contribution to sustainability. The need for ICT of the new wave of computing to be embedded in such forms is underpinned by the recognition that urban sustainability applications are deemed of high relevance to the contemporary research agenda of computing and ICT. To unlock and exploit the underlying potential, the field of sustainable urban planning is required to extend its boundaries and broaden its horizons beyond the ambit of the built form of cities to include technological innovation opportunities. This paper explores and substantiates the real potential of ICT of the new wave of computing to evaluate and improve the contribution of sustainable urban forms to the goals of sustainable development. This entails merging big data and context–aware technologies and their applications with the typologies and design concepts of sustainable urban forms to achieve multiple hitherto unrealized goals. In doing so, this paper identifies models of smart sustainable city and their technologies and applications and models of sustainable urban form and their design concepts and typologies. In addition, it addresses the question of how these technologies and applications can be amalgamated with these design concepts and typologies in ways that ultimately evaluate and improve the contribution of sustainable urban forms to the goals of sustainable development. The overall aim of this paper suits a mix of three methodologies: literature review, thematic analysis, and secondary (qualitative) data analysis to achieve different but related objectives. The study identifies four technologies and two classes of applications pertaining to models of smart sustainable city as well as three design concepts and four typologies related to models of sustainable urban form. Finally, this paper proposes a Matrix to help scholars and planners in understanding and analyzing how the contribution of sustainable urban forms to sustainability can be improved through ICT of the new wave of computing and its novel technologies and applications, as well as a data–centric approach into evaluating this contribution and a simulation method for strategically optimizing it.
Existing jobs-housing balance studies have relied heavily if not solely on small data. Via a case study of Shanghai, this study shows how cellular network data can be processed to derive useful information, job and housing locations of commuters in particular, for those studies. Based on cellular network data, this article quantifies and visualizes Shanghai's jobs-housing balance with a much larger sample (n = 6.3 million), finer spatial resolution and greater geographic coverage than ever before. It identifies and geocodes the local commuters by Base Transceiver Station (BTS), which has on average a service area of 0.16 km². After detecting jobs and housing by BTS, it aggregates them by subareas of particular interest (e.g., traffic analysis zones, inner city, suburbs and exurbs) to local planners and decision-makers. It also visualizes the traffic flows associated with the actual (Tact), theoretical minimum (Tmin) and maximum (Tmax) commutes. It shows that Shanghai's commuting pattern is far from the extremes (indicated by Tmax and Tmin traffic flows) and Shanghai's relative balance of jobs with respect to housing is decent (3.2 km) despite its huge population (24 million) and land area sizes (6800 km²). The cumulative distribution of the Tact and Tmin flows vary more significantly when the commuting distance is less than 6 km. In theory, there is high concentration of both jobs and housing within a 6-kilometer radius across different locales of the city. This potentially allows over 95% of all the local workers to find a job within 6 km of his/her residence or vice versa. In reality, a much lower percentage (71%) of workers can enjoy such a benefit. This can imply that there is qualitative mismatch between jobs and housing.
A group of researchers, consultants, software developers, and transit agencies convened in Santiago, Chile over 3 days as part of the Thredbo workshop titled “Harnessing Big Data”, to present their recent research and discuss the state of practice, state of the art, and future directions of big data in public transportation. This report documents their discussion. The key conclusion of the workshop is that, although much progress has been made in utilizing big data to improve transportation planning and operations, much remains to be done, both in terms of developing further analysis tools and use cases of big data, and of disseminating best practices so that they are adopted across the industry.
Today we have the opportunity without precedents to analyze human land use or mobility behavior in a city, country or even the globe. Some studies have analyzed existing data generated daily by mobile networks, mostly using geo-localization in Twitter, Foursquare or cell phone records. Most of these studies use a small portion of data (a few days or a couple million records). This time we will show a novel way to apply latent semantic topic models to detect Land Use Patterns in a real big dataset of 880,000,000 calls made in Santiago City (Chile) over 77days by about 3 million customers of a major telecommunications company. We proposed to use a latent variables clustering technique which allow us to detect four interesting clusters. We found out that the application of LDA allow us to discover two well known clusters (residential and office area clusters) but also we discover two new clusters: Leisure-Commerce and Rush Hour patterns.
Historically urban planners have been educated and trained to work in a data poor environment. Urban planning students take courses in statistics, survey research and projection and estimation that are designed to fill in the gaps in this environment. For decades they have learned how to use census data, which is comprehensive on several basic variables, but is only conducted once per decade so is almost always out of date. More detailed population characteristics are based on a sample and are only available in aggregated form for larger geographic areas.
But new data sources, including distributed sensors, infrastructure monitoring, remote sensing, social media and cell phone tracking records, can provide much more detailed, individual, real time data at disaggregated levels that can be used at a variety of scales. We have entered a data rich environment, where we can have data on systems and behaviors for more frequent time increments and with a greater number of observations on a greater number of factors (The Age of Big Data, The New York Times, 2012; Now you see it: simple visualization techniques for quantitative analysis, Berkeley, 2009). Planners are still being trained in methods that are suitable for a data poor environment (J Plan Educ Res 6:10–21, 1986; Analytics over large-scale multidimensional data: the big data revolution!, 101–104, 2011; J Plan Educ Res 15:17–33, 1995). In this paper we suggest that visualization, simulation, data mining and machine learning are the appropriate tools to use in this new environment and we discuss how planning education can adapt to this new data rich landscape. We will discuss how these methods can be integrated into the planning curriculum as well as planning practice.