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Demographic growth and the continued evolution of cities call for a new approach to better observe and research our understanding of cities. A new science based on big data, urban modelling and network theory is emerging, providing a different and rather new perspective for planners and decision-makers so that they might learn about both current and future cities. In this article, the new science is briefly introduced from four aspects: Aggregate dynamics; Form and function; High frequency cities; and New tools & techniques for planning. Examples are given to show how this new science illustrates the real-time city, as well as the structure and functional boundaries of a city, while future practice and further exploration of this new science in urban planning and policy making are explored.

The study of land use change in urban and regional systems has been dramatically transformed in the last four decades by the emergence and application of cellular automata (CA) models. CA models simulate urban land use changes which evolve from the bottom-up. Despite notable achievements in this field, there remain significant gaps between urban processes simulated in CA models and the actual dynamics of evolving urban systems. This article identifies contemporary issues faced in developing urban CA models and draws on this evidence to map out four interrelated thematic areas that require concerted attention by the wider CA urban modelling community. These are: (1) to build models that comprehensively capture the multi-dimensional processes of urban change, including urban regeneration, densification and gentrification, in-fill development, as well as urban shrinkage and vertical urban growth; (2) to establish models that incorporate individual human decision behaviours into the CA analytic framework; (3) to draw on emergent sources of ‘big data’ to calibrate and validate urban CA models and to capture the role of human actors and their impact on urban change dynamics; and (4) to strengthen theory-based CA models that comprehensively explain urban change mechanisms and dynamics. We conclude by advocating cellular automata that embed agent-based models and big data input as the most promising analytical framework through which we can enhance our understanding and planning of the contemporary urban change dynamics.

openaire: EC/H2020/856602/EU//FINEST TWINS By exploring and defining characteristics of a smart city research and innovation centre, we contribute to the discussion on smart city development capacity. To do so, using a qualitative method, we review definitions of the concept and map international groups and institutes affiliated with this domain. Our main result is an overview of global research centres dealing with smart cities. One of the key implications of this paper is that instead of a strict definition, the important aspect appears in the framing provided by the complex real-life challenges that require and enable cross-disciplinary research, even though the concept keeps evolving.

Great strides have been made in Geographic Information Systems (GIS) research over the past half-century. However, this progress has created both opportunities and challenges. From a geographic per...

This introduction outlines a portfolio of theory and methods in the chapters that develop a basic urban science for urban informatics. Inductive and deductive methods for generating data, analytics, and urban simulation, form the focus. In this first Part of the book, the emphasis is on mobility, space-time theory, energy and infrastructure, the spatial economy, and the role of modelling in understanding and planning the smart city.

Urban analytics combines spatial analysis, statistics, computer science, and urban planning to understand and shape city futures. While it promises better policymaking insights, concerns exist around its epistemological scope and impacts on privacy, ethics, and social control. This chapter reflects on the history and trajectory of urban analytics as a scholarly and professional discipline. In particular, it considers the direction in which this field is going and whether it improves our collective and individual welfare. It first introduces early theories, models, and deductive methods from which the field originated before shifting toward induction. It then explores urban network analytics that enrich traditional representations of spatial interaction and structure. Next it discusses urban applications of spatiotemporal big data and machine learning. Finally, it argues that privacy and ethical concerns are too often ignored as ubiquitous monitoring and analytics can empower social repression. It concludes with a call for a more critical urban analytics that recognizes its epistemological limits, emphasizes human dignity, and learns from and supports marginalized communities.

As cities increase in size, total wages grow superlinearly, meaning that average wages are higher in larger cities. This phenomenon, known as the urban wage premium, supports the notion that urbanization and the growth of cities contribute positively to human well-being. However, it remains unclear how the distribution of wages changes as cities grow. Here we segment the populations of U.S. cities into wage deciles and determine the scaling coefficient of each bracket's aggregate wages versus city size. We find that, while total wages of all these categories grow superlinearly with city size, the effect is uneven, with total wages of the highest wage earners growing faster than all other wage brackets. We show that this is partly due to the predominance of high-wage jobs in larger cities. Thus, the effects of urbanization are mixed -- it brings higher average wages but with increasing inequality, thus inhibiting prospects for long-term sustainability.

This introductory chapter provides a brief overview of the theories and models that constitute what has come to be called urban science. Explaining and measuring the spatial structure of the city in terms of its form and function is one of the main goals of this science. It provides links between the way various theories about how the city is formed, in terms of its economy and social structure, and how these theories might be transformed into models that constitute the operational tools of urban informatics. First the idea of the city as a system is introduced, and then various models pertaining to the forces that determine what is located where in the city are presented. How these activities are linked to one another through flows and networks are then introduced. These models relate to formal models of spatial interaction, the distribution of the sizes of different cities, and the qualitative changes that take place as cities grow and evolve to different levels. Scaling is one of the major themes uniting these different elements grounding this science within the emerging field of complexity. We then illustrate how we might translate these ideas into operational models which are at the cutting edge of the new tools that are being developed in urban informatics, and which are elaborated in various chapters dealing with modeling and mobility throughout this book.

In addition to incorporating 2020q2 data from the Financial Accounts, the 2020q2 release of the Distributional Financial Accounts (DFAs) includes three substantial updates. The most consequential is the incorporation of the newly released 2019 Survey of Consumer Finances (SCF).

In this article, written for The City Reader in 2019, Michael Batty, director of University College London’s Centre for Advanced Spatial Analysis, describes the smart city movement within urban planning and policy analysis. Computer systems have become ever more powerful, the storage of data vast, and costs continue to fall at an accelerating rate. For example the number of emails per second is about 2.4 million, some 205 billion each day. Batty describes how every aspect of the city is now subject to the influence of new information technologies. Computers imbedded into the built environment linked to sensors monitor and control many city functions. Massive streams of real-time “big data” can be “mined” to enhance our understanding of cities. Most cities will be completely networked by the end of this century. Transport is one of the major functions that is being informed by big data. Digital information is now widely available for systems such as the payment of fares, the status of the network (which is routinely transmitted to passengers), and about navigation for the driver (and the user). The delivery and collection of water, waste, energy, telecommunications, and other city services are also being informed by smart technologies that are focused on managing these services and their products better. For example, the supply and demand for London tube services is available in real time. Disruptive technologies such as Uber ride-sharing and Airbnb are reshaping private markets and cities.

Big data analytics and artificial intelligence, paired with blockchain technology, the Internet of Things, and other emerging technologies, are poised to revolutionise urban management. With massive amounts of data collected from citizens, devices, and traditional sources such as routine and well-established censuses, urban areas across the world have – for the first time in history – the opportunity to monitor and manage their urban infrastructure in real-time. This simultaneously provides previously unimaginable opportunities to shape the future of cities, but also gives rise to new ethical challenges. This paper provides a transdisciplinary synthesis of the developments, opportunities, and challenges for urban management and planning under this ongoing ‘digital revolution’ to provide a reference point for the largely fragmented research efforts and policy practice in this area. We consider both top-down systems engineering approaches and the bottom-up emergent approaches to coordination of different systems and functions, their implications for the existing physical and institutional constraints on the built environment and various planning practices, as well as the social and ethical considerations associated with this transformation from non-digital urban management to data-driven urban management.

Health policy is often designed to help protect patients' financial security. However, there is limited understanding of the role medical debt plays in household finances. We used credit report data on more than four million Americans to study the age profile of people whose medical bills were sent to a US collections agency in 2016. We found that, unlike health care use and spending, medical collections decreased substantially with age. The average size of medical debt decreased nearly 40 percent from patients age twenty-seven to sixty-four, with increases in health insurance coverage and incomes likely playing important mediating roles. However, the frequency of medical collections-that is, the proportion of people with a collection by age-was less closely tied to insurance coverage rates. A potential explanation is that most medical collections were relatively modest in size, with more than half of them less than $600 annually. As a result, medical collections could still occur under typical insurance plans. We discuss how these results could inform policies targeting medical debt and insurance regulation, such as restrictions on age rating.

It is often assumed that financial incentives of healthcare providers affect the care they deliver, but this issue is surprisingly difficult to study. The recent enactment of state laws that limit how much hospitals can charge uninsured patients provide a unique opportunity. Using an event study framework and panel data from the Nationwide Inpatient Sample, we examine whether these regulations lead to reductions in the amount and quality of care given to uninsured patients. We find that the introduction of a fair pricing law leads to a seven to nine percent reduction in the average length of hospital stay for uninsured patients, with no corresponding change for insured patients. These care reductions are not accompanied by worsening quality of inpatient care. Overall, our results provide strong evidence that hospitals actively alter their behavior in response to financial incentives, and are consistent with the laws promoting a shift towards more efficient care delivery. The findings also add to the growing evidence that hospitals can, and do, treat patients differently based upon insurance status.

Public transport is perhaps the most significant component of the contemporary smart city currently being automated using sensor technologies that generate data about human behaviour. This is largely due to the fact that the travel associated with such transport is highly ordered. Travellers move collectively in closed vehicles between fixed stops and their entry into and from the system is unambiguous and easy to automate using smart cards. Flows can thus be easily calculated at specific station locations and bus stops and within fine temporal intervals. Here we outline work we have been doing using a remarkable big data set for public transport in Greater London generated from the Oyster Card, the smart card which has been in use for over 13 years. We explore the generic properties of the Tube and Overground rail system focusing first on the scale and distribution of the flow volumes at stations, then engaging in an analysis of temporal flows that can be decomposed into various patterns using principal components analysis (PCA) which smoothes out normal fluctuations and leaves a residual in which significant deviations can be tracked and explained. We then explore the heterogeneity in the data set with respect to how travel behaviour varies over diff erent time intervals and suggest how we can use these ideas to detect and manage disruptions in the system.

This Note describes briefly how the Distributional Financial Accounts (DFAs) are constructed and highlights some of their key features.

This paper uses detailed high-frequency regulatory data to evaluate whether trading increases or decreases systemic risk in the U.S. banking sector. We estimate the sensitivity of weekly bank trading net profits to a variety of aggregate risk factors, which include equities, fixed-income, derivatives, foreign exchange, and commodities. We find that U.S. banks had large trading exposures to equity market risk before the introduction of the Volcker Rule in 2014 and that they curtailed these exposures afterwards. Pre-rule equity risk exposures were large across the board of the main asset classes, including fixed-income. There is also evidence of smaller exposures to credit and currency risk. We corroborate the main finding on equity risk with a quasi-natural experiment that exploits the phased-in introduction of reporting requirements to refine identification, and an optimal changepoint regression that estimates time-varying exposures to address rebalancing. A stress-test calibration indicates that the Volcker Rule was an effective financial-stability regulation, as even a 5% drop in stock market returns would have led to material aggregate trading losses for banks in the pre-Volcker period, as large as about 3% (1.5%) of sector-wide market risk weighted assets (tier 1 capital).

The net worth of households and nonprofit organizations grows by $249 billion to reflect the value of policies that were previously missing from the Financial Accounts of the United States (Financial Accounts). Businesses also capture $81 billion from previously missing policies. The rest of the world (ROW) sector loses $130 billion on net because they are the reinsurer for many of the policies that are newly recorded. This note describes these changes and their associated effects in more detail.

The analysis of big data is deemed to define a new era in urban research, planning and policy. Real-time data mining and pattern detection in high-frequency data can now be carried out at a large scale. Novel analytical practices promise smoother decision-making as part of a more evidence-based and smarter urbanism, while critical voices highlight the dangers and pitfalls of instrumental, data-driven city making to urban governance. Less attention has been devoted to identifying the practical conditions under which big data can realistically contribute to addressing urban policy problems. In this paper, we discuss the value and limitations of big data for long-term urban policy and planning. We first develop a theoretical perspective on urban analytics as a practice that is part of a new smart urbanism. We identify the particular tension of opposed temporalities of high-frequency data and the long duree of structural challenges facing cities. Drawing on empirical studies using big urban data, we highlight epistemological and practical challenges that arise from the analysis of high-frequency data for strategic purposesand formulate propositions on the ways in which urban analytics can inform long-term urban policy.

My Classroom Economy is a simulated economy where students have the opportunity to engage in financial decisions on a daily basis. It provides financial literacy instruction without requiring significant classroom time, and is relatively simple for teachers to implement. Compared to schools where My Classroom Economy was delayed or never implemented, students showed strong improvements in financial knowledge. Compared to a traditional lecture-based curriculum, students showed similar gains in learning. Based on standardized math test scores, there was no substitution away from core school curriculum after the program began. Experiential financial education appears to be an effective strategy to teach financial literacy, even at lower grade levels. It is also a relatively low-cost approach that does not require extensive teacher preparation.

The prevalence of gentrification and housing marketisation processes in many cities points to increasingly wealthy inner-city areas and potentially greater population segregation by income. It is plausible that these trends are contributing to regional accessibility inequalities, though quantitative research testing this link is limited. This paper examines differences in employment accessibility between Standard Occupational Classification groups in the London Metropolitan Region for 2011 for car, transit, bus only and walking modes. Additionally, changes in occupational class populations 2006–2016 are considered, revealing continuing inner-city gentrification. Employment accessibility is calculated using cumulative measures, based on travel times from multi-modal network modelling. The results show that while car accessibility is relatively equal between occupational classes, public transport, bus and walk accessibility have significant inequalities favouring professional classes. Low income groups have lower accessibility for the most affordable bus and walk modes, and inequalities are greater for residents in the wider metropolitan region. Furthermore, professional groups combine accessibility advantages with the highest rates of owner occupation, maximising housing wealth benefits. Lower income groups are exposed to rent increases, though this is offset by social housing, which remains the most prevalent tenure in Inner London for low income classes.

In this paper, we propose a computational approach to Jane Jacobs’ concept of diversity and vitality, analyzing new forms of spatial data to obtain quantitative measure- ments of urban qualities frequently employed to evaluate places. We use smart card data collected from public transport to calculate a diversity value for each research unit. Diver- sity is composed of three dynamic attributes: intensity, variability and consistency, each measuring different temporal variations of mobility flows. We then apply a regression model to establish the relationship between diversity and vitality, using Twitter data as a proxy for human activity in urban space. Final results (also validated using data sourced from OpenStreetMap) unveil which are the most vibrant areas in London.

This note describes new data on household debt-to-income ratios (DTI) that is being provided in interactive maps as part of the Enhanced Financial Accounts (EFA).

Cities are places where populations cluster to engage in economic exchange usually around a central market which represents an efficient point of distribution in a wider hinterland where various goods are produced. Although most cities still have well-defined central cores that function to tie economic and social activities together, the networks that sustain these cores are becoming ever more complex, diversified, and diffuse. Flows of people and commodities represent material interactions and tend to be more visible than electronic flows that occupy the ether which makes them much less visible. As these varieties of flow proliferate in a global world, the complexity of cities becomes ever greater and the challenge to our understanding ever more daunting. A consequence of complexity thinking is that cities get ever more complex as they grow. This is beyond complicatedness in that cities change qualitatively as they get larger and as their form adapts to embrace more and more human and physical interactions.

The availability of large amounts of mobility data has stimulated the research in discovering patterns and understanding regularities. Comparatively, less attention has been paid to the study of variability, which, however, has been argued as equally important as regularities, since variability identifies diversity. In a transport network, variability exists from person to person, from place to place, and from day to day. In this paper, we present a set of measuring of variability at individual and aggregated levels using multi-day smart-card data. Statistical analysis, correlation matrix and network-based clustering methods are applied and potential use of measured results for urban applications are also discussed. We take Singapore as a case study and use one-week smart-card data for analysis. An interesting finding is that though the number of trips and mobility patterns varies from day to day, the overall spatial structure of urban movement always remains the same throughout a week. This finding showed that a systemic framework with well-organized analytical methods is indeed, necessary for extracting variability that may change at different levels and consequently for uncovering different aspects of dynamics, namely transit, social and urban dynamics. We consider this paper as a tentative work toward such generic framework for measuring variability and it can be used as a reference for other research work in such a direction.

Models again: their role in planning and prediction In the early months of this year the Planning Educators Electronic Mail Network, PLANET, was dominated by a vibrant discussion about the role of complexity in thinking about cities and their planning. The debate was started by Martin Krieger (17February 2015)(1) in a post to the list which he titled “chaos, nonlinearity, path dependence, complex adaptive systems, agent-based modeling, butterfly, uncertainty (as in Heisenberg UP), fuzzy, catastrophe”.

Cities are complex systems. They contain different functional areas originally defined by planning and then reshaped by actual needs and use by the inhabitants. Estimating the functions of urban space is of significant importance for detecting urban problems, evaluating planning strategies, and supporting policy making. In light of the potential of data mining and spatial analysis techniques for urban analysis, this paper proposes a method to infer urban functions at the building level using transportation data obtained from surveys and smart card systems. Specifically, we establish a two-step framework making use of the spatial relationships between trips, stops, and buildings. Firstly, information about the travel purposes for daily activities is deduced using passengers’ mobility patterns based on a probabilistic Bayesian model. Secondly, building functions are inferred by linking daily activities to the buildings surrounding the stops based on spatial statistics. We demonstrate the proposed method using large-scale public transportation data from two areas of Singapore. Our method is applied to identify building functions at building level. The result is verified with master plan, street view, and investigated data, and limitations are identified. Our work shows that the presented method is applicable in practice with a good accuracy. In a broader context, it shows the effectiveness of applying integrated techniques to combine multi-source data in order to make insights about social activities and complex urban space.

Prior to the industrial revolution, record-keeping was an intensive but modest affair with manual technologies constraining the growth of data. Historically, data were always big with respect to the available means by which they could be manipulated. Introduced by Brian Berry, an early data typology that has withstood the test of time is the 'geographic matrix'. Real-time data pertaining to the socio-economic structure of the city are much more problematic to collect using sensing devices. Big data which are streamed in real time represents the cutting edge of new data about the functioning of cities. Since the 1950s, data have been collected in continuous time for traffic flow analysis. Data on energy flows and usage in the smart city are not focal as yet, while the analysis of big data associated with social media may well remain in some preliminary form for many years.

© 2018 selection and editorial matter, Laurie A. Schintler and Zhenhua Chen. Big data that record mobility patterns have the potential to provide important insight to transportation agencies about how to better plan and operate the transportation system. Those agencies often already have access to valuable data assets, but faced with limited resources and a lack of experience working with emerging data sources and methods, those data may go under-utilized. In the case of real-time data feeds, the data may be lost altogether if they are not archived. This paper provides recommendations for planning and establishing big data programs at transportation agencies. These recommendations are derived from the lessons learned during the development of a big data fusion tool for measuring transportation system changes in San Francisco. It focuses on broad data management issues that staff at a public agency may face, such as identifying and prioritizing data sources and uses, managing privacy considerations and data sharing policies, and addressing data issues that may arise in contracting situations.

© 2015, IGI Global. All rights reserved. This chapter introduces a range of analytics being used to understand the smart city, which depends on data that can primarily be understood using new kinds of scientific visualisation. We focus on short term routine functions that take place in cities which are being rapidly automated through various kinds of sensors, embedded into the physical fabric of the city itself or being accessed from mobile devices. We first outline a concept of the smart city, arguing that there is a major distinction between the ways in which technologies are being used to look at the short and long terms structure of cities, and we then focus on the shorter term, first examining the immediate visualisation of data through dashboards, then examining data infrastructures such as map portals, and finally introducing new ways of visualising social media which enable us to elicit the power of the crowd in providing and supplying data. We conclude with a brief focus on how new urban analytics is emerging to make sense of these developments.

Hospitals in the United States maintain chargemasters that contain the official list prices for all billable services. The prices vary widely across hospitals and are more than three times what hospitals are paid for treating a patient, on average. From this it is tempting to conclude that list prices are a strange, yet ultimately inconsequential, quirk of US health care. However, using both state and national data sets covering the period 2002-14, we found considerable evidence suggesting that list prices reflect hospitals' strategic behavior and have meaningful effects on payments made by and on behalf of patients. Specifically, we found that list prices varied predominantly across hospitals and within markets, were well predicted by observable hospital characteristics, and were positively related to prices actually paid by patients and their insurers. Moreover, analyses of data before and after the implementation of California's Hospital Fair Pricing Act suggest that high list prices causally increased payments from the uninsured. However, list prices had at most a limited relationship with care quality.

We introduce the 14 articles in the Royal Society Open Science themed issue on City Analytics. To provide a high-level, strategic, overview, we summarize the topics addressed and the analytical tools deployed. We then give a more detailed account of the individual contributions. Our overall aims are (i) to highlight exciting advances in this emerging, interdisciplinary field, (ii) to encourage further activity and (iii) to emphasize the variety of new, public-domain, datasets that are available to researchers.

This research aims to develop software tools to support the fusion and analysis of large, passively collected data sources for the purpose of measuring and monitoring transit system performance. This study uses San Francisco as a case study, taking advantage of the automated vehicle location (AVL) and automated passenger count (APC) data available on the city transit system. Because the AVL-APC data are only available on a sample of buses, a method is developed to expand the data to be representative of the transit system as a whole. In the expansion process, the General Transit Feed Specification (GTFS) data are used as a measure of the full set of scheduled transit service.

New sources of data originating from new ways of sensing and collecting data are fast emerging as sensors and software are being rapidly embedded into physical and social environments. The data that is being generated usually incorporates time as well as space in much greater locational and temporal precision than anything we have had access to hitherto. This is ‘big data’, so called because it is often orders of magnitude larger in volume than that collected from conventional ‘manual’ sources such as the traditional population census and household interview. This is stretching our notion of the system and problems of cities that we might model and it is shortening our attention span, forcing us to concentrate on more immediate problems in both a spatial and temporal sense than what has been the norm in the past. Our tools for urban planning and design are likely to be transformed by such developments as emerging sources of big data associated with spatial behavior in terms of location and transport interactions are revealing new horizons for urban modeling. Since its inception nearly 50 years ago, urban modeling has had an unapologetically practical and long-term focus on physical interventions in urban land use, accessibility, building location and infrastructure. Its primary purpose was and continues to be to understand the city and then to reshape it to meet long-term goals associated with equity and efficiency – the longevity of urban infrastructure remains an anchor for the need of longer term predictions of the lasting effects of major interventions and cumulative causation over decades. However at the other extreme, much narrower time horizons have been associated with immediate problems and actions in cities which have pushed shorttermism to the top of the policy agenda. Suddenly, models are being tasked to inform both shortand long-term issues with respect to urban policy, on matters that pertain to minutes and hours as well as those that pertain to years and decades. The opportunities for urban modeling to fulfill both such immediate and longer term tasks have become much more significant in the last 20 years: more rounded understanding of human behavior and institutions, an explosion of new data sources, and new means to monitor urban activities (through crowd sourcing for example) are providing the context for using models in more immediate applications. This is particularly the case with respect to transport planning. Moreover, widespread availability of fast computing is heralding a new surge in activity monitoring, research, model-building and policy applications. Slowly but surely our land use and transport models are being adapted to these new tasks. Almost 20 years ago, Wegener (1994) foresaw some of these new directions for the field of land use and transport modeling, in terms of both the necessity and opportunities for traditional models (which applied static, cross-sectional and aggregate methods) to incorporate different temporal dynamics at increasingly disaggregate spatial scales. A new generation of models such as cellular automata (CA) based models dealing with urban growth followed by more generic agent-based models (ABM) lie in the vanguard of a new concern for temporal

We are building a series of fast, visually accessible, cross-sectional, hence static urban models for large metropolitan areas that will enable us to rapidly test many different scenarios pertaining to both short-term and long-term urban futures. We call this framework SIMULACRA which is a forum for developing many different model variants which can be finely tuned to different problem contexts and future scenarios. The models are multisector, dealing with residential, retail/service, and employment location, are highly disaggregate, and subject to constraints on land availability and transport capacities. They have an explicit urban economic focus around transport costs, incomes, and house prices and thus encapsulate simple market-clearing mechanisms. Here we will briefly outline this class of models, paying particular attention to their structure and the way physical flows and locations are mirrored by economic flows in terms of costs and prices. Several versions of the model now exist, but we will focus, first, on the simplest ‘one-window’ desktop pilot version with the most obvious graphical interface; and, second, on a much more elaborated framework developed for web access, extensible to web service architectures and other related services. To demonstrate its flexibility and intelligibility, we define the various interfaces and demonstrate how the aggregate model can be calibrated to the wider London region to which it is applied. We will demonstrate the model, albeit briefly with respect to the rapid assessment of different urban futures—“what-if” scenarios, based on the impact of new London airports in the Thames Estuary. The key feature of this entire project is that the model and its variants can be run in a matter of seconds, thus entirely changing the traditional dialogue associated with their use and experimentation. Keywords: Land Use Transport Interaction (LUTI) models, economic flows, trip distribution, rapid visualisation, web-based portals, Greater and Outer London

New smart card datasets are providing new opportunities to explore travel behaviour in much greater depth than anything accomplished hitherto. Part of this quest involves measuring the great array of regular patterns within such data and explaining these relative to less regular patterns which have often been treated in the past as noise. Here we use a simple method called DBSCAN to identify clusters of travel events associated with particular individuals whose behaviour over space and time is captured by smart card data. Our dataset is a sequence of three months of data recording when and where individual travellers start and end rail and bus travel in Greater London. This dataset contains some 640 million transactions during the period of analysis we have chosen and it enables us to begin a search for regularities at the most basic level. We first define measures of regularity in terms of the proportions of events associated with temporal, modal (rail and bus), and service regularity clusters, revealing that the frequency distributions of these clusters follow skewed distributions with different means and variances. The analysis then continues to examine how regularity relative to irregular travel across space, demonstrating high regularities in the origins of trips in the suburbs contrasted with high regularities in the destinations in central London. This analysis sets the agenda for future research into how we capture and measure the differences between regular and irregular travel which we discuss by way of conclusion.

© 2014 by IGI Global. All rights reserved. This chapter defines the smart city in terms of the process whereby computers and computation are being embedded into the very fabric of the city itself. In short, the smart city is the automated city where the goal is to improve the efficiency of how the city functions. These new technologies tend to improve the performance of cities in the short term with respect to how cities function over minutes, hours or days rather than over years or decades. After establishing definitions and context, the author then explores questions of big data. One important challenge is to synthesize or integrate different data about the city's functioning and this provides an enormous challenge which presents many obstacles to producing coherent solutions to diverse urban problems. The chapter augments this argument with ideas about how the emergence of widespread computation provides a new interface to the public realm through which citizens might participate in rather fuller and richer ways than hitherto, through interactions in various kinds of decision-making about the future city. The author concludes with some speculations as to how the emerging science of smart cities fits into the wider science of cities.

One of Peter Hall’s main themes in his research was the impact of technology on cities and regions. Although his early work was largely about the form and function of cities, particularly world cities, and how the planning system in Britain and America was changing the shape of cities, his first visits to the Far East energised his interest in the way cities were crucibles of creativity and innovation.

We have adapted METRONAMICA, an established cellular automata (CA) modelling system, to simulate the historical growth of a section of a large world city. Our model is tuned to reflect the morphology of land use patterns more accurately than traditional CA models, which abstract those patterns to more aggregate spatial scales. We explore the spatial determinants of land use patterns with detailed empirical data, documenting the historical growth of West London at an unusually high level of spatial and temporal resolution. The results of the study provide support for our considered speculations: (1) that the spatial relationships between land uses and the physical environment are remarkably consistent through time, showing little variation relative to changes in historical context; and (2) that these relationships constitute a basic code for urban growth which determines the spatial signature of land development in a given metropolitan area.

The quantitative revolution in geography has been fifty years in the making and during this time, we have made enormous progress in building and implementing models of geographical systems. These have been driven by changes in our ability to represent spatial systems in digital terms while the idea that this science must be much richer than was first assumed has led to dramatic developments in how we conceive of systems with great heterogeneity in highly disaggregate, temporally dynamic forms. As GIS has developed, various attempts have been made to integrate various models into new software environments, often as plug-ins as well as through specific routines and algorithms for simulation, notwithstanding the difficulty of strongly coupling large-scale models to GIS. However, as policy has begun to respond to much bigger ‘grand’ challenges such as climate change, urbanization, aging, migration, security, energy and so on, there is now a need for coupling together larger-scale models to form integrated assessments of such impacts across a range of spatial and temporal scales. In this paper, we discuss these issues, using various examples of urban land use transportation models that we are building for Greater London, using aggregate and disaggregate approaches describing an example of assessing the long term impacts of sea level rise and energy change in the Greater London region. Through these ideas, we will reassess the role of geographic information science and suggests ways it might both enrich and be enriched from many cognate perspectives.

Mashups, composed of mixing different types of software and data, first appeared in 2004 and ‘map mashups’ quickly became the most popular forms of this software blending. This heralded a new kind of geography called ‘Neogeography’ in which non-expert users were able to exploit the power of maps without requiring the expertise traditionally associated, in the geographic world, with cartography and geographic information science, and, in computer science, with data structures and graphics programming. First we suggest the need for a typology of map mashups while arguing that such a typology is premature. We then discuss the need for standards and formats, moving on to questions of security, privacy and confidentiality. We follow this by introducing the key issues of creating spatial data for mashups through crowd-sourcing. To ground this presentation in applications, we explore some classic exemplars from our own and related work with map mashups and portals such as MapTube (http://www.maptube.org/). We th...

We present a summary of the kinds of outputs submitted to the Geography and Environmental Studies sub-panel (H-32) for the 2008 Research Assessment Exercise (RAE), and examine the relationships between the peer assessment of research quality that the RAE process has typified, and alternative modes of assessment based on bibliometrics. This comparison is effected using (in aggregate form) some of the results from the RAE, together with citation data gathered after completion of the RAE assessment, specifically for the purpose of this paper. We conclude that, if it continues to be necessary and desirable to assess, in some measure and however imprecisely, research quality, then peer assessment cannot be replaced by bibliometrics. Bibliometrics permit measurement of something that may be linked to quality but is essentially a different phenomenon – a measure of ‘impact’, for example.

In this article, we explore the concepts and applications of Web 2.0 through the new media of NeoGeography and its impact on how we collect, interact and search for spatial information. We argue that location and space are becoming increasingly important in the information technology revolution. To this end, we present a series of software tools which we have designed to facilitate the non-expert user to develop online visualisations which are essentially map-based. These are based on Google Map Creator, which can produce any number of thematic maps which can be overlaid on Google Maps. We then introduce MapTube, a technology to generate an archive of shared maps, before introducing Google Earth Creator, Image Cutter and PhotoOverlay Creator. All these tools allow users to display and share information over the web. Finally, we present how Second Life has the potential to combine all aspects of Web 2.0, visualisation and NeoGeography in a single multi-user three-dimensional collaborative environment.

The authors describe how we are harnessing the power of web 2.0 technologies to create new approaches to collecting, mapping, and sharing geocoded data. The authors begin with GMapCreator that lets users fashion new maps using Google Maps as a base. The authors then describe MapTube that enables users to archive maps and demonstrate how it can be used in a variety of contexts to share map information, to put existing maps into a form that can be shared, and to create new maps from the bottom-up using a combination of crowdcasting, crowdsourcing, and traditional broadcasting. The authors conclude by arguing that such tools are helping to define a neogeography that is essentially ‘‘mapping for the masses,’’ while noting that there are many issues of quality, accuracy, copyright, and trust that will influence the impact of these tools on map-based communication.

We propose that the emergent phenomenon know as “desakota,” the rapid urbanization of densely populated rural populations in the newly developed world, particularly China, can be simulated using agent-based models that combine bottom-up actions with global interactions. We argue that desakota represents a surprising and unusual form of urbanization well-matched to processes of land development that are locally determined but moderated by the higher-level macroeconomy. We develop a simple logic that links local household reform to global urban reform, translating these ideas into a model structure that reflects these two scales. Our model first determines the rate of growth of different spatial aggregates using linear statistical analysis. It then allocates this growth to the local level using “developer agents” who determine the transformation or mutation of rural households to urban pursuits based on local land costs, accessibilities, and growth management practices. The model is applied to desa...