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Since the late 1990s, Information and Communication Technologies (ICTs) have been hailed as a potentially revolutionary feature of the planning and management of Western cities. Economic regeneration and place promotion strategies have exploited these new technologies; city management has experimented with electronically distributed services, and participation in public life and democratic decision-making processes can be made more flexible by the use of ICTs. All of these technological initiatives have often been presented and accessed via an urban front-end information site known as 'digital city' or 'city network.' Illustrated by a range of European case studies, this volume examines the social, political and management issues and potential problems in the establishment of an electronic layer of information and services in cities. The book provides a better understanding of the direction European cities are going towards in the implementation of ICTs in the urban arena. Alessandro Aurigi, Alessandro is Professor of Urban Design in the School of Architecture, Design and Environment at the University of Plymouth. He holds a Laurea in Architecture from Florence University, Italy, and a PhD from the University of Newcastle upon Tyne, UK. He has worked previously at University College London. His main research interest is studying the relationships between the emergence of the 'information society' and the ways we imagine, conceive, represent, and manage buildings and cities. Contents: The Context: Public Space and Cyberspace: Introduction; Cyberspace and the city's public sphere; Exploring the digital city. The Early Steps of the Digital City: The web cities phenomenon in Europe; History of a civic network: Iperbole in Bologna; When Bristol went digital. Issues, Dilemmas and the Future: Lessons to be learnt from Bologna and Bristol; Conclusions: what next for the digital city?; Bibliography; Index.

Cities shape the lives and outlooks of billions of people, yet they have been overshadowed in contemporary political thought by nation-states, identity groups, and concepts like justice and freedom. The Spirit of Cities revives the classical idea that a city expresses its own distinctive ethos or values. In the ancient world, Athens was synonymous with democracy and Sparta represented military discipline. In this original and engaging book, Daniel Bell and Avner de-Shalit explore how this classical idea can be applied to today's cities, and they explain why philosophy and the social sciences need to rediscover the spirit of cities. Bell and de-Shalit look at nine modern cities and the prevailing ethos that distinguishes each one. The cities are Jerusalem (religion), Montreal (language), Singapore (nation building), Hong Kong (materialism), Beijing (political power), Oxford (learning), Berlin (tolerance and intolerance), Paris (romance), and New York (ambition). Bell and de-Shalit draw upon the richly varied histories of each city, as well as novels, poems, biographies, tourist guides, architectural landmarks, and the authors' own personal reflections and insights. They show how the ethos of each city is expressed in political, cultural, and economic life, and also how pride in a city's ethos can oppose the homogenizing tendencies of globalization and curb the excesses of nationalism. The Spirit of Cities is unreservedly impressionistic. Combining strolling and storytelling with cutting-edge theory, the book encourages debate and opens up new avenues of inquiry in philosophy and the social sciences. It is a must-read for lovers of cities everywhere.

PurposeThe purpose of this paper is to rethink the focus of the smart cities debate and to open it to policymaking and strategy considerations. To this end, the origins of what is termed normative bias in smart cities research are identified and a case made for a holistic, scalable and human-centred smart cities research agenda. Applicable across the micro, mezzo and macro levels of the context in which smart cities develop, this research agenda remains sensitive to the limitations and enablers inherent in these contexts. Policymaking and strategy consideration are incorporated in the agenda this paper advances, thus creating the prospect of bridging the normative and the empirical in smart cities research.Design/methodology/approachThis paper queries the smart cities debate and, by reference to megacities research, argues that the smart city remains an overly normatively laden concept frequently discussed in separation from the broader socio-political and economic contexts in which it is embedded. By focusing on what is termed the normative bias of smart cities research, this paper introduces the nested clusters model. By advocating the inclusion of policymaking and strategy considerations in the smart cities debate, a case is made for a holistic, scalable and human-centred smart cities agenda focused, on the one hand, on individuals and citizens inhabiting smart cities and, on the other hand, on interdependencies that unfold between a given smart city and the context in which it is embedded.FindingsThis paper delineates the research focus and scope of the megacities and smart cities debates respectively. It locates the origins of normative bias inherent in smart cities research and, by making a case for holistic, scalable and human-centred smart cities research, suggests ways of bypassing that bias. It is argued that smart cities research has the potential of contributing to research on megacities (smart megacities and clusters), cities (smart cities) and villages (smart villages). The notions of policymaking and strategy, and ultimately of governance, are brought into the spotlight. Against this backdrop, it is argued that smart cities research needs to be based on real tangible experiences of individuals inhabiting rural and urban space and that it also needs to mirror and feed into policy-design and policymaking processes.Research limitations/implicationsThe paper stresses the need to explore the question of how the specific contexts in which cities/urban areas are located influence those cities/urban areas’ growth and development strategies. It also postulates new avenues of inter and multidisciplinary research geared toward building bridges between the normative and the empirical in the smart cities debate. More research is needed to advance these imperatives at the micro, mezzo and macro levels.Practical implicationsBy highlighting the connection, relatively under-represented in the literature, between the normative and the empirical in smart cities research, this paper encourages a more structured debate between academia and policymakers focused on the sustainable development of cities/urban areas. In doing so, it also advocates policies and strategies conducive to strengthening individuals’/citizens’ ability to benefit from and contribute to smart cities development, thereby making them sustainable.Social implicationsThis paper makes a case for pragmatic and demand-driven smart cities research, i.e. based on the frequently very basic needs of individuals and citizens inhabiting not only urban but also rural areas. It highlights the role of basic infrastructure as the key enabler/inhibitor of information and communication technology-enhanced services. The nested clusters model introduced in this paper suggests that an intimate connection exists between individuals’ well-being, their active civic engagement and smart cities sustainability.Originality/valueThis paper delineates the relationship between megacities and smart cities research. It identifies the sources of what is termed normative bias in smart cities research. To address the implications of that bias, a nested clusters model for smart cities is introduced, i.e. a conceptual framework that allows us to redraw the debate on smart cities and establish a functional connection between the array of normatively laden ideas of what a smart city could be and what is feasible, and under which conditions at the policymaking level.

Big data analytics has gained wide attention from both academia and industry as the demand for understanding trends in massive datasets increases. Recent developments in sensor networks, cyber-physical systems, and the ubiquity of the Internet of Things (IoT) have increased the collection of data (including health care, social media, smart cities, agriculture, finance, education, and more) to an enormous scale. However, the data collected from sensors, social media, financial records, etc. is inherently uncertain due to noise, incompleteness, and inconsistency. The analysis of such massive amounts of data requires advanced analytical techniques for efficiently reviewing and/or predicting future courses of action with high precision and advanced decision-making strategies. As the amount, variety, and speed of data increases, so too does the uncertainty inherent within, leading to a lack of confidence in the resulting analytics process and decisions made thereof. In comparison to traditional data techniques and platforms, artificial intelligence techniques (including machine learning, natural language processing, and computational intelligence) provide more accurate, faster, and scalable results in big data analytics. Previous research and surveys conducted on big data analytics tend to focus on one or two techniques or specific application domains. However, little work has been done in the field of uncertainty when applied to big data analytics as well as in the artificial intelligence techniques applied to the datasets. This article reviews previous work in big data analytics and presents a discussion of open challenges and future directions for recognizing and mitigating uncertainty in this domain.

Road traffic congestion continues to manifest and propagate in cities around the world. The recent technological advancements in intelligent traveler information have a strong influence on the route choice behavior of drivers by enabling them to be more flexible in selecting their routes. Measuring traffic congestion in a city, understanding its spatial dispersion, and investigating whether the congestion patterns are stable (temporally, such as on a day-to-day basis) are critical to developing effective traffic management strategies. In this study, with the help of Google Maps API, we gather traffic speed data of 29 cities across the world over a 40-day period. We present generalized congestion and network stability metrics to compare congestion levels between these cities. We find that (a) traffic congestion is related to macroeconomic characteristics such as per capita income and population density of these cities, (b) congestion patterns are mostly stable on a day-to-day basis, and (c) the rate of spatial dispersion of congestion is smaller in congested cities, i.e. the spatial heterogeneity is less sensitive to increase in delays. This study compares the traffic conditions across global cities on a common datum using crowdsourced data which is becoming readily available for research purposes. This information can potentially assist practitioners to tailor macroscopic network congestion and reliability management policies. The comparison of different cities can also lead to benchmarking and standardization of the policies that have been used to date.

Background This study aims to assess geographical distribution of hospitals and extent of inequalities in hospital beds against socioeconomic status (SES) of residents of five metropolitan cities in Iran. Methods A cross-sectional analysis was conducted to measure geographical inequality in hospital and hospital bed distributions of 68 districts in five metropolitan cities during 2016 using geographic information system (GIS), and Gini and Concentration indices. Correlation analysis was performed to show the relationship between the SES and inequality in hospital beds densities. Results The study uncovered marked inequalities in hospitals and hospital beds distributions. The Gini indices for hospital beds were greater than 0.55. The aggregated concentration indices for public and private hospital beds were 0.33 and 0.49, respectively. The GIS revealed that 216 (70.6%) hospitals were located in two highest socioeconomic status classes in the cities. Only 29 (9.5%) hospitals were located in the lowest class. The public, private, and the cumulative hospitals beds distributions in Tehran and Esfahan showed significant ( p  < 0.05) positive correlation with SES of the residents. Conclusions The high inequalities in hospital and hospital beds distributions in our study imply an overlooked but growing concern for geographical access to healthcare in rapidly urbanizing metropolitan cities in Iran. Thus, regardless of ownership, decision-makers should emphasize the disadvantaged areas in metropolitan cities when need arises for the establishment of new healthcare facilities in order to ensure fairness in healthcare. The metropolitan cities and rapid urbanization settings in other countries could learn lessons to reduce or prevent similar issues which might have hampered access to healthcare.

To identity differences between a general access index (Accessibility/ Remoteness Index of Australia; ARIA+) and a specific acute and aftercare cardiac services access index (Cardiac ARIA). Exploratory descriptive design. ARIA+ (2011) and Cardiac ARIA (2010) were compared using cross-tabulations (chi-square test for independence) and map visualisations. All Australian locations with ARIA+ and Cardiac ARIA values were included in the analysis (n = 20,223). The unit of analysis was Australian locations. Of the 20,223 locations, 2757 (14% of total) had the highest level of acute cardiac access coupled with the highest level of general access. There were 1029 locations with the poorest access (5% of total). Approximately two thirds of locations in Australia were classed as having the highest level of cardiac aftercare. Locations in Major Cities, Inner Regional Australia, and Outer Regional Australia accounted for approximately 98% of this category. There were significant associations between ARIA+ and Cardiac ARIA acute (χ2 = 25250.73, df = 28, p<0.001, Cramer's V = 0.559, p<0.001) and Cardiac ARIA aftercare (χ2 = 17204.38, df = 16, Cramer's V = 0.461, p<0.001). Although there were significant associations between the indices, ARIA+ and Cardiac ARIA are not interchangeable. Systematic differences were apparent which can be attributed largely to the underlying specificity of the Cardiac ARIA (a time critical index that uses distance to the service of interest) compared to general accessibility quantified by the ARIA+ model (an index that uses distance to population centre). It is where the differences are located geographically that have a tangible impact upon the communities in these locations-i.e. peri-urban areas of the major capital cities, and around the more remote regional centres. There is a strong case for specific access models to be developed and updated to assist with efficient deployment of resources and targeted service provision. The reasoning behind the differences highlighted will be generalisable to any comparison between general and service-specific access models.

Twitter has become an important platform for geo-spatial analyses, providing high-volume spatial data on a wide variety of social processes. Understanding the relationship between population density and Twitter activity is therefore of key importance. This study reports a systematic relationship between population density and Twitter use. Number of tweets, number of users and population per unit area are related by power law functions with exponents greater than one. These relations are consistent with each other and hold across a range of spatial scales. This implies that population density can accurately predict Twitter activity, but importantly, it also implies that correct predictions are not given by a naive linear scaling analysis. The observed super-linearity has implications for any spatial analyses performed with Twitter data and is important for understanding the relationship between Twitter use and demographics. For example, the robustness of this relationship means that we can identify 'anomalous' geographic areas that deviate from the observed trend, identifying several towns with high/low usage relative to expectation; using the scaling relationship we are able to show that these anomalies are not caused by age structure, as has been previously proposed. Proper consideration of this scaling relationship will improve robustness in future geo-spatial studies using Twitter.