The study examines the long run as well as short run relationship between the fiscal deficits, which is outcome of high government expenditure over the level of tax revenue collection, and poverty. The results reveal a negative relationship between government expenditure and poverty based on time series data from 1976 to 2010. The short run and long run relationships between poverty and other variables are identified by ECM model and Johnson Cointegration test respectively. The results show that there exist short run as well as long run relation between the poverty and government expenditure.
Today the concept of smart cities is discussed in scientific society and politics. A core function of smart cities is transportation. This paper gives a short overview on the concepts for Intelligent Transport Systems (ITS) for smart cities and proposes a framework for the design of an autonomic transportation system that provides personalized mobility services to its users in a smart city setting. The transportation poses extreme environment for ICT systems due to fast moving vehicles and users, requiring real-time acquisition and high performance processing of large scale data, and rapidly changing communication networks topologies and node densities. The aim of this paper is to propose a framework that will act as a reference for the design of future transportation systems that are able to cope with the ever rising system complexities and users' demands. Therefore, a backbone system providing information at different levels was designed following the principles of a corporative ICT that were proposed in [1]. The framework fulfills the main requirement providing suitable information about the local decision engines in vehicles and infrastructure interacting in smart cities traffic systems.
Today's societies are connected to a level that has never been seen before. The COVID-19 pandemic has exposed the vulnerabilities of such an unprecedently connected world. As of 19 November 2020, over 56 million people have been infected with nearly 1.35 million deaths, and the numbers are growing. The state-of-the-art social media analytics for COVID-19-related studies to understand the various phenomena happening in our environment are limited and require many more studies. This paper proposes a software tool comprising a collection of unsupervised Latent Dirichlet Allocation (LDA) machine learning and other methods for the analysis of Twitter data in Arabic with the aim to detect government pandemic measures and public concerns during the COVID-19 pandemic. The tool is described in detail, including its architecture, five software components, and algorithms. Using the tool, we collect a dataset comprising 14 million tweets from the Kingdom of Saudi Arabia (KSA) for the period 1 February 2020 to 1 June 2020. We detect 15 government pandemic measures and public concerns and six macro-concerns (economic sustainability, social sustainability, etc.), and formulate their information-structural, temporal, and spatio-temporal relationships. For example, we are able to detect the timewise progression of events from the public discussions on COVID-19 cases in mid-March to the first curfew on 22 March, financial loan incentives on 22 March, the increased quarantine discussions during March–April, the discussions on the reduced mobility levels from 24 March onwards, the blood donation shortfall late March onwards, the government's 9 billion SAR (Saudi Riyal) salary incentives on 3 April, lifting the ban on five daily prayers in mosques on 26 May, and finally the return to normal government measures on 29 May 2020. These findings show the effectiveness of the Twitter media in detecting important events, government measures, public concerns, and other information in both time and space with no earlier knowledge about them.
PurposeThe purpose of this paper is to advance knowledge of the transformative potential of big data on city-based transport models. The central question guiding this paper is: how could big data transform smart city transport operations? In answering this question the authors present initial results from a Markov study. However the authors also suggest caution in the transformation potential of big data and highlight the risks of city and organizational adoption. A theoretical framework is presented together with an associated scenario which guides the development of a Markov model.Design/methodology/approachA model with several scenarios is developed to explore a theoretical framework focussed on matching the transport demands (of people and freight mobility) with city transport service provision using big data. This model was designed to illustrate how sharing transport load (and capacity) in a smart city can improve efficiencies in meeting demand for city services.FindingsThis modelling study is an initial preliminary stage of the investigation in how big data could be used to redefine and enable new operational models. The study provides new understanding about load sharing and optimization in a smart city context. Basically the authors demonstrate how big data could be used to improve transport efficiency and lower externalities in a smart city. Further how improvement could take place by having a car free city environment, autonomous vehicles and shared resource capacity among providers.Research limitations/implicationsThe research relied on a Markov model and the numerical solution of its steady state probabilities vector to illustrate the transformation of transport operations management (OM) in the future city context. More in depth analysis and more discrete modelling are clearly needed to assist in the implementation of big data initiatives and facilitate new innovations in OM. The work complements and extends that of Setia and Patel (2013), who theoretically link together information system design to operation absorptive capacity capabilities.Practical implicationsThe study implies that transport operations would actually need to be re-organized so as to deal with lowering CO2footprint. The logistic aspects could be seen as a move from individual firms optimizing their own transportation supply to a shared collaborative load and resourced system. Such ideas are radical changes driven by, or leading to more decentralized rather than having centralized transport solutions (Caplice, 2013).Social implicationsThe growth of cities and urban areas in the twenty-first century has put more pressure on resources and conditions of urban life. This paper is an initial first step in building theory, knowledge and critical understanding of the social implications being posed by the growth in cities and the role that big data and smart cities could play in developing a resilient and sustainable transport city system.Originality/valueDespite the importance of OM to big data implementation, for both practitioners and researchers, we have yet to see a systematic analysis of its implementation and its absorptive capacity contribution to building capabilities, at either city system or organizational levels. As such the Markov model makes a preliminary contribution to the literature integrating big data capabilities with OM capabilities and the resulting improvements in system absorptive capacity.
The urbanization problems we face may be alleviated using innovative digital technology.However, employing these technologies entails the risk of creating new urban problems and/orintensifying the old ones instead of alleviating them. Hence, in a world with immense technologicalopportunities and at the same time enormous urbanization challenges, it is critical to adopt theprinciples of responsible urban innovation. These principles assure the delivery of the desired urbanoutcomes and futures. We contribute to the existing responsible urban innovation discourse byfocusing on local government artificial intelligence (AI) systems, providing a literature and practiceoverview, and a conceptual framework. In this perspective paper, we advocate for the need forbalancing the costs, benefits, risks and impacts of developing, adopting, deploying and managinglocal government AI systems in order to achieve responsible urban innovation. The statements madein this perspective paper are based on a thorough review of the literature, research, developments,trends and applications carefully selected and analyzed by an expert team of investigators. Thisstudy provides new insights, develops a conceptual framework and identifies prospective researchquestions by placing local government AI systems under the microscope through the lens of re-sponsible urban innovation. The presented overview and framework, along with the identifiedissues and research agenda, offer scholars prospective lines of research and development; wherethe outcomes of these future studies will help urban policymakers, managers and planners to betterunderstand the crucial role played by local government AI systems in ensuring the achievement ofresponsible outcomes.
