scholarly journals Urban Crime and Security

Author(s):  
Tao Cheng ◽  
Tongxin Chen

AbstractScientists have an enduring interest in understanding urban crime and developing security strategies for mitigating this problem. This chapter reviews the progress made in this topic from historic criminology to data-driven policing. It first reviews the broad implications of urban security and its implementation in practice. Next, it focuses on the tools to prevent urban crime and improve security, from analytical crime hotspot mapping to police resource allocation. Finally, a manifesto of data-driven policing is proposed, with its practical demand for efficient security strategies and the development of big data technologies. It emphasizes that data-driven strategies could be applied in cities due to their promising effectiveness for crime prevention and security improvement.

2022 ◽  
Vol 9 (1) ◽  
pp. 205395172110706
Author(s):  
Marthe Stevens ◽  
Rik Wehrens ◽  
Johanna Kostenzer ◽  
Anne Marie Weggelaar-Jansen ◽  
Antoinette de Bont

Recent buzzes around big data, data science and artificial intelligence portray a data-driven future for healthcare. As a response, Europe's key players have stimulated the use of big data technologies to make healthcare more efficient and effective. Critical Data Studies and Science and Technology Studies have developed many concepts to reflect on such overly positive narratives and conduct critical policy evaluations. In this study, we argue that there is also much to be learned from studying how professionals in the healthcare field affectively engage with this strong European narrative in concrete big data projects. We followed twelve hospital-based big data pilots in eight European countries and interviewed 145 professionals (including legal, governance and ethical experts, healthcare staff and data scientists) between 2018 and 2020. In this study, we introduce the metaphor of dreams to describe how professionals link the big data promises to their own frustrations, ideas, values and experiences with healthcare. Our research answers the question: how do professionals in concrete data-driven initiatives affectively engage with European Union's data hopes in their ‘dreams’ – and with what consequences? We describe the dreams of being seen, of timeliness, of connectedness and of being in control. Each of these dreams emphasizes certain aspects of the grand narrative of big data in Europe, makes particular assumptions and has different consequences. We argue that including attention to these dreams in our work could help shine an additional critical light on the big data developments and stimulate the development of responsible data-driven healthcare.


2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Simon Elias Bibri

AbstractAs materializations of trends toward developing and implementing urban socio-technical and enviro-economic experiments for transition, eco-cities have recently received strong government and institutional support in many countries around the world due to their ability to function as an innovative strategic niche where to test and introduce various  reforms. There are many models of the eco-city based mainly on either following the principles of urban ecology or combining the strategies of sustainable cities and the solutions of smart cities. The most prominent among these models are sustainable integrated districts and data-driven smart eco-cities. The latter model represents the unprecedented transformative changes the eco-city is currently undergoing in light of the recent paradigm shift in science and technology brought on by big data science and analytics.  This is motivated by the growing need to tackle the problematicity surrounding eco-cities in terms of their planning, development, and governance approaches and practices. Employing a combination of both best-evidence synthesis and narrative approaches, this paper provides a comprehensive state-of-the-art and thematic literature review on sustainable integrated districts and data-driven smart eco-cities. The latter new area is a significant gap in and of itself that this paper seeks to fill together with to what extent the integration of eco-urbanism and smart urbanism is addressed in the era of big data, what driving factors are behind it, and what forms and directions it takes. This study reveals that eco-city district developments are increasingly embracing compact city strategies and becoming a common expansion route for growing cities to achieve urban ecology or urban sustainability. It also shows that the new eco-city projects are increasingly capitalizing on data-driven smart technologies to implement environmental, economic, and social reforms. This is being accomplished by combining the strengths of eco-cities and smart cities and harnessing the synergies of their strategies and solutions in ways that enable eco-cities to improve their performance with respect to sustainability as to its tripartite composition. This in turn means that big data technologies will change eco-urbanism in fundamental and irreversible ways in terms of how eco-cities will be monitored, understood, analyzed, planned, designed, and governed. However, smart urbanism poses significant risks and drawbacks that need to be addressed and overcome in order to achieve the desired outcomes of ecological sustainability in its broader sense. One of the key critical questions raised in this regard pertains to the very potentiality of the technocratic governance of data-driven smart eco-cities and the associated negative implications and hidden pitfalls. In addition, by shedding light on the increasing adoption and uptake of big data technologies in eco-urbanism, this study seeks to assist policymakers and planners in assessing the pros and cons of smart urbanism when effectuating ecologically sustainable urban transformations in the era of big data, as well as to stimulate prospective research and further critical debates on this topic.


2021 ◽  
pp. 269-288
Author(s):  
Sonja Zillner

AbstractWith the recent technical advances in digitalisation and big data, the real and the virtual worlds are continuously merging, which, again, leads to entire value-added chains being digitalised and integrated. The increase in industrial data combined with big data technologies triggers a wide range of new technical applications with new forms of value propositions that shift the logic of how business is done. To capture these new types of value, data-driven solutions for the industry will require new business models. The design of data-driven AI-based business models needs to incorporate various perspectives ranging from customer and user needs and their willingness to pay for new data-driven solutions to data access and the optimal use of technologies, while taking into account the currently established relationships with customers and partners. Successful data-driven business models are often based on strategic partnerships, with two or more players establishing the basis for sustainable win-win situations through transparent resource-, investment-, risk-, data- and value-sharing. This chapter will explore the different data-driven business approaches and highlight in this context the importance of functioning ecosystems on the various levels. The chapter will conclude with an introduction to the data-driven innovation framework, a proven methodology to guide the systematic investigation of data-driven business opportunities while incorporating the dynamics of the underlying ecosystems.


2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Tolulope J. Ashaolu ◽  
Abayomi Ajayi-Majebi ◽  
Sarhan M. Musa

In the data-driven economy, turning data into actionable analytics is the best way to boost efficiency, quality, and productivity. The manufacturing processes are getting more and more complex due to increasing demands. Manufacturers of all types of products are finding significant value in big data. The application of big data technologies in manufacturing sector is a relatively new interdisciplinary research area which incorporates automation, engineering, and data analytics. This paper provides an introduction on the use of big data in manufacturing.


Author(s):  
Yang Lv ◽  
Chenwei Ma ◽  
Xiaohan Li ◽  
Min Wu

IntroductionThis study aims to explore the potential of big data technologies for controlling COVID-19 transmission and managing effectively.Material and methodsA systematic review guided by PRISMA guidelines has been followed to obtain the key elements.ResultsThis study identified the most relevant 32 documents for qualitative analysis. It also reveals 10 possible sources and 8 key applications of big data for analyzing the virus infection trend, transmission pattern, virus association, and differences of genetic modifications.ConclusionsThe findings will provide new insight and help policymakers, and administrators to develop data-driven initiatives to tackle and manage the COVID-19 crisis.


Author(s):  
D. R. Mukhametov

The article deals with various aspects related to the use of Big Data technologies in political processes. Digital technologies have an ambivalent impact on the social and political processes, creating the “grey zone” of opportunities and resources that are the subject of conflicts and competition among various political agents. This statement is equally true concerning election campaigns. Firstly, the author describes the concept of data-driven campaign, which is rapidly spreading due to the demand for flexible management mechanisms and the formation of the “attention economy”. The implementation of the concept includes processes of data mining and analysis, microtargeting — the article reveals the content of each stage on the example of recent cases. The essential advantage of using big data analysis in political processes is concluded not only in the scale of the data mining but also in the possibility to examine deep causal relationships and dependencies, which extends the range of opportunities to influence political agents behaviour. Secondly, it is possible to extrapolate mechanisms of data-driven campaign to the level of data-driven politics. The author formulates the major risks and threats associated with the use of Big Data in political processes: funnel of mistrust in political institutions and technologies, blurring political institutions and plebiscite democracy, the preservation and confidentiality of personal data, the consequences of algorithms cognitive restrictions. As a result, in the short term it will be relevant to provide institutional regulation of data using, as well as to support the development of human capital as the basic skills of personal data protection and the use of modern technologies.


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