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/or intensifying the old ones instead of alleviating them. Hence, in a world with immense technological opportunities and at the same time enormous urbanization challenges, it is critical to adopt the principles of responsible urban innovation. These principles assure the delivery of the desired urban outcomes and futures. We contribute to the existing responsible urban innovation discourse by focusing on local government artificial intelligence (AI) systems, providing a literature and practice overview, and a conceptual framework. In this perspective paper, we advocate for the need for balancing the costs, benefits, risks and impacts of developing, adopting, deploying and managing local government AI systems in order to achieve responsible urban innovation. The statements made in 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. This study provides new insights, develops a conceptual framework and identifies prospective research questions by placing local government AI systems under the microscope through the lens of responsible urban innovation. The presented overview and framework, along with the identified issues and research agenda, offer scholars prospective lines of research and development; where the outcomes of these future studies will help urban policymakers, managers and planners to better understand the crucial role played by local government AI systems in ensuring the achievement of responsible outcomes.

Responsible Urban Innovation with Local Government Artificial Intelligence (AI): A Conceptual Framework and Research Agenda

Smart cities and artificial intelligence (AI) are among the most popular discourses in urban policy circles. Most attempts at using AI to improve efficiencies in cities have nevertheless either struggled or failed to accomplish the smart city transformation. This is mainly due to short-sighted, technologically determined and reductionist AI approaches being applied to complex urbanization problems. Besides this, as smart cities are underpinned by our ability to engage with our environments, analyze them, and make efficient, sustainable and equitable decisions, the need for a green AI approach is intensified. This perspective paper, reflecting authors’ opinions and interpretations, concentrates on the “green AI” concept as an enabler of the smart city transformation, as it offers the opportunity to move away from purely technocentric efficiency solutions towards efficient, sustainable and equitable solutions capable of realizing the desired urban futures. The aim of this perspective paper is two-fold: first, to highlight the fundamental shortfalls in mainstream AI system conceptualization and practice, and second, to advocate the need for a consolidated AI approach—i.e., green AI—to further support smart city transformation. The methodological approach includes a thorough appraisal of the current AI and smart city literatures, practices, developments, trends and applications. The paper informs authorities and planners on the importance of the adoption and deployment of AI systems that address efficiency, sustainability and equity issues in cities.

https://www.mdpi.com/2071-1050/13/16/8952/pdf

Green Artificial Intelligence: Towards an Efficient, Sustainable and Equitable Technology for Smart Cities and Futures

Since the advent of the second digital revolution, the exponential advancement of technology is shaping a world with new social, economic, political, technological, and legal circumstances. The consequential disruptions force governments and societies to seek ways for their cities to become more humane, ethical, inclusive, intelligent, and sustainable. In recent years, the concept of City-as-a-Platform was coined with the hope of providing an innovative approach for addressing the aforementioned disruptions. Today, this concept is rapidly gaining popularity, as more and more platform thinking applications become available to the city context—so-called platform urbanism. These platforms used for identifying and addressing various urbanization problems with the assistance of open data, participatory innovation opportunity, and collective knowledge. With these developments in mind, this study aims to tackle the question of “How can platform urbanism support local governance efforts in the development of smarter cities?” Through an integrative review of journal articles published during the last decade, the evolution of City-as-a-Platform was analyzed. The findings revealed the prospects and constraints for the realization of transformative and disruptive impacts on the government and society through the platform urbanism, along with disclosing the opportunities and challenges for smarter urban development governance with collective knowledge through platform urbanism.

https://www.mdpi.com/2073-445X/10/1/33/pdf

The Evolution of City-as-a-Platform: Smart Urban Development Governance with Collective Knowledge-Based Platform Urbanism

COVID-19 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has a case-fatality rate of 2–3%, with higher rates among elderly patients and patients with comorbidities. Radiologically, COVID-19 is characterised by multifocal ground-glass opacities, even for patients with mild disease. Clinically, patients with COVID-19 present respiratory symptoms, which are very similar to other respiratory virus infections. Our knowledge regarding the SARS-CoV-2 virus is still very limited. These facts make it vitally important to establish mechanisms that allow to model and predict the evolution of the virus and to analyze the spread of cases under different circumstances. The objective of this article is to present a model developed for the evolution of COVID in the city of Manizales, capital of the Department of Caldas, Colombia, focusing on the methodology used to allow its application to other cases, as well as on the monitoring tools developed for this purpose. This methodology is based on a hybrid model which combines the population dynamics of the SIR model of differential equations with extrapolations based on recurrent neural networks. This combination provides self-explanatory results in terms of a coefficient that fluctuates with the restraint measures, which may be further refined by expert rules that capture the expected changes in such measures.

https://www.mdpi.com/2079-9292/10/7/799/pdf

A Hybrid Model for COVID-19 Monitoring and Prediction

A smart city is an environment that uses innovative technologies to make networks and services more flexible, effective, and sustainable with the use of information, digital, and telecom-munication technologies, improving the city’s operations for the benefit of its citizens. Most cities incorporate data acquisition elements from their own systems or those managed by subcontracted companies that can be used to optimise their resources: energy consumption, smart meters, lighting, irrigation water consumption, traffic data, camera images, waste collection, security systems, pol-lution meters, climate data, etc. The city-as-a-platform concept is becoming popular and it is in-creasingly evident that cities must have efficient management systems capable of deploying, for instance, IoT platforms, open data, etc., and of using artificial intelligence intensively. For many cities, data collection is not a problem, but managing and analysing data with the aim of optimising resources and improving the lives of citizens is. This article presents deepint.net, a platform for capturing, integrating, analysing, and creating dashboards, alert systems, optimisation models, etc. This article shows how deepint.net has been used to estimate pedestrian traffic on the streets of Melbourne (Australia) using the XGBoost algorithm. Given the current situation, it is advisable not to transit urban roads when overcrowded, thus, the model proposed in this paper (and implemented with deepint.net) facilitates the identification of areas with less pedestrian traffic. This use case is an example of an efficient crowd management system, implemented and operated via a platform that offers many possibilities for the management of the data collected in smart territories and cities.

An efficient management platform for developing smart cities: Solution for real-time and future crowd detection

This paper presents an efficient cyberphysical platform for the smart management of smart territories. It is efficient because it facilitates the implementation of data acquisition and data management methods, as well as data representation and dashboard configuration. The platform allows for the use of any type of data source, ranging from the measurements of a multi-functional IoT sensing devices to relational and non-relational databases. It is also smart because it incorporates a complete artificial intelligence suit for data analysis; it includes techniques for data classification, clustering, forecasting, optimization, visualization, etc. It is also compatible with the edge computing concept, allowing for the distribution of intelligence and the use of intelligent sensors. The concept of smart cities is evolving and adapting to new applications; the trend to create intelligent neighbourhoods, districts or territories is becoming increasingly popular, as opposed to the previous approach of managing an entire megacity. In this paper, the platform is presented, and its architecture and functionalities are described. Moreover, its operation has been validated in a case study where the bike renting service of Paris—Velib’ Metropole has been managed. This platform could enable smart territories to develop adapted knowledge management systems, adapt them to new requirements and to use multiple types of data, and execute efficient computational and artificial intelligence algorithms. The platform optimizes the decisions taken by human experts through explainable artificial intelligence models that obtain data from IoT sensors, databases, the Internet, etc. The global intelligence of the platform could potentially coordinate its decision-making processes with intelligent nodes installed in the edge, which would use the most advanced data processing techniques.

Pablo Plaza-Martínez

Papers

Deepint.net: A Rapid Deployment Platform for Smart Territories.

A Hybrid Supervised/Unsupervised Machine Learning Approach to Classify Web Services