INFORMATION & COMMUNICATION TECHNOLOGIES
8 ECTS credits.
The area is based on the idea that the intensive deployment and usage of Information and Communications Technologies (ICT) contribute to improved wealth creation and social cohesion in the urban context. In particular, the most relevant trends within the ICT domain –mobile uptake, ultrabroadband deployment, Internet of Things, big data, social networking expansion and innovations in services, applications and content- are explored in this area.
Next Generation Infrastructures
Next generation infrastructures (NGI) will support a renewed electronic communication structure where opportunities lie in the provision of ubiquitous ultra-broadband connectivity, novel applications, appealing contents and the general support to the sustainable development of all the economic sectors. From their deployment it is –much– expected a wealth of innovations, jobs creation and economic growth.
In particular, within the city framework, NGI appear as the key technology for the provision of smart transportation, smart grids, Internet of Things, big data, open government and all types of new services and applications. However, beyond specific aspects of the urban context, the rise of what
has been called the knowledge economy or new economy has reinforced the role of telecommunications as a strategic investment in urban areas. The consensus regarding the importance of telecommunications has changed the reasoning at play. It no longer includes the existence of an adequate infrastructure as a factor affecting local development. Instead, its absence is considered a sign of underdevelopment.
In this context, this module of the MCS will cover present and future NGI technological developments,
techno-economic deployment scenarios in a urban landscape, citizens’ demands and expectations, and regulatory and policy-making options to promote their uptake.
Internet of Things (IoT)
Internet of Things (IoT) will be the supporting technology for any type of smart environment. It is based on a network of sensors on physical objects equipped with Internet protocols –therefore seamlessly integrated within Internet- and able to create communications networks automatically and send / receive information without direct user intervention –machine-to-machine communications, M2M. Data acquisition, data processing, wireless transmission and routing, and data fusion and consolidation are the basic building blocks of any IoT deployment.
M2M has been cited frequently as the next engine for growth of wireless. In fact, in 2020 between 12 to 50 billion machines are expected be connected with each other, a 12- to 50-fold growth from 2014. Within the urban landscape, IoT will be a key component of consumer electronics, intelligent buildings, utility sector, transportation and healthcare. From the perspective of citizens, IoT/M2M services will translate in a number of applications based on information provided by sensors and devices surrounding the user and supplying highly valuable context information.
In the case of industry –vertical- urban applications, the automotive sector (or “smart transportation”) is a paradigmatic case. Here, IoT/M2M involves sensors and connected devices that monitor the car as whole, the outside environment and traffic, as well as the driving itself. On a more macro level it also involves managing traffic and safety as a whole. In this sense, cars are forecasted to become the most connected machine we use and they are inherently mobile. For instance, emergency calling devices will automatically signal to emergency services when the car is involved in an accident or is in danger.
Big data as a concept is loosely referred to as a term based on the size of some dataset, although there is no formal or informal demarcation above which a dataset shall be considered “big”, apart from some tacit agreement that this value is larger than some terabytes (as of 2014). Big data is also usually implied when considering the huge amounts of miscellaneous data stored by both public and private sector as a result of their regular activities. Both of those ideas—size and/or heterogeneity—hint at the problematic—but full of promises—management entailed by these data. In these senses the term has become increasingly popular in all types of policy declarations, initiatives and documents, whether scientific or not, and in particular in the domain of smart cities.
Complementarily, there is another strand of definitions more akin to an economic perspective. Thus, big data can be defined as a set of processes, technologies and business models that are based on data and on capturing the value hidden in the data itself. Insisting in the value and business point of view, big data could be considered then as a new class of economic asset and the basis of a drift toward data-driven discovery and decision-making.
Also around the term big data, other concepts such as data science, data mining or data visualization have arisen. Probably “data science” should be highlighted as the global term encompassing all the others including big data itself. Data science would be different from statistics and other similar disciplines because of data increasingly heterogeneous and unstructured and the new processes required to analyse it. In fact, big data needs to be acquired, ingested, processed, persisted, integrated, analysed and exposed to produce results.
Big data is also deeply linked to other related concepts such as “Internet of Things, (IoT)” or “open data”, which are also becoming increasingly popular on their own. IoT is an obvious source of big data. The second term makes reference data that is available and can be reused by anyone at no cost, subjected to a pre-defined license under which, the user/distributor of the data has to provide appropriate credit to the primary owner of the data. Last but not least, in order to tackle with this relatively new discipline a new kind of professional has emerged, the data scientist, who combines the skills of software programmer, statistician and creativity to extract value from data.
Spatial & Visual Analitics
Cities face a rapidly increasing amount of data that have radically influenced the manner in which we deal with information. Most of the time, data is stored without filtering and refinement for later use. However, raw data has no value in itself; instead, we want to extract the information contained in it.
In this context data science and complexity theory can help cities formulate and evaluate policies to stimulate a balanced economic activity and a sustainable urban development. In particular, Spatial and Visual Analytics are among the most powerful tools aiming to cope with the information overload problem. This refers to the danger of getting lost in data, which may be irrelevant to the current task at hand, as well as processed and/or presented in an inappropriate way.
Visualization is a process to communicate content through different pictorial techniques in order to allow users to get information and gain knowledge from a specific topic or process. In the ICT context, it is mainly used as the most effective way to discover unexpected patterns and relationships among big and often heterogeneous datasets and/or to present / evaluate / explore / simulate / play with several facets of the real world.
Furthermore, a visual analytics process combines automatic and visual analysis methods with a tight coupling through human interaction in order to gain knowledge from data. In this context, this module of the MCS will cover present and future research and technological development in the field of Spatial Analytics and Visual Analytics and its applications. It will also provide an overview of the ongoing work on visualization of data mining and simulation tools, among other related disciplines with a combined potential to support decision making in the urban environment.