The world is increasingly urbanised, and in just a few decades it will be even more so. Cities only make up a tenth of the world’s land, yet today more than 55% of the total population (7,800 million people) live in them. By 2050 this percentage will have reached 70% of the world’s population, estimated at 10,000 million. These are the figures handled by UN-Habitat, the United Nations programme devoted to housing and sustainable urban development, i.e. to ensuring that human settlements are adequate and decent for people and that they respect the environment.

The process of urbanisation –with all its social, economic and environmental repercussions– is happening on a global scale, at an increasingly fast pace and spontaneously, giving rise to urban settlements that lack the minimum infrastructure and services to ensure the quality of life and development of their inhabitants. Adequate planning of both urban growth and transport networks –especially in large metropolitan areas– is one of the keys to making cities into habitable environments that are sustainable, safe, fair and friendly for their inhabitants.

We cannot talk of city planning from one sole point of view or one sole model: we have to consider what makes each urban area unique in order to offer effective solutions that respond to specific problems.

For this, we require political will, commitment from all actors (state, private and civilian), as well as availability of economic and financial resources, which will enable policies and actions to be agreed to achieve a sustainable development model.

In the current context of rapid urbanisation, planning has new challenges to confront, such as slowing down climate change, backing sustainability and fighting against growing social inequality. For this, it is necessary to ensure universal access to basic services such as transport, water, sanitation, energy, communications and equipment.

A highly organised urban model with sufficient equipment and public spaces, affordable housing and sustainable mobility offers people more opportunities of employment and training as well as access to essential services like healthcare and education, among others, thus minimising urban imbalances and inequality.

The United Nations Conference on Housing and Sustainable Urban Development, Habitat III, held in Quito, Ecuador, from 17–20 October 2016, brought together over 35,000 participants and covered all these topics through numerous conferences and events in which the various agents debated and presented their proposals to tackle the urban problems of the future. Among its main conclusions were the pursuit of social inclusion and eradication of poverty, sustainable and inclusive urban prosperity, and the assurance of a sustainable, resilient environmental balance through city planning.

The result of this meeting, translated into the so-called New Urban Agenda, gathered and took on the conclusions and commitments made by the international community in another two global forums of colossal importance for the planet’s development: the historic Paris Agreement on Climate (COP21) in December 2015, in which 195 countries signed the first binding global agreement to reduce global warming and slow down climate change, and the 17 goals of the UN’s Sustainable Development Agenda 2030.

In the first row, the Ecuadorian Minister for Urban Development and Housing, Ms Ángeles Duarte, the then Secretary General of the United Nations, Mr Ban Ki-moon, and the Executive Director of UN-Habitat, former Barcelona mayor Mr Joan Clos.

Mobility for urban development: Ineco’s experience

Ineco, as part of the Spanish government’s delegation, took part in this global conference, presenting its planning, consultancy and transport engineering experience, a field in which we have decades of experience, as well as in other, more recently developed sectors linked to sustainable development, such as management of water resources and waste or smart cities.

The company has taken on an extensive range of engineering and consultancy work in these fields, to which it takes a comprehensive approach, marrying the interests of public administrations, businesses and society, and always including the environmental and social aspect to products through environmental assessments and socialisation projects.

As such, Ineco has successfully completed projects of all kinds in relation to urban and interurban mobility: from technical, economic, financial, legal and environmental impact feasibility studies (such as those performed on the Bi-oceanic Corridor for the governments of Bolivia and Peru) to drawing up projects and supervising infrastructure construction (conventional and high-speed railway lines and stations in Spain, Arabia, Turkey, India, etc.), airports, highways, access to ports and logistics centres, etc.

Among the studies carried out by Ineco to improve bus transport have been the reordering of buses in Algiers, the Bus Transport Strategic Plan in Oman, and the sustainable technology study for the buses of São Paulo. In metro systems, we have extensive experience in Spain (Madrid, Barcelona, Valencia, Seville, etc.) and in Medellín, São Paulo and Santiago de Chile. In terms of trams and light metros, also in Spain, we have worked on studies and projects in Madrid, Bilbao, Logroño, Zaragoza, León, Tenerife and Alicante, and on new schemes in Belgrade and Kuwait, as well as studies for tram renovation in Tallinn, in Latvia and in Pavlodar in Kazakhstan. Our suburban railway work includes the comprehensive projects between Caracas and the Valles del Tui in Venezuela, the studies for building a railway system in San José, Costa Rica, the Belgrade Light Metro and the Buenavista-Cuautitlán line in Mexico.

Comprehensive strategic, multimodal planning on a national, regional or local scale is another of the company’s specialities; for over four decades we have cooperated with the Spanish government to develop their national plans –PITVI (the Infrastructure, Transport and Housing Plan) is the most recent– but also with other governments such as those of Ecuador, Costa Rica, Oman and Algeria. Croatia and Malta, which are also planning their national strategies, commissioned a vital part of their plans to Ineco: that of preparing their national transport models (see pages 34-37) which, in Malta’s case, enabled Ineco to take part in the development of the National Transport Strategy, the National Master Plan, and finally the Strategic Environmental Assessment.

On a local level, it is worth mentioning the drawing up of Urban Mobility Plans, management tools to structure mobility policies towards methods for more sustainable movement in municipalities such as Hospitalet de Llobregat (151,000 inhabitants), Logroño (228,000) and A Coruña (244,000), where in addition to optimising public transport we also seek to strengthen non-motorised modes of transport, such as travel by foot or by bicycle.

For example, in Muscat, the capital of Oman, the starting point was one in which there was considerable presence of private vehicles and absence of railway networks, and it was concluded that a new, well-run network of buses would be the basis for the future public transport network. Ineco designed and presented a plan for the city in 2015 (starting with route proposals towards a new management model based on a single transport authority, among many other aspects) and subsequently the Bus Transport Strategic Plan for national public transport operations. The Omani government acquired a modern, state-of-the-art vehicle fleet to equip new urban and long-distance routes, and has put in place, among other means, a new legislative framework which is transforming the public transport system in the Sultanate (see IT57).

Towards the future of the city

Ineco has expanded its activity to planning other public services like water and waste:  as such, it prepared the Master Plan for Comprehensive Waste Management in the Metropolitan District of Quito (see IT58), based on a circular economy strategy with direct application methods and an effective legal framework; and studies for supervising the National Irrigation Plan in Ecuador with the aim of optimising water resource management, and it is elaborating Panama’s National Plan for the Collection and Treatment of Solid Waste, which will set out the means necessary to solve national waste management problems.

For Smart Cities the use of technology enables dynamic, real-time information to be obtained by installing sensors (the “internet of things”) and the vast quantity of data gathered via Big Data platforms to be processed. The Smart City model enables the management of multiple services to be optimised, from waste collection to traffic management, with the resulting benefit to the environment of reducing emissions, energy consumption and water, among other resources. It also enables citizen participation and administrative transparency to be increased. In this field, Ineco is working on the development of its platform CityNECO, with a pilot project for Granada City Council.

In short, the company, which presented some of its projects at Habitat (see News in this issue), believes in and works towards an urban model planned with an agreed comprehensive approach that is economically, socially and environmentally sustainable, with cleaner air, more space for pedestrians, greater abundance of water and biodiversity, and greater involvement of citizens, at the heart of a more polycentric, fluid urban structure in which information is available to assist people’s development and wellbeing.

The growth projection by 2020 is almost 40ZB (zettabyte, 10²¹ bytes), the majority generated by human beings, followed by physical devices connected to the Internet. Another indicator that allows us to verify this trend is that the Big Data analytics and technology market grows at an annual rate of 20-30%, with an estimated world market of 50 billion euros by 2018.

But it is not simply the amount of data that makes the concept of Big Data unique. We tend to take this concept literally and associate it with a large amount of information, but, as we will see later on, a set of data must have more qualities in order to be considered Big Data.

DEFINITION OF BIG DATA AND ASSOCIATED PROBLEMS

We can talk about Big Data when large amounts of information are generated (Volume) very quickly (Velocity), with heterogeneous types of data (Variety). Recently, the industry has started to add a fourth ‘V’ to these three classic features (the three V’s): Veracity. Given that a large portion of information is directly generated by people, it is necessary that the origin of the data be granted the quality of veracity. There is no point in having a full set of data that is not reliable.

To a great extent, the rise in Big Data technologies has been caused by the social networks, as far as the volume and variety of data are concerned, and by the marketing sector, with regard to the possibilities of demonstrating the value of all the information being generated. Banking is another classic sector that generates and exploits Big Data. The study of the information on uses and habits that can be obtained from banking information makes it possible to design products tailored to customers, or to predict behaviours, such as outstanding payments, according to the correlation of the information available. Engineering firms are also beginning to identify cases of use for which the capacity of Big Data analytics is a competitive advantage.

Finally, the field of the IoT (Internet of Things) and Smart Cities should be noted.
The concept of a Smart City involves an intensive use of information technologies for collecting and processing the information that the city generates using the sensors deployed or other data sources, such as traffic cameras or any other source of unstructured information.

The four qualities that information must have in order to identify with
the concept of big data are: volume, velocity, variety and veracity

THE INDUSTRY’S APPROACH

Big Data projects cannot be efficiently addressed using traditional technologies. The requirements for storing and exploiting such quantities of data, with their qualities of velocity and heterogeneity, have forced the industry to design new technologies that make it possible to work with information in real time, including the previously mentioned characteristics of data volume and variety.

Among the different paradigms presented by the industry when tackling Big Data projects, we can highlight In-Memory (IMDB) technologies and Distributed Systems. In-Memory technology allows all of the information that is necessary to work to be loaded into a memory where the processing is much faster. Furthermore, solutions based on distributed systems are oriented towards parallel processing, allowing a complex problem to be broken down and sorted out by using different machines responsible for solving each part of the original problem. This breakdown allows for the use of affordable computers which together make up a large processing platform. The appearance of Open Source solutions such as Hadoop and Storm has supported this trend.

Additionally, there is a tendency to implement Big Data platforms using cloud services. The problem raised in Big Data projects is infrastructure dimensioning and scalability (growth potential). For this reason, these sorts of projects need to have an infrastructure that is elastic and which allows available resources to be expanded or reduced depending on our requirements at any given moment.

Solutions based on cloud services are going to take the place of private infrastructure contracting (on-premise), as this allows companies to be free from infrastructure installation and maintenance, in order to focus on tasks which contribute value to the project. We are no longer talking about acquiring machines (virtual or physical) where we have installed and configured our own solution, but rather about utilising the services we need at any given time, paying only for the processing time and the storage. For instance, if we need an automatic learning service where we can define a prediction algorithm that works with our own information, contracting the cloud service and only paying for the period of use is sufficient.

WHAT BIG DATA IS HIDING

Once we have this vast amount of data, how do we generate value from our information? There is a misconception that Big Data projects involve storing the existing information and applying a relatively complex technology to analyse what we can obtain. A Big Data project should begin prior to starting to compile information. It is necessary to be sure about the objectives that motivate the project and the type of information we need, as well as to consider all of the constraints involved in the collection and processing of this information.

As opposed to Big Data technology, classic Business Intelligence systems are based on the consolidation of the information which lets us carry out operations with that pre-calculated data. The new Big Data paradigm forces us, on one hand, to be able to analyse the flow of information in real time, and, on the other, to store the raw information. With regard to temperature sensors, for example, we need to record all measurements that the sensor has generated. It is not enough to simply control the average daily temperature, since having the additional information does not allow us to analyse details to be able to predict parameter behaviour or identify behaviour patterns. That is to say that we need to be able to store and analyse the information in its original form, or at a much lower level of detail than in traditional analytical systems.

BIG DATA IN ENGINEERING

The areas of application are far-reaching, ranging from solutions for Smart Cities to automatic learning techniques for predictive maintenance activities. At Ineco we are aware of the importance and the possibilities Big Data technologies have in the field of engineering. Therefore, the Information Technologies division studies and exploits the characteristics of Big Data in different areas. In terms of Smart Cities we work in different fields, among which we can highlight the Smart CityNECO platform, for the integration of information from the various city services (mobility, environment, etc.) allowing for a correct management based on the control panels of the different services provided by the city. In addition, also within the field of Smart Cities, but more specifically concerning the axis of mobility (Smart Mobility), Ineco works in the study and optimisation of mobility in cities by creating prediction and simulation environments in real time that allow the optimal mobility regulation parameters in the different areas of the city to be determined. This solution is based on integrating the simulation models, as well as on the automatic learning techniques, by working with the information concerning the city’s state of mobility in real time.

A big data project must be sure about the objectives and the type of information we need, as well as consider the constraints involved in the collection and processing of this information

Within the field of infrastructure maintenance, predictive maintenance is based on anticipating the problem before it becomes a reality, or before its state loses the optimal conditions. This way, we lengthen the time between maintenance activities, thus improving availability while saving on costs. In this field, we develop predictive techniques using measurements from different parameters thanks to sensors which allow a relationship with their service life to be established. The difference with traditional techniques lies in automatically combining all information regarding their state, characteristics, exploitation and environmental conditions.

Within the area of mobility surveys and capacity, Ineco works on a mobile device survey platform that allows all information relevant to these types of studies to be compiled, including the responses provided by the user, location information provided by the GPS, etc. Additionally, with regard to the answers given using natural speech, we can conduct what is called a ‘Sentiment Analysis’ (opinion mining) which lets us identify the speaker’s attitude towards an issue.

Furthermore, we cannot forget that Big Data does not only consider alphanumeric information. Thus, another area of research focuses on image processing. The objective is to locate defects or objects in an automated way.

To sum up, we are undergoing a digital transformation which, combined with interconnection capacities, is exponentially increasing the amount of information generated. We live in the ‘Time of Data’ and the capacity to analyse that information is going to mark the difference in all fields of business.