Ongoing Challenges

The data itself has no value, the value derives from its use or, more specifically, when through its processing we are able to create information that supports decision making

True urban intelligence will only happen when those who govern the territory are able to establish strategies that lead to the construction of the city as a platform, creating the necessary and sufficient conditions to, taking advantage of information management and data science leveraged in big data, radically change the paradigm of planning and management of our cities and towns.

But the data itself has no value, the value stems from its use or, more specifically, when through its processing we are able to create information that supports decision making and leads to action (information is given in context).

In this sense, on this page, you can consult some of the data analytics and visualization solutions supported in big data that are still under development for the various partners of NOVA Cidade – Urban Analytics Lab and that are open to contributions and participation of interested researchers.

Analytical model based on data from environmental sensors in Lisboa

Context / Description

Big Data is increasingly used to help meet the goals of climate health, environmental sustainability and resilience in countries and cities worldwide. The data used in this process is generated from sensors that keep track of environmental variables such as CO2, NO2, O3, etc., and measure their levels in the atmosphere. This information can be used in the development of analytical and machine learning models that make it possible to describe, predict and prescribe the environment status in a current moment and its evolution, access the level of air pollution and, associate it with the communities’ behaviours regarding, for example, traffic and industrial production.

This challenge aims at developing these sorts of analytics in the context of the city of Lisbon, under the scope of the “Dados ao Serviço de Lisboa” project, that joins the Municipality of Lisbon with the Universities ISEL and NOVA IMS – the students developing their thesis on this challenge will receive advisory from both universities.

Goals

More specifically, the goals of these challenges are the following:

  • Build a “smart environment” analytical model that can describe environmental health and air quality patterns in the city of Lisbon;
  • This model should also be able to determine the biggest contributors to atmospheric deterioration; cluster and profile the places in the city based on air quality variables, inform about the location characteristics and demographic attributes of the population in the different clusters; predict air quality and build if-then scenarios based on changes in the attributes of the people’s and place’s profiles;
  • Build real-time descriptive Dashboards fed with the model’s output;
  • Implement this model in the Lisbon Municipality intelligence services;
  • Publish a paper on the subject matter.

Analytical model based on data from mobile devices in Lisboa

Context / Description

Big Data is increasingly used to face the challenges of today’s society. In this context, the production of data about the use of mobile devices proliferates and, among others, informs about the location of its users. This information can then be used in the development of analytical and machine learning models that make it possible to describe, predict and prescribe mobility patterns, compute origin-destination travel demand, build if-then scenarios, understand demographic attributes and, also, gauge mobility’s relationship with virus transmission (in the context of the COVID-19 pandemic).

This challenge aims at developing these sorts of analytics in the context of the city of Lisbon, under the scope of the “Dados ao Serviço de Lisboa” project, that joins the Municipality of Lisbon with the Universities ISEL and NOVA IMS – the students developing their thesis on this challenge will receive advisory from both universities.

Goals

More specifically, the goals of these challenges are the following:

  • Build a “smart mobility” analytical model that can describe mobility patterns in the city of Lisbon;
  • This model should be able to compute origin-destination matrices; cluster and profile the places in the city with the highest demand, inform about the demographic attributes of the moving population in Lisbon; predict mobility and build if-then scenarios based on changes in the attributes of the people’s and place’s profiles;
  • Build real-time descriptive Dashboards fed with the model’s output;
  • Support implementation of this model in the Lisbon Municipality intelligence services;
  • Publish a paper on the subject matter.

Design a 3D Model of Lisbon for Microsoft HoloLens

Augmented reality is one of the most exciting technologies of the past recent years. It allows showing real-time 3D holograms of any physical environment, opening new possibilities for geographical data description and analysis through the digital representation of physical objects, such as buildings, streets, or even entire cities (in the form of a digital twin).

In this regard, one of the ground-breaking products that allow for this type of analysis is the HoloLens (https://www.microsoft.com/en-us/hololens/hardware). These futuristic glasses developed by Microsoft, enable an interface between the user and holograms that can be uploaded by a designer, with the ability to interact via speech and gesture.

Goals

The goals of this challenge are the following:

  • Create a holographic (3D model) view of Lisbon using, for example, Visual Studio and Unity 3D (see https://www.sitepoint.com/getting-started-with-microsoft-hololens-development/ for an introduction).
  • Integrate relevant data of the city in the 3D model, allowing for an interactive interface that shows the data features of each object, with gesture and voice input and audio output.
  • Set up the developed model in the HoloLens App.

Spatial behaviour of urban tourists

Context/Description
There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data when they visit cities. Public Wi-Fi networks can be used to track their presence through their activities and analyze their behaviours. The city of Lisbon is the capital of Portugal, having an area of approximately 100 Km2, and 500 000 inhabitants. Accordingly, with the tourism statistics provided by the Statistic Portugal, visited the Lisbon Metropolitan Area about 5 483 600 tourists during 2018. So it becomes essential to understand tourists behaviour to Lisbon city managers develop strategies to provide better services to tourists that visit Lisbon.

Research questions
• What are the demographic characteristics of the tourists that use the public Wi-Fi network in Lisbon?
• How is the spatial and temporal profile of the tourist’s Wi-Fi connections during weekdays and during the day?
• What are the POIs in the surroundings of the Wi-Fi access points?
• What are the demographic characteristics of tourists that visit a certain city area and at what time?

Outcome
Cluster and profiling Wi-Fi spots considering tourists demography, Wi-Fi connections, and the POIs in the surroundings of the Wi-Fi access points

Data sources
• Public Wi-Fi network data: The data used corresponds to a sample of 20%, collected in Lisbon between 1 March 2018 to 31 May 2018, and contains information about the device used to make the connection to the network, lead, access point, spot, user gender and age.
• POIs Lisbon

Adopting blockchain community currencies

Description: Community currencies provide a means for local governments to stimulate their local economies, encourage local production and consumption, and incentivise positive citizen behaviours like recycling. Blockchain technologies have been applied to create crypto currencies at many different scales, from Bitcoin down to much smaller projects, and allow a potential means to easily deploy new community currencies. However, many challenges remain from keeping citizens engaged in such schemes to knowing if people will accept blockchain cryptocurrencies as a trusted system.

Questions: In this challenge, you will look at applying a technology adoption framework to determine the drives of adoption of this critical new technology. The research can focus more on features of the technology, or characteristics of citizens and early adopters.

Determining the impact of the pandemic by COVID 19 on mobility and the environment

Description: In certain cases, hotels, institutions, ministries, political parties, among others, have parking allocated by the municipality through licensing and payment of the due fee. In this context, it is intended to identify which parking lots are allocated and whether fees are being paid.

Expected results: It is expected to obtain an estimate of the value of fees not charged by the municipality of Lisbon, by crossing information sources.

Start date: 04-09-2020

End date: ongoing

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Design of a methodology for real estate price forecasting taking advantage of the geospatial dimension

Context / Description

Econometric models for real estate price forecasting have been used for a long time, resorting to OLS methods, and making functions autonomous for specific geographic levels, by county or groups of councils. These models ignore the relationship between the value of the real estate and that of those who they circle geographically. Likewise, they do not take advantage of the possibility of using data from real estate transactions in the geographic vicinity, carried out in the past. The recent advances in Geographic Systems Information (GIS) and the technologies they entail has opened the door to the micro-location of real estate and, with it, the production of statistics not subject to an administrative division. This way, the main objective of the process to be developed is the design of a methodology for real estate price forecasting that takes advantage of econometric models together with the geospatial dimension.

Data sources

The data sets for the challenge will be presented in-depth by the start of the challenge. In summary, they comprise information for each asset sold, such as the sale price, number of rooms, area, postal code, etc. The datasets have a history of data since 2007. They cover mainland Portugal but focus on the main cities and metropolitan areas, where the market has more expression.

Goals

The goals of this challenge are the following:

  • Analyse the historical evolution of real estate price in Portugal over the last 15 years, concerning their location and configuration;
  • Development of an econometric framework for real estate price estimation (forecasting), resorting to state-of-the-art approaches found in the related literature;
  • Geographically locate each property and its surrounding properties using GIS technologies, to understand the extent to which there is a correlation between sale prices of neighbour assets;
  • Incorporate this data in the price estimation model to improve its prediction accuracy and analyse the outcomes and their quality;
  • Introduce the new real estate price forecasting model into production, by making it available for third party agents to make predictions regarding the properties they have under analysis.

Hyperlocal news

Description: Promote the development of a hybrid model of hyperlocal news production supported by open data sources and using artificial intelligence capable of supporting the edition of news bulletins for local markets (neighborhoods or parish councils), virtuously reconciling the latest developments in data science in the use of unstructured data with journalistic skills.

Start date: 1-2-2021

End date: on going

Mobility

Challenges: Impact of Covid19 in the mobility of cities.

Questions: To what extent did the Covid19 pandemic effectively change Mobility in the Cities?

Start date: 01-02-2021

End date:

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Mobility

Challenges: Government Measures Impact on Mobility.

Questions: To what extent do government measures affect Mobility in Cities?

Start date: 01-02-2021

End date:

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Mobility

Challenges: A Normal Day in the mobility flows of Cities.

Questions: Who travels the City? A study of commuting in the City.

Start date: 01-02-2021

End date:

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Taxation of private parking in the municipality of Lisbon

Description: In certain cases, hotels, institutions, ministries, political parties, among others, have parking allocated by the municipality through licensing and payment of the due fee. In this context, it is intended to identify which parking lots are allocated and whether fees are being paid.

Expected results: It is expected to obtain an estimate of the value of fees not charged by the municipality of Lisbon, by crossing information sources.

Start date: 14-10-2019

End date: ongoing

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Parking

Problem: To create new models either to predict or to generate viable alternatives for illegal parking in the city.

Research question: What are the characteristics of the locations where exists an higher number of occurrences regarding irregular parking? Where and at what time of day is expected to exist an higher number of illegalities regarding irregular parking?

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Demarked Region of Douro and Porto

Challenges: Dynamic Dashboard for assessing and benchmarking vineyard operational costs.

Questions: Comparing to other winegrowers in my parish is my cost per kg of grapes higher?

Start date: 01-02-2021

End date: 

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Smart Lisboa Observatory

Context / Description

Smart Cities Observatories allow for the diffusion of a smart development of urban systems by supporting data-based governance of cities, by providing the technological, infrastructural, economic, and social enabling factors of Smart Cities and by continually evaluating the socio-economic and environmental benefits associated with smart applications.

This challenge aims at developing this sort of infrastructure in the context of the city of Lisbon, under the scope of the “Dados ao Serviço de Lisboa” project, that joins the Municipality of Lisbon with the Universities ISEL and NOVA IMS – the students developing their thesis on this challenge will receive advisory from both universities.

Goals

More specifically, the goals of these challenges are the following:

  • Identify and characterize the state of the art of existing models for the domains of the Observatory to identify models to implement;
  • Present a proposal for indicators for the Lisbon Smart Observatory, considering the standards and good practices identified;
  • Operationalization of the Observatory, through the creation of dashboards that allow monitoring the proposed indicators and made available on the Lisbon Intelligent portal;
  • Publish a paper on the subject matter;
  • Other original ideas about this topic are also welcome.

Adopting peer-2-peer energy communities

Description: The rise of decentralized home production of energy through solar, wind, and batteries has allowed for a new local paradigm of energy consumption. With peer-2-peer energy communities, neighbourhoods share locally produces renewable energy and make use of smart devices to coordinate the timing of their energy use. However, this new way of powering households and interacting with a community faces many hurdles to full-scale adoption.

Questions: In this challenge, you will look at applying a technology adoption framework to determine the drives of adoption of this critical new technology. The research can focus more on features of the technology, or characteristics of citizens and early adopters.

Delivering smart cities with blockchain

Description: Blockchain systems allow us to create a trusted shared environment for collaborative enterprises, economic exchange, and information sharing. Blockchain allows us to create applications where the users are the owners and citizens can democratically determine the future of their infrastructure. As such blockchain has been touted as one of the technologies with the potential to help deliver truly smart cities. However, blockchain applications are in their infancy and there is significantly more work to do to realise their potential.

Questions: In this challenge, you will look to apply the characteristics of blockchain technology to a smart city application of your choice. Examples include peer-2-peer energy networks, car-sharing services, community currencies, tracing municipal services such as waste and recycling, among many others.

Students can look to design blockchain systems or implement existing systems in a simple blockchain framework.

Impact of COVID-19 on Local Housing (AL)

Description: With this challenge it is intended to estimate the effect that COVID had on stays in Local Accommodation through the technique of Web Scraping to local accommodation platforms.

Start date: 04-09-2020

End date: ongoing

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The 15 Minutes City

​Description: Develop a model for assessing the response capacity of cities to the challenge of the “15 Minutes City” through the use of open data and crossing a business intelligence approach with geographic information systems capable of allowing citizens to identify any point of interest and the system provides information on services that can be reached in 15 minutes on foot or by bicycle.

Start date: 1-2-2021

End date: ongoing

Fix my street biased city governance

Description: Today one of the best practices in the participatory governance of cities taking advantage of collective intelligence, the applications of the “Fix my street” type allow to identify in real time and through direct communication of citizens using their smart phones the problems of cities and promote their resolution as quickly and efficiently as possible. However, the generalization of their use has raised the question of the digital divide and the doubt about their capacity to respond to the needs of the city and ensure one of the ambitions of SDG 11 – Sustainable Cities and Communities, inclusion, that is, the way of managing the city guarantees the quality and the level of service to all citizens and is not biased by the degree of digital literacy of the different socio-economic realities of the urban fabric of the city. In this project we intend to cross-check the data collected by “fix my street” applications with socio-demographic data to better understand the phenomenon. 

Start date: 1-2-2021

End date: on going

Mobility

Challenges: Model Risk Associated with variations in Mobility.

Questions: Is there a correlation and can I model infection risk through Mobility Data?

Start date: 01-02-2021

End date:

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Mobility

Challenges: Infrastructure Planning.

Questions: How can I improve the City’s infrastructure through Mobility Data?

Start date: 01-02-2021

End date:

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Mobility

Challenges: Descriptive Analysis of Cities.

Questions: How can we define metrics to describe and compare Mobility at the City level?

Start date: 01-02-2021

End date:

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Mobility

Problem: To support new planning and management approaches altogether with new tools to evaluate impact and prediction of behaviours.

Research question: What are the demographic, environmental and infrastructural characteristics of the GIRA stations with more demand? What are the estimated pickups and drop offs in GIRA stations for a certain day period?

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Waste management

Problem: To identify patterns to support the prediction of the production of urban waste associated with a variety of context information (e.g. events, climate situation, etc.)

Research question: What is the social and economic profile of the citizens that produce more undifferenciated waste? What is the predicted quantity of produced undifferenciated waste that have to be collected by the municipality on a weekly basis?

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Pollution

Problem: To develop predictive models for the propagation of liquid and atmospheric pollutants.

Research question: How is the propagation of air and liquid pollutants in the city, in case of an accident with hazardous substances?

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Creation of general traffic indicator and indicators for each of the main routes of entry into the city

Problem: The characterization of traffic in the municipality, using a single global indicator, is undoubtedly a determining factor for correlation with other domains, both management and administration. In addition to the single global indicator for traffic characterisation, the perception of the state of the main roads in Lisbon as well as the main roads of the city, in addition to constituting the basis for the allocation of the global indicator, allow itself to assess the state of traffic on the outskirts of the municipality, with implication in the behaviour of roads managed by other road operators, and whose municipality does not directly control.

Expected results: This unique global indicator for the municipality, in addition to introducing a holistic component in the overall perception of the city, allows enhancing the promotion of sustainable mobility, correlating on time with global indicators of other means of transport. In addition to the single global indicator, it is intended to have indicators of the main roads of the city.

Start date: 04-09-2020

End date: ongoing​

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Emergency

Problem: To develop predictive models for the prediction of traffic accidents.

Research question: What are the characteristics of the locations where exists an higher number of traffic accidents? Where and at what time of day is expected to exist an higher number of traffic accidents?

Start date: 15-03-2020

End date: ongoing

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Demarked Region of Douro and Porto

Challenges: Analytical model that can help to:

  • identify relationship between physical and chemical data x quality (perceived) x market x parcels;
  • identify economic agents’ profiles; and
  • generate information relevant to the laboratory’s operational work (metrics related to quantities and response times)

Questions: Is there any relationship between physical chemical data x quality x market x parcels?

Start date: 01-02-2021

End date:

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