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.
Characterization of commuting movements on the main access routes to the city
Description
The characterization of traffic in the municipality is something fundamental to plan life in the city of Lisbon, particularly with regard to the volume of people entering it daily during the morning (7:30am-10:00pm) and afternoon (5:00pm-7:30pm) rush hours, which generates traffic on the main access roads. For the 11 main entry and exit points of the city there are data that allow us to know the number of mobile devices entering and exiting through each of these points every 15-minute period. The challenge is to try to characterize these daily flows during the two periods mentioned and their relationship to factors such as school calendars and rainfall.
Expected results
Among other results that the participants in the challenge may find interesting, namely by using other data sources, it is intended to know the following:
A. For the morning rush hour period (7:30am-10:00pm)
- Characterize the total volume of entries and exits from the city during the rush hour period,
- Characterize the volume of entries and exits from the city during the peak hour period for each of the 11 entry and exit points,
- Compare with other periods of the day,
- Relate the previous point with variables such as school calendars and the occurrence of rainfall,
- Analysis of the zones of origin of those entering the city,
- Analysis of the destination zones of those leaving the city
B. For the afternoon peak period (5:00pm-7:30pm)
- Characterize the total volume of entries and exits from the city during the peak hour period,
- Characterize the volume of entries and exits from the city during the peak hour period for each of the 11 entry and exit points,
- Compare with other periods of the day,
- Relate the previous point with variables such as school or vacation periods and the existence of rainfall,
- Analysis of the destination zones of those leaving the city,
- Analysis of the zones of origin of those entering the city.
Promotor / Recipient of the found solution
CGIUL – Centro de Gestão e Inteligência Urbana de Lisboa (Lisbon Center for Urban Management and Intelligence)
Data to be made available
- Number of cell phones entering and exiting the city every 15 minutes on the 11 main entry axes into the city of Lisbon – Axes of the city of Lisbon;
- Identification of the 11 entry and exit points of Lisbon;
- Observations from IPMA’s weather stations in Lisbon: Geofísico, Gago Coutinho and Tapada da Ajuda;
Additional information
School calendar
Determining the impact of the pandemic by COVID 19 on traffic and the environment
Description
The effects of the pandemic caused by COVID 19 had a significant impact on traffic and environmental pollution in the city. It is important to understand these variations and their connection with the implemented restriction measures.
Expected results
The challenge aims to develop analytics based on the traffic and air quality data made available, considering the evolution of the pandemic situation, allowing to obtain one or more of the following:
- identify patterns and trends in the usage private cars and shared bikes;
- identify changes in air quality and environment, in the city of Lisbon.
Promotor / Recipient of the found solution
Direção Municipal de Mobilidade e Direção Municipal do Ambiente, Estrutura Verde, Clima e Energia (Municipal Department of Mobility and Municipal Department of Environment, Green Structure, Climate and Energy)
Data to be made available
- Data from the GIRA bicycle network in Lisbon city: stations location, number of available bicycles in each dock, total number of docks and number of empty docks
- Number of daily trips on Lisbon’s bike sharing network – GIRA
- Automatic bicycle counters, bidirectional, located at Av. Duque de Ávila. Entrances (west-east direction) and exits (east-west direction)
- Map describing the bicycle lane and bicycle parking network in Lisbon
- Traffic congestion data recorded through the WAZE platform
- Nitrogen dioxide (NO2) concentration level, at the fixed stations of the air quality measurement network
- Observations from the IPMA weather stations in Lisbon: Geofísico, Gago Coutinho and Tapada da Ajuda
- COVID pandemic statuses
Identification of Lisbon's green covers
Description
Green covers provide economic, environmental, and socio-community benefits, contributing in a passive way to the obtainment of comfortable environments inside buildings. This type of structure can be implemented on buildings or underground structures, reducing energy consumption.
Expected results
The challenge consists in using image recognition technologies with cross-referencing of cartographic information to obtain:
- identification and georeferencing of the green covers;
- Measurement of the area occupied by each of the green covers.
In the scope of this challenge, the following study areas are considered:
- Area A more central, encompassing the parishes of Avenidas Novas, Arroios, Santo António and Santa Maria Maior;
- The more peripheral Area B, with the parish of Parque das Nações.
Promotor / Recipient of the found solution
Direção Municipal do Ambiente, Estrutura Verde, Clima e Energia (Municipal Department of Environment, Green Structure, Climate and Energy)
Data to be made available
- Survey of green spaces
- Green covers
- Non-subterranean buildings
- Underground building
- Orthophotomaps of the city of Lisbon for two study areas of the challenge
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.
Identification of waste deposited outside of containers
Description
Waste collection in the city implicates a complex and extensive operation, where the correct deposit of waste in the adequate containers is an important success factor. Regardless of a set of awareness campaigns in that direction, there are still problems related to the wrongful disposal of waste outside of the disposal equipment destined to that end (recycling bins, etc.) which imposes an additional effort of the teams that carry out the collection. It would be of great added value to the Direção Municipal de Higiene Urbana (Department of Municipal Urban Hygiene) if their teams could access near real-time information about the places where there were undue deposits and the volume of waste, as well as planning if there was an identification of problematic spots, based on their history.
Expected results
The challege has the goal to resort to image and/or video recognition technology to get one or more of the following points about solid waste disposal outside of the usual deposit locations:
- classification of the type of waste (great volume, undifferentiated waste)
- waste volume
Promotor / Recipient of the found solution
Direção Municipal de Higiene Urbana (Department of Municipal Urban Hygiene)
Data to be made available
- Photos of recycling locations in the city of Lisbon, with wrongfully disposed waste around it, obtained by the operational teams of the DMHU
- Videos of recycling locations in the city of Lisbon, with wrongfully disposed waste around it, obtained by the operational teams of the DMHU
- Photos taken by residents with wrongfully disposed waste around it and submitted through the “Na Minha Rua” portal
Business Intelligence Tool for Analyzing the Sustainability of Urban Food Systems
To face challenges such as food waste, the need to adapt to climate change and the contrasting double childhood obesity/hunger, it is necessary to rethink food systems according to a holistic, systemic and integrated vision, supported by circularity strategies, aiming at sustainability, in its social-environmental-economic whole.
Within the scope of the Living Lab of Hub Criativo do Beato, its potential contribution to the sustainability of the Food System in the city of Lisbon will be evaluated, with the participation of the different actors in the food value chain on site and in neighboring parishes, promoting the circular economy in the food chain, namely through strategies to focus on local production, short circuits, and strategies for closing nutrient cycles.
To support the analysis of the sustainability of the chain, it is intended to develop a tool that allows capturing data on the food that is consumed in the Hub, agnostic in relation to the different stock and sales management systems, and that provides information on different aspects related to the sustainability of the HCB food system (namely distance of supply, food typology, production methods, destination). The tool will receive and process the input (food products) and output (produced food waste) data from the food and associate it with environmental analysis elements (from existing databases), joining the processed data in a centralized data repository. This repository will serve as the basis for the development of analytical applications to analyze the sustainability of urban food systems.
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.
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.
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.
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
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
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:
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:
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:
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?
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
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?
Movement of people in nightlife areas
Description
Nightlife areas with strong occupation of public space have a great impact on the life of the city and its management is important in order to respond to the interests of various stakeholders, including merchants, residents and users. In this way, and using data from mobile devices, we intend to know the movement and permanence of people between nightlife areas. Given the economic interest of tourism and the impact of tourists on nightlife areas, this analysis will also include data on roaming mobile devices. It is also of interest to know the effects of these movements of people on the outdoor noise environment, namely to know the evolution of the values of the parameter equivalent continuous sound level (LAeq).
Expected results
In addition to other information that may result from the data analysis, with this challenge it is intended to:
1 – Determine the time period and the grid squares in which the use of public space ceases to be normal and becomes related to nighttime entertainment activities in public spaces (outdoors),
2 – For the grid squares and time periods identified in the previous point (these periods can be variable from zone to zone) characterize, among other aspects, the following variables throughout the period of nightlife fun
- permanence of residents and tourists,
- mobility in nightlife areas,
- assessment of possible transfers of users between nightlife areas,
3 – Relate the movements of people with the noise levels recorded in the environmental sensors located in the study area considered.
Promotor / Recipient of the found solution
CGIUL – Centro de Gestão e Inteligência Urbana de Lisboa / Direção Municipal do Ambiente, Estrutura Verde, Clima e Energia (Lisbon Center for Urban Management and Intelligence / Municipal Department of Environment, Green Structure, Climate and Energy)
Data to be made available
- Number of cell phones entering, staying and leaving per 200m/200m grid in a 5 minute period – Lisbon city grid;
- Mapping of Vodafone’s grid cells;
- Environmental sensors (noise);
- Location of nightlife establishments.
Identification of waste deposited outside of containers
Description
Mobility is seen as one of the greatest challenges that cities must face today. Recently, the use of soft modes of mobility, such as the use of bicycles and electric scooters, in the commuting movements of the population, has had significant growing adoption in the city of Lisbon. Moreover, as the amount of available information and the pressure to enforce sustainable and safe policies increases, stakeholders and policy makers require faster and more targeted actions. As such, the need to provide decision support systems to municipal authorities is greater than ever. Understanding and quantifying the past and current state of mobility is crucial for this purpose. As such, we proposed to use data from bicycle lane counter sensors, through EMEL’s open data portal. The goal is to elaborate a structured data collection and deeper knowledge in terms of contextual variables, and to develop relevant models which can allow the recognition of commuting patterns, from soft mobility modes, in the city of Lisbon.
Objective
More specifically, the objectives of this challenge are as follows:
- Develop a structured data collection, including data regarding EMEL’s bicycle meters, and relevant contextual data;
- Develop a prescriptive model, capable of identifying commuting patterns, in the city of Lisbon, from soft mobility modes;
- Create descriptive dashboards, in real time, fed with the model’s results;
- Support the implementation of this model in the intelligence services of the Municipality of Lisbon;
- Publish an article about the work developed;
- Other original ideas using the available data are also welcome.
Identification of road accident incidence points and their correlation with other factors
Description
Road fatalities are a problem in metropolitan areas that depend on the condition, design, slope and orientation of roads, road users, as well as weather and traffic factors.
Expected results
The challenge aims to use analytics to obtain one or more of the following points about the accidents that occur in the city of Lisbon
- identification of critical points for the occurrence of accidents;
- identification of factors that contribute to the occurrence of accidents.
Promotor / Recipient of the found solution
Direção Municipal de Mobilidade (Municipal Department of Mobility)
Data to be made available
- Data from serious accidents recorded by ANSR
- Road accidents data registered by RSB
- Altimetric cartography of Lisbon (contour lines) at a scale of 1/1000
- Slope map by location
- Location of signalized intersections (GEODADOS)
- Traffic congestion data recorded through the WAZE platform
- Observations from IPMA weather stations: Geophysical, Gago Coutinho and Tapada da Ajuda
Mobility in the city based on cell phone data
Description
City services need to know the commuting movements in the morning and afternoon rush hours to better manage the city.
Expected results
Using analytical solutions, this challenge aims to use data regarding cell phones entering and moving in the city to:
- identify the patterns of entry and movement in the city in the morning rush hour (8-10am);
- identify patterns of movement into and out of the city during the afternoon rush hour (5-7pm).
Promotor / Recipient of the found solution
Centro de Gestão e Inteligência Urbana de Lisboa / Serviços Municipais (Lisbon Center for Urban Management and Intelligence / Municipal Services)
Data to be made available
- Number of cell phones entering, staying and leaving per 200m/200m grid in a 5-minute period – Lisbon city grid
- Number of cell phones entering and leaving the city every 5 minutes in the 11 main entry axes in Lisbon city – Lisbon city axes
- Mapping of Vodafone’s grid
- Identification of Lisbon’s 11 entry and exit points
- Road network data of the city of Lisbon
- Traffic level data recorded through the WAZE platform
- Traffic constraints (EMEL)
Creation of a general traffic indicator and indicators for each of the main entry roads into the city
Description
The characterization of traffic in the municipality, using a single global indicator, is undoubtedly a determining factor for the correlation with other areas, both in management and administration. In addition to the characterization of traffic in the city, it is important to know the status of the main points of entry of Lisbon and the main roads within the city (in addition to establishing the basis for the allocation of the global indicator, they allow an assessment of the state of traffic on the outskirts of the municipality, with implications for the behavior of roads managed by other road operators, and which the municipality does not directly control). Thus, in addition to introducing a holistic component in the global perception of the city, it will be possible to contribute to the promotion of sustainable mobility (correlating with global indicators of other means of transport in the long term).
Expected results
The challenge aims to:
- build a single global indicator to characterize the state of traffic in the municipality in real time, as well as indicators for the main roads of the city ( N10, A1, A12, A30, A5, N6 (Av. Marginal), A37(IC19), IP7(Eixo N-S), Cç Carriche, A2, 2nd Circular, Radial Benfica, CRIL, Av. Santos e Castro, Av. Forças Armadas – Av. M. António Spinola, Eixo Central (Entrecampos – Pç da Restauração), Av. Calouste Gulbenkian, Av. de Berna, Av. João XXI, Av. Afonso Costa, Praça Marquês de Pombal – Av. da Liberdade, Av. Almirante Reis – Av. Almirante Gago Coutinho, Av. Infante D. Henrique, Av. Infante Santo – Rua da Estrela – Av. Álvares Cabral – Rato – R. Alexandre Herculano – Rua do Conde Redondo – R. Jacinta Marto – Rua Morais Soares – Av. Afonso II);
- Develop the same indicator referred in the previous point but with predictive values for the following 2 hours.
Promotor / Recipient of the found solution
Direção Municipal de Mobilidade (Municipal Department of Mobility)
Data to be made available
- Traffic congestion data recorded through the WAZE platform
- Traffic constraints (EMEL)
- Observations from IPMA weather stations in Lisbon: Geofísico, Gago Coutinho and Tapada da Ajuda
Identification of graffiti (tags and other illegal/unauthorized)
Description
One of the current challenges in cities is the issue of illegal/unauthorized graffiti on various surfaces. It is very important for the work of the city’s Urban Hygiene and surveillance teams to identify them so that prevention and removal operations can be planned.
Expected results
The challenge aims to use image recognition technologies to get one or more of the following points about graffiti, tags and other illegal/unauthorized:
- classification of the elements (tag, or other illegal/unauthorized and area covered);
- quantify the surface area affected.
Promotor / Recipient of the found solution
Direção Municipal de Higiene Urbana (Municipal Department of Urban Hygiene)
Data to be made available
- Photos of Lisbon’s graffiti taken by the operational teams, in specific moments
- Photos of graffiti taken by residents through the portal “Na Minha Rua”
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.
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.
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.
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
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:
Mobility
Challenges: Infrastructure Planning.
Questions: How can I improve the City’s infrastructure through Mobility Data?
Start date: 01-02-2021
End date:
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:
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?
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?
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
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
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:
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
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:
