[et_pb_section fb_built=”1″ admin_label=”Top banner” module_class=”title-header” _builder_version=”4.16″ background_image=”\/wp-content\/themes\/NovaCidade\/images\/header_bg.jpg” custom_padding=”0px||||false|false” saved_tabs=”all” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text admin_label=”Titulo” module_class=”page-title” _builder_version=”4.16″ _dynamic_attributes=”content” global_colors_info=”{}”]@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF90aXRsZSIsInNldHRpbmdzIjp7ImJlZm9yZSI6IiIsImFmdGVyIjoiIn19@[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ admin_label=”Laborat\u00f3rios” module_class=”page-content laboratorios” _builder_version=”4.16″ global_colors_info=”{}”][et_pb_row _builder_version=”4.16″ global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_text _builder_version=”4.16″ global_colors_info=”{}”]<\/p>\n
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.<\/p>\n
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).<\/p>\n
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 \u2013 Urban Analytics Lab and that are open to contributions and participation of interested researchers.<\/p>\n
[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.16″ _module_preset=”default” locked=”off” collapsed=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_video src=”https:\/\/www.youtube.com\/watch?v=izlhAT4Ipbc” image_src=”https:\/\/novacidade.pt\/wp-content\/uploads\/2021\/02\/cc-2.jpg” admin_label=”Video” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_video][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ _builder_version=”4.21.0″ locked=”off” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Analysis of Airbnb in Lisbon and Porto” url_new_window=”on” alt=”Transportes” admin_label=”Analysis of Airbnb in Lisbon and Porto” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}” disabled_on=”on|on|on” disabled=”on”]<\/p>\n
The primary objective of this challenge is to gain deep insights into the Airbnb rental market in Portugal. By analyzing the dataset, you will seek to answer important questions about the hosting landscape, demand patterns, pricing dynamics, popular regions, and other intriguing aspects that influence the tourism industry in the country.<\/p>\n
Dataset:
http:\/\/insideairbnb.com\/get-the-data\/<\/p>\n
You have the freedom to choose from a variety of tools and techniques to tackle this challenge. Here are some options:<\/p>\n
Python: Leverage the power of Python’s data science ecosystem, including libraries like Pandas, NumPy, and Matplotlib, to handle data manipulation, visualization, and statistical analysis.<\/p>\n
Power BI: Create interactive and visually appealing dashboards using Power BI, enabling you to showcase key insights and patterns from the Airbnb data effectively.<\/p>\n
Machine Learning: For advanced students, consider employing machine learning algorithms to predict rental prices, understand customer preferences, or classify property types.<\/p>\n
Spatial Analysis: Utilize geospatial libraries like GeoPandas and Folium to analyze the spatial distribution of Airbnb listings and understand regional variations.<\/p>\n
Text Mining: Extract valuable information from guest reviews using natural language processing techniques to gain insights into popular features and aspects that influence bookings.<\/p>\n
Data sources: local accomodation registries<\/p>\n
Skills: statistical analysis, geographic information systems, data visualization, reporting<\/p>\n
[\/et_pb_blurb][et_pb_blurb title=”AI Generated News: Unleashing the Power of AI in Local Journalism” url_new_window=”on” alt=”Transportes” admin_label=”AI Generated News: Unleashing the Power of AI in Local Journalism” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n
This master’s thesis challenge aims to explore the application of generative language modelling techniques in the context of local news generation. The project will focus on developing a framework to generate realistic and coherent local news articles. The research will involve collecting a large corpus of local news data, preprocessing and cleaning the data, training and fine-tuning a generative language model, and evaluating the generated output using metrics such as fluency, coherence, and relevance. The skills required for this challenge include proficiency in Python programming, experience with deep learning frameworks (e.g., PyTorch), and a solid understanding of natural language processing techniques.<\/p>\n
Data sources: The challenge can utilize various data sources, such as API, news portals’ web scraping potentially other relevant data sources available through open data initiatives or partnerships with local news companies.<\/p>\n
Skills: Natural Language Processing, information retrieval, data preprocessing, web scraping, Python programming, Large Language Models, Pytorch, Promp Engineering<\/p>\n
[\/et_pb_blurb][et_pb_blurb title=”Walkability and its relationship with Traffic and Air Quality: A Multi-dimensional Analysis for Urban Environments” url_new_window=”on” alt=”Trafego” admin_label=”Walkability and its relationship with Traffic and Air Quality: A Multi-dimensional Analysis for Urban Environments” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}” disabled_on=”on|on|on” disabled=”on”]<\/p>\n
This master’s thesis challenge focuses on developing a comprehensive walkability indicator and examining its relationship with traffic patterns and air quality in urban environments, with a specific focus on Lisbon. The objective is twofold: (1) to create a robust walkability indicator that encompasses various factors such as pedestrian infrastructure, street connectivity, amenities, and safety, and (2) to investigate the correlation between walkability, traffic levels, and air quality to understand how these variables interrelate. The challenge involves collecting and\/or preprocessing data from diverse sources, including street conditions, location of amenities, traffic flow data, air quality measurements, and other relevant urban datasets. The final solution should be delivered in the form of an dynamic dashboard. By analyzing and modeling the data, the study aims to uncover insights that can inform urban planning strategies, transportation policies, and environmental interventions to improve walkability, reduce traffic congestion, and mitigate air pollution in Lisbon.<\/p>\n
Data sources: Traffic (Waze), mobile network data (vodafone), air quality monitoring stations (Lx open data), Open Street Maps, and potentially other relevant data sources available through open data initiatives or partnerships with local authorities.<\/p>\n
Skills: Data integration & preprocessing, statistical analysis, factor analysis, data visualization (Power BI), programming (e.g., Python), spatial analysis (Geopandas)<\/p>\n
[\/et_pb_blurb][et_pb_blurb title=”NOVA IMS Assistant: Enhancing Information Access and Campus Engagement through an Intelligent Chatbot” url_new_window=”on” alt=”Trafego” admin_label=”NOVA IMS Assistant: Enhancing Information Access and Campus Engagement through an Intelligent Chatbot” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n
This master’s thesis challenge aims to design and develop an intelligent chatbot tailored specifically to NOVA Information Management School (NOVA IMS). The goal is to create an intuitive community question answering (CQA) platform that facilitates information retrieval and fosters engagement within the NOVA IMS community. The chatbot will leverage natural language processing techniques to understand and respond accurately to queries related to various aspects of the school, including courses, programs, faculty, events, facilities, and administrative procedures. By providing prompt and context-aware answers, the chatbot will enhance information access, streamline campus communication, and promote a seamless user experience for students, faculty, and staff.<\/p>\n
Data sources: The challenge can utilize various data sources, including existing FAQs and documentation from NOVA IMS, official school websites, course catalogs, event calendars, faculty profiles, and relevant administrative resources.<\/p>\n
Skills: Natural Language Processing, information retrieval, data preprocessing, web scraping, Python programming, Large Language Models, Pytorch, Promp Engineering<\/p>\n
[\/et_pb_blurb][et_pb_blurb title=”Analysis of patterns on the evolution of local accomodation – Powered by OesteCIM” url_new_window=”on” alt=”Transportes” admin_label=”Analysis of patterns on the evolution of local accomodation – Powered by OesteCIM” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n
Local accommodation analysis in Portugal offers valuable insights into the changing landscape and trends within the lodging industry. By closely examining the patterns and trends in the development and utilization of local accommodations, stakeholders can gain a comprehensive understanding of the factors shaping this sector’s growth and performance. This analysis involves evaluating factors such as the number of accommodations, occupancy rates, pricing trends, and the impact on tourism demand. By identifying patterns in the evolution of local accommodation, stakeholders can make informed decisions about investment opportunities, market positioning, and the development of strategies to cater to the evolving needs and preferences of travelers. Ultimately, this analysis supports the sustainable development of the local accommodation sector in Portugal, ensuring its competitiveness and contribution to the overall tourism industry.<\/p>\n
Data sources: local accomodation registries<\/p>\n
Skills: statistical analysis, geographic information systems, data visualization, reporting<\/p>\n
[\/et_pb_blurb][et_pb_blurb title=”Analysis of patterns of business companies performance – Powered by OesteCIM” url_new_window=”on” alt=”Transportes” admin_label=”Analysis of patterns of business companies performance – Powered by OesteCIM” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n
Patterns of business companies’ performance in the Oeste Region of Portugal yields valuable insights into the economic landscape and competitive dynamics specific to this region. By examining key economic indicators, financial performance metrics, market dynamics, and sector-specific strengths, stakeholders can gain a comprehensive understanding of the factors shaping business success and growth in the area. This analysis enables informed decision-making, resource allocation, and the formulation of tailored strategies to capitalize on emerging opportunities and address challenges within the Oeste Region’s business environment, ultimately fostering sustainable economic growth and development.<\/p>\n
Data sources: Companies economic activity indicators<\/p>\n
Skills: statistical analysis, geographic information systems, data visualization, reporting<\/p>\n
[\/et_pb_blurb][et_pb_blurb title=”Analysis of pass sales and traffic patterns of public road transport – Powered by OesteCIM” url_new_window=”on” alt=”Transportes” admin_label=”Analysis of pass sales and traffic patterns of public road transport – Powered by OesteCIM” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n
The Portuguese Intermunicipal Community of the West (OesteCIM) is a public entity whose mission is to contribute to the promotion of sustainable development and the improvement of quality of life in its municipalities. Its vision is to be an Intermunicipal Community of national reference for excellence in public management oriented towards quality, innovation, efficiency and effectiveness, optimizing existing resources and structures.<\/span>\u00a0<\/span><\/p>\n Through the processing and analysis of data from the o<\/span>perational systems that manage pass and ticket sales, as well as validations and traffic<\/span><\/b> on the road network, the objective of this challenge is to <\/span>develop analytical models <\/span><\/b>and visualization tools to characterize<\/span> pass sales. and traffic patterns of public road transport<\/span><\/b> at OesteCIM.<\/span>\u00a0<\/span><\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Analysis of the production, collection and destination of urban waste – Powered by OesteCIM” url_new_window=”on” alt=”Residuos” admin_label=”Waste CIMOeste” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}” disabled_on=”on|on|on” disabled=”on”]<\/p>\n The Portuguese Intermunicipal Community of the West (OesteCIM) is a public entity whose mission is to contribute to the promotion of sustainable development and the improvement of quality of life in its municipalities. Its vision is to be an Intermunicipal Community of national reference for excellence in public management oriented towards quality, innovation, efficiency and effectiveness, optimizing existing resources and structures.<\/span>\u00a0<\/span><\/p>\n Through the processing and analysis of data from the <\/span>operational waste management systems<\/span><\/b>, the objective of this challenge is to develop <\/span>analytical models and visualization tools<\/span><\/b> to characterize the <\/span>production, collection and destination<\/span><\/b> (recycling or undifferentiated collection) of <\/span>waste produced<\/span><\/b> at OesteCIM .<\/span><\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Analysis of monetary transactions and characterization of consumption – Powered by OesteCIM” url_new_window=”on” alt=”SIBS” admin_label=”Transactions CIMOeste” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n The Portuguese Intermunicipal Community of the West (OesteCIM) is a public entity whose mission is to contribute to the promotion of sustainable development and the improvement of quality of life in its municipalities. Its vision is to be an Intermunicipal Community of national reference for excellence in public management oriented towards quality, innovation, efficiency and effectiveness, optimizing existing resources and structures.<\/span>\u00a0<\/span><\/p>\n Through the processing and analysis of data from the operational systems of <\/span>monetary transactions via ATM card<\/span><\/b>, the objective of this challenge is the <\/span>development of analytical models and visualization tools<\/span><\/b> for the <\/span>characterization of consumption and the consumer<\/span><\/b> at OesteCIM.<\/span><\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Characterization of commuting movements on the main access routes to the city” url=”https:\/\/lisboainteligente.cm-lisboa.pt\/lxdatalab\/desafios\/movimentos-pendulares-nas-principais-vias-de-acesso-a-cidade-com-base-em-dados-de-telemoveis\/” url_new_window=”on” admin_label=”#73 – Commuting” _builder_version=”4.21.0″ link_option_url=”https:\/\/lisboainteligente.cm-lisboa.pt\/lxdatalab\/desafios\/movimentos-pendulares-nas-principais-vias-de-acesso-a-cidade-com-base-em-dados-de-telemoveis\/” link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n Description<\/strong><\/p>\n 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.<\/p>\n Expected results<\/strong><\/p>\n 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:<\/p>\n A. For the morning rush hour period (7:30am-10:00pm)<\/p>\n B. For the afternoon peak period (5:00pm-7:30pm)<\/p>\n Promotor \/ Recipient of the found solution<\/strong><\/p>\n CGIUL – Centro de Gest\u00e3o e Intelig\u00eancia Urbana de Lisboa (Lisbon Center for Urban Management and Intelligence)<\/p>\n Data to be made available<\/strong><\/p>\n Additional information<\/strong><\/p>\n School calendar<\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ global_colors_info=”{}”][et_pb_blurb title=”Sentiment Analysis of Tourists’ Social Media Data for Urban Mobility in Lisbon” url_new_window=”on” alt=”Trafego” admin_label=”Sentiment Analysis of Tourists’ Social Media Data for Urban Mobility in Lisbon” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n This master’s thesis challenge aims to analyze the sentiment of tourists’ social media posts to gain insights into the perception and experiences related to urban mobility in Lisbon. The challenge involves collecting and preprocessing social media data from platforms like Twitter and Instagram, utilizing APIs or web scraping techniques. The gathered data will be used to train a sentiment analysis model tailored to Lisbon’s context, considering aspects such as traffic congestion, public transportation usage, and popular destinations. The analysis will provide valuable information to improve urban mobility infrastructure and enhance tourists’ experiences in Lisbon.<\/p>\n Data sources: Twitter API, Instagram API, web scraping techniques for social media data retrieval.<\/p>\n Skills: Natural Language Processing, sentiment analysis, data preprocessing, Python, Machine Learning<\/p>\n [\/et_pb_blurb][et_pb_blurb title=”The Urban Dynamic Indicator: The case study of Lisbon” url_new_window=”on” alt=”Transportes” admin_label=”The Urban Dynamic Indicator: The case study of Lisbon” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}” disabled_on=”on|on|on” disabled=”on”]<\/p>\n This master’s thesis challenge aims to develop a composite indicator that comprehensively measures urban dynamic by integrating data from diverse sources such as traffic, mobile data, air quality, and bike counting sensors. The goal is to follow the framework applied to Porto: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2210670722000464 that combines and analyzes these heterogeneous datasets to provide insights into urban mobility, environmental sustainability, and overall livability. The challenge involves collecting and preprocessing data from different sources, designing appropriate normalization and weighting schemes, and utilizing advanced data fusion and analysis techniques. The final solution should be delivered in the form of an dynamic dashboard. The resulting composite indicator will be use to measure the impact of events in the city and enable policymakers and urban planners to monitor, assess, and make informed decisions to enhance urban development strategies in Lisbon.<\/p>\n Data sources: Traffic (Waze), mobile network data (vodafone), air quality monitoring stations (Lx open data), bike counting sensors (EMEL open data), and potentially other relevant data sources available through open data initiatives or partnerships with local authorities.<\/p>\n Skills: Data integration & preprocessing, statistical analysis, time-series processing, factor analysis, data visualization (Power BI), programming (e.g., Python), spatial analysis (Geopandas)<\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Predicting Real Estate Prices in Portugal – Powered by Confid\u00eancial Imobili\u00e1rio” url_new_window=”on” admin_label=”Previs\u00e3o pre\u00e7os imobiliario” _builder_version=”4.21.0″ global_colors_info=”{}” disabled_on=”on|on|on” disabled=”on”]<\/p>\n This master thesis challenge aims to develop an advanced predictive model that utilizes machine learning algorithms and real estate data to accurately forecast property prices in Portugal. The study will explore various features, including location, property characteristics, economic indicators, and market trends, to build a robust and scalable model capable of assisting buyers, sellers, and investors in making informed decisions in the dynamic Portuguese real estate market.<\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Chatbot for Lisbon: Enhancing Information Access and Community Engagement” url_new_window=”on” alt=”Transportes” admin_label=”Chatbot for Lisbon: Enhancing Information Access and Community Engagement” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n This master’s thesis challenge focuses on designing and developing an intelligent chatbot specifically catered to the needs of residents and visitors in Lisbon. The goal is to create a user-friendly community question answering (CQA) platform that empowers users to seek information and engage with the local community. The chatbot will utilize natural language processing techniques to understand and respond accurately to queries related to various aspects of the city, including local events, transportation, dining options, cultural activities, and more. The platform will provide timely, context-aware answers, facilitating knowledge sharing, and enhancing the overall experience of individuals interacting with the city of Lisbon.<\/p>\n Data sources: The challenge can utilize various data sources, such as existing FAQs, official tourism websites, open data initiatives from Lisbon City Council, local event listings, restaurant directories, and cultural activity databases.<\/p>\n Skills: Natural Language Processing, information retrieval, data preprocessing, web scraping, Python programming, Large Language Models, Pytorch, Promp Engineering<\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Analysis of docked bike sharing service in the city of Lisbon” url_new_window=”on” alt=”Trafego” admin_label=”Analysis of docked bike sharing service in the city of Lisbon” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n The analysis of the docked bike sharing service in the city of Lisbon provides valuable insights into the patterns and impact of this transportation alternative. By closely examining the usage patterns, ridership trends, and infrastructure requirements of the docked bike sharing system, stakeholders can gain a comprehensive understanding of its effectiveness in promoting sustainable urban mobility. This analysis involves evaluating factors such as user adoption rates, trip durations, popular routes, and the integration of bike sharing with existing transportation networks. By understanding the patterns and dynamics of the docked bike sharing service, stakeholders can make informed decisions about expanding the network, optimizing station locations, and improving user experience. Ultimately, this analysis supports the development of evidence-based strategies to enhance the bike sharing service in Lisbon, promoting environmentally friendly transportation options and contributing to the city’s sustainable mobility goals.<\/p>\n Data sources: bike docks occupation ratio from the GIRA service<\/p>\n Skills: statistical analysis, geographic information systems, data visualization, reporting<\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Analysis of use of recycling rewarding system – Powered by OesteCIM” url_new_window=”on” alt=”Trafego” admin_label=”Analysis of use of recycling rewarding system – Powered by OesteCIM” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n The use of a recycling rewarding system in the Oeste Region of Portugal provides valuable insights into the effectiveness and impact of incentivizing recycling behaviors. By closely examining the patterns and trends within the implementation of such a system, stakeholders can assess its effectiveness in promoting sustainable waste management practices. This analysis involves evaluating factors such as participation rates, recycling rates, and the overall environmental impact. By understanding the patterns of adoption, engagement, and success of the recycling rewarding system, stakeholders can make informed decisions about optimizing the program, expanding its reach, and identifying potential areas for improvement. Ultimately, this analysis supports the development of evidence-based strategies to encourage and incentivize recycling behaviors, contributing to the region’s environmental sustainability goals.<\/p>\n Data sources: Reverse vending machines collected information (number of users, number of packages recycled, dicscounts)<\/p>\n Skills: statistical analysis, geographic information systems, data visualization, reporting<\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Analysis of alerts, incidents and traffic patterns – Powered by OesteCIM” url_new_window=”on” alt=”Trafego” admin_label=”Analysis of alerts, incidents and traffic patterns – Powered by OesteCIM” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n The Portuguese Intermunicipal Community of the West (OesteCIM) is a public entity whose mission is to contribute to the promotion of sustainable development and the improvement of quality of life in its municipalities. Its vision is to be an Intermunicipal Community of national reference for excellence in public management oriented towards quality, innovation, efficiency and effectiveness, optimizing existing resources and structures.<\/span>\u00a0<\/span><\/p>\n Through the processing and analysis of data from the <\/span>WAZE application<\/span><\/b>, the objective of this challenge is the <\/span>development of analytical models<\/span><\/b> and visualization tools for the characterization and prediction of <\/span>traffic patterns, alerts and incidents<\/span><\/b> at OesteCIM.<\/span><\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Analysis of climate change and carbon neutrality – Powered by OesteCIM” url_new_window=”on” alt=”Clima” admin_label=”Climate CIMOeste” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n The Portuguese Intermunicipal Community of the West (OesteCIM) is a public entity whose mission is to contribute to the promotion of sustainable development and the improvement of quality of life in its municipalities. Its vision is to be an Intermunicipal Community of national reference for excellence in public management oriented towards quality, innovation, efficiency and effectiveness, optimizing existing resources and structures.<\/span>\u00a0<\/span><\/p>\n Through the processing and analysis of data from <\/span>operational systems to adapt to climate change<\/span><\/b>, the objective of this challenge is to develop <\/span>analytical models<\/span><\/b> and visualization tools to <\/span>characterize climate change and carbon neutrality goals <\/span><\/b>at OesteCIM.<\/span><\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Movement of people in nightlife areas” url_new_window=”on” admin_label=”#74 – Nightlife areas” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}”]<\/p>\n Description<\/strong><\/p>\n 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).<\/p>\n Expected results<\/strong><\/p>\n In addition to other information that may result from the data analysis, with this challenge it is intended to:<\/p>\n 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),<\/p>\n 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<\/p>\n 3 – Relate the movements of people with the noise levels recorded in the environmental sensors located in the study area considered.<\/p>\n Promotor \/ Recipient of the found solution<\/strong><\/p>\n CGIUL \u2013 Centro de Gest\u00e3o e Intelig\u00eancia Urbana de Lisboa \/ Dire\u00e7\u00e3o Municipal do Ambiente, Estrutura Verde, Clima e Energia (Lisbon Center for Urban Management and Intelligence \/ Municipal Department of Environment, Green Structure, Climate and Energy)<\/p>\n Data to be made available<\/strong><\/p>\n [\/et_pb_blurb][et_pb_blurb title=”Identification of road accident incidence points and their correlation with other factors” url_new_window=”on” admin_label=”#51 – Identification of road accident incidence points” _builder_version=”4.21.0″ link_option_url_new_window=”on” global_colors_info=”{}” disabled_on=”on|on|on” disabled=”on”]<\/p>\n Description<\/strong><\/p>\n 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.<\/p>\n Expected results<\/strong><\/p>\n 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<\/p>\n Promotor \/ Recipient of the found solution<\/strong><\/p>\n Dire\u00e7\u00e3o Municipal de Mobilidade (Municipal Department of Mobility)<\/p>\n Data to be made available<\/strong><\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n<\/span>","protected":false},"excerpt":{"rendered":" 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 […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":2490,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":" [et_pb_section fb_built=\"1\" admin_label=\"Top banner\" module_class=\"title-header\" _builder_version=\"4.4.8\" background_image=\"\/wp-content\/themes\/NovaCidade\/images\/header_bg.jpg\" custom_padding=\"0px||||false|false\" global_module=\"641\" locked=\"off\" collapsed=\"off\"][et_pb_row _builder_version=\"4.4.1\"][et_pb_column type=\"4_4\" _builder_version=\"4.4.1\"][et_pb_text admin_label=\"Titulo\" module_class=\"page-title\" _builder_version=\"4.4.8\" _dynamic_attributes=\"content\" hover_enabled=\"0\"]@ET-DC@eyJkeW5hbWljIjp0cnVlLCJjb250ZW50IjoicG9zdF90aXRsZSIsInNldHRpbmdzIjp7ImJlZm9yZSI6IiIsImFmdGVyIjoiIn19@[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=\"1\" admin_label=\"Laborat\u00f3rios\" module_class=\"page-content laboratorios\" _builder_version=\"4.4.2\"][et_pb_row _builder_version=\"4.4.6\"][et_pb_column type=\"4_4\" _builder_version=\"4.4.6\"][et_pb_text _builder_version=\"4.4.8\" hover_enabled=\"0\"]<\/p> Nesta p\u00e1gina, pode consultar alguns dos dashboards desenvolvidos para os diversos parceiros do NOVA Cidade - Urban Analytics Lab e que aqui partilhamos com o p\u00fablico.<\/p> [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=\"1_2,1_2\" _builder_version=\"4.4.1\"][et_pb_column type=\"1_2\" _builder_version=\"4.4.1\"][et_pb_blurb title=\"COVID-19 Insights\" image=\"https:\/\/novacidade.pt\/wp-content\/uploads\/2020\/05\/urban_intel_logo_covid19insights.png\" admin_label=\"COVID-19 Insights\" _builder_version=\"4.4.8\" link_option_url=\"https:\/\/insights.cotec.pt\/\"]<\/p> A plataforma COVID-19 Insights <\/i>apresenta indicadores de natureza prospetiva de apoio \u00e0 decis\u00e3o da comunidade empresarial, comunidade acad\u00e9mica e p\u00fablico em geral.<\/p> O projeto integra m\u00faltiplas vari\u00e1veis representativas de diferentes aspetos econ\u00f3micos, sociais e epidemiol\u00f3gicos, que s\u00e3o analisadas a partir de m\u00e9todos anal\u00edticos avan\u00e7ados.<\/p> A COVID-19 Insights<\/i> fornece informa\u00e7\u00e3o, atualizada diariamente \u2013 ou com frequ\u00eancia semanal, para alguns indicadores \u2013 sobre a situa\u00e7\u00e3o internacional, a situa\u00e7\u00e3o em Portugal, an\u00e1lise econ\u00f3mica (brevemente dispon\u00edvel), \u00edndice de risco, mobilidade e modelos epidemiol\u00f3gicos.<\/p> Todos os indicadores est\u00e3o dispon\u00edveis tanto em valor absoluto, como relativizados pela popula\u00e7\u00e3o dos v\u00e1rios pa\u00edses.<\/p> Saber mais<\/a>[\/et_pb_blurb][et_pb_blurb title=\"Estado da Intelig\u00eancia Urbana em Portugal\" image=\"https:\/\/novacidade.pt\/wp-content\/uploads\/2020\/05\/urban_intel_logo_radar.png\" admin_label=\"Estado da Intelig\u00eancia Urbana em Portugal\" _builder_version=\"4.4.8\"]Analisar e divulgar o estado do investimento em intelig\u00eancia urbana nos munic\u00edpios portugueses. Mais informa\u00e7\u00e3o<\/a><\/p> [\/et_pb_blurb][et_pb_blurb title=\"LX Open Trees\" image=\"https:\/\/novacidade.pt\/wp-content\/uploads\/2020\/03\/urban_intel_logo_cml.png\" admin_label=\"LX Open Trees\" _builder_version=\"4.4.8\"]<\/p> Saber mais<\/a><\/p> [\/et_pb_blurb][et_pb_blurb title=\"Lisboa Aberta - EMEL\" image=\"https:\/\/novacidade.pt\/wp-content\/uploads\/2020\/03\/urban_intel_logo_gira.png\" admin_label=\"Lisboa Aberta - EMEL\" _builder_version=\"4.4.8\" locked=\"off\"]Saber mais<\/a>[\/et_pb_blurb][\/et_pb_column][et_pb_column type=\"1_2\" _builder_version=\"4.4.1\"][et_pb_blurb title=\"Projeto LX Analytics Hub\" image=\"https:\/\/novacidade.pt\/wp-content\/uploads\/2020\/05\/urban_intel_logo_rsb.png\" admin_label=\"Projeto LX Analytics Hub\" _builder_version=\"4.4.8\"]<\/p> \u200bO projeto de inova\u00e7\u00e3o LxAnalyticsHub<\/i> surge da necessidade demonstrada pelo Regimento de Sapadores Bombeiros (RSB) em dispor de uma solu\u00e7\u00e3o que permita prever a melhor disposi\u00e7\u00e3o dos seus meios para o dia seguinte de forma a otimizar o tempo de resposta \u00e0s ocorr\u00eancias.<\/p> Para responder a este desafio, a C\u00e2mara Municipal de Lisboa (CML) promoveu uma parceria com a SAP, Microsoft, NOVA IMS \u2013 Information Management School, ISEL \u2013 Instituto Superior de Engenharia de Lisboa e Altice, para a qual cada entidade contribuiu com a disponibiliza\u00e7\u00e3o de alguns dos seus melhores recursos humanos e tecnol\u00f3gicos.<\/p> Com a colabora\u00e7\u00e3o destas entidades, foi desenvolvido um modelo anal\u00edtico com recurso a algoritmos de an\u00e1lise preditiva, visando disponibilizar informa\u00e7\u00e3o que permita adequar o posicionamento dos meios operacionais do RSB, para cumprir um tempo de resposta \u00e0s ocorr\u00eancias n\u00e3o superior a 5 e 10 minutos.<\/p> O modelo anal\u00edtico recorreu \u00e0s seguintes fontes de dados:<\/p> Foi poss\u00edvel testar o modelo anal\u00edtico preditivo desenvolvido pelos cientistas de dados da NOVA IMS e suportado num datalake<\/i> criado e estruturado pelo ISEL, utilizando tecnologia Microsoft.<\/p> Dado o grande potencial demonstrado pelo modelo, prev\u00ea-se a sua integra\u00e7\u00e3o na Plataforma de Gest\u00e3o Inteligente de Lisboa e disponibiliza\u00e7\u00e3o ao RSB para apoio na opera\u00e7\u00e3o di\u00e1ria das equipas.<\/p> [\/et_pb_blurb][et_pb_blurb title=\"Lisboa Inteligente\" image=\"https:\/\/novacidade.pt\/wp-content\/uploads\/2020\/03\/urban_intel_logo_lisboainteligente.png\" admin_label=\"Lisboa Inteligente\" _builder_version=\"4.4.8\"]<\/p> Neste projeto a C\u00e2mara Municipal de Lisboa pretende dar a conhecer o trabalho da autarquia no desenvolvimento de iniciativas que contribuam para a transforma\u00e7\u00e3o da autarquia numa cidade inteligente.<\/p> O projeto visa contribuir para o desenvolvimento de uma cultura de gest\u00e3o suportada em dados, integrada, proativa, que promova a efici\u00eancia, a transpar\u00eancia, a inova\u00e7\u00e3o e o desenvolvimento de novos servi\u00e7os ao cidad\u00e3o.<\/p> O portal desenvolvido no \u00e2mbito deste projeto, permite \u00e0s pessoas terem conhecimento sobre o que a autarquia est\u00e1 a desenvolver relativamente \u00e0s smart cities.<\/p> Neste portal est\u00e3o tamb\u00e9m agregadas todas as iniciativas municipais que a autarquia ir\u00e1 lan\u00e7ar no \u00e2mbito das smart cities.<\/p>\n
\n
\n
<\/ul>\n
\n
\n
\n
\n
A anal\u00edtica urbana representa um pilar fundamental do trabalho que \u00e9 todos os dias desenvolvido pelo NOVA Cidade \u2013 Urban Analytics Lab<\/h3>
Elaborado com a tecnologia Power BI e contou com a colabora\u00e7\u00e3o e os importantes contributos de 49 membros da Sec\u00e7\u00e3o de Munic\u00edpios \u201cCidades Inteligentes\u201d, localizados de norte a sul do territ\u00f3rio continental e nas Regi\u00f5es Aut\u00f3nomas.<\/p>