IAAC’s Master in City & Technology (1 or 2-year program) is a unique program oriented towards redefining the analysis, planning, and design of twenty-first-century cities and beyond. The program offers expertise in the design of digitally enhanced, ecological and human-centered urban environments by intersecting the disciplines of urbanism and data science. Taking place in Barcelona, the capital of urbanism, the Master in City & Technology is training the professionals that city administrations, governments, industries, and communities need, to transform the urban environment in the era of big data.
Background Public transportation plays a crucial role in urban planning globally. Extensive research indicates that bus transit has become a highly favored mass mobility system. This preference stems from its cost-effective infrastructure, flexible planning, and discrete architecture that supports incremental growth. Bus transit systems exhibit lower infrastructure costs compared to rail or subway networks, making … Read more
The Environmental Asset Resilience (EAR) Tool is a response to the pressing global challenge of environmental instability, particularly in urban areas. These areas face substantial costs associated with environmental problems and climate change. However, decision-making in addressing these issues is complicated by conflicting priorities, limited resources, and a lack of expertise. The EAR Project aims … Read more
Unraveling Footfall Patterns for Enhanced Urban Planning: A Spatial and Temporal Analysis Introduction Footfall prediction plays a crucial role in urban planning decision-making processes. Understanding the intricate relationship between footfall and its two dimensions—space and time—is essential. In this project, we delve into the workflow and key findings of a project aimed at comprehending the … Read more
Alejandro Aravena, renowned for his role in facilitating the recovery of a city struck by an earthquake and a tsunami, asserts that Participatory design transcends mere inclusivity and offers enhanced efficiency. Despite recognizing the significance of public participation in urban planning, we delve into the persisting factors that hinder its optimal efficiency. Conventional methods may … Read more
During the Internet of Building studio we developed a strategy of predicting the typologies of buildings in New Delhi, India by data, using algorithmic approaches as KMEANS, DBSCAN and PCA aswell a a heuristig rule-set. The objective of this project is to predict if a building in New Delhi is part of a formal settlement … Read more
OVER THE NEXT FEW DACADES HELSINKI EXPECTS TO ADD AROUND 250.000 NEW RESIDENTS IN 30-40KM OF TRANSFORMED MOTORWAY – LIKE HIGHWAYS PROJECT DESCRIPTION: In the future, a transition from highways and roadways to boulevards is anticipated due to a projected decrease in the number of cars. This shift is expected to address several issues commonly … Read more
The project focuses on promoting the physical, mental, and social well-being of older adults in the city of Barcelona through various initiatives, the goal is to empower older adults to remain active and engaged in their communities, and to promote healthy ageing and independence. Projected population aged 65+ What is Ageing? Health in the older … Read more
The New York City Taxi Trip Duration competition is a challenge to develop a model that predicts the total ride time of taxi trips in New York City. Yellow medallion taxicabs, which number 12,779 in New York City, generate a substantial revenue of $1.8 billion per year by providing transportation services to around 240 million … Read more
Goal: to predict trip duration of NYC cabs using machine learning models. Tools: Python + Nympy + Pandas + Datetime + Plotly.express + Matplotlib + Math + Seaborn + Bokeh + Sklearn Stages of project: data cleaning, data analysis, data preparation, data testing, evaluating prediction accuracy. Data cleaning The first dataset visualization with splitting datasets … Read more
Machine Learning-based Analysis of NYC Cab Trip Duration “Data-Driven Rides” is an entry for the first MaCT Machine Learning Competition that hosted on Kaggle which involves predicting the duration of taxi rides in New York City. The dataset provided for this competition is based on the 2016 NYC Yellow Cab trip record dataset and the … Read more
The MaCT01 students were tasked with training a model that would be used to predict NYC yellow taxi ride durations using machine learning. The dataset included pickup and drop-off datetimes, location coordinates and passenger count. Visualizing the data helped to understand the correlation between the columns and remove the highly correlated values Understanding the distribution … Read more
The source dataset provided for this project is derived from the 2016 NYC Yellow Cab trip records, which were made publicly available on the Big Query platform of the Google Cloud. The data was originally collected and published by the New York City Taxi and Limousine Commission (TLC). This dataset serves as the foundation for … Read more
#Objective #A Kaggle Competition to MaCT01 students to show their knowledge, designing an end-to-end machine learning project to predict the “Trip Duration” of NYC Taxi trips. #Workflow #First of all a workflow hast do be developed, which represents a classic approach for training machine learning models, analysing the provided training data provided by the submission, … Read more
The aim of the project is to predict trip duration, using 2016 NYC YELLOW CAB TRIP DATA. Structuring the dataset The analysis begins with outlier identification. The passenger_count variable has two outliers: 0 and 9, which compared to the amount of people allowed by the NY Limousine law, is impossible. Also, there were some pickup … Read more
INTRODUCTION Urban planning decisions have a significant impact on the development of cities, and using machine learning can provide decision-makers with valuable insights to make informed decisions. By clustering urban areas based on various factors such as population density, built density, POI density, green cover, and build diversity, we can reveal spatial patterns that can … Read more
