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.
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 objective of this exercise is to build a machine learning model that will predict taxicab trip durations based on 2016 NYC Yellow Cab trip record data. Data Fields Based on this, this project will develop a machine learning regression algorithm capable of predicting the duration of a trip based on the variables provided by … 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
Climate change is increasing the frequency and severity of extreme weather events, and cities like Mumbai with a population of over 12 million people, is particularly vulnerable to the impacts of climate change. In fact, studies have shown that Mumbai is one of the cities most at risk from rising sea levels more intense floods, … Read more
PROBLEM A lack of activities and engagement in certain streets can lead to a decrease in their vibrancy and vitality. This can manifest in empty streets, dilapidated buildings, and a dearth of social and cultural events. Over time, these areas may become less desirable places to live or visit, perpetuating the cycle of neglect and … Read more
Do you nip out to get your lunch from your favourite café, or perhaps use the Uber Eats app to get your lunch delivered to your office? In India, lunch is a whole different ball game. It’s people-powered – made at home that morning and delivered straight to you, all absent of any technology in time. … Read more
This is a short visual story about my personal digital space usage patterns and our need to be aware of our digital carbon footprint. Tracking my personal habits has revealed some shocking facts, which led towards reflecting on intangibility of the digital world, hopelessness in perceiving it as a hyper object and an urge to … Read more
a story about Spanish festivals Does it possible to party all year long from festival to festival throughout Spain? The story starts from the New Year holiday at the 1st of January For the better clusterisation we will use administrative division of Spain in autonomous communities (comunidades) and provinces. Each province of Spain has its … Read more
A Visual Story by: According to World Health Organization, 1/5 adolescents engage in some form of substance use. The Question of 100 School Phycologists: How planning could contribute towards the understanding of geographic patterns of substance use in order to distribute human resources all around the country? This visual story, seeks to explore the geographical … Read more
Every single search query, every streamed song or video and every email sent, billions of times over all around the world – it all adds up to an ever-increasing global demand for electricity, and to rising CO2 emissions too. Our increasing reliance on digital tools has an environmental impact that’s becoming increasingly harder to ignore. … Read more
Austin, a Lonely City Context Over the last decade, Austin’s status as a state-of-art technology hub along with its high concentration of venture apital, warm climate and vibrant cultural offerings has set the Texan city as the 2nd faster-growing U.S. city in 2022, only after San Francisco. But paradoxically , urban loneliness reflects as one … Read more
Buildings are an intrinsic part of our lives, where we spend most of our time and unfold as human beings. However, through its different forms, the built environment is responsible for the majority of our energy consumption (around 40 %) and a primary source of greenhouse emissions GHG (around 36 %) (Anderson et al., 2015). … Read more
