Introduction to Urban Data Science 2017

Introduction to Urban Data Science 2017

Course Description

In this course, attendees will be provided with an overview perspective on location-based technologies with a focus on urban applications. From human mobility models and
theory to neighborhood detection and finding an appropriate location for placing a shop or amenity attendees will be introduced to the mechanics and building blocks of a number of research works with a strong application orientation. Historically important theoretical concepts in the area will be covered that include Central Place Theory and Reilly’s law of retail gravitation. Next, the spotlight will be put on newer advancements in the area of the so-call gig economy, introducing services like Uber and Airbnb, highlighting opportunities for research using corresponding transport and hospitality datasets.

Course topics

Urban data science, human mobility, spatial data mining.

Course tools

Python, Scipy, Scikit-Learn, Networkx.



Dr. Anastasios Noulas

A Moore-Sloan Fellow at New York University Center for Data Science

Affiliation: New York University, USA


Fields of interests: Location-based Technologies, Human Mobility, Urban Data Science

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