Understanding city characteristics through clustering analysis
Organization
Bolt
Abstract
Operating in 100+ cities and 40+ countries includes a lot of complexity for reporting and ML applications. To reduce this complexity we can cluster the cities based on map, traffic, and/or business data and describe their characteristics through prototypical examples, so called “exemplars”. The immense advantage of exemplars lies in understanding business wide dynamics on a handful of examples and generalising their characteristics to other members of the same cluster.
In this topic you will get the opportunity to handle geospatial data in a real business context. You will use different clustering techniques and evaluate their cluster quality. By serving “cluster exemplars” you will understand different city characteristics and their impact on our business. Your analysis has the potential to impact the entire reporting and modelling at Bolt.
In this topic you will get the opportunity to handle geospatial data in a real business context. You will use different clustering techniques and evaluate their cluster quality. By serving “cluster exemplars” you will understand different city characteristics and their impact on our business. Your analysis has the potential to impact the entire reporting and modelling at Bolt.
Graduation Theses defence year
2022-2023
Supervisor
Sophie Laturnus
Spoken language (s)
English
Requirements for candidates
Level
Masters
Keywords
Application of contact
Name
Sophie Laturnus
Phone
E-mail