New Paper: Synthetic Populations with Social Networks

When developing geographically explicit agent-based models, one thing we spend a lot of time on is building synthetic populations and then linking the agents in the synthetic population to each other.  To overcome this issue we have a new paper pu…

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New Paper: Synthetic Populations with Social Networks

When developing geographically explicit agent-based models, one thing we spend a lot of time on is building synthetic populations and then linking the agents in the synthetic population to each other.  To overcome this issue we have a new paper pu…

Continue reading »

The Full Stack: Tools & Processes for Urban Data Scientists

Recently, I was asked to give talks at both UCL’s CASA and the ETH Future Cities Lab in Singapore for students and staff new to ‘urban data science’ and the sorts of workflows involved in collecting, processing, analysing, and reporting on … Continue reading 

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Mesa: An Agent-Based Modeling Framework in Python

Just a short post to say two of our PhD students, David Masad and Jackie Kazil have been developing an agent-based modeling framework in Python called Mesa.
To quote from David’s talk abstract:

“Agent-based modeling is currently a hole in in Python’s robust and growing scientific ecosystem. Mesa is a new open-source package meant to fill that gap. It allows users to quickly create agent-based models using built-in core components (such as agent schedulers and spatial grids) or customized implementations; visualize them using an innovative browser-based interface; and analyze their results using Python’s robust data analysis tools. Its goal is to be a Python 3-based alternative to other popular frameworks based in other languages such as NetLogo, Repast, or MASON.”

Below is short presentation outlining Mesa from SciPy 2015:

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Mesa: An Agent-Based Modeling Framework in Python

Just a short post to say two of our PhD students, David Masad and Jackie Kazil have been developing an agent-based modeling framework in Python called Mesa.
To quote from David’s talk abstract:

“Agent-based modeling is currently a hole in in Python’s robust and growing scientific ecosystem. Mesa is a new open-source package meant to fill that gap. It allows users to quickly create agent-based models using built-in core components (such as agent schedulers and spatial grids) or customized implementations; visualize them using an innovative browser-based interface; and analyze their results using Python’s robust data analysis tools. Its goal is to be a Python 3-based alternative to other popular frameworks based in other languages such as NetLogo, Repast, or MASON.”

Below is short presentation outlining Mesa from SciPy 2015:

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A Week in the Life of London’s Public Transit System

I’ve been meaning to post this for ages but have had a great deal on my plate (more posts and visualisations to follow in the next week I hope) so this has kept slipping, together with the six or seven other ‘draft’ posts I’ve got going. Anyway, this visualisation shows average entries at each and every Underground, […]

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Weighted Mean Direction Surfaces in Python

I work a lot with flows and spatial interactions, one thing that I’ve wanted to do for a while is compute a mean flow direction surface. Unfortunately, arithmetic means don’t work for angular data, this is because it cannot account for the circular nature of the distribution of angular measurements. For instance the angles 5 […]

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