2020-07-30 –, Red Track
We will show how to perform modeling and large scale simulation of complex graphs using the OpenStreetMapX.jl package.
Any transportation network can be represented as a complex directed graph where vertices are spread an Euclidean space. The library provides a bridging functionality between real world spatial data available in the OpenStreetMap project and LightGraphs.jl and makes it possible to run real-life-sized experiment on transportation networks along with various visualizations.
A transportation system or even an entire city can be represented as a complex directed graph embedded in an Euclidean space. Such graph can model real world in 1:1 scale and be used to perform various numerical experiments. The OpenStreetMapX.jl package makes it possible to load the data from the OpenStreetMap.org project and processes such graphs with Julia. The package is using LightGraphs.jl to represent the directed graph structure object along with meta related to spatial information.
In this talk the following areas will be discussed:
- processing of OpenStreetMap data in Julia to obtain graph structures
- visualizing graphs, maps and spatial data with OpenStreetMapXPlot.jl (GR, PyPlot backends) as well as integration with Leaflet via folium and PyCall
- technical issues and tips for running massive scale agent based simulations (e.g. with 1 million agents) of an entire city with real-world spatial data in 1:1 scale
- various examples and scenarios of graph dynamics analysis with simulation models reflecting behavior of people and vehicles in virtual model of a city.
This project is co-financed by the Polish National Agency for Academic Exchange.
I am a researcher in the fields of operations research and computational social science.
For development work I mostly use the Julia language.
You can find more information about me and my work on my personal website or GitHub.
Przemysław Szufel is an Assistant Professor in Decision Support and Analysis Unit at Warsaw School of Economics, an Adjunct Professor in The Cybersecurity Research Lab, Ryerson University, Toronto and a member of Computational Methods in Industrial Mathematics Lab in Fields Institute, Toronto. He is also a Management Committee member of the European Social Simulation Association.
Przemysław Szufel main research focus is in applying advanced analytics methods, and in particular, machine learning, simulation and optimization in modelling and automating decision business processes. He is a co-author of "Julia 1.0 Programming Cookbook: Over 100 Numerical and Distributed Computing Recipes for Your Daily Data Science Workflow" that has recently been translated by O'Reilly to Japanese. He is a co-author of over 30 publications, including handbooks and journal papers, in the area of applying advanced analytics, machine learning and simulation methods to making optimal business decisions.
Przemysław Szufel has delivered several workshops showing advanced analytics and simulation in Julia including Canada (University of Toronto, Ryerson University), Ireland (University College Dublin), Sweden (Stockholm University) and Central Bank of Poland and top consulting copmpanies. He is also a regular presenter for Julia hands-on-workshop at the Supercomputing Frontiers Europe conference. He also teaches Julia programming and advanced analytics PhD course in the Information Technology Department at Salerno University and he teaches several post graduate and PhD simulation and data science courses at Warsaw School of Economics, Poland.
He has also been working as an auditor for venture capital companies - he specializes in assessing technology of data-oriented startups. Dr Przemyław Szufel has been also co-managing SilverDecisions.pl project (that aims for representing and supporting business decisions and was part of a larger grant financed by the H2020 European Union grant), which has been elected by the European Commission to the Innovation Radar programme grouping the best European innovations financed by the EU funds.