Covid-19: Preparing the simulation results for spatial visualisation

You can take the full course here: www.udemy.com/...
In this video, you will start preparing everything for the animated spatial visualisation. In particular, you will load the city grid file and change its coordinate reference system for plotting. I will also walk you step-by-step through understanding how the Numpy arrays storing the simulation results are structured so you have a clear idea of how to use them in your visualisations!
Interested in learning how to create spatial animated visualisations in Python? Want to learn it on the example of the Covid-19 coronavirus epidemic spreading in a real city with a real human mobility dataset? Then this course is for you!
You will learn how to use basic Python (3 or higher) to model the Covid-19 epidemic spreading in a city, do data analysis of real urban mobility data, run simulations of the epidemic in Jupyter Notebooks, and create beautiful complex animated visualisations on a city map.
Covid-19 is a great case example for learning how to use Python for spatial analysis and visualisation. After completing this course you will be able to apply the techniques from this course to many other types of projects dealing with spatial data analysis and visualisation.
Assuming just a basic familiarity with Python numpy and matplotlib libraries, we will go step-by-step through using real urban mobility data for modelling, simulating and visualising the spread of the epidemic in an urban environment. On the way, you will learn lots of tricks and tips for enhancing your Python coding skills and making even more compelling and complex data visualisations.
The course consists of the following sections:
Introduction, where you will learn about the Python GeoPandas library and how to use it for making nice spatial visualisations right in the Jupyter Notebook
Understanding the spatial epidemiological models, in which you will get a firm intuition and a solid understanding of the maths behind the spatial epidemiological models
Coding the spatial epidemiological model in Python, where you will learn how to use Python and numpy to write efficient code for the epidemic simulation engine
Simulating the Covid-19 epidemic in a city, in which you will use the epidemic model code and a real dataset of urban mobility flows to run simulations of the Covid-19 epidemic in a city
Covid-19 urban spatio-temporal visualisation, where you will put all the acquired knowledge to create a beautiful animated spatial visualisation on a city map, showing how the virus spreads in the city!
This course is a hands-on, practical course, making sure you can immediately apply the acquired skills to your own projects. The acquired spatial modelling, data visualisation, and spatial data science skills will be a valuable addition to your data science toolbox.
I will be there for you throughout this journey for any questions and doubts, so don't hesitate to begin and have a successful and satisfying experience!