Map Data with Python - a beginners' course

This section starts with creating some basic data for your map.  A spreadsheet program such as Excel will be useful here.

Then we move on to our first Python code.  We're not going to explain all the coding principles in Python.  Instead we dive in with the first piece of code and how to link it to a package. 

At the end of this section you will understand:

• How to specify map data in latitude and longitude

• How to write Python code using the "tkinter" package to find a file on your PC

Now we look at a development environment to run our code.  We'll be using the simple but powerful Jupyter Notebook, probably the most popular coding tool for data science.  The session covers how to install this and how to use it to run Python code.

Now we move on to our second line of Python code which reads our data.  With Jupyter installed we can now try running our code.

At the end of this section you will understand:

• How to set up and run the Jupyter Notebook environment to create Python code

• How to write Python code using the "pandas" package to read your data off file

• How to run Python code using Jupyter Notebook

In this session we look at the specifics of mapping data.  Mapping data is not just numbers, but lines, shapes and points.  So we need to convert our data into a mapping format.  Fortunately Python makes this easy.

We look at how the "geopandas" package can be used to convert numbers into points on a map and build a set of points which we can later represent on a map.

At the end of this section you will understand:

• The difference between numerical data and shape data

• How to write Python code using the "geopandas" package to create map points from raw data

Up to this point we have been manipulating data internally in the computer.  In this session we move on to visualising the data on a map.

We look at how the "geopandas" package can plot data points on the screen to draw a map based on our original data.

Using Jupyter Notebooks we look at the results of the code to see how it represents the data.

At the end of this section you will understand:

• How to write Python code using the "geopandas" package to plot data points

• How to use Jupyter Notebooks to draw a map

In this session we introduce map projections.  Latitude and longitude are measurements on a globe and we need to consider how we are going to show this on a flat screen.

Now we look at "basemaps".  These are the background on which we plot our points.  Typically these would be a street plan or landscape map.  This is a key part of making your map look professional.  The good news is that basemaps can be available for free.

Finally we bring all of this together in Jupyter Notebooks to generate a map with the data points from your initial list of data.

At the end of this section you will understand:

• What map projections are and why they are needed

• How to use a coordinate reference system for map projection

• How to write Python code using the "geopandas" package to implement a map projection

• What basemaps are, why they are needed and how they can be accessed

• How to write Python code using the "contextily" package to access basemaps

• How to use Jupyter Notebooks to draw a full map with a basemap

After the earlier parts of the course, we have a functioning map.  In the later sections we look at how we might enhance the map.  You should feel free to make your own experiments and see the material on the video as suggestions.

In this section we look at improving the display of the map area, adding titles, removing unnecessary clutter, changing the shape and size of data points

At the end of this section you will understand how to modify the Python code to make changes such as:

• Add a title

• Remove or add axis labels

• Change the shape, size or color of data points

After the earlier parts of the course, we have a functioning map.  In the later sections we look at how we might enhance the map.  You should feel free to make your own experiments and see the material on the video as suggestions.

In this section we look in more detail at the base map.  This defines how the map "feels" whether topographical, minimalistic or street-based.

At the end of this section you will understand how to modify the Python code to:

• Access a range of base maps

• Change the base map to change the background and "feel" of the map

After the earlier parts of the course, we have a functioning map.  In the later sections we look at how we might enhance the map.  You should feel free to make your own experiments and see the material on the video as suggestions.

In this section we look in more detail at data points.  How can we change the way data is represented on the map?

At the end of this section you will understand how to modify the Python code to:

• Size data points according to a data value

• Color data points according to a data value

• Access a wide range of color scales for use with maps