rapidmaps

Wales

The maps of Wales take you through the first few steps you take when mapping economic data.

• Map 1. The first step is to locate a JSON file that contains the coordinates needed to draw the map. This is then loaded in the usual way, either directly from an API, or saved to a hosting service such as GitHub and delivered to your page as a raw file.
• Map 2. The second map adds colour. In many of the GEOJSON files that you access there may be interesting data that you wish to use to colour your chart. To do this you need to access those value and use this field to supply the colour to your graph. In this teaching example, I am using the first name of the place to add colour.
• Map 3. The third map show how you can filter geographic data, to exclude areas that you do not want to show. In this case I ranked the place names and only want to include the top 5. The other areas of the map are not shown at all.
• Map 4. The fourth map forms the basis of most maps. It combines the JSON that specifies the boundaries of each of the wards, and then matches these using “lookup”, with the values in a csv file of population.

England

Start to build more interactive maps using tooltips and year selectors.

• Map 1. Again the first step is to check our base map works as we want it to.
• Map 2. As with the Wales example this map adds colour, picking colours based on the first letter of the place name. This is done using a string function.
• Map 3. Population. This map uses a secondary data set that contains information on population by wards. The cities can clearly be seen. The tooltip has been added, so you can hover over the areas. However, some areas are white, since the match between the dataset and the map is imperfect.
• Map 4. House prices. This map matches house prices in an interactive way. The drop down box acts as a filter, so the map can be drawn for different years.

World

Linking maps of the World to IMF, World Bank and UN data.

The core challenge in economic cartography is linking your economic data to your map. These maps show some of the problems, and how to overcome them.

The first map is a simple base map. This one uses the Natural Earth Projection. The data has country codes, but not country names. Since we want to use country names in our Tooltip, these will need to be matched in from a second data source.

The second map adds country names via a "lookup" transform. Countries are then coloured based on their names. Some are white, and hovering the mouse over them will reveal that the name is "null". This is because the match has not worked properly.

In the third map all of the countries appear, and the tooltip works properly.The problem was that our match of country ids had three numbers stored as string in one dataset and number in another. For countries with a leading zero {Afghanistan, code "004"} this means the match is assessing "004"==4, which is false. The solution is to convert the string to a number (so that "004" becomes 04) before the match takes place.

The fourth map starts to add in economic data from the IMF. Again, some countries dissappear. And again this is based on a matching problem. Any time you see the US or UK dissappear you should suspect this, since these (and many other) countries has multiple forms (US, USA; UK, GB and NI, Ivory Coast, Cote d'Ivoire). The way to get around this is to match on codes, rather than names.

The final map matches economic data based on 'alpha3' codes (e.g., usa, gbr, civ). At this point you can move to aesthetic tweaks, choosing color pallettes that best represent your data. This one uses a pre-set scheme, "turbo".

USA

Maps of the US. Research assistance and map drafts by Josh Hellings.

The set set of maps demonstrates how to use data filters to make your cartography interactive.

The first map is a simple base map. The only addition is a tooltip: hover your mouse over each state.

The second US map plots a single crime variable: motor theft. The key addition to the code is a link to a second data source, and instructions for how to match with the map (i.e., based on the state) and which variable to retrieve.

The third map generalises the second. We add more data in the match [lookup] part of the code, and add a drop-down box. This sets a temporary parameter that colours the map based on the user's selection.

The final map has a few more steps. First, the user selects a year, and this filters the data, so that only numbers for that year are used. Next, the code changes the data from wide to long (aka 'tidy') form using a "fold" transform. Finally, the topoJSON file is matched, based on the state name. Eash of these steps are discussed in detail in class.

United Kingdom | International Terretorial Levels

Maps of the UK - Richard Davies and Josh Hellings.

These UK maps demonstrate differing levels of detail.

These maps show the NUTS-1, -2 and -3 breakdown for the UK. Also the ITL1, 2 and 3.

London | Bikes and Crimes

Maps of London - by Josh Hellings.

Plotting bike docking stations and suspected crimes on a map.