Let's go through the steps of uploading a basic dataset to CARTO and making a web map.
Then follow these steps:
- In a web browser, visit https://carto.com/help/getting-started/student-accounts/ to create an account.
- Scroll down to the GitHub Student Dev Pack - The Process section and follow the steps to create an login via GitHub.
- This process can take some time, so get started as soon as you can.
- When you've been notified that your account has been created, return to the GitHub Student Dev Pack - The Process site and follow the link to Sign Up for CARTO for Students.
- After you log in, you will see two options in a dropdown menu at the top of the CARTO.com homepage: Your Maps and Your Datasets. We'll use the Datasets link to upload and view our datasets, and the Maps link to design some maps from them.
- Click Datasets and click New Dataset. Examine all the input options: you can import data from Google Drive, ArcGIS Server, and Dropbox, for example. We're just going to use the simplest option here and bring it in from a raw file.
- Choose Data File, click the Browse button, and browse to csa_farm_dropoffs.geojson. Then click Connect Dataset. If you don't see the Browse button, try maximizing your web browser or otherwise enlarging the view.
You should now see a table of this dataset. These are locations where local farms around Portland will bring their produce into the city. They drop it off at these points for urban residents who have signed up for community-supported agriculture (CSA) subscriptions. Mapping these points is one way to see which areas of Portland are currently being served by CSAs (and which ones are not).
- Click Create Map, and you'll see the points overlayed on a basemap of Portland. A labels layer sits on top. You're now in CARTO Builder, which is a map design center similar to the ArcGIS.com map viewer or Mapbox Studio (although Builder is more for styling thematic maps rather than basemaps).
- Click the Basemap link and explore the variety of basemaps offered by CARTO. For example, try the Positron layer. These are built from OpenStreetMap data. You can choose your favorite one for use in this exercise.
- Go back to the layer list and click the title of the csa_farm_dropoffs layer.
- Play around with changing the size, fill, and stroke (outline) properties of the points. Below I've changed the size to 10 and applied a pink fill with black stroke. You can set this up however you like.
Sometimes it's easier to visually make sense of a bunch of dense points by aggregating them to polygons of uniform size. Hexagons are a popular choice because they are compact and tessellate (fit together) easily. Let's aggregate these points to hexagons, or "hexbins" as cartographers sometimes call them.
- Still viewing the properties of the csa_farm_dropoffs layer, choose the hexagon aggregation (under the Aggregation menu marked number 1) and select a color ramp and size for the hexbins that appeals to you. This is somewhat of an art; if the hexbins are too small, you won't learn much more than if you viewed the raw points, and if the hexbins are too large the patterns in the data will be muted.
- Click the Fill color ramp and notice the options to change the number of buckets, or classes, that the data is divided into. You do this by clicking the ....
You can also change the classification scheme. Right now it's set at Quantiles, which ensures that there will be an equal number of hexbins in each class, or color. You can apply other classification schemes commonly used by cartographers, such as equal interval or Jenks natural breaks.
- Experiment with adjusting the classification system by changing it to Jenks with 4 buckets.
- Set a classification scheme and number of buckets that you feel best tells the story about where Portland is serviced by CSAs. Notice how much power that you have over the message implicit in the map patterns. This is true with all cartography and not just online tools, yet interactive web GIS of this nature brings the power of the cartographer to the forefront.
In some situations you might want to aggregate to well-known boundaries of political units or some other polygons of non-uniform shape. In the next part of the lesson, we'll see how you could aggregate to neighborhood boundaries and share the resulting map.