New to CARTO? Discover the first 5 things to try, from running a cloud-native spatial analysis to creating your first map.
So you’re ready to start your CARTO journey - congratulations!
If you’re new to CARTO and not sure where to start, you’re in the right place! In this post, we'll run through the first five things you should try.
Not got an account? That’s easily solved! Sign up for a free 14-day trial account here (which is exactly the same as a non-trial account, just… well, it only lasts for 14 days!).
Once set up, head to app.carto.com and login. You should see a window that looks a little something like the one below. This is your CARTO Workspace and will be the hub for all things CARTO. No software download, no slow install - just login and go!
So… shall we?
If you aren’t already aware, one of the things that makes CARTO special is that it’s entirely cloud-native. That means you can connect directly to the cloud and run all of your processing and analytics directly inside your data warehouse. Your data never has to leave your data warehouse, and you don’t have to worry about long and complex ETL processes, or the pain that goes with storing data across multiple locations.
To connect to your data warehouse:
- Hover over the panel on the left of your workspace and select Connections
- Select New Connection
- Choose your data warehouse provider
- Enter the details of your data warehouse
Back on the left hand side of the screen, select Data Explorer > Connections. You’ll see you can now access all of your data - it’s that easy!
Not a problem! Every CARTO user is provided with access to the CARTO Data Warehouse connection. This will grant you access to cloud storage and computing, the amount of which will depend on your subscription plan. You can also import data from URLs or local files into this location.
To really scale your analytics we recommend setting up spatial infrastructure on the cloud. We have a host of resources to help you with this, such as this step-by-step guide to migrating from postgreSQL to Snowflake.
From the Data Explorer, you can import data from URLs or local files like .CSVss, geojsons and shapefiles. The Import button is to the top right of the Data Explorer window (see below).
Now let’s skip straight to the good bit - let’s make a map! Check out the video for a quick-start guide, or follow the steps below.
Once you’re happy with your visualization, hit the share button (the padlock icon to the top-right of the screen) and select who can view your map (private, public, password-protected or organization-only) - you’ll now be given a link which you can use to show the world your hard work!
Ever heard the statistic that most people working with data spend 60-80% of their time cleaning, formatting and ETL-ing that data? We’re trying to take some of the pain out of that process with our Spatial Data Catalog. From here, you can subscribe to over 12,000 different spatial datasets from around the world, ranging from geographic boundaries to consumer spend data.
To access a dataset:
- Head to the Data Observatory tab.
- Explore! When you click on a dataset, you’ll be able to see more metadata about that dataset as well as a map and tabular preview.
- When you’ve decided you want to subscribe to a dataset, click subscribe on the preview page - and that’s it!
There are two types of subscription available: public and premium. You can access any public dataset with a CARTO account, whereas premium datasets require an additional subscription. For these datasets you will see a “Request a subscription” option and a CARTO representative will be in touch with you.
Not sure which dataset to subscribe to? We recommend subscribing to a Spatial Features dataset, which includes a range of demographic, environmental and economic data. Even better, these datasets make use of Spatial Indexes - a super lightweight type of spatial data designed for quick and efficient analysis.
Once you’ve subscribed to a dataset you’ll be taken back to the Data Explorer, but this time to the Data Observatory tab. Here you’ll see and access all of your data subscriptions and samples.
Now you know how to access your own data and subscribe to third party sources, isn’t it time we get to the good stuff and run some analysis?
Let’s go for something super simple and analyze the population within a catchment area of an asset. You can use your own datasets for this, but we’ll be using:
- Assets: we'll be using Retail Stores from CARTO Data Warehouse > Demo Data > Demo tables, which is made available by default to all users.
- Population: we'll be using the United States Spatial Features (H3) for this.
So let’s get this analysis running!
- In your workspace, head to Workflows and select New workflow.
- Select a connection; either the CARTO Data Warehouse, or whichever connection your own data is stored under.
- You’ll now have a new workflow ready for you to design!
- On the left of the screen, under Sources navigate to whichever table you want to use - for us, that’s Retail Stores. Drag and drop this onto the canvas. You’ll now be able to preview this in tabular and map forms at the bottom of the screen.
- One of the most commonly used tools in any data analysis is filter, which selects only data which meets a certain criteria, e.g. only stores in Massachusetts. Switch to the Components tab on the right of the window, and drag and drop the Simple Filter component (under Data Preparation, or you can just search for it) to the right of the Retail stores source.
- Connect the node on the right of retail stores to the node on the left of the filter; this will make it an input to that component.
- With Simple Filter selected, to the right of the screen, change the column to state, ensure the operator is set to equal to, and finally type MA (for Massachusetts) into the Value box.
- Click run! When complete, the component will turn green and list the number of output features it has returned.
- Add an ST Buffer component to your workflow, to the right of the filter. This will create a fixed-distance ring around the input geometry.
- Connect the top right output of the filter (which contains the features which have matched your filter) to the node on the left of ST Buffer.
- Change the parameters distance and units to 1,000 and meters respectively. Run! (Note you don’t have to run every component in a workflow individually, this is just for learning purposes!).
So now you’ve established the catchments of each store. Now we want to work out the total population within each, which we’ll be calculating using the Spatial Indexes data.
- First, we need to add the Spatial Indexes table to the workflow. As this data is a Data Observatory subscription, we currently can’t add it as a source but have to import the data into the workflow via a Select component, so drag this onto the canvas (ideally beneath retail stores to keep it nice and tidy).
- Open another CARTO Workspace tab and navigate to the Data Explorer > Data Observatory > CARTO > Spatial Features - United States of America (H3 Resolution 8). Select Access In > CARTO Data Warehouse and copy the SQL code into the Select component’s statement box.
- Before running, replace the “... * EXCEPT(geoid)...” section of code with “population.” This will return only the population and H3 ID columns rather than the entire table.
- Next, return to the original “strand” of the workflow, and add a H3 Polyfill component. This will output every H3 cell which overlaps the buffers. Connect the left node of ST Buffer to the right node of H3 Polyfill to do this. Make sure the H3 resolution is set to 8 (the same as Spatial Features) and Keep input table columns is enabled.
- Nearly there! Next add a Join component to the right hand side of the workflow to join our two H3 tables together. Connect the output of H3 polyfill to Join’s top-left node, and the output of Select to the bottom-left node. In the Join parameters, make sure the join column from both tables is set to H3.
- Now finally… we want to know the total population within 1km of each store, so the last component we will add is Group by. Set the Group by field to store_id, then set the aggregation fields and type to:
- Geom (ANY). This will retain the geometry of the retail store.
- Population_joined (SUM). This will sum the total population within each 1km store buffer.
When you’ve completed all of those steps, your workflow should look a little like the below - with your final Group by component now containing the total population in each 1km store catchment!
To continue this analysis, connect a Save as table component to the Group by component. This will save your results, and you can return to step 2 of this article to explore the data in a map!
So, what next?
You’ve connected to your data, created a map, subscribed to external data sources and run some simple analysis… but your Location Intelligence journey has only just started!
Head to CARTO Academy for a wide range of resources and courses designed to enhance geospatial analysis skills and empower users to leverage location data effectively. The content covers various topics, including data visualization, spatial analysis and geospatial fundamentals.
Want to learn more about how you can leverage Location Intelligence for your use case? Request a demo with one of our experts!