This section of CARTO Academy explores the essential foundations of handling spatial data in the modern geospatial tech stack.

Spatial data encompasses a wide range of information that is associated with geographical locations. This data can represent anything from points on a map to complex geographic features, and it plays a central role in a multitude of applications.

• What is location data?

• Types of location data

In the past few years, geospatial technology has fundamentally changed. Data is getting bigger, faster, and more complex. User needs are changing too, with an increasing number of organizations and business functions adopting data-centric decision making, leading to a broader range of users undertaking this kind of work. Geospatial cannot any longer be left on a silo.

In this rapidly evolving landscape, the traditional desktop-based Geographic Information Systems (GIS) of the past have given way to a new way of doing spatial analysis, focused on openness and scalability over proprietary software and desktop analytics. This new way of working with geospatial data is supported by a suite of cloud-native tools and technologies designed to handle the demands of contemporary data workflows - this is what we call the modern geospatial analysis stack.

This shift to more open and scalable geospatial technology offers a range of benefits for analysts, data scientists and the organizations they work for:

• Interoperability between different data analysis teams working on a single source of truth database in the cloud.

• Scalability to analyze and visualize very large datasets.

• Data security backed by the leading cloud platforms.

However, while the modern geospatial analysis stack excels in offering scalable and advanced analytical and visualization capabilities for your geospatial big data, there are some data management tasks - like geometry editing over georeferenced images - for which traditional open-source desktop GIS tools are great solutions for.

This section of the CARTO Academy will share how you can complement your modern geospatial analysis stack - based on CARTO and your cloud data warehouse of choice - with other GIS tools to ensure all your geospatial needs and use-cases are covered, from geometry editing to advanced spatial analytics and app development.

Platforms which deal with spatial data - like CARTO - are able to translate encoded location data into a geographic location on a map, allowing you to visualize and analyze data based on location. This includes mapping where something is, and the space it occupies.

There are two main ways that "location" is encoded.

1. Geographic Coordinates (Geography): Geographic coordinates, also known as geographic or unprojected coordinates, use latitude and longitude to specify a location on the Earth's curved surface. Geographic coordinates are based on a spherical or ellipsoidal model of the Earth and provide a global reference system.

2. Projected Coordinates (Geometry): Projected coordinates, also referred to as geometriesy or projected coordinates, utilize a two-dimensional Cartesian coordinate system to represent locations on a flat surface, such as a map or a plane. Projected coordinates result from applying a mathematical transformation to geographic coordinates, projecting them onto a flat surface. This projection aims to minimize distortion and provide accurate distance, direction, and area measurements within a specific geographic region or map projection.

The choice between geographic or projected coordinates depends on the purpose and scale of the analysis. Geographic coordinates are commonly used for global or large-scale analysis, while projected coordinates are more suitable for local or regional analysis where accurate distance, area, and shape measurements are required. Furthermore, web mapping systems may often require your data to be a geography, as these systems often use a global, geographic coordinate system.

When you begin a new map in CARTO Builder, the left panel is your starting point, providing the tools to add data sources that will be visualized as layers on your map. In Builder, each data source creates a direct connection to your data warehouse, allowing you to access your data without the need to move or copy it. This cloud-native approach ensures efficient and seamless integration of large datasets.

Once a data source is added, CARTO's advanced technology renders a map layer that visually represents your data, offering smooth and scalable visualization, even with extensive datasets.

In this section, we'll take you through the various data source formats that CARTO Builder supports. We'll also explore the different types of map layers that can be rendered in Builder, enhancing your understanding of how to effectively visualize and interact with your geospatial data.

Not sure where to start? Check out our recommended learning path !

Working with geospatial data

The two primary spatial data types are raster and vector - but what’s the difference?

Raster data

Raster data is represented as a grid of cells or pixels, with each cell containing a value or attribute. It has a grid-based structure and represents continuous values such as elevation, temperature, or satellite imagery.

Common raster file types

Common file types for raster data include:

• GeoTIFF: a popular raster file format with embedded georeferencing.

• JPEG, PNG & BMP: ubiquitous image files which can be georeferenced with a World or TAB file. PNG supports lossless compression and transparency, making it particularly useful for spatial visualization.

• ASCII: stores gridded data in ASCII text format. Each cell value is represented as a text string in a structured grid format, making it easy to read and manipulate.

You may also encounter: ERDAS, NetCDF, HDF, ENVI, xyz.

Vector data

Vector data represents geographic features as discrete points, lines, and polygons.It has a geometry-based structure in which each element in vector data represents a discrete geographic object, such as roads, buildings, or administrative boundaries. Vector data is scalable without loss of quality and can be easily modified or updated.

Vector data is useful for spatial analysis operations such as overlaying, buffering, and network analysis, facilitating advanced geospatial studies. Vector data formats are also well-suited for data editing, updates, and maintenance, making them ideal for workflows that require frequent changes.

Common vector file types

Shapefiles

Shapefiles are a format developed by ESRI. They have been widely adopted across the spatial industry, but their drawbacks see them losing popularity. These drawbacks include:

1. Shareability: They consist of multiple files (.shp, .shx, .dbf, etc.) that comprise one shapefile, which can make them tricky for non-experts to share and use.

2. Limited Attribute Capacity: Shapefiles are limited to a maximum of 255 attributes.

3. Lack of Native Support for Unicode Characters: This can cause issues when working with datasets that contain non-Latin characters or multilingual attributes.

Limited File Size Limitations: Shapefile size is limited to 2 GB.

Other vector file types

1. GeoJSON (Geographic JavaScript Object Notation): GeoJSON is an open standard file format based on JSON (JavaScript Object Notation). It allows for the storage and exchange of geographic data in a human-readable and machine-parseable format.

2. KML/KMZ (Keyhole Markup Language): KML is an XML-based file format used for representing geographic data and annotations. It was originally developed for Google Earth but has since become widely supported by various GIS software. KMZ is a compressed version of KML, bundling multiple files together.

3. GPKG (Geopackage): GPKG is an open standard vector file format developed by the Open Geospatial Consortium (OGC). It is a SQLite database that can store multiple layers of vector data along with their attributes, styling, and metadata. GPKG is designed to be platform-independent and self-contained.

Everything in-between

There is a small area in between raster and vector data types, with Spatial Indexes being one of the most ubiquitous data types here.

Spatial Indexes are global grids - in that sense, they are a lot like raster data. However, they render a lot like vector data; each "cell" in the grid is an individual feature which can be interrogated. They can be used for both vector-based analysis (like running intersections and spatial joins) and raster-based analysis (like slope or hotspot analysis).

But where they really excel is in their size, and subsequent processing and analysis speeds. Spatial Indexes are "geolocated" through a reference string, not a long geometry description (like vector data). This makes them small, and quick. So many organizations are now taking advantage of Spatial Indexes to enable highly performant analysis of truly big spatial data. Find out more about these in the ebook

So, you've decided to start scaling your analysis using Spatial Indexes - great! When using these grid systems, some common spatial processing tasks require a slightly different approach to when using geometries.

To help you get started, we've created a reference guide below for how you can use Spatial Indexes to complete common geoprocessing tasks - from buffers to clips. Once you're up and running, you'll be amazed at how much more quickly - and cheaply - these operations can run! Remember - you can always revert back to geometries if needed.

All of these tasks are undertaken with CARTO Workflows - our low-code tool for automating spatial analyses. Find more tutorials on using Workflows .

In this section you can find step-by-step guides focused on bringing your data visualization to life with Builder. Each tutorial utilizes available demo data from the CARTO Data Warehouse connection, enabling you to dive straight into map creation right from the start.

In this webinar we showcase how to leverage the component in Workflows to optimize and help us understand the results of a spatial analysis.

In this tutorial, we will calculate the rate of traffic accidents (number of accidents per 1,000 people) for Bristol, UK. We will be using the following datasets. The first one is available in the demo tables section of the CARTO Data Warehouse, while the latter two are freely available in our Spatial Data Catalog.

• Bristol traffic accidents (CARTO Data Warehouse)

• Census 2021 - United Kingdom (Output Area) [2021] (Office for National Statistics)

• Lower Tier Local Authority (Office for National Statistics)

Step 1: Converting accident data to Spatial Indexes

In this step, you'll convert the individual accident point data to aggregated H3 cells.

1. Create a Workflow using the CARTO Data Warehouse connection.

2. First, drag the Lower Tier Local Authority data onto the canvas. It can be found under Sources > Data Observatory > Office for National Statistics.

3. Connect this to a Simple Filter component. Set the filter to do_label is equal to "Bristol, City of".

The result of this will be a H3 index covering the Bristol area with a count for the number of accidents which have taken place within each cell. Now let's put those counts into context!

Step 2: Enrich the grid with population data

In this section of the tutorial, we will enrich the H3 grid we have just created with population data from the UK Census.

1. Drag the Census 2021 - United Kingdom (Output Area) [2021] table onto the canvas from Sources > Connections > Office for National Statistics.

2. Drag an Enrich H3 Grid onto the canvas. Connect the Join component (Step 1 point 4) to the top input, and the Census data to the bottom output.

3. The component should detect the H3 and geometry columns by default. From the Variables drop down, add "ts001_001_ff424509" (total population, you can reference Variable descriptions for any dataset on our Data Observatory) and specify the aggregation method as SUM. This will estimate the total population living in each H3 cell based on the area of overlap with each Census Output Area.

Run the workflow.

Step 3: Calculating the accident rate & hotspot analysis

Now we have all of the variables collected into the H3 support geography, we can start to turn this into insights.

1. First, we'll calculate the accident rate. Connect the output of Enrich H3 Grid to a new Create Column component. Call the new column "rate".

2. Set the expression as CASE WHEN h3_count_joined IS NULL THEN 0 ELSE h3_count_joined/(ts001_001_ff424509_sum/1000) END. This code calculates the number of accidents per 1,000 people, unless there has been no accident in the area, in which case the accident rate is set to 0.

3. Now, let's explore hotspots of high accident rates . Connect the output of Create Column to a new

You can explore the results below!

💡 Note that to be able to visualize a H3 index in CARTO Builder, the field containing the index must be called H3.

Featured resources

These resources have been designed to get you started. They offer an end-to-end tutorial for creating, enriching and analyzing Spatial Indexes using data freely available on the CARTO platform.

Spatial Statistics

For your use case

On this page, you'll learn how to take advantage of some of the unique properties of Spatial Indexes.

• Use parent and children hierarchies; seamlessly move data between index resolutions.

• Create K-rings; define areas of interest without requiring the use of geometries.

• ; when and how to do this.

• ; how to aggregate data from a spatial index to a geometry.

Use parent and children hierarchies

Being able to seamlessly move data between resolutions is one of the reasons Spatial Indexes are so powerful. With geometries, this would involve a heavy spatial join operation whereas Spatial Indexes enable an efficient string process.

Resolutions are referred to as having "parent" and "child" relationships; less detailed hierarchies are the parents, and more detailed hierarchies are the children. In this tutorial, we'll share how you can easily move between these resolutions.

💡 You will need a Spatial Index table to follow this tutorial. You can use your own or follow the steps in the tutorial. We'll be using "" which you can access as a demo table from the CARTO Data Warehouse.

Our source dataset (USA Spatial Features H3 - resolution 8) has around 12 million cells in it - which is a huge amount! In this tutorial, we'll create the workflow below to move down a hierarchy to resolution 7 to make this slightly more manageable.

1. In the CARTO workspace, head to Workflows > Create a new workflow. Choose the relevant connection for where your data is stored; if you're following this tutorial you can also use the CARTO Data Warehouse.

2. Drag your Spatial Index table onto the canvas.

3. Next, drag a H3 to Parent component onto the canvas. Note you can also use a Quadbin to Parent component if you are using quadbins.

At this point, it is good practice to use a Rename Column component to rename the H3_Parent column "H3" so it can be easily identified as the index column.

Create K-rings

K-rings are a simple concept to understand, but can be a powerful tool in your analytics arsenal.

A ring is the adjacent cells surrounding an originating, central cell. The origin cell is referred to as “0,” and the adjacent cells are ring “1.” The cells adjacent to those are ring “2,” and so on - as highlighted in the image below.

What makes this so powerful is that it enables fast and inexpensive distance-based calculations; rather than having to make calculations based on - for example - buffers or isolines, you could instead stipulate 10 K-rings. This is a far quicker and cheaper calculation as it removes the requirement for use heavy geometries.

💡 You will need a Spatial Index table to follow this tutorial. We have used the Retail Stores dataset from demo tables in the CARTO Data Warehouse, and used a Simple Filter to filter this table to stores in Boston. We've then used H3 from GeoPoint to convert these to a H3 table. Please refer to the tutorial for more details on this process.

1. Connect your H3 table to a H3 KRing component. Note you can also use a Quadbin KRing component if you are using this type of index.

2. Set the K-ring to 1. You can use and this to work out how many K-rings you need to approximate specific distances. For instance, we are using a H3 resolution of 8 which has a long-diagonal "radius" of roughly 1km. This means our K-ring of 1 will cover an area approximately 1km away from the central cell.

3. Run your workflow! This will generate a new field called

So how can you use this? Well, you can see an example in the workflow above in the "Calculate the population" section, where we analyze the population within 1km of each store.

We run a Join (inner) on the results of the K-ring, joining it by the kring_index column to the H3 column in USA Spatial Features table (available for free to all CARTO users via the ). Next, with the Group by component we aggregate by summing the population, and grouping by H3_joined. This gives us the total population in the K-ring around each central cell, approximately the population within 1km of each store. Finally, we use a Join (left) to join this back to our original H3 index which contains the store information.

With this approach, we leverage string-based - rather than geometry-based - calculations, for lighter storage and faster results - ideal for working at scale!

Convert indexes into a geometry

There are some instances where you may want to convert Spatial Indexes back into a geometry. A common example of this is where you wish to calculate the distance from a Spatial Index cell to another feature, for instance to understand the distance from each cell to its closest 4G network tower.

There are two main ways you can achieve this - convert the index cell to a central point, or to a polygon.

💡 You will need a Spatial Index table to follow this tutorial. You can use your own or follow the steps in the tutorial. We have used the USA States dataset (available for free to all CARTO users via the ) and filtered it to California. We then used H3 Polyfill to create a H3 index (resolution 5) to cover this area. For more information on this process please refer to the tutorial.

• Converting to a point geometry: connect any Spatial Index component or source to a H3 Center component. Note you can alternatively use Quadbin Center.

• Converting to a point geometry: connect any Spatial Index component or source to a H3 Boundary component. Note you can alternatively use Quadbin Boundary.

So, which should you use? It depends completely on the outcome you're looking for.

Point geometries are much lighter than polygons, and so will enable faster analysis and lighter storage. They can also be more representative for analysis. Let's illustrate by returning to our example of finding the distance between each cell and nearby 4G towers. By calculating the distance from the central point, you are essentially calculating the average distance for the whole cell. If you were to use a polygon boundary, your results would be skewed towards the side of the cell which is closest to the tower. On the other hand, polygon boundaries enable "cleaner" visualizations and are more appropriate for any overlay analysis you may need to do.

But remember - because Spatial Index grids are geographically "fixed" it's easy to move to and from index and geometry, or different geometry types.

Enriching a geometry with a Spatial Index

So, you've learned how to convert a , and how to convert that . Another really common task which is made more efficient with Spatial Indexes is to use them to enrich a geometry - for instance to calculate the population within a specified area.

In this tutorial, we'll calculate the total population within 25 miles of Grand Central Station NYC. You can adapt this for any example; all you need is a polygon to enrich, and a Spatial Index to do the enriching with.

For this specific example, you will need access to the USA Spatial Features H3 table (available for free to all CARTO users either in the CARTO Data Warehouse > demo data > demo tables, or via the ). In addition, the workflow below creates a polygon of 25 miles from Grand Central Station, which we've manually digitized using the component.

To run the enrichment, follow the below steps:

1. In addition to your polygon, drag your Spatial Index layer onto the canvas.

2. Connect the ST Buffer output to a component (note you can also use a if you are using this Spatial Index type).

3. Set the resolution of H3 Polyfill to the same resolution as your input Spatial Index; for us that is 8. If you have multiple polygon input features, we recommend enabling the Keep input table columns option. Optional: run the workflow to check out the interim results! You should have a H3 grid covering your polygon.

And what's the result?

The benefit of this approach is that after you've run the H3 Polyfill component, all of the calculations are based on string fields, rather than geometries. This makes the analysis far less computationally expensive - and faster!

Check out more examples of data enrichment in the !

Enhance your sharing and collaborating skills with Builder through our detailed guides. Each tutorial, equipped with demo data from the CARTO Data Warehouse, showcases how Builder facilitates the sharing and collaboration of insights, ensuring ease of understanding and effective communication in your maps.

Explore a range of tutorials in this section, each designed to guide you through solving various geospatial use-cases with Builder and the wider CARTO Platform. These tutorials leverage available demo data from the CARTO Data Warehouse connection, enabling you to dive straight into map creation right from the start.

MCP Tools in CARTO are built from Workflows. Each tool you create defines how to solve a specific spatial problem, what inputs it needs, and what results it returns.

This guide shows you how to configure a Workflow as an MCP Tool.

Step 1: Create a Workflow

Build a Workflow that solves the specific problem you want agents to handle. For example, if you want agents to find nearby stores, create a workflow that performs that spatial query.

Step 2: Add an MCP Tool Output

Add the MCP Tool Output component to your workflow. This defines what the tool returns.

Choose the output mode:

• Sync: Returns results immediately (use for fast queries)

• Async: Returns results after processing completes (use for long-running operations)

Step 3: Configure the tool

Click the three-dot menu in the top-right corner and select the MCP Tool settings.

When the dialog opens up, write a clear description explaining what the tool does. Make sure to also. Define all input parameters with descriptive names and descriptions and enable the tool to make it available through the MCP Server.

Example description: "Finds the 5 nearest retail stores to a given location and returns their addresses and distances."

Step 4: Sync changes

When you update a workflow, click Sync to propagate changes to the MCP Tool. This ensures agents always use the latest version.

Step 5: Use your own tools

Once your workflow is configured as an MCP Tool, you can:

• Connect external agents (like Gemini CLI) to your CARTO MCP Server. See Using MCP Tools with CARTO.

• Give CARTO AI Agents access to the tool. See Adding MCP Tools to AI Agents.

In this section you can find a set of tutorials with step-by-step instructions on how to solve a series of geospatial use-cases with the help of Agentic GIS.

Spatio-temporal analysis is crucial in extracting meaningful insights from data that possess both spatial and temporal components. By incorporating spatial information, such as geographic coordinates, with temporal data, such as timestamps, spatio-temporal analysis unveils dynamic behaviors and dependencies across various domains. This applies to different industries and use cases like car sharing and micromobility planning, urban planning, transportation optimization, and more.

In this example, we will perform a hotspot analysis to identify space-time clusters and classify them according to their behavior over time. We will use the location and time of accidents in London in 2021 and 2022, provided by Transport for London. This tutorial builds upon this previous one, where we explained how to use the spacetime Getis-Ord functionality to identify traffic accident hotspots.

Data

The source data we will use has two years of weekly aggregated data into an H3 grid, counting the number of collisions per cell. The data is available at cartobq.docs.spacetime_collisions_weekly_h3 and it can be explored in the map below.

Spacetime Getis-Ord

We start by performing a spacetime hotspot analysis to identify hot and cold spots over time and space. We can use the following call to the Analytics Toolbox to run the procedure:

For further detail on the spacetime Getis-Ord check out and .

By performing this analysis, we can check how different parts of the city become “hotter” or “colder” as time progresses.

Understanding hot and cold spots

Once we have identified hot and cold spots, we can classify them into a set of predefined categories so that the results are easier to digest. For more information about the categories considered and the specific criteria, please check .

We can run the analysis by calling the procedure using the previously obtained Getis-Ord results.

We can see how now we have different types of behaviors at a glance in a single map. There are several insights we can extract from this map:

• There is an amplifying hotspot in the city center that shows an upward trend in collisions.

• The surroundings of that amplifying hotspot are mostly occasional.

• The periphery of the city is mostly cold spots, but most of them are fluctuating or even declining.

Identify best billboards to target a specific audience

This workflow example computes an index in order to analyze what are the best billboards to target a specific audience, then it filters the top 100 best billboards.

Download example

CARTO's Analytics Toolbox for BigQuery is a set of UDFs and Stored Procedures to unlock Spatial Analytics. It is organized in a set of modules based on the functionality they offer. Visit the SQL Reference to see the full list of available modules and functions. In order to get access to the Analytics Toolbox functionality in your BigQuery please read about the different access methods in our documentation.

In this section, you can explore our step-by-step guides designed to enhance your data analysis skills using Builder. Each tutorial features demo data from the CARTO Data Warehouse connection, allowing you to jump directly into creating and analyzing maps.

CARTO AI Agents provide a powerful conversational interface that allows anyone, regardless of technical expertise, to ask questions in natural language and receive instant, actionable insights. This marks a fundamental shift beyond dashboards to a dynamic, intuitive way of exploring your geospatial data.

You can create agents directly in Builder and link them to your maps. This transforms static maps into interactive experiences where end-users can ask questions, explore data, and extract insights through conversation.

What is an AI Agent?

An AI Agent is a system powered by a large language model (LLM) that can interact with your data and tools to answer questions. Unlike a simple chatbot, agents can decide which tools to use, analyze results, and take multiple steps to solve a problem.

MCP Tools let you expose Workflows as tools that AI Agents can use. This means you can build custom geospatial operations in Workflows and make them available to any MCP-compliant agent.

For example, you could create a Workflow that finds optimal delivery routes, then expose it as an MCP Tool. An agent like Gemini CLI could then call that tool automatically when someone asks a routing question.

The CARTO MCP Server enables AI Agents to use geospatial tools built with Workflows. By exposing workflows as MCP Tools, GIS teams can empower agents to answer spatial questions with organization-specific logic. Each tool follows the MCP specification, including a description, input parameters, and output, making them accessible to any MCP-compliant agentic application.

How it works:

1. Build a Workflow that solves a specific problem

Estimate population around top performant retail stores

This example demonstrates how to use workflow to filter out the top retail stores that belong to a specific category and computes the population living around them.

Create a classification model

The CARTO Analytics Toolbox is a suite of functions and procedures to easily enhance the geospatial capabilities available in the different leading cloud data warehouses.

It is currently available for Google BigQuery, Snowflake, Redshift, Databricks and PostgreSQL.

The Analytics Toolbox contains more than 100 advanced spatial functions, grouped in different modules. For most data warehouses, a core set of functions are distributed as , while the most advanced functions (including vertical-specific modules such as retail) are distributed only to CARTO customers.

In this example we will see how we can identify customers potentially affected by an active fire in California using CARTO Workflows. This approach is one of the building blocks of spatial analysis and can be easily adapted to any use case where you need to know which features are within a distance of another feature.

All of the data that you need can be found in the CARTO Data Warehouse (instructions below).

• To begin, click on "+ New workflow" in the main page of the Workflows section. If it will be your first workflow, click on "Create your first workflow".

• To begin, click on "+ New workflow" in the main page of the Workflows section. If it will be your first workflow, you will instead see the option to "Create your first workflow".

From here, you can drag and drop data sources and analytical components that you want to use from the explorer on the left side of the screen into the Workflow canvas that is located at the center of the interface.

• Let's add the usa_states_boundaries data table into our workflow from the demo_tables dataset available in the CARTO Data Warehouse connection. You can find this under Sources > Connection > demo data > demo_tables.

• Then filter only the boundary of the state of California using the Simple Filter component; set the column as name, the operator as equal to and the value as California.

• Run your workflow!

You can run the workflow at any point in this tutorial - only new or edited components will be run, not the entire workflow. You can also just wait to run until the end.

Next, let's explore fires in this study area.

• From the same location that you added usa_states_boundaries, add fires_worldwide to the canvas. For ease later, you'll want to drop it just above the Simple Filter component from the previous step.

• Next, add a Spatial Filter component to filter only the fires that fall inside the digital boundary of the state of California. Connect fires_worldwide to the top input and Simple Filter to the bottom. Specify both geo columns as "geom" and the spatial predicate as intersect (meaning the filter will apply to all features where any part of their shape intersects California).

• To keep your workflow well organized, use the Add a note (Aa) tool at the top of the window to draw a box around this section of the workflow. You can use any markdown syntax to format this box - our example uses

• Now, use the ST Buffer component to generate a 5 km radius buffer around each of the active fires in California.

• Next, add third data source with a sample of customer data from an illustrative CRM system. You can find it as customers_geocoded in demo_tables inside your CARTO Data Warehouse.

• Now let’s add another Spatial Filter component to know which of our customers live within the 5 km buffer around the active fires and thus could potentially be affected.

• You'll notice we now have a couple of instances of duplicated records where these intersect multiple buffers. We can easily remove these with a Remove duplicated component. Now is also a great time to add a second note box to your workflow, this time called ## Filter customers.

You can explore the results of this analysis at the bottom panel of the window, via both the Data and Map tabs. From the map tab, you can select Create map to automatically create a map in CARTO Builder.

Head to the section of the Academy next to explore tutorials for building impactful maps!

In this example we use CARTO Workflows to ingest data from a remote file containing temperature forecasts in the US together with weather risk data from NOAA, and data with the location of our stores; we will identify which of the stores are located in areas with weather risks or strong deviations in temperature.

To start creating the workflow, please click on "+ New workflow" in the main page of the Workflows section. If it will be your first workflow, click on "Create your first workflow".

Choose the data warehouse connection that you want to use. In this case, please select the CARTO Data Warehouse connection to find the data sources used in this example.

Now you can drag and drop the data sources and components that you want to use from the explorer on the left side of the screen into the Workflow canvas that is located at the center of the interface.

Now, let's add the noaa_warnings data table into our workflow from the demo_tables dataset available in the CARTO Data Warehouse connection.

After that, let’s add the retail_stores data table from the demo_tables dataset, also available in the CARTO Data Warehouse connection.

Now let's use the SPATIAL_JOIN component to know which of our retail_stores are in the warning areas.

At that point we already have our stores within a NOAA Weather Warning and, if we deem it appropriate, we can send an email to share this warnings to anyone interested in this information using the SEND_BY_EMAIL component.

After that, we can use the IMPORT_FROM_URL component to import the temperature forecast from using this URL in particular to take the latest temperature forecast in a Shapefile: . These data will be consulted again with each execution of the workflow. It means that the results of the workflow will change if the data has been updated.

Now, we are going to drop the geom_joined column to keep only one geom column in order to avoid confusions.

We will proceed to make a new SPATIAL_JOIN in order to have the temperature forecast associated to the stores.

Finally, we conclude with this example saving the outcome in a new table using SAVE_AS_TABLE component. Remember that you should specify the fully qualified name of the new dataset in the field of this component.

We can use the "Create map" button in the map section of the Results panel to create a new Builder map and analyze the results in a map.

Territory Balancing

In this template, we’ll explore how to optimize work distribution across teams by analyzing sales territory data to identify imbalances and redesign territories.

Focusing on a beverage brand in Milan, we’ll evenly assign Point of Sale (POS) locations to sales representatives by dividing a market (a geographic area) into a set of continuous territories. This ensures fair workloads, improves customer coverage, and boosts operational efficiency by aligning territories with demand.

Location Allocation - Maximize Coverage

Managing a modern telecom network requires balancing cost, coverage, and operational efficiency. Every network node—a set of cell towers—represents demand that must be effectively served by strategically placed facilities.

In this tutorial, we’ll explore how network planners can determine the optimal locations for Rapid Response Hubs, ensuring that each area of the network is monitored and maintained efficiently through Location Allocation. More specifically, we aim to maximize network coverage so that whenever an emergency occurs (i.e. outages, equipment failures, or natural disaster impacts), the nearest facility can quickly respond and restore service.

Location Allocation - Minimize Total Cost

Managing a modern telecom network requires balancing cost, coverage, and operational efficiency. Every network node—a set of cell towers—represents demand that must be effectively served by strategically placed facilities.

In this example, we’ll explore how network planners can determine the optimal locations for Maintenance Hubs, ensuring that each area of the network is monitored and maintained efficiently through Location Allocation. More specifically, we aim to minimize total operational costs for ongoing inspections and servicing, respecting resource capacities, and ensuring that routine maintenance is delivered cost-effectively. Our goal will be to expand our existing facilities by adding one selected site per county in Connecticut to serve rising network demand.

Spatial Indexes - sometimes referred to as Data Cubes or Discrete Global Grid Systems (DGGs) - are global grid systems which tessellate the world into regular, evenly-shaped grid cells to encode location. They are available at multiple resolutions and are hierarchical, with resolutions ranging from feet to miles, and with direct relationships between “parent”, “child” and “neighbor” cells.

They are gaining in popularity as a support geography as they are designed for extremely fast and performant analysis of big data. This is because they are geolocated by a short reference string, rather than a long geometry description which is much larger to store and slower to analyze.

To learn more about Spatial Indexes you can get a copy of our free ebook .

The tutorials on this page will teach you the fundamentals for working with Spatial Indexes; how to create them!

• ; convert a point geometry dataset to a Spatial Index grid, and then aggregate this information.

• .

Builder enhances data interaction and analysis through two key features: and . Widgets, linked to individual data sources, provide insights from map-rendered data and offer data filtering capabilities. This functionality not only showcases important information but also enhances user interactivity, allowing for deeper exploration into specific features.

Meanwhile, SQL Parameters act as flexible query placeholders. They enable users to modify underlying data, which is crucial for updated analysis or filtering specific subsets of data.

Widgets

This tutorial leverages the H3 to visualize origin and destination trip patterns in a clear, digestible way. We'll be transforming 2.5 million origin and destination locations into one H3 frequency grid, allowing us to easily compare the spatial distribution of pick up and drop off locations. This kind of analysis is crucial for resource planning in any industry where you expect your origins to have a different geography to your destinations.

You can use any table which contains origin and destination data - we'll be using the NYC Taxi Rides demo table which you can find in the CARTO Data Warehouse (BigQuery) or the listing on the Snowflake Marketplace.

In this tutorial, we’ll explore how to optimize work distribution across teams by analyzing sales territory data to identify imbalances and redesign territories using .

Focusing on a beverage brand in Milan, we’ll use the component, a feature available in the , to evenly assign Point of Sale (POS) locations to sales representatives by dividing a market (a geographic area) into a set of continuous territories. This ensures fair workloads, improves customer coverage, and boosts operational efficiency by aligning territories with demand.

Setting up your workflow

In addition to easily subscribing to data on the cloud via the CARTO Data Observatory, another way you can easily access spatial data is via API.

Data is increasingly being published via API feeds rather than static download services. By accessing data this way, you can benefit from live feeds and reduce data storage costs.

In this tutorial, we will walk through how to import data from an external REST API into CARTO Workflows.

What are we aiming for? We're going to extract data from the US Current Air Quality API and map it. Then we're going to keep doing that, every hour (at least for a while), so we can monitor those changes over time - but you won't have to lift a finger - once you've set up your workflow, that is! By the end of it, you'll end up with something that looks like this 👇

All the data we'll be using here is free and openly available - so all you need is your CARTO account.

Step 1: Accessing data from an API

We're going to be using CARTO Workflows to make this whole process as easy as possible.

• Sign into the CARTO platform and head to the Workflows tab.

• Create a new Workflow using any connection - you can also use the CARTO Data Warehouse here.

• Open the Components tab (on the left of the window) and search for the Import from URL component. Drag it onto the canvas.

• Note that the Import from URL component requires you to run the import before proceeding to further workflow steps - so let's Run! Once complete, you should be able to select the component to view the data, just like with any other component.

This is pretty much the most straightforward API call you can make to access spatial data - things can obviously get much more complicated!

First, let's say we want to only return a handful of fields. We would do this by replacing the outFields=* portion of the URL with a list of comma-separated field names, like below.

Next, let's image we only want to return air quality results from a specified area. You can see how the URL below has been adapted to include a geometry bounding box.

Let's leave the URL editing there for now, but do make sure to check the documentation of the API you're using to explore all of the parameters supported. Many will also supply UI-based custom API builders to help you to create the URL you need without needing to code.

Before we move on to analyzing this data, there are an extra couple of considerations to be aware of:

1. This API is fully open, but many require you to set up an API and/or application key to access data. This can usually be easily appended to your URL with the code %APIKEY. If an API service is private and requires further authentication or access tokens, you should first use a HTTP Request component to obtain an authentication token by sending your credentials to the service. From here, you can extract the token and use it in a subsequent HTTP Request component to access the data, including the token in the appropriate header as specified by the service. Similarly, current the Import from URL supports CSV and GeoJSON formats - for other data formats HTTP Request should be used.

2. Many APIs impose a limit to the number of features you can access, whether in a single call or within a time period. This limit is not imposed by CARTO, and if you require more features than the API allows you should contact the service provider.

Now, let's do something exciting with our data!

Step 2: Adding contextual information

Before creating a map, let's add some contextual information to our data to make it even more useful for our end users.

We'll do this with the below simple workflow, which we'll build on the one which we already started.

1. Create local timestamp: as we start to build up a picture of air quality changes over time, we'll need to know when each recording was taken. It's important to know this in local time, as it's likely changes will be affected by time-sensitive patterns like commutes. For this, connect a Create Column component to your Import from URL. Call the field "local_time" and use the below formula for this calculation:

1. USA_counties: let's make sure our users can find out which state and county each sensor can be found in. If you're working in the CARTO Data Warehouse, find the table usa_counties under Sources > Connection data > Organization data > demo tables. If not, you can locate and subscribe to this data via the Data Observatory and add this table through there.

2. Join to counties with the following components:

   1. A Spatial Join to join counties to air quality sensors.

Now we have a snapshot of this data from the time we ran this - now let's make some tweaks to the workflow so we can keep fetching the results every hour.

Step 3: Hourly updates

To prepare for our incoming hourly data, let's make the below tweaks to our workflow.

1. First, give your page a refresh.

2. Under Sources, navigate to wherever you saved your output in the previous step. Drag it onto the canvas, roughly below Edit schema.

3. Delete the connection between Edit schema and Save as Table, instead connecting both Edit schema and your existing table to a new Union All component. Now, every time you run this workflow your table will have the new values appended to it.

Now your workflow will be set up to run hourly until you come back here to select Delete schedule. You should also come here to sync your scheduled workflow whenever you make changes to it.

While we're waiting for our table to be populated by the next hour of results... shall we build a map ready for it?

Step 3: Building a dashboard

1. In your workflow, select the Save as Table component, and open the Map preview on the bottom of the screen - from here you can select Create Map to open a new CARTO Builder map with your data ready to go!

2. Under Sources to the bottom-left of the screen, select Data freshness. Open the Data freshness window from here and set the data freshness to every 1 hour (see below).

3. Open the map legend (bottom right of the screen) and click the three dots next to your newly generated Layer 1, and select Zoom to to fly to the extent of your layer.

Now let's build out our map:

1. Rename the map (top left of the screen) "USA Air Quality"

2. Rename the layer (3 dots next to the layer name - likely Layer 1) "Air Quality Index - PM 2.5"

3. Style the layer:

Your map should be looking a little like this...

Now let's add some widgets to help our users understand the data.

1. In the Widgets panel to (the left of Interactions), create a New Widget using your sensor locations layer.

2. Change the widget type to Time Series, setting the below parameters:

   1. Name: PM2.5 AQI hourly changes. You can change this in the same way you change layer names.

As we have data for multiple time zones on the map, you should already be able to see some temporal patterns and interaction with the time series widget.

Let's add a couple more widgets to tell more of a story with this data:

1. Add a new Formula Widget called "Average PM2.5 AQI." This should use the average of the pm25_aqi column with 2 decimal place formatting.

2. Add a new Category Widget called "PM2.5 AQI - top 5 counties." Set the operation to average, the column to name_joined and the aggregation column to pm25_aqi column. Again make sure you set the formatting to 2 decimal places.

Can you notice a problem with this? There are multiple counties in the US with the same names, so we need to do something to differentiate them or the widget will group them together.

1. In the Sources window (bottom left of the screen), click on the three dots next to your source and select Query this table/Open SQL console (the display will depend on whether you have opened the console before.

2. Between the * and FROM, type , CONCAT(name_joined, ', ', state_name_joined) AS county_label. So your entire console will look something like:

1. Run the code, then head back to your category widget. Switch the SQL Query field from name_joined to county_label. Much better!

Altogether, your map should be looking something like...

Finally, if you'd like to share the results of your hard work, head to the Share options at the top of the screen!

What's next?

Why not explore some of our space-time statistics tools to help you draw more advanced conclusions from spatio-temporal data?

Context

URL parameters allow you to share multiple versions of the same map, without having to rebuild it depending on different user requirements. This tutorial will guide you through embedding a Builder map in a low code tool that can be controlled using URL parameters to update the map's view based on users input. Through these steps, you'll learn to make your embedded maps more engaging and responsive, providing users with a seamless and interactive experience.

Resources for this tutorial:

In this tutorial, we're providing you with an existing Builder map as a hands-on example to guide you through the process. This example map highlights historic weather events. If you're interested in creating a similar map, this is for you.

• Public map URL:

• Embed code:

Step-by-Step Guide:

In this guide, we'll walk you through:

Accessing your map URL and embed code

To access your map's URL and/or embed code, first ensure that your map has been shared — either within your organization, with specific groups, or publicly. After sharing the map, you can proceed with the following steps:

1. Map Link: This direct URL to your map can be quickly obtained in two ways:

   • Through a quick action from the 'Share' button.

   • Within the sharing modal in the left bottom corner.

Dynamically updating URL parameters in Builder

Leveraging with Builder maps enables dynamic customization for specific audience views without creating multiple map versions. This method simplifies sharing tailored map experiences by adjusting URL parameters, offering a personalized viewing experience with minimal effort.

In the viewer mode of a Builder map, any modifications you make are instantly updated in the URL. For example, if you zoom to a specific level in the loaded Builder map, the zoom level gets added to the URL. Here's how it looks:

https://clausa.app.carto.com/map/5d942679-411f-4ab7-afb7-0f6061c9af63?zoom=4

Below you can see how when you interact with the map: navigating, filtering widgets, parameters, etc. automatically change the URL displaying that specific map view.

Updating an embedding map using URL parameters

You also have the option to manually insert URL parameters to customize your map's viewer mode further. This option is particularly useful for tailoring map content to specific user queries or interests when the map is embedded, making the application more engaging and interactive.

In this section we'll illustrate how to integrate a Builder map in a custom application using , a development platform for creating apps rapidly.

1. Begin by inserting an iFrame component in your Retool application. In the URL section, use your map URL. You can use the provided map URL: .

1. Add a container in Retool to neatly organize UI components that will interact with your map.

2. Implement UI elements to enable users to filter the map view based on criteria like state, type of severe weather event, and the event's date range. Start by adding a multi-select dropdown to allow users select a specific state. Name this element as State, and pre-fill it with the names of all U.S. states in alphabetical order:

1. Include a checkbox group element named Event. This component will enable users to select the type of severe weather event they are interested in, such as hail, tornadoes, or wind, with one option set as the default.

1. Add two date pickers, one named as StartDate and the other EndDate. These components will define the timeframe of the event, providing default start and end dates to guide the user's selection. For the provided map example, let's ensure we are matching the temporal frame of the weather events by setting the start date to 1950-01-03 and end date to 2022-01-03.

1. Create a transformer named mapUrlParameters to dynamically construct the iframe's URL based on the user's selections. Use JavaScript to fetch the values from the UI components and assemble them into the URL parameters.

1. Add a button component labelled Apply that, when clicked, updates the iFrame URL with the new parameters selected by the user. This action ensures the map is only refreshed when the user has finalized their choices, making the map interaction more efficient and user-friendly.

1. To further enhance user experience, implement a secondary event that zooms to the map's focal point in the iFrame when the "Apply" button is clicked. This ensures the map is centered and zoomed appropriately for the user.

Additionally, customize your application by adding headers, more interactive elements, and so on, to increase usability and aesthetic appeal.

Context

Understanding population distribution has important implications in a wide range of geospatial analysis such as human exposure to hazards and climate change or improving geomarketing and site selection strategies.

In this tutorial we are going to represent the distribution of the most populated places by applying colours to each type of place and a point size based on the maximum population. Therefore, we can easily understand how the human settlement areas is distributed with a simple visualization that we can use in further analysis.

Context

The basemap is the foundational component of a map. It provides context, geographic features, and brand identity for your creations. Every organization is unique, and CARTO allows you to bring your own basemaps to fit your specific needs.

In this tutorial, you'll learn to customize your visualizations in Builder by using tailor-made basemaps. Don't have a custom basemap already? We'll start with the creation of a custom basemap using Maputnik, a free and open-source visual editor.

Prerequisites: You need to be an Admin user to add custom basemaps to your CARTO organization.

What is CARTO Workflows

CARTO Workflows is a visual modeling tool that allows you to create multi-step analyses without writing any code. With Workflows, you can orchestrate complex spatial analyses with as many steps as needed which can be edited, updated, duplicated, and run as many times as needed.

In this tutorial, you will learn how to optimize the site selection process for EV charging stations at scale. While this guide focuses on EV charging stations, you can adapt this process to optimize site selection for any service or facility.

You will need...

Polyfill a set of polygons with H3 indexes

Spatio-temporal analysis is crucial in extracting meaningful insights from data that possess both spatial and temporal components. By incorporating spatial information, such as geographic coordinates, with temporal data, such as timestamps, spatio-temporal analysis unveils dynamic behaviors and dependencies across various domains. This applies to different industries and use cases like car sharing and micromobility planning, urban planning, transportation optimization, and more.

In this example, we will perform spatio-temporal analysis to identify areas with similar traffic accident patterns over time using the location and time of accidents in London in 2021 and 2022, provided by . This tutorial builds upon where we explained how to use to identify traffic accident hotspots.