# Step-by-step tutorials

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 CARTO Workflows.&#x20;

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* [Insurance](#insurance)
* [Telco](#telco)
* [Transport & Logistics](#transport-and-logistics)
* [Retail & CPG](#retail-and-cpg)
* [Cross-industry & Miscellaneous](#cross-industry-and-miscellaneous)

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## Insurance

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th></th><th data-hidden></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Using crime data &#x26; spatial analysis to assess home insurance risk</strong></td><td>In this tutorial, we'll be using individual crime location data to create a crime risk index. This analysis is really helpful for insurers looking to make more intelligent policy decisions - from customized pricing of premiums to tailored marketing.</td><td></td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FI95ZQ4CpVgpGqGggbAaJ%2FCaptura%20de%20pantalla%202024-01-05%20a%20las%2017.36.12.png?alt=media&#x26;token=4eea9945-83db-4f5f-963c-ff6488bac973">Captura de pantalla 2024-01-05 a las 17.36.12.png</a></td><td><a href="step-by-step-tutorials/using-crime-data-and-spatial-analysis-to-assess-home-insurance-risk">using-crime-data-and-spatial-analysis-to-assess-home-insurance-risk</a></td></tr><tr><td><strong>Finding stores in areas with weather risks</strong></td><td>Leverage CARTO Workflows and Builder to analyze weather risk data from NOAA, to figure out which retail stores locations are exposed to a weather-related risk in the US.</td><td></td><td> </td><td><a href="https://content.gitbook.com/content/FEElAdsRIl9DzfMhbRlB/blobs/Le9XHS0El0wOg6KcvbB4/workflows_ex2_importurl.png">workflows_ex2_importurl.png</a></td><td><a href="step-by-step-tutorials/finding-stores-in-areas-with-weather-risks">finding-stores-in-areas-with-weather-risks</a></td></tr><tr><td><strong>Creating a composite score for fire risk</strong></td><td>Combine climate and historic fire extents to calculate fire risk.</td><td></td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2Fub58eEAUUxcIe6deZUua%2Fcomposite%20score_header.png?alt=media&#x26;token=c9be2062-f06c-41bc-8bec-6e1da7df2b31">composite score_header.png</a></td><td><a href="step-by-step-tutorials/creating-a-composite-score-for-fire-risk">creating-a-composite-score-for-fire-risk</a></td></tr><tr><td><strong>Real-Time Flood Claims Analysis</strong></td><td>Share the potential impact of floods on assets in real-time.</td><td></td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FUWFbZtjI37VRDHs1IeKo%2FReal-time%20flood%20claims.png?alt=media&#x26;token=8319daae-0ebd-447c-90d0-7b95eadb1347">Real-time flood claims.png</a></td><td><a href="step-by-step-tutorials/real-time-flood-claims-analysis">real-time-flood-claims-analysis</a></td></tr><tr><td><strong>Space-time anomaly detection for real-time portfolio management</strong></td><td>Improve portfolio management for real estate insurers by identifying vacant buildings in areas experiencing anomalously high rates of violent crime.</td><td></td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FLZOaucfBdMZ9V7gCcS9W%2FScreenshot%202025-02-05%20at%2010.56.06.png?alt=media&#x26;token=dfd14686-8ba0-4359-b12f-b3b0d9171d13">Screenshot 2025-02-05 at 10.56.06.png</a></td><td><a href="step-by-step-tutorials/space-time-anomaly-detection-for-real-time-portfolio-management">space-time-anomaly-detection-for-real-time-portfolio-management</a></td></tr></tbody></table>

## Telco

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Estimate the population covered by LTE cells</strong></td><td>Leverage CARTO Workflows and Builder to estimate and analyze the population that is covered by telecom network cells based on the LTE technology. </td><td></td><td><a href="https://content.gitbook.com/content/FEElAdsRIl9DzfMhbRlB/blobs/nnkSo5tRf0wyFbBUEL9a/Captura%20de%20pantalla%202023-05-08%20a%20las%2018.13.34.png">Captura de pantalla 2023-05-08 a las 18.13.34.png</a></td><td><a href="step-by-step-tutorials/estimate-the-population-covered-by-lte-cells">estimate-the-population-covered-by-lte-cells</a></td></tr><tr><td><strong>Train a classification model to estimate customer churn</strong></td><td>Learn how telecom providers can leverage BigQuery ML to predict customer churn using Workflows.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FugE7k6OCyiIULNqrg134%2FScreenshot%202025-01-27%20at%2011.37.11.png?alt=media&#x26;token=9f7b79b2-889c-4eb7-93f6-b77d4bc9b95d">Telco BQ ML.png</a></td><td><a href="step-by-step-tutorials/train-a-classification-model-to-estimate-customer-churn">train-a-classification-model-to-estimate-customer-churn</a></td></tr><tr><td><strong>Identify buildings in areas with a deficit of mobile phone antennas</strong></td><td>Learn how to pinpoint busy locations lacking sufficient mobile phone antennas using CARTO Workflows.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FESaUPsZLOppDOX1BvrpV%2Foverture-tutorial-banner.png?alt=media&#x26;token=fde5e1a0-fc60-4062-a250-1a969768e6b6">overture-tutorial-banner.png</a></td><td><a href="step-by-step-tutorials/identify-buildings-in-areas-with-a-deficit-of-cell-network-antennas">identify-buildings-in-areas-with-a-deficit-of-cell-network-antennas</a></td></tr></tbody></table>

OOH

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Identify the best billboards and stores for a multi-channel product launch campaign</strong></td><td>Select what are the best billboards and retail stores in order to create a targeted product launch marketing campaign across multiple channels: out of home advertising and in-store promotions.</td><td></td><td><a href="https://content.gitbook.com/content/FEElAdsRIl9DzfMhbRlB/blobs/En6NoEt9ZF23yty0eCjT/Final.png">Final.png</a></td><td><a href="step-by-step-tutorials/identify-the-best-billboards-and-stores-for-a-multi-channel-product-launch-campaign">identify-the-best-billboards-and-stores-for-a-multi-channel-product-launch-campaign</a></td></tr><tr><td><strong>A no-code approach to optimizing OOH advertising locations [ Video 🎥 ]</strong> </td><td>Leveraging Spatial Indexes along with human mobility and spend data to optimize locations for OOH billboards in a low-code environment. While this example focuses on OOH, the approach could be utilized in other sectors such as CPG, retail and telecoms. </td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FFuFcgHKpZEEkn1qDzypB%2FCaptura%20de%20pantalla%202023-11-30%20a%20las%2012.33.24.png?alt=media&#x26;token=93ad3a50-8f30-4caa-9591-9041759df8b3">Captura de pantalla 2023-11-30 a las 12.33.24.png</a></td><td><a href="step-by-step-tutorials/a-no-code-approach-to-optimizing-ooh-advertising-locations">a-no-code-approach-to-optimizing-ooh-advertising-locations</a></td></tr><tr><td><strong>Geomarketing techniques for targeting sportswear consumers [ Video 🎥 ]</strong> </td><td>Webinar in which we show how to implement with workflows geomarketing techniques to help businesses target sportsfans &#x26; sportswear consumers.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FtKaIGTVbKH8swovfalx3%2FCaptura%20de%20pantalla%202023-11-30%20a%20las%2012.51.18.png?alt=media&#x26;token=6012d5c1-db21-40e9-80a5-bbdab3c26875">Captura de pantalla 2023-11-30 a las 12.51.18.png</a></td><td><a href="step-by-step-tutorials/geomarketing-techniques-for-targeting-sportswear-consumers">geomarketing-techniques-for-targeting-sportswear-consumers</a></td></tr></tbody></table>

## Transport & Logistics

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Optimizing site selection for EV charging stations</strong></td><td>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. </td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FqThx7kfhf9PM7xLLCXGT%2Facademy_ev-charging_workflow2.png?alt=media&#x26;token=fb1121d8-a132-4a59-8063-5e6374e5a533">academy_ev-charging_workflow2.png</a></td><td><a href="step-by-step-tutorials/optimizing-site-selection-for-ev-charging-stations">optimizing-site-selection-for-ev-charging-stations</a></td></tr><tr><td><strong>How to run scalable routing analysis the easy way</strong> <strong>[ Video 🎥 ]</strong> </td><td>Spatial Spotlight webinar in which we showcase how to run scalable routing analysis in your cloud data warehouse with a workflow built in CARTO.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FVoe1DvC11U7wFXcMCu4F%2FCaptura%20de%20pantalla%202023-11-30%20a%20las%2012.21.52.png?alt=media&#x26;token=a08438d8-790f-48fa-8a7e-6dd40f180b00">Captura de pantalla 2023-11-30 a las 12.21.52.png</a></td><td><a href="step-by-step-tutorials/how-to-run-scalable-routing-analysis-the-easy-way">how-to-run-scalable-routing-analysis-the-easy-way</a></td></tr><tr><td><strong>Analyzing origin and destination patterns</strong></td><td>Aggregate huge datasets to a H3 Index to compare the differences in origins and destinations, using the example of NYC taxi trips.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2Fz3WITENWkjFPNHdkeDO1%2Facademy_nyc%20taxi%20trips_hero%20image.png?alt=media&#x26;token=d8d2fd0d-5f41-4a31-ab47-4deab53948d2">academy_nyc taxi trips_hero image.png</a></td><td><a href="step-by-step-tutorials/analyzing-origin-and-destination-patterns">analyzing-origin-and-destination-patterns</a></td></tr><tr><td><strong>Understanding accident hotspots</strong></td><td>Transform points to a H3 grid and calculate hotspots, before relating this back to physical cycling infrastructure.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2F1xpjiNophyFRLzPIFfwG%2Facademy_accident%20hotspots_hero%20image.png?alt=media&#x26;token=064d2d35-a315-4129-88e8-9876e66f5dfc">academy_accident hotspots_hero image.png</a></td><td><a href="step-by-step-tutorials/understanding-accident-hotspots">understanding-accident-hotspots</a></td></tr></tbody></table>

## Retail & CPG

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-card-cover data-type="files"></th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>Measuring merchant attractiveness and performance</strong></td><td>In this tutorial, we’ll be scoring potential merchants across Manhattan to determine the best locations for our product: canned iced coffee!.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FFgIgEj4mr4jVKmM47ivN%2Facademy_scoring_full%20workflow.png?alt=media&#x26;token=b1bfe35a-b4ec-4186-9e6b-091936b1de4e">academy_scoring_full workflow.png</a></td><td><a href="step-by-step-tutorials/spatial-scoring-measuring-merchant-attractiveness-and-performance">spatial-scoring-measuring-merchant-attractiveness-and-performance</a></td></tr><tr><td><strong>Calculate population living around top retail locations</strong></td><td>Create walk time isolines for selected retail locations. It includes some examples of simple data manipulation, including filtering, ordering and limiting datasets. </td><td></td><td><a href="https://content.gitbook.com/content/FEElAdsRIl9DzfMhbRlB/blobs/8ubDTV1PBO5awcC5Jqqn/workflows_isolines_8.png">workflows_isolines_8.png</a></td><td><a href="step-by-step-tutorials/calculate-walk-time-isolines-for-top-retail-locations">calculate-walk-time-isolines-for-top-retail-locations</a></td></tr><tr><td><strong>Identifying customers potentially affected by an active fire in California</strong></td><td>Use CARTO Workflows to import and filter a public dataset that contains all active fires worldwide; apply a spatial filter to select only those happening in California. <br>Create buffers around the fires and intersect with the location of customers to find those potentially affected by an active fire.</td><td></td><td><a href="https://content.gitbook.com/content/FEElAdsRIl9DzfMhbRlB/blobs/7KgbONqPqRjodzPvoAuz/workflows_ex1_createmap.png">workflows_ex1_createmap.png</a></td><td><a href="step-by-step-tutorials/identifying-customers-potentially-affected-by-an-active-fire-in-california">identifying-customers-potentially-affected-by-an-active-fire-in-california</a></td></tr></tbody></table>

## Cross-industry & Miscellaneous

<table data-card-size="large" data-view="cards"><thead><tr><th></th><th></th><th data-hidden></th><th data-hidden data-card-cover data-type="image">Cover image</th><th data-hidden data-card-target data-type="content-ref"></th></tr></thead><tbody><tr><td><strong>How to optimize location planning for wind turbines [ Video 🎥 ]</strong> </td><td>Example on how to run a wind farm site feasibility analysis, including assessing terrain, demographics and infrastructure with an easy to build workflow. </td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FpWhhRXyCSVzrrarYpIpe%2FCaptura%20de%20pantalla%202023-11-30%20a%20las%2012.45.58.png?alt=media&#x26;token=4f8bf1b8-317a-4fa0-8ff7-497920bd846b">Captura de pantalla 2023-11-30 a las 12.45.58.png</a></td><td><a href="step-by-step-tutorials/how-to-optimize-location-planning-for-wind-turbines">how-to-optimize-location-planning-for-wind-turbines</a></td></tr><tr><td><strong>How to use GenAI to optimize your spatial analysis [ Video 🎥 ]</strong> </td><td>Spatial Spotlight webinar in which we showcase how to use the ML Generate Text component to help us understand the results of our analysis.</td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FQgrosziDw3zHTlYpjZNu%2FCaptura%20de%20pantalla%202024-01-05%20a%20las%209.19.21.png?alt=media&#x26;token=6352adac-8b16-4472-a1ea-86092942e6c1">Captura de pantalla 2024-01-05 a las 9.19.21.png</a></td><td><a href="step-by-step-tutorials/how-to-use-genai-to-optimize-your-spatial-analysis">how-to-use-genai-to-optimize-your-spatial-analysis</a></td></tr><tr><td><strong>Optimizing workload distribution through Territory Balancing</strong></td><td><p>In this tutorial, we’ll explore how to optimize work distribution across teams by analyzing sales territory data to identify imbalances and redesign territories. </p><p><br></p></td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2FXQW55kYMhVGFXLfJk2Hq%2FScreenshot%202025-10-01%20at%2016.35.34.png?alt=media&#x26;token=916c0e2d-d1c3-48fb-9786-726605290465">territory-balancing.png</a></td><td><a href="step-by-step-tutorials/optimizing-workload-distribution-through-territory-balancing">optimizing-workload-distribution-through-territory-balancing</a></td></tr><tr><td><strong>Transforming Telco Network Management Decisions with Location Allocation</strong></td><td>In this tutorial, we’ll explore how network planners can determine the optimal locations for maintenance hubs or support facilities, ensuring that each area of the network is monitored and maintained efficiently through Location Allocation.<br></td><td></td><td><a href="https://3015558743-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FFEElAdsRIl9DzfMhbRlB%2Fuploads%2F0KbXfRkmHPaoUIAXKZig%2FScreenshot%202025-10-01%20at%2016.36.26.png?alt=media&#x26;token=74c8f458-b98e-4b42-a45c-79b043251c0a">location-allocation.png</a></td><td><a href="step-by-step-tutorials/transforming-telco-network-management-decisions-with-location-allocation">transforming-telco-network-management-decisions-with-location-allocation</a></td></tr></tbody></table>
