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  • CARTO Academy
  • Working with geospatial data
    • Geospatial data: the basics
      • What is location data?
      • Types of location data
      • Changing between types of geographical support
    • Optimizing your data for spatial analysis
    • Introduction to Spatial Indexes
      • Spatial Index support in CARTO
      • Create or enrich an index
      • Work with unique Spatial Index properties
      • Scaling common geoprocessing tasks with Spatial Indexes
      • Using Spatial Indexes for analysis
        • Calculating traffic accident rates
        • Which cell phone towers serve the most people?
    • The modern geospatial analysis stack
      • Spatial data management and analytics with CARTO QGIS Plugin
      • Using data from a REST API for real-time updates
  • Building interactive maps
    • Introduction to CARTO Builder
    • Data sources & map layers
    • Widgets & SQL Parameters
    • AI Agents
    • Data visualization
      • Build a dashboard with styled point locations
      • Style qualitative data using hex color codes
      • Create an animated visualization with time series
      • Visualize administrative regions by defined zoom levels
      • Build a dashboard to understand historic weather events
      • Customize your visualization with tailored-made basemaps
      • Visualize static geometries with attributes varying over time
      • Mapping the precipitation impact of Hurricane Milton with raster data
    • Data analysis
      • Filtering multiple data sources simultaneously with SQL Parameters
      • Generate a dynamic index based on user-defined weighted variables
      • Create a dashboard with user-defined analysis using SQL Parameters
      • Analyzing multiple drive-time catchment areas dynamically
      • Extract insights from your maps with AI Agents
    • Sharing and collaborating
      • Dynamically control your maps using URL parameters
      • Embedding maps in BI platforms
    • Solving geospatial use-cases
      • Build a store performance monitoring dashboard for retail stores in the USA
      • Analyzing Airbnb ratings in Los Angeles
      • Assessing the damages of La Palma Volcano
    • CARTO Map Gallery
  • Creating workflows
    • Introduction to CARTO Workflows
    • Step-by-step tutorials
      • Creating a composite score for fire risk
      • Spatial Scoring: Measuring merchant attractiveness and performance
      • Using crime data & spatial analysis to assess home insurance risk
      • Identify the best billboards and stores for a multi-channel product launch campaign
      • Estimate the population covered by LTE cells
      • A no-code approach to optimizing OOH advertising locations
      • Optimizing site selection for EV charging stations
      • How to optimize location planning for wind turbines
      • Calculate population living around top retail locations
      • Identifying customers potentially affected by an active fire in California
      • Finding stores in areas with weather risks
      • How to run scalable routing analysis the easy way
      • Geomarketing techniques for targeting sportswear consumers
      • How to use GenAI to optimize your spatial analysis
      • Analyzing origin and destination patterns
      • Understanding accident hotspots
      • Real-Time Flood Claims Analysis
      • Train a classification model to estimate customer churn
      • Space-time anomaly detection for real-time portfolio management
      • Identify buildings in areas with a deficit of cell network antennas
    • Workflow templates
      • Data Preparation
      • Data Enrichment
      • Spatial Indexes
      • Spatial Analysis
      • Generating new spatial data
      • Statistics
      • Retail and CPG
      • Telco
      • Insurance
      • Out Of Home Advertising
      • BigQuery ML
      • Snowflake ML
  • Advanced spatial analytics
    • Introduction to the Analytics Toolbox
    • Spatial Analytics for BigQuery
      • Step-by-step tutorials
        • How to create a composite score with your spatial data
        • Space-time hotspot analysis: Identifying traffic accident hotspots
        • Spacetime hotspot classification: Understanding collision patterns
        • Time series clustering: Identifying areas with similar traffic accident patterns
        • Detecting space-time anomalous regions to improve real estate portfolio management (quick start)
        • Detecting space-time anomalous regions to improve real estate portfolio management
        • Computing the spatial autocorrelation of POIs locations in Berlin
        • Identifying amenity hotspots in Stockholm
        • Applying GWR to understand Airbnb listings prices
        • Analyzing signal coverage with line-of-sight calculation and path loss estimation
        • Generating trade areas based on drive/walk-time isolines
        • Geocoding your address data
        • Find similar locations based on their trade areas
        • Calculating market penetration in CPG with merchant universe matching
        • Measuring merchant attractiveness and performance in CPG with spatial scores
        • Segmenting CPG merchants using trade areas characteristics
        • Store cannibalization: quantifying the effect of opening new stores on your existing network
        • Find Twin Areas of top-performing stores
        • Opening a new Pizza Hut location in Honolulu
        • An H3 grid of Starbucks locations and simple cannibalization analysis
        • Data enrichment using the Data Observatory
        • New police stations based on Chicago crime location clusters
        • Interpolating elevation along a road using kriging
        • Analyzing weather stations coverage using a Voronoi diagram
        • A NYC subway connection graph using Delaunay triangulation
        • Computing US airport connections and route interpolations
        • Identifying earthquake-prone areas in the state of California
        • Bikeshare stations within a San Francisco buffer
        • Census areas in the UK within tiles of multiple resolutions
        • Creating simple tilesets
        • Creating spatial index tilesets
        • Creating aggregation tilesets
        • Using raster and vector data to calculate total rooftop PV potential in the US
        • Using the routing module
      • About Analytics Toolbox regions
    • Spatial Analytics for Snowflake
      • Step-by-step tutorials
        • How to create a composite score with your spatial data
        • Space-time hotspot analysis: Identifying traffic accident hotspots
        • Computing the spatial autocorrelation of POIs locations in Berlin
        • Identifying amenity hotspots in Stockholm
        • Applying GWR to understand Airbnb listings prices
        • Opening a new Pizza Hut location in Honolulu
        • Generating trade areas based on drive/walk-time isolines
        • Geocoding your address data
        • Creating spatial index tilesets
        • A Quadkey grid of stores locations and simple cannibalization analysis
        • Minkowski distance to perform cannibalization analysis
        • Computing US airport connections and route interpolations
        • New supplier offices based on store locations clusters
        • Analyzing store location coverage using a Voronoi diagram
        • Enrichment of catchment areas for store characterization
        • Data enrichment using the Data Observatory
    • Spatial Analytics for Redshift
      • Step-by-step tutorials
        • Generating trade areas based on drive/walk-time isolines
        • Geocoding your address data
        • Creating spatial index tilesets
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On this page
  • Estimate population covered by a telecommunications cell network
  • Mobile pings within Area of Interest
  • Population Statistics
  • Emergency Response
  • New tower site selection in Denver
  • Competitor's coverage analysis
  • Path profile and path loss analysis
  • Path profile and path loss analysis with raster sources

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  1. Creating workflows
  2. Workflow templates

Telco

Last updated 3 months ago

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Estimate population covered by a telecommunications cell network

This example demonstrates how to use Workflows to estimate the total population covered by a telecommunications cell network, by creating areas of coverage for each antenna, creating an H3 grid and enriching it with data from the CARTO Spatial Features dataset.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Mobile pings within Area of Interest

This example demonstrates how to use Workflows to find which mobile devices are close to a set of specific locations, in this case, supermarkets of competing brands.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Population Statistics

This example demonstrates how to use Workflows to carry on with a common analysis for telco providers: analyze their coverage both by area (i.e. square kilometers) and by population covered.

In this analysis we will analyze the coverage for AT&T LTE Voice based on the public data from the Federal Communications Commission FCC.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Emergency Response

This example demonstrates how to use Workflows to leverage Telco providers' advanced capabilities to respond to natural disasters. Providers can use geospatial data to better detect at risk areas for specific storms. In this analysis we will analyze buildings and cell towers in New Orleans to find clusters of at risk buildings for flooding and potential outages.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

New tower site selection in Denver

Selecting a new location for a tower requires understanding where customers and coverage gaps are, however, we can also identify buildings that might be suitable for a new tower. We do that in this analysis.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Competitor's coverage analysis

This example shows how a telco provider could use Workflows to identify areas where they don't have 5G coverage while their competitors do.

Later, adding some socio-demographic variables to these areas would help them prioritize and plan for network expansion.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Path profile and path loss analysis

This template acts as a guide to perform path loss and path profile analysis for an area of interest. This template uses vector data of clutter for the analysis.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Path profile and path loss analysis with raster sources

This template acts as a guide to perform path loss and path profile analysis for an area of interest. This template uses raster data of clutter for the analysis.

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

For this template, you will need to install the extension package.

Read the to learn more.

For this template, you will need to install the extension package.

Read the to learn more.

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Telco Signal Propagation Models
full guide
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Telco Signal Propagation Models
full guide
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