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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
  • Identify hotspots of specific Point of Interest type
  • Space-time hotspot analysis
  • Spacetime hotspot classification: Understanding collision patterns
  • Time series clustering: Identifying areas with similar traffic accident patterns
  • Computing the spatial auto-correlation of point of interest locations
  • Applying GWR to model the local spatial relationships in your data
  • Create a composite score with the supervised method (BigQuery)
  • Create a composite score with the unsupervised method (BigQuery)
  • Detect Space-time anomalies

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  1. Creating workflows
  2. Workflow templates

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Last updated 3 months ago

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Identify hotspots of specific Point of Interest type

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

This example demonstrates how to identify hotspots using Getis Ors Gi* statistic. We use OpenStreetMap amenity POIs in Stockholm.

Read to learn more.

Space-time hotspot analysis

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

This example shows how to identify spacetime clusters. In particular, we will perform space temporal analysis to identify traffic accident hotspots using the location and time of accidents in the city of Barcelona in 2018.

Spacetime hotspots are computed using an extension of the Getis Ord Gi* statistics that measures the degree to which data values are clustered together in space and time.

Spacetime hotspot classification: Understanding collision patterns

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

This example shows how to use Workflows to identify space-time clusters and classify them according to their behavior over time.

Time series clustering: Identifying areas with similar traffic accident patterns

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

This example shows how to use Workflows to identify areas with similar traffic accident patterns over time using their location and time.

Computing the spatial auto-correlation of point of interest locations

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

This example demonstrates how to use Workflows to analyze the spatial correlation of POI locations in Berlin using OpenStreetMap data and the Moran’s I function available in the statistics module.

Applying GWR to model the local spatial relationships in your data

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

This example demonstrate how to use Worklfows to apply a Geographically Weighted Regression model to find relationships between a set of predictor variables and an outcome of interest.

In this case, we're going to analyze the relationship between Airbnb’s listings in Berlin and the number of bedrooms and bathrooms available at these listings.

Create a composite score with the supervised method (BigQuery)

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Create a composite score with the unsupervised method (BigQuery)

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

Detect Space-time anomalies

CARTO DW
BigQuery
Snowflake
Redshift
PostgreSQL

This example workflow uses the Detect Space-time Anomalies component to find the most significant clusters of anomalous data.

We’ll create a workflow to improve portfolio management for real estate insurers by identifying vacant buildings in areas experiencing anomalously high rates of violent crime.

Read to learn more.

Read to learn more.

Read guide to learn more.

Read to learn more.

A is an aggregation of variables which aims to measure complex and multidimensional concepts which are difficult to define, and cannot be measured directly. Examples include , or .

A is an aggregation of variables which aims to measure complex and multidimensional concepts which are difficult to define, and cannot be measured directly. Examples include , , , and so on.

In this example, we will use the component, to identify areas in Milan with a larger market potential for a wellness & beauty center mainly aimed for teenage and adult women.

Download example
this guide
Download example
this guide
Download example
this
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this full guide
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composite indicator
innovation
human development
environmental performance
Download example
composite indicator
innovation
human development
environmental performance
Create Score Unsupervised
Download example
Download example
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