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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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  • Raster data
  • Common raster file types
  • Vector data
  • Common vector file types
  • Everything in-between

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  1. Working with geospatial data
  2. Geospatial data: the basics

Types of location data

Raster, Vector & everything in-between

Last updated 3 months ago

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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.

  4. Lack of Topology Information: Shapefiles do not inherently support topological relationships, such as adjacency, connectivity, or overlap between features.

  5. No Native Support for Time Dimension: No native time field type.

  6. Lack of Direct Data Compression: Shapefiles do not provide built-in compression options, which can result in larger file sizes.

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.

  4. FGDB (File Geodatabase): FGDB is a proprietary vector file format developed by Esri as part of the Esri Geodatabase system.


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).

GML (Geography Markup Language): GML is an XML-based file format developed by the .

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

OGC
Spatial Indexes 101.
Introduction to Spatial Indexes