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Use cases

Geospatial Analytics

CrateDB enhances geospatial analytics by enabling real-time processing of location-based data, while also supporting full-text and contextual search. This allows organizations to not only track and analyze spatial data but also query and filter it using natural language or relevant context.

By combining advanced geospatial capabilities with full-text and vector search, CrateDB enables more powerful insights, such as identifying assets based on location and descriptive data, optimizing operations like route planning, and delivering actionable, location-aware intelligence.

Optimizing E-Scooter Fleets with IoT and Real-Time Geospatial Analytics

This presentation from AI & Big Data Expo 2024 explores how CrateDB enables efficient management of e-scooter fleets across cities. Learn how real-time tracking, geospatial data, and diverse analytics streamline fleet operations, improve resource allocation, and solve urban mobility challenges.
geospatial_queries

CrateDB Academy Lesson: "Geospatial Data"

In this lesson, you will learn why CrateDB excels at storing, analyzing, and tracking the location of people and objects in real time. Whether you're managing a fleet of vehicles, offering customers information about services near their location, or analyzing historical traffic patterns, CrateDB's geospatial data types and functions have you covered.

Discover the lesson ›

 

Related web pages on time series data

User stories

Spatially Health is a provider of location intelligence software.They use CrateDB for their data scientists to find geographic insights through the patterns and relationships in their data by analyzing terabytes of data. They plan to scale CrateDB to handle hundreds of terabytes as it continues to expand to other cities.

"Postgres couldn't keep up with the data we have; Datastax Enterprise had ingest scaling issues with spatial data; Cassandra didn't have spatial query operations. CrateDB was the only database we found that could smoothly process data for our users and for our data science team. We fell in love with it immediately."

Kartik Venkatesh
CTO
Spatially Health

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Spatially-Health
AVUXI provides location context for travel and real estate. They use CrateDB to collect and query geotagged data from over 65 million locations, processing over 20 million events every day.
 

"It is a pleasure working with companies like yours, that listen and care about their customers."

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Avuxi

Clickdrive.io tracks automotive vehicle fleets in real-time. They use CrateDB to enable storage and analysis of up to 2,000 data points per second, per car.

"Most of the cars in our customers' fleets are in use almost 24 hours a day, and we need to store and analyse the massive amounts of data they generate in real time. We tried a few different SQL and NoSQL databases, and CrateDB offered the best combination of high performance, scalability and ease of use."

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Clickdrive

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FAQ

GIS (Geographic Information System) is a tool used to collect, store, and visualize geographic data, primarily for mapping and spatial analysis. It provides the infrastructure to manage and manipulate spatial data. Geospatial analytics, on the other hand, refers to the process of analyzing geographic data to uncover patterns, trends, and insights. While GIS focuses on data management and visualization, geospatial analytics emphasizes the interpretation and extraction of actionable insights from that data, often integrating advanced techniques like AI, machine learning, and predictive modeling to solve complex spatial problems.

Geospatial analytics involves analyzing data that is tied to specific geographical locations, often represented through maps or spatial coordinates. Unlike traditional data analytics, which typically focuses on numerical or categorical data, geospatial analytics adds a locational context to data, allowing users to visualize and analyze spatial patterns, relationships, and trends. This added dimension enables more accurate decision-making in fields like urban planning, environmental monitoring, and transportation logistics.

Geospatial analytics is widely used across multiple industries, including:

  • Transportation and Logistics: For route optimization, fleet management, and tracking vehicles or assets in real-time.
  • Urban Planning and Smart Cities: To analyze land use, infrastructure, and traffic patterns, supporting sustainable urban growth.
  • Agriculture: To monitor crop health, manage resources, and optimize yields based on soil and climate data.
  • Energy and Utilities: For managing infrastructure, predicting demand, and optimizing energy distribution.
  • Retail and Real Estate: To analyze customer demographics and locations to improve marketing strategies and site selection.
Geospatial analytics relies on various types of data, including:
  • GPS and Satellite Data: Provides location information, often used for tracking and monitoring assets.
  • IoT Sensor Data: Used for real-time monitoring of infrastructure, vehicles, or environmental conditions in smart cities or logistics networks.
  • Geotagged Social Media Data: Captures user-generated content with location metadata, useful for real-time insights.
  • Demographic Data: Helps understand population distribution, income levels, and other societal factors in a geographic area.
  • Remote Sensing Sata: Involves the collection of data about the Earth’s surface from aircraft or satellites, often used in agriculture, environmental monitoring, and land use planning.

 

CrateDB manages geospatial data by providing native support for geographic data types, such as points, lines, and polygons. It allows users to store, query, and analyze geospatial data efficiently, leveraging SQL for easy querying. CrateDB supports geospatial indexing, enabling fast lookups and proximity searches. It also integrates with common geospatial standards like GeoJSON, making it compatible with a wide range of applications. Additionally, CrateDB’s scalability ensures that it can handle large datasets, making it suitable for real-time geospatial analytics across industries such as logistics, IoT, and smart city initiatives. More information here.