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Location intelligence

Location intelligence

(also location awareness, LI)

Location intelligence definition

Location intelligence (LI) involves extracting valuable insights from geospatial information to guide decision-making, streamline operations, and improve user interactions. By merging data from geographic information systems (GIS), GPS, and additional sources, LI examines patterns, trends, and connections across diverse fields, including marketing, logistics, urban development, and security.

See also: artificial intelligence, geotagging, machine learning

Location intelligence examples

  • Retail site selection: Businesses can use location intelligence to identify the best locations for new stores based on factors like population density, customer demographics, and proximity to competitors.
  • Emergency response: By analyzing real-time location data, emergency responders can allocate resources more effectively and reach affected areas faster.
  • Targeted advertising: Marketers can utilize location intelligence to deliver personalized ads to consumers based on their geographic location and behavioral patterns.

Location intelligence vs. location-based services

While both location intelligence and location-based services (LBS) involve the use of geographic data, they serve different purposes. Location intelligence focuses on analyzing location data to derive actionable insights, whereas location-based services provide users with functionalities or information based on their location.

Pros and cons of location intelligence


  • Improved decision-making: Location intelligence can provide businesses with valuable insights to make more informed decisions.
  • Enhanced customer experiences: By understanding consumer behavior and preferences, businesses can tailor their offerings and promotions accordingly.
  • Increased operational efficiency: Location intelligence can help organizations optimize their logistics and supply chain management.


  • Higher costs: Computationally expensive compared to simpler machine learning tasks.
  • More resources needed: Requires specialized algorithms and representations.

Tips for using location intelligence effectively

  • Ensure data privacy: Collect and analyze location data in compliance with data protection regulations, and be transparent with users about how their information is being used.
  • Prioritize data quality: Use reliable data sources and validate the accuracy of the geospatial data to ensure the insights generated are trustworthy.
  • Integrate with other data sources: Combine location intelligence with other types of data, such as demographic and behavioral information, to gain a more comprehensive understanding of the target audience or market.

Further reading

Ultimate digital security