Location Intelligence
Definition
Location intelligence is the practice of deriving business or policy insight from the locations of people, assets, and events. It blends geospatial data (demographics, POIs, movement, networks) with operational data (sales, inventory, service calls) to answer where-focused questions: Where is demand rising? Which stores cannibalize each other? Which neighborhoods are underserved? Modern stacks use cloud data warehouses, geospatial indexes, and notebooks to fuse large datasets with privacy and governance controls. Good programs prioritize clear decision pathways, not just maps: each visualization supports an action such as opening a site, adjusting inventory, or targeting outreach. Ethics and compliance are essential when working with mobility or personal data; aggregation and consent rules must be explicit.
Application
Retailers plan stores and delivery zones; banks manage branches and ATMs; logistics optimizes depots and routes; telecoms plan network densification; governments allocate resources and evaluate equity. Location intelligence also powers fraud detection (suspicious geographies), catastrophe modeling, and site-selection due diligence.
FAQ
What distinguishes location intelligence from basic mapping?
A focus on decisions and measurable outcomes. It integrates data engineering, analytics, and domain knowledge so that spatial patterns lead to actions.
How do we protect privacy when using mobility data?
Aggregate to coarse grids or cohorts, apply thresholds and noise, document consent chains, and ban re-identification attempts in contracts.
What skills make a strong LI team?
Spatial analysis, data engineering, experimentation design, visualization, and stakeholder communication—plus deep knowledge of the business domain.
How should success be measured?
Track lift from decisions (sales, service times, equity metrics) versus control geographies. Maintain a backlog of experiments with clear hypotheses.