Geospatial Data Analysis → Predictive Modeling → Business Intelligence

Import geospatial data from AR applications showing where users spend time. Clean the dataset, remove outliers, and aggregate location points into meaningful zones using clustering algorithms.

Upload processed location data to BigQuery for fast querying. Set up tables that can handle millions of location data points with timestamp and user behavior attributes.

Create machine learning models that predict foot traffic patterns based on historical AR user behavior, weather data, local events, and seasonal trends. Use time series forecasting to predict future hotspots.

Set up automated workflows that regularly pull new location data, run it through your predictive models, and update business intelligence dashboards without manual intervention.

Build heat maps and predictive visualizations showing current and predicted foot traffic patterns. Include filters for time of day, demographics, and seasonal trends to inform retail strategy decisions.