Eliminate the 60-80% overhead before modeling starts.
Data scientists working on location-influenced models spend most of their time acquiring, cleaning, and joining geographic data. xMap eliminates that overhead with a single, clean, versioned data layer purpose-built for analytical consumption.
THE DATA
Every layer, analyst-ready.
Schema-stable, versioned, and built for ML feature engineering and statistical analysis.
📍
poi
Structured points of interest with category, brand, coordinates, hours. Schema-stable. Agent-ready.
🚗
car_traffic
Road segment volumes and speeds. Hourly resolution. Direction of travel. Timeseries for ML feature engineering.
📡
gps_mobility
Aggregated origin-destination flows, dwell time, visit frequency. Privacy-safe. Population-level.
🏛
parcel_data
Ownership, zoning, assessed value, structure type. 98% US coverage. Versioned snapshots for longitudinal analysis.
👥
demographics
Block-group level population, income, age, household composition. Joinable on GEOID. Updated on census cycle.
💻
sdk_access
Python, TypeScript, Go, Rust, Swift SDKs. Identical method names and response shapes. Full async support.
WORKFLOWS
What data scientists build on xMap.
01
Demand Forecasting Models
Build location-influenced demand models using xMap’s mobility, demographics, and POI layers as structured ML features.
POI density features by category and radius
Traffic percentile rank by city
Mobility-derived trade area catchment
Demographic composite scores at block-group
02
Site Scoring & Risk Models
Score candidate locations against custom site criteria models with reproducible training sets from versioned xMap snapshots.
Versioned data for reproducible training sets
Parcel velocity as leading indicator of change
Competition pressure index by brand tier
Agent confidence vs. outcome correlation
03
Network Optimization
Optimize hub placement, route design, and service territory coverage using real traffic and population movement data.
Drive-time isochrone calculation at scale
Batch distance matrix for thousands of nodes
H3 hexagonal grid for mobility aggregation
Async batch processing with Python SDK
PYTHON SDK IN ACTION
Five lines to your first site score.
The xMap Python SDK is designed for the workflows data scientists actually run. Full async support, GeoPandas-compatible output, versioned snapshots for reproducible training sets.
SDK WORKFLOW · Site scoring with Python
pip install
pip install xmap-sdk · installs in 12s · no system dependencies