Build, automate, and scale spatiotemporal movement pipelines.

A field manual for mobility data scientists, urban analysts, Python GIS developers, and logistics engineering teams. Every guide is focused on coordinate and temporal precision, production-ready Python, and the messy reality of debugging real-world movement data — not theoretical GIS overviews.

Trajectory segmentation, stay-point detection, time-window mapping, GPS error correction, and pipeline synchronization — written so you can drop patterns straight into your stack and ship reliable analytics from day one.

Spatiotemporal Data Foundations & Structures

Data models, coordinate systems, sampling architecture, GPS error handling and storage patterns for movement data.

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Movement Pattern Extraction & Trajectory Analysis

Segmentation, stay-point detection, kinematic profiling, directionality analysis and change detection at scale.

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Temporal Aggregation & Window Mapping

Turn asynchronous telemetry into structured spatiotemporal matrices: time binning, rolling stats, gap filling, seasonal alignment.

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Frequently reached guides

These pages are the most direct entry points for common real-world problems. Each is a self-contained, executable guide you can work through in an afternoon.

Handling GPS Drift in Raw Trajectory Logs Temporal regularization, kinematic thresholding, and Savitzky-Golay smoothing. DBSCAN for Stay-Point Clustering in Python scikit-learn DBSCAN with Haversine metric, radian conversion, and temporal filtering. CRS Transformations in Movement Data Safe pyproj pipelines, 3D coordinate handling, and output validation. Kalman Filter Gap Filling for GPS Dropouts 2D constant-velocity Kalman filter for irregular timestamps with production tuning. Instantaneous Speed from Discrete GPS Points Vectorized haversine distance, UTC normalization, noise filtering. Structuring Trajectory Data in GeoPandas Long-format GeoDataFrame schema, CRS setup, spatial indexing, and segmentation.
What's inside

Three sections, every topic engineered end-to-end

Each section is broken into narrowly scoped topics. Every page contains executable Python you can lift into your own stack, with checklists, validation strategies, and links to the foundational concepts.

Temporal Aggregation & Window Mapping

Turn asynchronous telemetry into structured spatiotemporal matrices: time binning, rolling stats, gap filling, seasonal alignment.

Dynamic Time-Binning Strategies for Movement Telemetry Read the guide → Gap Filling in Sparse Trajectories Read the guide → Rolling Statistics for Mobility Metrics Read the guide → Seasonal & Cyclical Alignment for Movement Data Read the guide → Time-Series Synchronization Strategies for Temporal Aggregation Read the guide →
Why this site

For the engineers shipping movement analytics

Coordinate & temporal precision UTC-first ingestion, projected CRS for metric work, deterministic window boundaries.
Production-ready Python Polars, GeoPandas, Shapely, pyproj, MovingPandas — patterns that scale past notebooks.
Real debugging GPS drift, multipath, signal loss, clock jitter, projection mismatches — diagnosed and fixed.
Pipeline-shaped Every guide ends with validation gates, observability, and operational SLAs.