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Integration • Automation • Analytics • Conversion • Data Quality • GIS • CAD • BIM • 3D/AR/VR • Imagery • Software/Applications • Enterprise Architecture • Web/Cloud • Sensors/IoT • Real-time • AI/ML

WhiteStar LegalMapper

WhiteStar asked us to build a USGS Public Land Survey System grid slicer/dicer (the WhiteStar Legal Mapper) that interprets legal land descriptions for points (WellSpotter) or polygons (LeaseBuilder) and returns accurate geometries for same.  Multiple nested half- and quarter-calls, lots and tracts are supported for each legal land description. The solution implements a microservices architecture including:

  • a PostGIS database under Amazon Web Services’ Relational Database Service;
  • a Ruby on Rails web application, deployed with VueJS via a Docker container on Heroku, that exposes both a web user interface and a RESTful API;
  • a Resque/Redis service to queue, and schedule batch job processing;
  • a Sinatra (Ruby) legal land description processor mid-tier deployed via a Docker container by Amazon Web Services Elastic Beanstalk, using the GEOS library to aid in spatial computations and shapelib for shapefile production;
  • an FME custom transformer on FME Hub for customer use;
  • and an ArcGIS plugin with corresponding feature loader in Python.

The web application allows legal land descriptions to be entered either via a form, or uploaded from a CSV or GeoJSON file of arbitrary size for batch processing.  The results—rendered with correct topology in as few disjoint geometries as possible—are made available in real time for review as they are generated in the background.

The geometry of any legal land description so processed can then be rendered on an overlay map by an ArcGIS Server, and the whole batch can be downloaded en masse as a GeoJSON file, a CSV file, or a ZIPped shapefile.

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