
Gianluca Crivelli contributed to the OurPlanscape/Planscape repository by engineering robust backend features and data management workflows over five months. He developed dynamic map layer generation with efficient MVT tiling, integrated Redis caching for performance, and enhanced API endpoints for public data access. Using Python, Django, and SQL, Gianluca implemented scalable database migrations, improved geospatial data handling with PostGIS, and modernized CLI tools for data import and processing. His work included refining authentication flows, strengthening code quality through refactoring and testing, and enabling advanced scenario modeling. These efforts improved system reliability, data integrity, and developer productivity across the project.

October 2025 – Highlights for OurPlanscape/Planscape. Delivered a major Treatment Goals Framework update with new groupings and backfilled geometry; enhanced search to default to raster data types and support data-layer-type filtering; enabled last-login tracking and session-based authentication with updated tests; modernized SQL tooling by renaming install_layers.py to install_functions.py, expanding the core integration, and refining help text. Together, these changes improve data accuracy, usability, security auditing, and developer productivity.
October 2025 – Highlights for OurPlanscape/Planscape. Delivered a major Treatment Goals Framework update with new groupings and backfilled geometry; enhanced search to default to raster data types and support data-layer-type filtering; enabled last-login tracking and session-based authentication with updated tests; modernized SQL tooling by renaming install_layers.py to install_functions.py, expanding the core integration, and refining help text. Together, these changes improve data accuracy, usability, security auditing, and developer productivity.
September 2025: Focused delivery on data-layer processing controls, scenario capability enhancements, and targeted code quality improvements. These efforts improved data integrity, maintainability, and preparation for scalable backfills, while balancing performance considerations and governance around raster handling.
September 2025: Focused delivery on data-layer processing controls, scenario capability enhancements, and targeted code quality improvements. These efforts improved data integrity, maintainability, and preparation for scalable backfills, while balancing performance considerations and governance around raster handling.
Concise monthly summary for 2025-08 highlighting key features delivered, major bugs fixed, and overall impact. The team completed a coordinated set of migration, data-layer, and views enhancements for Planscape, stabilized testing, and strengthened code quality, delivering business value through more reliable treatment-goal data, scalable migrations, and clearer developer workflows.
Concise monthly summary for 2025-08 highlighting key features delivered, major bugs fixed, and overall impact. The team completed a coordinated set of migration, data-layer, and views enhancements for Planscape, stabilized testing, and strengthened code quality, delivering business value through more reliable treatment-goal data, scalable migrations, and clearer developer workflows.
June 2025 monthly summary for OurPlanscape/Planscape: Delivered user-facing DataLayer enhancements enabling URL-based creation with EXTERNAL_SERVICE storage, enforcing exactly one data source (URL or original_name), requiring layer_type when URL is used, initializing URL-created layers to READY, and exposing public read access to dataset and datalayer endpoints. Implemented Planning Area improvements with optional region_name, expanding validation and ensuring compatibility with v1/v2 APIs, supported by accompanying tests. Performed code quality and test maintenance efforts to improve readability, fix a typo in models, lint tests, and modernize test client usage.
June 2025 monthly summary for OurPlanscape/Planscape: Delivered user-facing DataLayer enhancements enabling URL-based creation with EXTERNAL_SERVICE storage, enforcing exactly one data source (URL or original_name), requiring layer_type when URL is used, initializing URL-created layers to READY, and exposing public read access to dataset and datalayer endpoints. Implemented Planning Area improvements with optional region_name, expanding validation and ensuring compatibility with v1/v2 APIs, supported by accompanying tests. Performed code quality and test maintenance efforts to improve readability, fix a typo in models, lint tests, and modernize test client usage.
May 2025 performance highlights for OurPlanscape/Planscape. Delivered key feature enhancements for dynamic layer generation with robust handling of dynamic table names and polygon support to enable efficient MVT tiling; implemented fixes to map tile URL generation and updated tests to ensure correct URL formation for dynamic tiles; introduced DataLayer.map_service_type with a backfill migration and admin/frontend exposure; added read-only protection for DataLayer.info in Django Admin to preserve data integrity; integrated Redis caching to boost Planscape performance; added CLI capability to drop z-dimension during data import (ogr2ogr -dim XY) to streamline data ingestion. These changes collectively improve map rendering performance, data accuracy and governance, admin UX, and overall system responsiveness.
May 2025 performance highlights for OurPlanscape/Planscape. Delivered key feature enhancements for dynamic layer generation with robust handling of dynamic table names and polygon support to enable efficient MVT tiling; implemented fixes to map tile URL generation and updated tests to ensure correct URL formation for dynamic tiles; introduced DataLayer.map_service_type with a backfill migration and admin/frontend exposure; added read-only protection for DataLayer.info in Django Admin to preserve data integrity; integrated Redis caching to boost Planscape performance; added CLI capability to drop z-dimension during data import (ogr2ogr -dim XY) to streamline data ingestion. These changes collectively improve map rendering performance, data accuracy and governance, admin UX, and overall system responsiveness.
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