Building a PostGIS Spatial ETL Pipeline for Greek Urban AnalysisBuilding a PostGIS Spatial ETL Pipeline for Greek Urban Analysis
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Konstantinos Chantsaris
• Oct 4
Standard municipal data is misleading.
If you want to find 15-Minute Cities within Greece's urban landscape, you have to rely on more than just city-wide averages. I've been creating a containerized PostGIS spatial data warehouse to identify undervalued, structurally neglected and high-potential urban micro-fragments.
The data includes:
6,138 official ELSTAT communes
12,505 objective government pricing polygons
132,000+ OpenStreetMap amenities
Fusing different spatial and flat tabular data together presented challenges. Last night, I published an article on Medium documenting the process of building this spatial ETL pipeline. I cover the architectural wins, and of course the bugs encountered along the way.
These include:
Spatial Sliver Confusion: How a 150m² geographic sliver falsely inherited the population of a 20km² city, and why I had to build a Two-Tier Data Model to fix it.
CRS Misuse: How a projection mismatch between EPSG:3857 and EPSG:4326 caused the mapping software to panic and collapse into a blank screen.
Missing Spatial Index: Why dropping a functional GIST ((geometry::geography)) index turned a 2-second query into an hour-long frozen process.
Analytical Reward: Running an initial and experimental spatial linear regression in SQL, showcasing a €8.51/m² property value increase for every additional amenity per km².
If you are into Data Engineering, GIS or Spatial Engineering in general, check out the full technical breakdown here: 🔗 https://medium.com/@kchantsaris_84495/designing-a-spatial-etl-pipeline-in-postgis-72565b53aeb8
Completed work
Data AnalysisData VisualizationMatplotlibPostgreSQLPythonData Engineering
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