Marija Ercegovac
Senior Geospatial Analyst at Rockup, building neighborhood analytics tools. Previously Geospatial Researcher (R&D) at Habidatum, developing cross-country urban mobility pipelines for European policy institutions — OD matrices, temporal land-use profiling, service accessibility mapping across 16 countries. Former geospatial data scientist at Yandex, where I built GeoAI prediction models and led spatial feature engineering for service expansion. Invited lecturer on geospatial data science (MIPT Deep Learning School) and QGIS (RheinMain University, Germany). Jury member at IAAC Barcelona. Daily tools: Python, GeoPandas, PostGIS, QGIS. Admitted to MSc Geomatics at TU Delft. I run URBAN_MASH, a geoanalytics community (2,200+ subscribers).
Sessions
verture Maps Places offers 53 million POIs with a clean taxonomy — but what can you actually do with them? In 50 lines of Python and DuckDB, I turn raw Overture places into a visual neighborhood typology. Before/after, pipeline, and a ready-to-run notebook.
Which open mobility dataset should you trust for urban function analysis? Using Copernicus Urban Atlas polygons, we compare Eurostat experimental MNO statistics, OpenStreetMap GPS traces, and census commuting flows. You will learn their biases, and get a fully reproducible Python/PostGIS workflow.