Doing in code what you would otherwise do by clicking in QGIS — so it can be repeated over fifty wards, re-run when the data updates, and handed to someone else. The skill that separates a GIS operator from an urban data lead.
What you will be able to answer
Complaint locations arrive as a fresh CSV every month, and someone has been joining them to ward boundaries in QGIS by hand. How would you take that off their desk?
Read the points and the ward polygons into GeoPandas, put both into a projected coordinate system before anything is measured, spatially join the points to the wards and aggregate to a count per ward. The reprojection is the step that gets skipped and quietly ruins the result: distances and areas computed in degrees are meaningless numbers rather than merely imprecise ones. The output is a choropleth and a summary table produced in code, which also leaves a record of what was actually done for whoever inherits it.
One payment
₹99
The videos are free
Course outline
Concept 1
Concept 1 · Why write code when QGIS already has a button
Concept 2
After: why-python-for-spatial
A clean conda environment built around GDAL, and the compatibility traps behind most failed geospatial setups.
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Concept 3
After: environment-and-notebooks
A population CSV read into a DataFrame, inspected, then filtered on a rank condition.
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Concept 4
After: pandas-basics
Merging on key columns, then single- and multi-column groupby aggregation.
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Concept 5
After: pandas-basics
The geometry column inspected directly, proving a GeoDataFrame is a table with one extra column.
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Concept 6
After: geodataframe
Area computed in a geographic CRS beside the same area projected — one throws a warning and returns a meaningless number.
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Concept 7
After: coordinate-systems, grouping-and-joining
Point locations matched into state polygons with sjoin, including the CRS alignment it needs first.
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Concept 8
After: coordinate-systems
A buffer built at a stated distance around a layer, with the resulting geometry plotted.
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Concept 9
After: geodataframe
A choropleth given a legend colorbar, scaling and title — the parts that make it readable.
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Concept 10
After: geometric-operations
A street network pulled from OpenStreetMap, then a shortest path solved across it.
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9 candidates did not meet the course criteria.
Spatial-data and analytics roles — building GIS systems, remote-sensing pipelines, urban dashboards, and data products for planning clients, authorities, and tech/geospatial firms. Strong demand where planning meets data. Specialisation tags: Urban, Regional, Environmental (cross-cutting).
9 mapped employers
Explore path →Private transport planning and modelling — travel-demand models, comprehensive mobility plans, transit/BRT/metro feasibility, and traffic micro-simulation, at firms like AECOM, WSP, Ramboll, L&T, and specialist mobility consultancies. Strong, named demand for modellers. Specialisation tags: Transport, Infrastructure.
10 mapped employers
Explore path →