Cityseer is a Python library providing computational tools for network-based pedestrian-scale urban analysis. It enables users to model urban environments, analyze accessibility, connectivity, and other urban metrics using spatial data and graph theory. The current version is 4.24.1, and it typically sees regular updates, often with significant changes between major versions.
pip install cityseerVerified import paths — ran on the pinned version, not inferred.
This quickstart demonstrates how to create a `Cityseer` `City` object from an `OSMnx` graph and perform a basic accessibility analysis. It highlights the primary entry point (`City.from_osmnx`) and a common analysis method (`analyze_accessibility`). Ensure `osmnx` is also installed (`pip install osmnx`).
Review the official v4 migration guide. Replace direct `graph` object manipulation and standalone functions (e.g., `graph_from_bbox`, `accessibility`) with methods on the `cityseer.City` instance (e.g., `City.from_osmnx`, `city.analyze_accessibility`).
Always pass the `crs` argument with a valid EPSG code or WKT string to `City` constructors (e.g., `City('place', crs='EPSG:4326')` or `City.from_osmnx(G, crs=G.graph['crs'])`). Ensure your input data also has a defined CRS.For initial development and testing, work with smaller, representative areas. Consider simplifying the network or processing in smaller chunks for very large regions. Monitor memory usage and execution time.