network-accessibility-analysis

# Network & Accessibility Analysis Purpose: replace as-the-crow-flies guesswork with network-true travel costs, at the right scale and with honest assumptions about speeds and modes. First decision on every task: Euclidean distance is only acceptable as a declared approximation — flag it whenever you see it standing in for access. ## Tool selection by scale | Scale | Tool | |---|---| | Neighborhood-city, research flexibility | **OSMnx + NetworkX** | | City-region, many-to-many OD (>10⁴×10⁴) | **r5py** (multimodal + transit w/ GTFS) or **pandana** (contraction-hierarchy speed) | | Production routing service | Valhalla / OSRM / OpenRouteService API | | Proprietary stacks | ArcGIS Network Analyst (script it headlessly) | NetworkX chokes on metro-scale many-to-many — don't loop `shortest_path` over thousands of origins; switch tools instead. ## Graph construction (OSMnx) ```python import osmnx as ox G = ox.graph_from_place("City, Country", network_type="drive") # walk/bike/all G = ox.add_edge_speeds(G) # imputes from highway tags where maxspeed missing G = ox.add_edge_travel_times(G) # edge attr: travel_time (s) G = ox.project_graph(G) # metric CRS before any distance work ``` - **network_type matters**: pedestrian analysis on a `drive` graph misses paths, stairs, plazas; driving on `all` uses footpaths. Match mode. - Imputed speeds are averages by road class — a systematic bias, not noise. State it; calibrate against known trips when stakes ar...