Performance Improvements

[a-regal]

Performance improvements with minimal changes across modules

Before moving further with publishing in conda and other improvements to the package in general, I think a general QOL update to our packages' performance was important. I went through our modules one by one with Claude Code to figure out where we can gain the most with the least changes. In total, five per-module commits on top of master with the following breakdown:

54d6d90 fix/perf(download): vectorize point construction, fix hdx_dataset typos
b30960b fix/perf(utils): no-op fillna fallback in overpass_to_gdf; one-pass relation polys
3f93ce2 perf(accessibility): vectorize friction, cache centroids in travel_times
0fd8f2c perf(routing): batch OSRM via /table/, pooled Session, single-source Dijkstra in isochrones
d6aa459 perf(geom): deduplicate hex IDs before building polygons; one-pass stats writes; union_all

Benchmarks (synthetic data, best of 5)

Path	Old	New	Speedup
accessibility.friction (200k rows)	2103.6 ms	8.9 ms	237×
geom.osmnx_coefficient_computation write pattern (2k × 4)	917.6 ms	4.5 ms	202×
routing.compute_osrm_dist_matrix (10 × 10, local stub)	117.3 ms	1.6 ms	74× ¹
download.get_hdx_dataset Point construction (400k rows)	5231.2 ms	96.7 ms	54×
accessibility.travel_times centroid handling (20k units)	577.0 ms	18.3 ms	31×
routing.isochrone_from_graph (2k-node graph, 3 × 3)	572.3 ms	145.2 ms	3.9×
utils.process_overpass_relations (5k ways)	143.8 ms	111.4 ms	1.3×
geom.gen_hexagons (res-8 over 0.5°²)	103.7 ms	98.6 ms	1.05× ²
geom.merge_geom_downloads (6 overlapping gdfs)	2.22 ms	2.18 ms	≈ ³

¹ Local in-process HTTP stub — with a real OSRM server the gap is dominated by N·M HTTP round-trips, so this is a lower bound.
² Bottleneck is h3.geo_to_cells / cell_to_boundary themselves; new code's main win is avoiding duplicate Polygon construction on multi-part inputs.
³ union_all is the same speed as unary_union — change exists to clear the shapely 2 deprecation warning.

Correctness fixes shipped alongside

• utils.overpass_to_gdf: the per-key NaN fallback loop discarded the result of Series.fillna() (no assignment, no inplace=True), so secondary tag keys never populated poi_type. Now correctly fills from fallback keys.
• download.hdx_dataset: had resource.ends_with(...) / resource.starts_with(...) (AttributeError on the first call) and an unbound hdx_url = hdx_url branch. Both fixed.
• np.NaN → np.nan and k in tag.keys() → tag.get(k, np.nan) across utils.py and download.py.

Per-module change detail

geom (commit d6aa459)

• gen_hexagons: deduplicate H3 cell IDs across (multi)polygon parts before materializing shapely Polygons, dropping the post-hoc drop_duplicates(). Equal output for single-polygon cities; avoids redundant Polygon construction on multi-part inputs.
• osmnx_coefficient_computation: accumulate per-row stats into a list of dicts and assign once. Replaces O(N·K) .loc[i, col] = ... writes that forced repeated reindex / dtype upcasts.
• merge_geom_downloads (+ plotting.choropleth_map): switch deprecated GeoSeries.unary_union to GeoSeries.union_all() (shapely 2 path).

routing (commit 0fd8f2c)

• compute_osrm_dist_matrix: replace nested per-pair /route/ requests with a single OSRM /table/v1/ call; preserves the per-pair fallback for older OSRM builds.
• Module-level requests.Session with urllib3 Retry + connection pooling, reused by osrm_route, ors_api, isochrone_from_api. Removes per-call TCP/TLS handshake overhead and the ad-hoc time.sleep retry.
• isochrone_from_graph: run nx.single_source_dijkstra_path_length once per center and threshold the node set for each trip_time, instead of re-running ego_graph for every (center, time) pair. Also swap the GeoSeries(...).unary_union.convex_hull round-trip for a direct shapely.MultiPoint hull.
• type(x) == gpd.GeoSeries → isinstance(x, gpd.GeoSeries).

accessibility (commit 3f93ce2)

• friction: accept array input via np.where; preserves scalar behavior.
• hu_access_map: replace the two progress_apply(scalar friction(...), axis=1) passes with numpy distance computation on cached x/y arrays.
• travel_times: compute geometry.centroid once and reuse for both nn_search inputs and OSRM calls; pull nearest POI geometries up front instead of recomputing centroid + pois.iloc per row inside progress_apply.

utils (commit b30960b)

• overpass_to_gdf: assign the result of Series.fillna() back to gdf["poi_type"]. Switch tag[k] if k in tag.keys() else np.NaN → tag.get(k, np.nan).
• process_overpass_relations: replace three sequential .apply() passes (shell → length filter → Polygon) with a single Python loop, and call shapely.make_valid on the underlying array instead of per-row apply.

download (commit 54d6d90)

• get_hdx_dataset: df.apply(lambda r: Point(...), axis=1) → gpd.points_from_xy on the filtered slice; .copy() the slice; set crs="EPSG:4326" on the result.
• overpass_pois: same tag.get(...) rewrite as utils.
• hdx_dataset: .ends_with/.starts_with → .endswith/.startswith; fix unbound hdx_url = hdx_url branch.

Bench methodology

Each comparison runs the old and new implementations side-by-side in the same process against synthetic data sized to make per-call overhead visible (e.g. 200k / 400k / 2k rows). Reported numbers are best of 5. Network-dependent paths (OSRM /route, /table, Overpass) are exercised against a local in-process HTTP stub so we measure code-side overhead — the real-world gap on OSRM /table will be substantially larger because the per-pair path pays N·M HTTP round-trips.

[Claudio9701]

LGTM. One comment for a follow up related to the OSRM the user should be able to set a custom port if needed. we have port 5000 hardcoded in many places