Dataspryva

Use US Walkability, Transit & Bike Score Extractor with Python

Looks up Walk Score, Transit Score, and Bike Score for any US city or street address, returning each 0-100 score with its rating label and nearby amenity categories.

1. Choose the data you need

Review the published README for supported collection modes, examples, and limits. The recipe below uses the current published input definition.

2. Prepare a source-based input

This JSON contains published prefills and required-field defaults. Review every value for your task before running it; it is not a promise of successful collection.

{
  "addresses": [],
  "cities": [
    "Seattle, WA"
  ],
  "includeNeighborhoods": false,
  "maxItems": 1,
  "mode": "byCity",
  "neighborhoods": [],
  "state": "WA"
}

Download input.example.json and save your reviewed copy as input.json.

FieldTypeRequiredDescription
modestringrequiredWhat to look up.
addressesarrayoptionalUS street addresses, e.g. '1 Beach St, San Francisco, CA 94133'. walkscore.com/score/... URLs are also accepted.
citiesarrayoptionalCities as 'Seattle, WA', 'Seattle-WA', or a walkscore.com city URL.
neighborhoodsarrayoptionalNeighborhoods as 'Ballard, Seattle, WA' or a walkscore.com neighborhood URL.
statestringoptionalUS state whose city table to read.
includeNeighborhoodsbooleanoptionalAdd the city page's neighborhood ranking table to each city record.
maxItemsintegeroptionalMaximum records to emit (state summary counts as one).

3. Start an Actor run

Set APIFY_TOKEN in your environment. Python 3.10 or newer is sufficient; this recipe uses no third-party packages.

Execution may incur charges. Review current pricing on Apify. The request asks for a 120-second timeout and a charge limit; these settings are subject to Apify’s pricing and do not guarantee an all-inclusive spending cap.

import json
import os
import urllib.request

# Save the schema-derived example as input.json; review and edit it first.
with open("input.json", encoding="utf-8") as stream:
    actor_input = json.load(stream)

token = os.environ["APIFY_TOKEN"]
request = urllib.request.Request(
    "https://api.apify.com/v2/acts/e6LDeTzJu7sTkzu3x/runs?timeout=120&maxTotalChargeUsd=0.50",
    data=json.dumps(actor_input).encode("utf-8"),
    headers={"Authorization": "Bearer " + token, "Content-Type": "application/json"},
    method="POST",
)
# This starts a potentially billable run. Do not automatically retry this POST.
with urllib.request.urlopen(request, timeout=40) as response:
    run = json.load(response)["data"]
print("Run ID:", run["id"])
print("Status:", run["status"])
# Inspect and download results in Apify Console once the run succeeds.

4. Inspect the actual results

Open the run in Apify Console and wait for a successful completion. Use the published output schema to locate results and the dataset schema to understand declared views. Missing or optional record fields must not be treated as guaranteed values.

Address, city, neighborhood, state, and ranking records with Walk, Transit, and Bike scores, ratings, coordinates, transit lines, parks, neighborhood context, and live/archive provenance.

Questions before you run

Which inputs are required?

The published required fields are mode

What does this guide verify?

The fields, prefills, and schema links are rendered from the published build. This is a usage recipe, not a paid live extraction test. Source availability and collection results can vary.

What if the run request times out?

Check Apify Console before retrying. The first request may already have created a run. Do not automatically repeat a run-creation POST.

Generated from source build 4PDMfidL97ROpWPYL and its README and schemas. Source changes regenerate this guide; no third-party editorial claims are inferred.