Time Out Events & Things-To-Do Scraper

Collect publicly available Time Out (timeout.com) city listings — "things to do", events, theatre, music, art and more — as clean, structured records. Point it at a city and section; it reads the hub and enriches every card from its detail page so you get the full event, not just a headline.

What you get (one record per event)

field	description
event_id	stable slug id from the event URL
title	event / listing title
event_url	Time Out detail-page URL
location_name	venue name
address	full venue address (street, locality, postcode)
start_datetime / end_datetime	ISO datetimes (from the page's structured data)
price_range	price / price range with currency
ticket_booking_url	booking / ticket link when present
description	editorial summary
category	event type (TheaterEvent, MusicEvent, …) or section
image_url	lead image
rating_text	Time Out star rating when shown

Input

field	default	description
location	london	city slug as in the URL (e.g. newyork, paris, chicago, tokyo)
section	things-to-do	hub section (things-to-do, theatre, music, art, attractions, comedy)
search_query	—	optional keyword filter on title/summary
maxItems	50	cap on events (0 = whole section)
proxyConfiguration	RESIDENTIAL	uses Apify Residential by default — no external proxy key needed

How it works

The listing hubs are server-rendered, so the scraper parses each card by stable structural attributes (resistant to cosmetic markup changes) and pulls the structured per-event data (datetimes, venue, address, price, tickets) from each detail page's schema.org JSON-LD. Requests use a residential proxy for reliable access; you only supply the city and section.

Pricing

Pay per result — you are charged once per event record written to the dataset. Compute runs on your own Apify account.

Notes

Use the city/section slugs exactly as they appear in the timeout.com URL after the domain (e.g. https://www.timeout.com/london/things-to-do → location=london, section=things-to-do). This tool collects publicly available listing data for research and aggregation.

Usage statistics

This Actor creates a small, content-free summary at the end of each run. It is used only to monitor reliability and improve this Actor. A copy is saved as USAGE_STATS in your own Apify key-value store, so you can see the exact record created for your run.

Set disableUsageStats to true in the input to opt out. Nothing is sent then; your USAGE_STATS record only says that statistics were disabled.

Only these fields are recorded:

• schema version, Actor name and build number;
• UTC start and finish hour (not a precise timestamp);
• run duration, number of results and time to the first result, each as a coarse range;
• whether the result was empty, the end status, and an error type from a fixed list;
• memory setting and counts of charged events;
• names of the input fields you set, never their values;
• the selected option for input fields that offer a fixed list of choices (for example a sort order).

We do not collect input text, search terms, URLs, domains, usernames, email addresses, names, proxy credentials, tokens, scraped records, output items, raw error messages, stack traces, or hashes of any of those values. Records are kept for no longer than 13 months, used only as aggregated operational statistics, and never sold or shared.

Additional fields (Phase 2)

This Actor also records your Apify user ID, whether Apify marks the account as paying, the size range of list inputs, the selected country when the input offers a fixed list of countries, and one category from a fixed Actor taxonomy. We use these fields only for aggregate reliability, repeat-use and cross-Actor analysis; reports suppress any cell with fewer than five distinct users.

The same disableUsageStats: true input flag turns these fields off too. The user ID is removed after 13 months; we do not export, sell, share, or attempt to re-identify this data.

Run-outcome signals (v2)

To learn whether a run did what it was asked to do, the record also holds a few more coarse ranges and yes/no flags. None of them contains content:

• the result limit you asked for (a range, when the input has one) and what share of it was delivered;
• results delivered per input item you listed (a range);
• output quality as ranges: how fully the result fields were filled, the share of rows that look like errors, the share of duplicate rows, and how many different fields appeared. These are counted in memory while results are saved; no result content is kept;
• how the run was started (console, API, schedule, webhook, another Actor);
• how it ended: stopped by you, timed out, reached the requested limit, stopped by the charge limit, and how many times the platform moved the run;
• if this Actor reports it: how many items to process worked or failed (ranges) and one failure reason from a fixed list;
• a short code made from the names of the input fields you set, never their values.

Repeat-run fingerprint (v2)

When your Apify user ID is recorded (see above), the record also holds an 8-character one-way code made from your input (proxy settings left out) and this Actor's name. It only lets us see that the same account ran the same input again soon after an unsatisfying run; we never see the input itself. It is stored only in the database, never published, and reports use it in aggregate with the same five-user minimum. It is the one exception to the statement above that no hashes are collected, and disableUsageStats: true turns it off.