The booking fee here funds education projects rather than a margin. That changes the fee data from noise into something worth reading.
ScrapeIt runs the collection as a managed service. You name the places and categories; we enumerate through the published place sitemaps, keep ticket types and fees as separate records, type online events apart, and hand back CSV, JSON, Excel or a push into your warehouse.
Small events appear and change at short notice, so collection runs frequently enough to catch them rather than on a convenient monthly cycle.
We collect published listings only, honour the crawl rules and pace requests. No attendee data, no organiser account information. Terms restrict commercial reuse, so the dataset is for analysis rather than republication, and your counsel should see the use case before the project starts.
Event records carry the event name, organiser, category, venue name and address, city, country, start and end date and time, and the event page address.
Ticket type records are separate: type name, price, currency and the booking fee where published. Keeping the fee apart from the ticket price is the point on this source, since the fee is the platform's actual subject.
Place is a first class field because discovery is organised around it and because the value of the source is local coverage. A national aggregate of community events describes nothing that anybody plans around.
Online events are typed separately, since the place sitemaps include online as a location and an online event is not a fact about a city.
Every row carries the collection timestamp and the sitemap the address was discovered through, so coverage can be audited.
Local calendar assembly is the main use and works best combined with the large platforms rather than instead of them. Each covers what the other misses, and a complete picture needs both on one schema.
Fee benchmarking across ticketing platforms answers what it costs a small organiser to sell a ticket, which is a practical question with surprisingly little published comparison behind it.
Category mix analysis by place shows what kinds of community activity concentrate where - useful to councils and cultural funders, and only visible with the long tail included.
Scope: published event listings only. No attendee data, no organiser account information, no purchase flows. Organisers here are frequently small groups and individuals, and we collect the event rather than anything about the people running it beyond the organiser name as published.
Humanitix is a not-for-profit ticketing platform founded in Australia and operating across several countries. Its booking fees fund education projects rather than shareholder returns, which is the organising idea of the business rather than a marketing line attached to it.
That matters for data because the event mix follows the model. Community events, school and university functions, charity fundraisers, conferences and small cultural events are heavily represented - precisely the long tail that the large commercial platforms carry thinly or not at all.
For anyone mapping what actually happens in a city, the large platforms give you the arena shows and miss most of the calendar. This kind of source is where the rest lives.
Discovery works through place-based sitemaps: the sitemap index carries a set of place files, and the addresses in them are location and category pages that respond directly with substantial content. Crawl rules are published, allow the site and reference the sitemap.
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Event data projects usually start with the big platforms and produce a picture of a city that anybody who lives there would not recognise.
The large platforms carry the events with commercial scale: arena concerts, major sport, big theatre. What they carry thinly is the rest of the calendar - the community theatre, the school production, the charity dinner, the local conference. Those events are the majority by count and they are where most people's evenings actually go.
A platform whose model suits small organisers therefore holds data the big ones do not, and for local media, tourism bodies, councils and anyone building a genuinely complete local calendar, that is the reason to use it.
The second reason is fee transparency. Because the fee is the platform's purpose, it is published clearly, which makes this a usable reference point when comparing what ticketing actually costs a small organiser across platforms - a question small organisers ask constantly and rarely get a straight answer to.
The third is that small events have thin metadata by nature. Descriptions are short, categories are applied loosely, and venue addresses are sometimes informal. That is not dirty data to be cleaned away; it is what a community calendar looks like, and filtering for completeness would delete the long tail we came for.
Learn how to use web scraping to solve data problems for your organization
Leveraging advances in technology, the AI-powered web scraper has skyrocketed in demand and is helping to expand capabilities by automating tedious daily tasks and speeding up data collection from thousands of websites several times over.
Web scraping is a method of obtaining web data by extracting it from pages of web resources with the help of a program, that is, in automatic mode. It is used to syntactically convert web pages into more usable forms.
If you specialize in machine learning, you need to feed large amounts of data to the algorithms. Web scraping is the easiest and the most efficient method of collecting the data from all over the Internet.
ScrapeIt is a managed extraction company, not a tool you have to learn. Our team builds the pipeline, follows the place sitemaps as coverage grows, and repairs the collector before a local calendar starts missing the week ahead.
You see a sample first, in your format, over the places you actually cover, with fees separated and thin listings kept so you can see what the long tail really looks like.
Because they carry the arena shows and miss most of the calendar. Community theatre, school productions, charity dinners and local conferences are the majority by count, and a city picture built only from large platforms is one nobody who lives there would recognise.
Because on this platform the fee is the actual subject - it funds the organisation's purpose. Kept apart it is also a usable reference point for what ticketing costs a small organiser, which is a question with little published comparison behind it.
No, it is what a community calendar looks like. Short descriptions, loose categories and informal venue addresses come with small events. Filtering for completeness would delete exactly the long tail that makes this source worth collecting.
Through the place sitemaps the site publishes - the documented route. We also record which sitemap each address came from so coverage can be audited rather than assumed complete because nothing obvious was missing.
Only the organiser name as published on the listing. No account information, no contact details, no attendee data. Organisers here are often small groups and individuals, and the event is what we collect.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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