How I gather the data

Why this matters

I built GoTripAble because I could not find honest, practical answers to the questions that actually matter before you travel. Not "is there a ramp?" but "how far is the nearest toilet from the beach?" Not "accessible rooms available" but "will the cobblestones wreck your knees before you have made it to lunch?"

Generic travel content is built for the average person. Most of us with health conditions, disabilities, or additional needs are not the average person. So the data needs to be different too.

Here is exactly how I pull it together.

🗺️OpenStreetMap: the world's largest open geography database

OpenStreetMap (OSM) is a community-built map of the planet, and it contains far more than roads. I pull public toilet locations, bench and seating data, pedestrian zone classifications, gradient data, cobblestone surface tags, and accessibility features like dropped kerbs and tactile paving. This is all public, real-world data contributed by millions of mappers over decades. I query it via the Overpass API and use it to calculate terrain ratings, toilet frequency scores, and rest stop availability. For example, if a destination has sparse toilet data on OSM, that directly lowers the toilet access score. No guesswork.

🌡️OpenWeatherMap: live climate data, not travel brochure averages

I connect to OpenWeatherMap's climate API to pull actual temperature ranges, humidity levels, and UV index data by month for every destination. This feeds directly into the heat rating. A destination that is described as "Mediterranean and sunny" in a brochure might genuinely reach 38 degrees in August with 80% humidity, which is genuinely dangerous for people with MS, heart conditions, POTS, or heat intolerance. My heat ratings reflect that, not the marketing version.

🤖AI: used to organise, interpret and fill gaps, not to invent facts

I use large language models to help process the volume of information and structure it clearly. AI does not make up destination data. It organises, summarises and interprets what already exists from real sources. Where I ask AI to generate condition-specific notes (for example, what fibromyalgia travellers should know about a destination), it works from the terrain, heat, toilet and facility data I already hold, plus established medical guidance about those conditions. I run a fact-checking pass on every batch to make sure the output is grounded in reality, not hallucinated. Where the AI is not confident, I flag it rather than publish uncertain content.

🔬Medical research: condition-specific guidance, not guesswork

Each condition category on GoTripAble (fibromyalgia, MS, IBS, Parkinson's, wheelchair users, autism, and over 30 others) has a different set of needs. I have researched what actually matters for each condition, which rating categories carry more weight, what specific risks are present, and which environmental factors are most relevant. For example, for people with POTS or heat intolerance, I weight the heat rating much higher than terrain. For IBS travellers, toilet frequency and food options carry the most weight. These condition-specific scoring weights are baked into the personalised search results.

📸Photo and image auditing

I use AI vision to check every destination image, not just for quality but for accuracy. A stock photo of a Tuscan hilltop does not tell you whether the town centre is cobbled. I review destination imagery against the terrain data I hold, and flag mismatches. I would rather show a less glamorous photo that accurately represents the terrain than a beautiful shot that misleads someone into thinking it is flat.

👷Continuous enrichment: this is never finished

GoTripAble is not a dataset that was built once and left. Every destination is continuously re-enriched as new data becomes available. I run scheduled enrichment jobs that pull fresh weather data, re-check OSM for new features, refine condition notes based on accuracy audits, and add information as I learn more. If a destination gets new accessible transport links, or a beach installs a new beach wheelchair scheme, I want that reflected in the data as soon as I find out about it. The coverage level shown on each destination tells you how much data I currently hold.

What I do not do

• I do not accept payment from hotels, tourist boards, or destinations to improve their ratings.
• I do not publish AI-generated content without a human review pass for factual accuracy.
• I do not use marketing copy or tourism board press releases as data sources.
• I do not pretend to have data I do not have. Where coverage is thin, I say so.
• I do not guarantee that any destination is suitable for any individual, because accessibility is personal. Always verify directly with venues before you book.

The next layer: real traveller experiences

APIs and AI can only go so far. The things that matter most to disabled and health-affected travellers are often the things that do not appear in any database. The texture of the pavement outside the restaurant. Whether the accessible toilet in the museum actually had enough turning space. Whether the beach was so loud it triggered sensory overload.

That is why I am building a traveller experience layer, a place where real people can share what they actually found, and where that information can gradually inform and improve the ratings over time. I review every submission before it goes live, so the quality stays high and the information stays useful.

GoTripAble is a guide, not a guarantee. I do my best to be accurate, but information changes. Accessibility features get added and removed. Always contact venues directly to verify current accessibility before you travel. See the Terms for full liability information.