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Working notes rather than marketing. Each article covers a decision we make regularly and explains why it matters, with the trade-offs stated plainly.
After acquisition and QA, street view imagery moves through visual extraction and spatial aggregation. Each processing layer determines what the final dataset can support.
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The official route starts with free metadata, then permitted image requests. At city scale, sampling, quota, cost, retries, QA and a traceable manifest become the real collection job.
Ten platforms in five families, compared for real data collection work. Coverage, documented access, quotas, licensing and permitted delivery determine whether a source can support a city-scale or global project.
Before collecting thousands of images, use free metadata requests to measure coverage, image age and expected volume across the whole sampling frame.
Collection is only the first stage. A large street view image set becomes usable data when headings, classes, seasonality and validation are controlled across the whole pipeline.
Survey research tells you what people say. Spatial analysis tells you what is actually there. The two answer different halves of the same question.
Most spatial pipelines work perfectly once. The design decisions that matter are the ones that keep them working on run twenty.
A map of competitors is a starting point. Overlap, saturation and rate of change are what make it a decision tool.
Busy does not mean available. The areas that feel best on a Saturday visit are often the ones with the least room for another venue.
Bandwidth, normalisation and colour classification each change the conclusion. A heatmap without those three stated is decoration.
Four formats, four sets of limitations. Choosing the wrong one loses data silently, which is the worst way to lose it.
Not dashboards. Seven specific decisions where location data changes the answer, and what it costs to get there.
Mapping shows you where things are. Location intelligence tells you which option is stronger, by how much, and for what reason.
The hard part is not getting listings. It is getting all of them, once each, with categories that mean the same thing everywhere.
A POI record is a name, a category and a coordinate. Everything that makes POI data hard lives in those three fields.
Geospatial data is any data with a location attached. What makes it different is that distance, containment and adjacency between records carry meaning.
Editorial policy
We publish when we have something specific to say about work we actually do. Every article is written by the team that builds these datasets, and we would rather have twelve useful pieces than two hundred generated ones.
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Send us the specifics. We answer technical questions directly, whether or not there is a project in it.