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A scenario bundles everything a piece of spatial analysis needs before you type anything: a study area, the prepared datasets for it, a method that maps onto the platform’s analysis tools, and suggested questions that work with that data. Opening one turns an empty project into a project that is ready to answer a real question. The Scenarios catalog in the top menu, one card per built-in study area

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The six built-in scenarios

Every scenario is built from the built-in datasets catalog, so each layer arrives typed, styled, and with its source, licence, and attribution attached. Each scenario has its own page describing the planning question, the data it ships, the method it follows, the platform tools it uses and what a run can produce — start from the scenario you need or browse all six below.

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Where to find them

  • Top menu → Scenarios — the catalog, filterable and searchable alongside datasets.
  • New project screen — the scenario picker offers the same six scenarios when you start from scratch.
  • Empty chat — the workspace chat offers scenarios before the first message.

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Open a scenario

1

Pick a scenario

Open the catalog and select a card. The detail view lists the study area, the datasets included, the questions it is designed for, and the analysis tools behind it.
2

Open it in your project

Choose Open scenario. Every layer in the bundle is imported into the current project with its default style, legend, and attribution — no upload and no configuration.
3

Use a suggested question

The scenario ships with starter questions written for that study area. Click one to load it into the chat box, then send it, or edit it first. The AI recognises the scenario’s layers, so the question works without further setup.
4

Refine and deliver

Follow up in chat to adjust scope or thresholds, then export the result as a report or share the map. See Analysis for what each step produces.
If a project already contains the scenario’s layers, MapX asks before adding them again — the second import adds another layer set rather than replacing what you already styled.

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Scenario, dataset, or workflow?

Scenarios are the fastest way to see the platform work end to end, and a good way to learn prompt patterns: each one documents the questions it was designed for, and the analysis chain behind them.