I'll analyze the geographic distribution in this dataset. Let me start by examining the spatial axes mentioned in the card and then explore the actual distribution. ```json { "action": "tool_call", "reasoning": "The card shows multiple spatial axes across brewers, places, and availability tables. I'll start by querying the countries table to understand the top-level geographic distribution, then examine how data is distributed across these locations.", "tool": "query_sql", "args": { "sql": "SELECT c.name as country, c.continent, COUNT(DISTINCT b.brewer_id) as brewers, COUNT(DISTINCT p.place_id) as places FROM raw.countries c LEFT JOIN raw.brewers b ON c.country_id = b.country_id LEFT JOIN raw.places p ON c.country_id = p.country_id GROUP BY c.name, c.continent ORDER BY brewers DESC LIMIT 30", "purpose": "Get the distribution of brewers and places across countries to understand geographic coverage", "source": "raw" } } ```