This wasn't a leetcode-style question you can just pattern-match your way through.
Clarify the geography problem (e.g., nearest point of interest, route optimization) and choose an appropriate algorithm like k-d tree or Dijkstra. Then implement it with clean code, write unit tests covering edge cases, and debug systematically using logging and assertions.
Pro tip: Before coding, discuss trade-offs between different spatial data structures (e.g., k-d tree vs. quadtree) and their impact on performance and memory, showing you consider real-world constraints. Also, mention how you would handle floating-point precision issues in geographic calculations.
Ask questions to understand the exact problem, data size, expected query patterns, and performance requirements. Confirm whether it's a one-time computation or needs to support dynamic updates.
Select an appropriate spatial algorithm (e.g., k-d tree for nearest neighbor, Dijkstra for shortest path) and justify your choice based on trade-offs like time complexity, memory, and ease of implementation.
Write clean, modular functions for core logic (e.g., distance calculation, tree building) and separate I/O. Use meaningful variable names and add comments for complex parts.
Cover normal cases, edge cases (empty input, single point, points on boundaries), and error conditions. Use mocking for external dependencies and test for floating-point precision.
Use print statements, debuggers, or logging to trace issues. After fixing, discuss potential optimizations (e.g., caching, indexing) and how you would scale the solution.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.