地图对机器来说仍然重要吗：重新审视分级统计图在基础模型空间理解中的作用
Do Maps Still Matter for Machines: Revisiting the Role of Choropleth Maps in Foundation Model Spatial Understanding
AI总结：
研究探讨分级统计图对基础模型空间理解的作用，引入ChoroplethMap-Bench基准，在三种输入条件下评估22个模型，发现地图能显著提升空间推理，尤其结合符号数据及高层次空间模式理解任务时，证明地图是基础模型空间推理的重要外部表示。
AI中文摘要：
空间理解对基础模型至关重要，长期以来地图帮助人类组织和推理地理信息。本研究探讨当模型可直接处理结构化地理数据时，分级统计图对机器空间理解是否仍有用。引入ChoroplethMap-Bench基准，含2400个合成分级统计图、对应GeoJSON数据及12000个跨五个认知维度的问题。在三种输入条件下评估22个模型，结果表明地图显著提升空间推理，尤其与符号数据结合及用于高层次空间模式理解任务时。还分析多种因素影响，总体数据+地图条件性能最强，证明地图仍是基础模型空间推理的重要外部表示。
英文摘要：
Spatial understanding is crucial for foundation models (FMs), and maps have long helped humans organize and reason about geographic information. This study examines whether choropleth maps remain useful for machine spatial understanding when models can directly process structured geodata. We introduce ChoroplethMap-Bench, a controlled benchmark containing 2,400 synthetic choropleth maps, corresponding GeoJSON data, and 12,000 questions across five cognitive dimensions: Identify, Spatial Recognition, Compare, Rank, and Delineate. We evaluate 22 open-source and proprietary models under three input conditions: Data Only, Map Only, and Data + Map. The results show that maps substantially improve spatial reasoning, especially when combined with symbolic data and for tasks requiring higher-level understanding of spatial patterns. We further analyze the effects of map type, color hue, and spatial structure, as well as prompting strategies, language, geographic context, decoding settings, classification methods, and response stability. Overall, the Data + Map condition achieves the strongest performance, demonstrating that maps remain valuable external representations for foundation model spatial reasoning.
