  Maps are crucial in conveying geospatial data in diverse contexts such as
news and scientific reports. This research, utilizing thematic maps, probes
deeper into the underexplored intersection of text framing and map types in
influencing map interpretation. In this work, we conducted experiments to
evaluate how textual detail and semantic content variations affect the quality
of insights derived from map examination. We also explored the influence of
explanatory annotations across different map types (e.g., choropleth, hexbin,
isarithmic), base map details, and changing levels of spatial autocorrelation
in the data. From two online experiments with $N=103$ participants, we found
that annotations, their specific attributes, and map type used to present the
data significantly shape the quality of takeaways. Notably, we found that the
effectiveness of annotations hinges on their contextual integration. These
findings offer valuable guidance to the visualization community for crafting
impactful thematic geospatial representations.
