Mapping Truths and Tricks: How Visual Choices Shape Interpretation
Location
OC Auditorium
Start
4-3-2026 2:15 PM
Type of Presentation
Oral Presentation
Abstract
Maps are analytical instruments and rhetorical devices. This paper examines how three routine cartographic choices: spatial aggregation, kernel smoothing in density surfaces, and classification of thematic breaks, influence interpretation and can produce misleading spatial narratives. Using an empirical dataset, we generate controlled side-by-side comparisons that isolate each effect. For aggregation, we compare point level and multiple aggregated representations to quantify changes in local pattern visibility. For smoothing, we vary kernel bandwidths in density estimation to show how bandwidth alters hotspot location and extent. For classification, we apply equal interval, quantile, and natural breaks and measure shifts in priority areas. Visual comparisons are supplemented by statistical diagnostics to distinguish visually apparent clusters from statistically significant clusters. Results demonstrate that modest parameter changes can reorder priorities and create spurious hotspots. A concise checklist for mapmakers and map readers is provided to improve transparency and reduce misinterpretation and policy errors.
Mapping Truths and Tricks: How Visual Choices Shape Interpretation
OC Auditorium
Maps are analytical instruments and rhetorical devices. This paper examines how three routine cartographic choices: spatial aggregation, kernel smoothing in density surfaces, and classification of thematic breaks, influence interpretation and can produce misleading spatial narratives. Using an empirical dataset, we generate controlled side-by-side comparisons that isolate each effect. For aggregation, we compare point level and multiple aggregated representations to quantify changes in local pattern visibility. For smoothing, we vary kernel bandwidths in density estimation to show how bandwidth alters hotspot location and extent. For classification, we apply equal interval, quantile, and natural breaks and measure shifts in priority areas. Visual comparisons are supplemented by statistical diagnostics to distinguish visually apparent clusters from statistically significant clusters. Results demonstrate that modest parameter changes can reorder priorities and create spurious hotspots. A concise checklist for mapmakers and map readers is provided to improve transparency and reduce misinterpretation and policy errors.