Searching for an Optimal Hexagonal Shaped Enumeration Unit Size for Effective Spatial Pattern Recognition in Choropleth Maps

Abstract

Thoughtful consideration of the enumeration unit size in choropleth map design is important to ensure the correct communication of spatial information. However, the enumeration unit size and its influence on pattern conveying in choropleth maps have not yet been the subject of in-depth empirical studies. This research aims to address this gap. We focused on the issue concerning whether the ability to recognize spatial patterns on an Equal Area Unit Map is related to the hexagonal enumeration unit size, defined by the number of pixels. The aim is to indicate the range of the enumeration unit sizes, namely, at what point the upper and lower borders of the range where the spatial patterns start, and where the end is visible and recognizable by users. To address this problem, we conducted an empirical study with 488 users. The results show that the enumeration unit size has an impact on the users’ spatial pattern recognition abilities. Choropleth maps with enumeration unit sizes of 26, 52, and 104 pixels were, in the majority, indicated by participants as those most suitable for indicating spatial patterns. This was in contrast to choropleth maps with enumeration unit sizes of 1664 and 3328 pixels, which users indicated as not being useful. However, there were some exceptions to this general finding. Thus, determining the optimal enumeration unit size is a challenging task, and requires further insightful investigations.

Mapping Urban Structure Types Based on Remote Sensing Data—A Universal and Adaptable Framework for Spatial Analyses of Cities

“…As an alternative for equal-area units, hexagons are suggested by many studies to ideally divide an urban area into comparable units (Figure 7D). Hexagons are observed to reduce sampling bias at their edges and reportedly support the visual inspection of spatial patterns [148].…”

“…Result can contain irregular shapes or artefacts based on extreme geometries or suboptimal spatial distributions. Regular divisions, e.g., hexagonal grids Equal spatial units, reduced sampling bias$$Regular and highly objective Hard to determine the ideal scale [148]$$Might suppress patterns at higher or lower scales…”

Where Maps Lie: Visualization of Perceptual Fallacy in Choropleth Maps at Different Levels of Aggregation

“…It is very difficult to predict where such distortions are apt to occur. Many research papers are devoted to problems of spatial units in density mapping: modifiable spatial units [4][5][6][7][8]; disaggregation of spatial data [9,10]; aggregation of spatial Many research papers are devoted to problems of spatial units in density mapping: modifiable spatial units [4][5][6][7][8]; disaggregation of spatial data [9,10]; aggregation of spatial data; and spatial uncertainty [11][12][13]. The impact of the data aggregation level on the accuracy of mapping has been extensively discussed in the field of health geography.…”