Choropleth map

Abstract: This paper explores in detail a new and effective metaphor for visualising choropleth map uncertainty. The “level-of-detail” metaphor has been shown here to communicate attribute uncertainty, but also spatial uncertainty as a secondary expression. The metaphor is delivered to the map viewer via the regular tessellated output of the Hexagonal or Rhombus (HoR) quadtree spatial data structure, as a semi-transparent map layer that lies on top of the choropleth (termed the trustree when used in this manner). For testing, multiple images were created with differing resolution levels of output from the trustree and superimposed on a New Zealand 2001 census choropleth map of Dunedin City. An Internet survey was designed and run, to reveal the visual metaphors that the trustree communicates uncertainty through. The choice of metaphor offered was (1) a level of detail (or resolution) metaphor, where less detail (i.e. coarser resolution cells) represents more uncertainty (i.e. uncertainty is sketchy), or (2) a metaphor of clutter, where the data structure output can be sufficiently dense so as to cover spatial information, in effect hiding uncertain areas (i.e. uncertainty is a barrier). In this case the finer resolution cells indicate more uncertainty. Also, the survey aimed to determine a usable trustree tessellation resolution level to express uncertainty information. The results showed the trustree tessellation was more effective when representing a metaphor of detail and that attribute and spatial uncertainty can be effectively expressed, depending on the tessellation level used.

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.

Abstract: The cartographic community has taken a renewed interest in evaluating the effectiveness of automated map displays, given their increasing prevalence among general map users. The changing values of the mapped area from frame to frame in a dynamic thematic map constitute its main element of visual complexity, while many of the peripheral map components often change little (titles) or not at all (scale bars, color ramps). Building on recent research into visual complexity as it relates to dynamic thematic mapping, this study developed a raster-based GIS model for evaluating the graphical variability between sequences of choropleth maps as they would appear as scenes in a dynamic map. The evaluation of visual complexity is based on two previously established metrics, Basic Magnitude of Change (BMOC) and Magnitude of Rank Change (MORC), for describing the variability and average class 'jump' for enumeration units across map scenes. The model presented in this paper uses a neighborhood focal operator that sequentially moves across the entire map, replicating the user's viewing perspective as it divides the scene to instantaneously focus only on the part of the map within the foveal viewing area, a zone of enhanced visual-cognitive acuity. This model accepts a single vector map, uses its class membership attribute data as inputs, computes the BMOC and MORC variability, and writes the value to the focus. The model output is two smoothed map images depicting relative visual complexity values for the sequence of maps. While the neighborhood paradigm can theoretically be used to quantify change on either a vector or raster map, the raster-based approach suggests several improvements over one based on vector polygons. These include a potentially higher degree of accuracy in modeling the user's perspective, especially if enumeration units vary widely in size within the foveal area and map itself, plus the ability to use (with minimal customization) existing image-processing software such as ERDAS Imagine, ArcGIS Spatial Analyst and ENVI to perform analysis of dynamic map complexity.

Abstract: description and analysis of geographically indexed health data with respect to demographic, environmental, behavioural, socioeconomic, genetic, and infectious risk factors (Elliott andWartenberg 2004). Disease maps can be useful for estimating relative risk; ecological analyses, incorporating area and/or individual-level covariates; or cluster analyses (Lawson 2009). As aggregated data are often more readily available, one common method of mapping disease is to aggregate the counts of disease at some geographical areal level, and present them as choropleth maps (Devesa et al. 1999; Population Health Division 2006). Therefore, this chapter will focus exclusively on methods appropriate for areal data...