Assessing different approaches to visualise spatial and attribute uncertainty in socioeconomic data using the hexagonal or rhombus (HoR) trustree
Assessing different approaches to visualise spatial and attribute uncertainty in socioeconomic data using the hexagonal or rhombus (HoR) trustree
复制标题
使用六角形或菱形 (HoR) 信托树评估社会经济数据中空间可视化和属性不确定性的不同方法
DOI：
10.1016/j.compenvurbsys.2005.07.007
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
A. Moore
中科院分区：
文献类型：
--
作者：
J. Kardos;G. Benwell;A. Moore
可视化社会经济数据的不确定性是一项必要的任务，这样地图用户就可以做出明智和明确的决定。这对于choropleth mapping来说尤其如此，它以隐含地突出可修改的面积单位问题（MAUP）而闻名，导致经常出现虚假的choropleth面积单位边界，而不是从地理或社会经济现象中定义的。本文通过扩散固定和任意choropleth边界来解决这种形式的空间不确定性，同时通过可视化解决传统属性不确定性。本文提出了使用六边形或菱形（HoR）四叉树镶嵌（称为可信赖树）的替代方法。属性的不确定性通过镶嵌尺寸来表示，网格空间边界的不确定性通过镶嵌位置来表示。进行了一项互联网调查，以评估适用于新西兰2001年人口普查数据的六种不同的可信方法的可用性和有效性。结果表明，透明的HoR信任树覆盖在与原始choropleth相邻的choropleth地图上是最可用和最有效的表达空间和属性不确定性的方法。此外，覆盖其原始面积单位边界的按面积显示的HoR值几乎同样有效和可用。未来的研究应侧重于使用各种不确定性可视化方法评估现实世界的地图参与者。
The visualisation of uncertainty for socioeconomic data is a necessary task so that map users may make well-informed and unambiguous decisions. This is particularly the case for choropleth mapping, which is well-known for implicitly highlighting the modifiable areal unit problem (MAUP), resulting in often spurious choropleth areal unit boundaries, not defined from geographical or socioeconomic phenomena. This paper addresses this form of spatial uncertainty by diffusing fixed and arbitrary choropleth boundaries and simultaneously, traditional attribute uncertainty through visualisation. The paper presents alternative ways of using the Hexagonal or Rhombus (HoR) quadtree tessellation (termed the trustree) for this purpose. Attribute uncertainty is expressed via tessellation size, and choropleth spatial boundary uncertainty via tessellation location. An Internet survey was conducted to assess the usability and effectiveness of six different trustree methods applied to New Zealand 2001 census data. Results are given and show that a transparent HoR trustree overlaying a choropleth map shown adjacent to the original choropleth is the most usable and the most effective way to express spatial and attribute uncertainty. Also, the HoR value-by-area display with its original areal unit boundaries overlain was almost as effective and usable. Future research should focus on assessing real-world map participants using a variety of uncertainty visualisation methods.