The Visualisation of Uncertainty for Spatially Referenced Census Data Using Hierarchical Tessellations
The Visualisation of Uncertainty for Spatially Referenced Census Data Using Hierarchical Tessellations
复制标题
使用分层镶嵌对空间参考人口普查数据的不确定性进行可视化
DOI：
10.1111/j.1467-9671.2005.00203.x
复制
发表时间：
2005
影响因子：
2.4
通讯作者：
A. Moore
中科院分区：
文献类型：
--
作者：
J. Kardos;G. Benwell;A. Moore
本文解释了为什么在GIS中利用社会经济数据时考虑不确定性是至关重要的，重点是一种新颖直观的方法来直观地表示不确定性。与其他数据一样，不可能确切地知道社会经济数据离真相有多远。因此，当这些数据用于决策环境时，为数据的正确性给出的近似度量是一个重要组成部分。这是说明，使用choropleth映射技术的人口普查数据作为一个例子。属性和空间的不确定性被认为是与蒙特卡洛统计模拟被用来建模属性的不确定性。介绍了一种适当的可视化技术来管理人口普查类型数据中的某些分区问题和不确定性，同时满足属性和空间不确定性的要求。这是使用来自分层空间数据结构的输出来完成的，特别是区域四叉树和HoR（六边形或菱形）四叉树。这些结构的可变单元尺寸表示不确定性，较大的单元尺寸表示较大的不确定性，反之亦然。使用新西兰2001年人口普查数据和TRUST（使用尺度非特定镶嵌表示不确定性）软件套件来说明这种技术，该软件套件旨在显示空间和属性的不确定性，同时显示原始数据。
This paper explains why it is vital to account for uncertainty when utilising socioeco‐nomic data in a GIS, focusing on a novel and intuitive method to visually represent the uncertainty. In common with other data, it is not possible to know exactly how far from the truth socioeconomic data are. Therefore, when such data are used in a decision‐making environment an approximate measure given for correctness of data is an essential component. This is illustrated, using choropleth mapping techniques on census data as an example. Both attribute and spatial uncertainty are considered, with Monte Carlo statistical simulations being used to model attribute uncertainty. An appropriate visualisation technique to manage certain choropleth issues and uncer‐tainty in census type data is introduced, catering for attribute and spatial uncertainty simultaneously. This is done using the output from hierarchical spatial data structures, in particular the region quadtree and the HoR (Hexagon or Rhombus) quadtree. The variable cell size of these structures expresses uncertainty, with larger cell size indicating large uncertainty, and vice versa. This technique is illustrated using the New Zealand 2001 census data, and the TRUST (The Representation of Uncertainty using Scale‐unspecific Tessellations) software suite, designed to show spatial and attribute uncertainty whilst simultaneously displaying the original data.