Geovisualisation Beyond Maps: Addressing Challenges in Visualising Geospatial Data Using Non-map-based Geography Encodings

通常，地理空间数据的可视化是基于地图的。有许多既定类型的专题地图，例如，科洛普莱斯或比例符号地图。然而，特别是对于复杂的数据集，如网络数据[1]和大型多变量数据(其中除了地理位置之外还需要可视化许多变量)，地图面临着各种挑战，例如符号或标记重叠、视觉混乱和地理扭曲。除了这些视觉问题之外，关于地图的使用可能还有其他考虑因素，例如，事实证明，新冠肺炎案件编号的地图并不能增加公众的知识和行为[2]。这个博士项目基于这样一个假设，即通过使用不基于传统地图的替代视觉编码，可以(对于许多应用程序)完全避免这些问题，而不是试图通过基于地图的表示来缓解和减少这些问题。这种不基于地图的可视化设计不将地理空间信息编码为屏幕上的水平和垂直位置，而是使用基于失真、抽象、空间排序或分组、颜色编码等的替代编码。因此，他们可以使用元素在屏幕上的位置来可视化地理位置以外的属性。这为更灵活的可视编码提供了机会，这些编码集中在数据的其他方面，而不是以地图为基础的布局主导可视化。因此，它们主要适用于需要地理上下文作为参考而不是要传达的主要信息的应用。这一领域的一个主要类别是图案化地图，它“使地图更加可视化，强调数据的显示而不是地理上的准确性”[3，p.1]。在[1](地理空间网络)和[3](类似地图的可视化)中包括了这种技术的一些例子。这个项目的第一步是对现有的非基于地图的可视化和可视化技术进行概述，并确定创造新方法的途径。这可能会导致分类或结构化的设计空间，而不是基于地图的可视化。基于此，我计划在案例研究的背景下开发新的非基于地图的可视化技术，并使用与专家和非专家进行的受控定量和定性研究对其进行评估。此外，我将探索教用户如何阅读这些新颖的可视化的策略，因为它们可能需要支持才能理解不熟悉的可视编码。[1]Schöttler，S.，Yang，Y.，Pfister，H.，and Bach，B.(2021)《可视化和与地理空间网络的交互：勘测和设计空间》。将在计算机图形论坛上发表。[2]Thorpe，A.，Scherer，A.M.，han P.K.J.，Burpo，N.，Shaffer，V.，Scherer，L.，Fagerlin，A.(2021)“接触普通地理新冠肺炎流行地图和公共知识、风险认知和行为意图”。JAMA网络公开赛。Https://doi.org/10.1001/jamanetworkopen.2020.33538[3]HOGRäfer，M.，Heitzler，M.和Schulz，H.-J.(2020年)，类似地图的可视化技术的现状。计算机图形论坛，39：647-674。Https://doi.org/10.1111/cgf.14031

Typically, visualisations of geospatial data are based on maps. There are many established types of thematic maps, e.g., choropleth or proportional symbol maps. However, particularly for complex datasets, such as network data [1] and large multivariate data (in which many variables in addition to geographic locations need to be visualised), maps suffer from various challenges such as overlapping symbols or markers, visual clutter, and geographic distortion. Beyond these visual issues, there may be additional considerations regarding the use of maps, for example, maps of COVID-19 case numbers have been shown not to increase public knowledge and behaviour [2].This PhD project is based on the hypothesis that, instead of trying to mitigate and reduce these issues on map-based representations, they can (for many applications) be avoided entirely by using alternative visual encodings not based on conventional maps. Such non-map-based visualisation designs do not encode geospatial information as horizontal and vertical positions on a screen, but rather use alternative encodings based on distortion, abstraction, spatial ordering or grouping, colour-coding, and more. As such, they can use the position of elements on the screen to visualise attributes other than geolocation. This opens up opportunities for more flexible visual encodings that centre other aspects of the data, instead of the map-based layout dominating the visualisation. Therefore, they are mainly suitable for applications in which the geographic context is required as a reference, but not the primary information to be communicated. One major category in this space are schematized maps, which "[transform] cartographic maps to be more visualization-like, emphasizing the display of data over geographic accuracy" [3, p.1]. A number of examples of such techniques are included in [1] (geospatial networks) and [3] ('map-like' visualisations).The first step of this project is to get an overview of existing non-map-based visualisations and visualisation techniques and to identify avenues for creating new methods. This could lead to a taxonomy or structured design space of non-map-based visualisations. Based on this, I plan to develop novel non-map-based visualisation techniques in the context of a case study and evaluate them using controlled quantitative and qualitative studies with experts and non-experts. Furthermore, I will explore strategies to teach users how to read these novel visualisations, as they may require support to understand unfamiliar visual encodings.[1] Schöttler, S., Yang, Y., Pfister, H., and Bach, B. (2021) "Visualizing and Interacting with Geospatial Networks: A Survey and Design Space". To be published in Computer Graphics Forum.[2] Thorpe, A., Scherer, A.M., Han P.K.J., Burpo, N., Shaffer, V., Scherer, L., Fagerlin, A. (2021) "Exposure to Common Geographic COVID-19 Prevalence Maps and Public Knowledge, Risk Perceptions, and Behavioral Intentions". JAMA Netw Open. https://doi.org/10.1001/jamanetworkopen.2020.33538[3] Hogräfer, M., Heitzler, M. and Schulz, H.-J. (2020), The State of the Art in Map-Like Visualization. Computer Graphics Forum, 39: 647-674. https://doi.org/10.1111/cgf.14031