Publications

 

Topic: Spatial measures and human behavior

Quantifying space, understanding minds: A visual summary approach

Simpson, M., Richter, K.-F., Wallgrün, J. O., & Klippel, A. (2017). Quantifying space, understanding minds: A visual summary approach. Journal of Spatial Information Science, 14, 95–136. https://doi.org/10.5311/JOSIS.2017.14.292

Link to Publication (open access)

Link to ResearchGate Page

Abstract: This paper presents an illustrated, validated taxonomy of research that compares spatial measures to human behavior. Spatial measures quantify the spatial characteristics of environments, such as the centrality of intersections in a street network or the accessibility of a room in a building from all the other rooms. While spatial measures have been of interest to spatial sciences, they are also of importance in the behavioral sciences for use in modeling human behavior. A high correlation between values for spatial measures and specific behaviors can provide insights into an environment’s legibility, and contribute to a deeper understanding of human spatial cognition. Research in this area takes place in several domains, which makes a full understanding of existing literature difficult. To address this challenge, we adopt a visual summary approach. Literature is analyzed, and recurring topics are identified and validated with independent inter-rater agreement tasks in order to create a robust taxonomy for spatial measures and human behavior. The taxonomy is then illustrated with a visual representation that allows for at-a-glance visual access to the content of individual research papers in a corpus. A public web interface has been created that allows interested researchers to add to the database and create visual summaries for their research papers using our taxonomy.


Topic: Uncertainty visualization

Domains of uncertainty visualization research: a visual summary approach

Smith Mason, J., Retchless, D., & Klippel, A. (2016). Domains of uncertainty visualization research: A visual summary approach. Cartography and Geographic Information Science, 44(4), 296–309. https://doi.org/10.1080/15230406.2016.1154804

Link to Publication

Link to ResearchGate Page

Abstract: The inherent uncertainty of geospatial data has engendered a critical research agenda addressing all facets of uncertainty visualization due to the communicative efficiency of graphical representation. To organize this broad research area, we have reviewed literature on geospatial uncertainty visualization and systematically and iteratively classified research in this field. Upon creating a classification, we developed several visual summaries over time, refining the classification and subsequent graphic as new relevant topics emerged. This visual summary extends current existing approaches to taxonomies by allowing users a quick visual overview of relevant topics in a research area at a glance. For each research paper on uncertainty visualization, this classification can be used to visually represent which domains are covered. In order to ensure that the visual summary approach and the corresponding domains developed in this article can be used reliably, we performed an inter-rater agreement task. The high agreement reveals that the domains in the classification that were identified are intuitive and can lead to objective, reproducible classifications (visual summaries) of research papers. In future research, we plan to refine the visual classification/summary approach by providing guided classification via a web interface to visually classify the entire body of literature on geospatial uncertainty visualization and visually explore any trends in research topics, how they have changed over the years, and identify sparser topics that still need to be addressed.