Exploratory Geovisualizations for Supporting the Qualitative Analysis and Synthesis of Place-Related Emotion Data

Peer-reviewed

Journal Article

Author

Bleisch, S., and Hollenstein, D.

Published

2019

Doi

10.14714/CP91.1437

[pdf]

Abstract

Locations become places through personal significance and experience. While place data are not emotion data, per se, personal significance and experience are often emotional. In this paper, we explore the potential of using visual data exploration to support the qualitative analysis of place-related emotion data. To do so, we draw upon Creswell’s (2009) definition of place to define a generic data model that contains emotion data for a given location and its locale. For each data dimension in our model, we present symbolization options that can be combined to create a range of interactive visualizations, specifically supporting re-expression. We discuss the usefulness of example visualizations, created based on a data set from a pilot study on how elderly women experience their neighborhood. We find that the visualizations support four broad qualitative data analysis tasks: revising categorizations, making connections and relationships, aggregating for synthesis, and corroborating evidence by combining sense of place with locale information to support a holistic interpretation of place data. In conclusion, the paper contributes to the literature in three ways. It provides a generic data model and associated symbolization options, and uses examples to show how place-related emotion data can be visualized. Further, the example visualizations make explicit how re-expression, the combination of emotion data with locale information, and visualization of vagueness and linked data support the analysis of emotion data. Finally, we advocate for visualization-supported qualitative data analysis in interdisciplinary teams so that more suitable maps are used and so that cartographers can better understand and support qualitative data analysis.

Figures

Filtered views of selected sense of place data. Left: Route of a single participant and their expressions of positive valence in the categories of memories and aesthetics. Circle size approximates place extent. Center: The same participant and categories as on the left, now showing negative valences. Right: Accessible destinations (red circles) referred to by a participant while standing at the location marked with a yellow circle (topical associations). The circles are used to approximate the place’s location, while the overlaid opaque large dot and the rays indicate an uncertain extent associated with the location.

Overview of visual variables for the different data dimensions. Their use is illustrated in section 5. In italics, we provide the rationale for and/or references supporting choosing the respective visual variable.

An extract of an overview visualization showing all localized data chunks, their categorization, and valence. The background map is subtly emphasized within a buffer of 20 meters around the routes, to show the area covered by the walking interviews. Clicking the points allows the user to view the original data chunks.

Overviews of selected data aggregated across all participants. Left: Grid-based aggregation of negative valence in the category “comfort” from expressions of all participants. Right: Topical associations with a negative valence, across all participants. Note: the place of origin of the association may be perceived positively.

Gridded density overviews of the category “aesthetics,” overlaid with bivariate squares symbolizing the green space index (green) and bench index (purple), calculated for short road segments (Bleisch and Hollenstein 2017). The background features expressions with a positive valence (top), negative valence (middle), or both valences (bottom).

BibTeX

@article{bleisch_exploratoryGeoVisPlaceEmotionData_2019,
 abstract = {Locations become places through personal significance and experience. While place data are not emotion data, per se, personal significance and experience are often emotional. In this paper, we explore the potential of using visual data exploration to support the qualitative analysis of place-related emotion data. To do so, we draw upon Creswell’s (2009) definition of place to define a generic data model that contains emotion data for a given location and its locale. For each data dimension in our model, we present symbolization options that can be combined to create a range of interactive visualizations, specifically supporting re-expression. We discuss the usefulness of example visualizations, created based on a data set from a pilot study on how elderly women experience their neighborhood. We find that the visualizations support four broad qualitative data analysis tasks: revising categorizations, making connections and relationships, aggregating for synthesis, and corroborating evidence by combining sense of place with locale information to support a holistic interpretation of place data. In conclusion, the paper contributes to the literature in three ways. It provides a generic data model and associated symbolization options, and uses examples to show how place-related emotion data can be visualized. Further, the example visualizations make explicit how re-expression, the combination of emotion data with locale information, and visualization of vagueness and linked data support the analysis of emotion data. Finally, we advocate for visualization-supported qualitative data analysis in interdisciplinary teams so that more suitable maps are used and so that cartographers can better understand and support qualitative data analysis.},
 author = {Bleisch, Susanne and Hollenstein, Daria},
 doi = {10.14714/CP91.1437},
 journal = {Cartographic Perspectives},
 number = {91},
 title = {Exploratory {Geovisualizations} for {Supporting} the {Qualitative} {Analysis} and {Synthesis} of {Place}-{Related} {Emotion} {Data}},
 url = {https://cartographicperspectives.org/index.php/journal/article/view/1437},
 urldate = {2026-03-25},
 year = {2019}
}