In anticipation of acquisition of the full dataset (outlined in Week One post), I dive into the realm of ideas. This post concerns the first ~1/2 of the first book, a textbook, I am reading for the course… Slocum et al’s Thematic Cartography and Geovisualization 4th ed
This far into the text there has been little content on practical techniques of generating a geospatial visualization. The conceptual framework has been instrumental in educating me cartography, and especially about what I now realize were misinformed understanding of what professionals are referring to when they use the terms geovisualization (geoviz), geographic information system (GIS), and geographic information science (GIScience). Before sharing my thoughts on the theory of geoviz, I will mention that I have started interacting with two browser-based interactive sites suggested by the authors of the book: Projection Wizard and Mapbox.
Projection Wizard is an interactive learning tool that has helped with an understanding of content from the 7th, 8th, and 9th chapters. Having been raised on the practical application of maps and orienteering by a father who is a 20th century pilot (don’t they love maps) and officer in the United State Marine Corps (“land nav” was a dinner table term) for activities as common as driving (he still doesn’t have GPS in his car) and hiking, the broad concepts in these chapters were familiar to me. But I had never considered the implications of geodesy to the visualization of geographic space. Broadly conceptualizing the practice, the inherent dimensional reduction from three dimensional sphere to a two dimensional plane is an obvious Cartesian exercise. But the exact options that cartographers have for this necessary spatial transduction is much broader than I had imagined. The figures in the text, as my interaction with Projection Wizard, helped me to begin to appreciate the option space and nuanced impact that different visualization decisions have on the end cartographic product.
I have also downloaded quantum GIS (QGIS) to my laptop and started pursuing the tutorials per our discussion this week on open source GIS software as alternatives to the common albeit paid ArcGIS platform.
Now back to my learning from the text. At the most fundamental level, what I have realized from this book is my (unsurprising) prior misunderstanding of the design intent of geoviz. Even in the book, these practiced authors admit that from a certain standpoint (that of an end-user, lets say), it’s hard to philosophically state that a map drawn on a napkin with a pen is not a visualization of geographic information. But from the standpoint of the map maker, a geoviz is a different thing: (in my words) it is a defined practice of centering agency via the revelation of an individual’s interaction with an arena, compared to the practice of centering place via the revelation of a collective’s interaction with agency. The terminology used by the authors is probably more accessible: “[geoviz is] a private activity in which unknowns are revealed in a highly interactive environment […vs…] traditional static maps involves the opposite: It is a public activity in which knowns are presented in a noninteractive environment.” For me the most powerful differentiating content, the demarcation between (what they define as) “visualization vs communication” in cartography, is the map maker’s intent to use interactivity to reveal unknowns to a user. The author’s appropriately communicate this conceptualization via a visual:
In following chapters up to Ch. 7 is discussion of concepts that felt familiar: statistics and data classification (in which I am formally trained), symbolization, etc. I will say that the “fundamental operations of generalization” figure, Fig 6.4 on p 105, was an especially pleasing visual summary of ten modes (“operators”) of generalization.
I am eager to step into the next parts of the text while also coming to a more concrete aim for my project for the course. The topic is well defined but the methodological approach has yet to be defined. Part II of the text, on mapping techniques, will surely bring methodological inspiration to my project.
On anther note, I had good fun reading this blog-ish post titled “Friends Don’t Let Friends” (Make Bad Graphs) by Chenxin Li who is a data visualizer. I was especially fond of point 10, “friends don’t let friends make pie charts“, as I find my colleagues who are research scientists professional trained in the design and conduct of experiments to generate data to test hypotheses are experts in drawing evidence-based conclusions from their data but nonetheless not always the best at visualizing the data per se (…they make a lot of pie charts).