Retrospective Deconstruction of Statistical Maps
A Choropleth Case Study
Bibliographic Data
| ID | 3775209 |
|---|---|
| Authors | Marc P Armstrong (0000-0002-5983-7417, University of Iowa), N Xiao (0000-0002-6585-6294, The Ohio State University) |
| Year | 2018 |
| Volume | 108 |
| Issue | 1 |
| Pages | 179-203 |
| Publication date | 2018-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Annals of the American Association of Geographers (JOURNAL) |
| Journal identifiers | ISSN: 2469-4452 • E-ISSN: 2469-4460 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/24694452.2017.1356698 |
| OpenAlex | W2750658105 |
| Language | EN |
| References cited | 43 |
The process of creating printed statistical maps in the predigital era was expensive and time consuming. These and other interacting factors constrained the number of design alternatives, such as color choices, that a cartographer might reasonably have been able to consider. In this article, we develop an approach to map deconstruction that enables researchers to investigate the statistical choices made by cartographers by placing each printed map into the universe of all possible choices available to them. We place a particular focus on the specification of choropleth map class intervals for maps produced in the early twentieth century. Three published choropleth maps are used as case studies to illustrate the approach, using four evaluation criteria to evaluate the accuracy of the data classifications. The results indicate that the class interval selection choices made for the examined maps are inferior when compared with available alternatives and that, in one case, classification errors are not only evident, they are abundant
Cartography · Data mining · Data science · Geography · Operations research · Statistics · Computer Science · Data-Driven Disease Surveillance · Engineering · Geographic Information Systems Studies · Mathematics · Soil Geostatistics and Mapping · Artificial Intelligence
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| Citation velocity | historical |
|---|---|
| Highly cited | No |