13th Oct, 2020
University of West Attica
Question
Asked 1st Oct, 2020
How can we make cartographic design theory more relevant?
Maps are seemingly produced in ever-increasing numbers without principles of sound cartographic design. For example, despite the publication of successive cartographic texts and blogs that advise against the use of absolute numbers for choropleth maps, the practice is widespread in mapping cases of COVID-19. How can we ensure that cartographic design theory is implemented more widely?
Most recent answer
I think that each time you have to take a better look of your data. For example, like Cédric Aurelien Nguimdo Matsaguim said about highlighting differences at small scale like in the covid19 pandemic case. Nevertheless, each case of data must be addressed in a way that thematic maps would not lie or hide the truth...unless this is the goal!
All Answers (5)
3rd Oct, 2020
Hi @Kent
I think that is a matter of how well the design used for mapping help the final reader to better differenciate between relatively closed situations at the scale at which the map is presented.
It's particularly true for count data when you have to highlighted differences at small scale like in the covid19 pandemic case
Map designer have to deal with spatial features with different area and statistics, the goal to present the situation at the global scale and the necessity to produce a map for the general public. The latter imply readers with different technical level and aesthetic preferences
Best Regards
The simple answer is ... you cannot.
An increasing number of maps based on big data--for example many of the COVID-19 maps of global incidence--are not drawn by cartographers but by automated programs attached to the syndromic systems. Similarly, data from US census generates automatic maps of data selected.
There are as well a number of programs these days that transform raw data into maps. The problems are useful but few have the degree of control of programs like Esri's ArcGIS or QGIS.
The real and final answer is that those of us who make maps need to make the best maps we can while critiquing those that seem to fall short of this or that standard. The better the maps we produce, the greater the likelihood those making other maps will seek to emulate our work's standard.
Tom Koch
P.s. To see my work on maps, mapping and data go to http://kochworks.com
Not much to be done. Web mapping applications like Google Maps / Bing, along with the huge number of business applications which offer already pre-defined templates dwarf any attempt to educate. With perhaps one exception, the D3 JavaScript library. But really even geographers map things which really would be better expressed as a chart of graph.
I think that each time you have to take a better look of your data. For example, like Cédric Aurelien Nguimdo Matsaguim said about highlighting differences at small scale like in the covid19 pandemic case. Nevertheless, each case of data must be addressed in a way that thematic maps would not lie or hide the truth...unless this is the goal!
Similar questions and discussions
Which pattern do you feel more alive with?
Presented below are two patterns created from the data set: 1024 cities that follow precisely Zipf’s law, which implies that the first largest city is size 1, the second largest city is size 1/2, the third largest city is size 1/3, and the smallest city is size 1/1024. The left pattern was created from head/tail breaks (https://en.wikipedia.org/wiki/Head/tail_Breaks) that is a new classification scheme for data with a heavy-tailed distribution (Jiang 2013). The classification scheme partitions the data around its average into two unbalanced parts: those above the average for big values in the head (a minority), and those below the average for small values in the tail (a majority); this partition process continues recursively for the head or the large values until the notion of far more small values than large ones is violated. For this particular data, it ends up with 5 classes. The right pattern is created by natural breaks (Jenks 1967), with which variance within classes is minimized, while the variance between classes are maximized. The natural breaks is in fact the same as k-means clustering (https://en.wikipedia.org/wiki/K-means_clustering).
Jenks G. F. (1967), The data model concept in statistical mapping, International Yearbook of Cartography, 7, 186–190.
Jiang B. (2013), Head/tail breaks: A new classification scheme for data with a heavy-tailed distribution, The Professional Geographer, 65 (3), 482 – 494.
I have argued that the head/tail breaks helps revealing the underlying scaling of far more small values than large ones for data with a heavy-tailed distribution (Jiang 2013). My question to you is, do you feel more alive with the left pattern that is created by head/tail breaks? Or alternatively, do you feel the left pattern is more beautiful? do you feel you are more comfortable with the left rather than right pattern?
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