Hello everyone!

How are you doing today? I hope everything's fine. Everything is going great as far as I am concerned, only problem is I have been quite busy lately. Too many things going on that I need to handle and too much work I don't want to miss out on, so it goes without saying that my time is limited. This is also the main reason why I haven't published another part of this series in the last twenty days, so here I am to make it up to you. Let's waste no more time and get this started!

As promised in a previous Cartography post regarding the elements of Choropleth mapping, this time we will move on to the next category of thematic maps, which are broadly known as Dasymetric maps. We are going to elaborate on the fundamental differences between Choropleth and Dasymetric maps and the methods followed by Cartographers in the creation of suitable surface units. Moreover, we will talk about the two main categories of variables utilized in the process of gathering spatial data, so stay focused!

This is just a personal note for all of you who are new to this blog; this is just an introductory series about Cartography, which is the scientific field of creating 2D depictions of our 3D world with precision. This means that I am basically sharing broad information gathered through years of study and research in a simplified manner, so that nobody gets too bored reading this post! There's always a high chance of learning a thing or two by reading till the end of this publication, so stay tuned!

Image Source: pixabay.com(CC0 Creative Commons Author: BogdanaLS)

Nature & Geometry

As explained in the previous parts of this series, Choropleth mappings represent the original geometric structure of the units utilized in the process of gathering spatial data. It goes without saying by now that there are various cases in which those units do not match the characteristics of a geographic distribution efficiently. As a result, the Choropleth process might lead to an end product of low quality in such cases, mainly due to the fact that vast amounts of spatial information will inevitably not be included in our projection. However that won't stop Cartographers from achieving their goal of presenting their data efficiently and precisely, as there is a tool known as the Dasymetric process which makes things a bit less complicated. Let's see how.

Dasymetric maps are built with the utilization of the exact same types of spatial data used in the creation of simple Choropleth maps in most cases. However there are some fundamental differences, one of the most significant of which is the fact that in Dasymetric maps the boundaries between successive units are totally unrelated to the boundaries of surface units. The most obvious visual characteristic of Dasymetric maps is the combination of uniformal surfaces and zones of radical variation. In order to create those uniformal surfaces, Cartographers often use different sources of information regarding the geographic distribution being studied, by utilizing various tools of spatial analysis like maps of related phenomena etc.

Having gathered information from external sources, Cartographers are now ready to proceed to the next step, which basically includes the design and creation of new uniformal units that can be used in order to achieve a more efficent approximation for the actual nature of the surface variation of our geographic distribution. It is worth noting at this point that the definition of the dasymetric mapping boundaries is probably the hardest task in this job. The utilization of information regarding the spatial correlations of the data being used is necessary in order to define our precious dasymetric mapping boundaries. Geographic Information Systems are tools which can be used to process geographic databases that include all sorts of spatial information needed in this endeavour.

Image Source: pixabay.com(CC0 Creative Commons Author: Dariusz Sankowski)

Constraint Variables

The first category of variables that are used in the process of gathering information, which in return will enable Cartographers to access deeper knowledge regarding the characteristics that are being mapped, is the category of Constraint Variables. These variables are used in order to set upper limits in place regarding the amount of mapped phenomena that can be represented on a surface of limited, given dimensions. For instance, in cases where we are studying the land-use planning of specific areas, we can utilize data regarding the precise geographic location of places with different land uses in order to accomplish efficient distribution of the entire area's land uses. Therefore, we use percentages as upper limits for any specific type of land use, making sure to take into consideration the area's topography and natural environment.

Dasymetric maps provide the observer with a much more detailed analysis regarding the characteristics of the area of the natural terrestrial surface being studied. One of the main reasons why is the fact that the utilization of constraint variables in GIS operations enables Cartographers to create visual representations of uniformal surfaces with boundaries where radical variations are observed, thus assisting the creation of quality Dasymetric maps. Let's take a look at a fancy Dasymetric map and move on to the next category of variables.

Dasymetric Map of the Berlin-Brandenburg Area - Hue used as a primary visual variable in order to represent quantitative variations./ Image Source: commons.wikimedia.org (Creative Commons Attribution-Share Alike 3.0 Unported Author: Bennet Schulte)

Related Variables

The second category of variables that are used in the process of gathering information, with the aim to enable Cartographers to gather technical information regarding the characteristics that are being represented, is commonly known as the category of Related Variables. Related Variables are practically nothing but geographic phenomena that depict predictable variations in spatial correlation with the geographical phenomenon that is being mapped. In cases where our external sources of data are topographic maps, Cartographers need to possess knowledge regarding remote sensing applications and techniques in order to reach safe conclusions concerning the definition of the boundaries between areas with different land-use planning.

The utilization of Related Variables will have a less dramatic effect with a purely statistical nature on any projection; such variables enable Cartographers to make better decisions regarding the precision of the geographic location of the data being used, even though applying them correctly can be a really difficult job in the creation of Dasymetric Maps. Once again, rather useful GIS operations come in handy and assist Cartographers in their efforts regarding the utilization of Related Variables.

Dasymetric Map of Hardiness Zones in North America - Hue used as a primary visual variable in order to represent quantitative variations./ Image Source: commons.wikimedia.org (Public Domain Author: US Federal Government)

Alright everyone, that's it for today. Thanks for taking the time to read my work, another part of my Introduction to Cartography series has come to an end. There's still plenty of stuff to talk about, so make sure to follow me and stay tuned. If you have any questions regarding anything you read on this blog, please do make sure to let me know in the comments below and I will do my best to provide prompt and detailed answers. More Engineering stuff regarding Cartography and Geology coming out within the next days, so stay tuned and follow me for more!

PREVIOUS PARTS OF THE SERIES:

1. INTRODUCTION TO CARTOGRAPHY

2. CLASSIFICATION & BROAD CATEGORIES

3. CARTOGRAPHIC TRANSFORMATIONS

4. CONFORMAL PROJECTIONS

5. EQUAL AREA PROJECTIONS

6. AZIMUTHAL PROJECTIONS

7. SCALE FACTOR & TRANSFORMATION

8. CARTESIAN COORDINATES & GRID SYSTEMS

9. 3D COLOR SPACE

10. COLOR SYSTEMS

11. COLOR & PATTERN IN SYMBOLIZATION (Part I)

12. COLOR & PATTERN IN SYMBOLIZATION (Part II)

13. CARTOGRAPHIC SYMBOLIZATION & VISUAL VARIABLES

14. SYMBOLIZING POINT DATA

15. SYMBOLIZING LINEAR DATA

16. SYMBOLIZING SURFACE DATA

17. SYMBOLIZING VOLUMETRIC DATA

18. STATISTICAL SURFACE & POINT SYMBOLS

19. STATISTICAL SURFACE & LINEAR SYMBOLS

20. ISARITHMIC MAPPING

21. ELEMENTS OF ISARITHMIC MAPPING

22. ISARITHMIC ANALYSIS

23. CHOROPLETH MAPPING

24. ELEMENTS OF CHOROPLETH MAPPING

IMAGE SOURCES:

• pixabay.com

• commons.wikimedia.org

REFERENCES:

University Textbooks & Course Lectures:

• Χαρτογραφία Ι/ Cartography I -TSOULOS(National Technical University of Athens, School of Rural & Surveying Engineering, Course Lecture Notes)

Internet Links:

• https://pubs.usgs.gov/tm/2005/11B01/05tm11b01.html

• https://www.colorado.edu/geography/leyk/geog_4203/readings/mennis%20and%20hultgren%20CaGIS.pdf

• http://sites.tufts.edu/gis/files/2013/02/Nelson_Jason.pdf

• https://prism.ucalgary.ca/bitstream/handle/1880/45758/2001-693-16.pdf?sequence=2&isAllowed=y

• https://gisgeography.com/choropleth-maps-data-classification/

• https://www.tandfonline.com/doi/abs/10.1559/152304006779077309?journalCode=tcag20

Video Credits @gtg
GIF by @katerinaramm

Thank you for your attention!
Hope you enjoyed this post and did learn a thing or two.
Follow me and stay tuned for more engineering blogs.

Highest Regards
@lordneroo