<center>**Hello everyone!**</center>

How is everything going? I sincerely hope everybody is having a good time. As we move further into the Fall season, the weather is still really hot over here, making it even harder to concentrate on one's work and stay productive. However, it is always a good chance to visit the beach and spend a couple relaxing hours. Life is good as far as I am concerned, I am just super busy; I doubt if that's ever going to change as I am a workaholic. You guessed right, I am back to provide the next part of this series, so let's waste no more time!

**As promised in a previous [Cartography post](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-elements-of-isarithmic-mapping) regarding the elements of isarithmic mapping, this is the twenty-second part of this *Introduction to Cartography* series. This time I am taking the chance to share broad technical information regarding the different types of errors that are generated during the isarithmic process. After making an introduction regarding the elements of isarithmic mapping, I feel like readers are now ready to learn more about how Cartographers need to follow specific rules with caution while gathering data or making crucial decisions. Bare with me as this is going to be a rather interesting read for the science lovers.**

This is the point where I always include a personal note for those of you who are new to my blog; this is just an introductory series about Cartography, which is the art and science of making observations, gathering measurements and creating 2D depictions of 3D objects with precision. This means that I will be presenting general information in a simplified manner while throwing a couple failed jokes, so that nobody gets too bored while reading this. There's a high chance of learning a thing or two by reading through this blog, so stay tuned!

![globalisation-3390877_960_720.jpg](https://cdn.steemitimages.com/DQmZDBjaTTEL9RrfbBSDonptTpZmtLcjmctC4ZDKSsa6QmL/globalisation-3390877_960_720.jpg)
<center>*Image Source: [pixabay.com](https://pixabay.com/el/%CF%80%CE%B1%CE%B3%CE%BA%CE%BF%CF%83%CE%BC%CE%B9%CE%BF%CF%80%CE%BF%CE%AF%CE%B7%CF%83%CE%B7-internet-%CE%B5%CF%80%CE%B9%CE%BA%CE%BF%CE%B9%CE%BD%CF%89%CE%BD%CE%AF%CE%B1-3390877/)([CC0 Creative Commons](https://pixabay.com/el/service/terms/#usage) Author: [TheDigitalArtist](https://pixabay.com/el/users/TheDigitalArtist-202249/))*</center>

# Symbolization Problems
As explained multiple times in my previous posts, the symbolization process is a rather complicated and difficult job that demands theoretical knowledge related to various different scientific fields. The design of the appropriate symbols that will be utilized to represent all different sorts of spatial data, and the proper selection of the most effective visual variables by taking into consideration the nature of the data being depicted, the map scale that will be utilized and the way the human sensory system works, determine the quality of the end product. Obviously, due to the fact that we are dealing with a rather complicated situation the outcome of which can be affected by multiple factors, there are various categories of errors generated.

In this blog we are going to discuss problems that refer to the different sorts of errors generated, which of course affect the precision and the quality of the information provided on a map. There are many factors that induce errors when dealing with metric information, most of which are directly related to low quality measurements or false understanding of theoretical concepts and fundamental principles. However, there are also cases where the utilization of inappropriate equipment or carelessness during the process of observation can literally downgrade the quality of our end product by generating large errors. In order to solve a problem we have to map it first, which is also what has to be done in order to track and eliminate errors as well.

Cartographers need to be able to spot and point out the specific characteristics of the factors that induce generation of large errors in the process of designing and building isarithmic maps. One of the main factors consists of the three elements of isarithmic mapping we discussed in the [previous part of this series](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-elements-of-isarithmic-mapping), which by the way are the number and location of control points, and the accuracy of the interpolation process. Moreover, another significant factor is the quality of the data measured and gathered, which is always of huge significance in Engineering in general. Finally, the selection of the appropriate isarithmic interval for the map plays a rather important role as well. Let's take a deeper look at the different categories of errors often generated in isarithmic maps.

![world-1626692_960_720.jpg](https://cdn.steemitimages.com/DQmUpwLFwhGCHHAcZTuVEcKW2uTHCCxk2AV8C83Xs9d7iTK/world-1626692_960_720.jpg)
<center>*Image Source: [pixabay.com](https://pixabay.com/el/%CE%BA%CF%8C%CF%83%CE%BC%CE%BF-%CE%B1%CF%86%CF%81%CE%B9%CE%BA%CE%AE-%CF%87%CE%B1%CF%81%CF%84%CE%B9%CE%BA%CE%AC-%CE%B4%CF%8E%CF%81%CE%B1-1626692/)([CC0 Creative Commons](https://pixabay.com/el/service/terms/#usage) Author: [PIRO4D](https://pixabay.com/el/users/PIRO4D-2707530/))*</center>

#  Systematic Method Errors
Systematic method errors are caused by imperfect methods of observation or low quality instruments in combination with inexperienced observers. Errors that stem from the three key elements of isarithmic mapping and the process followed are such errors in our case. Those three key elements (number of control points, position of control points, and interpolation process) affect each other and therefore create a living organism which can downgrade the quality of the end product if not given the proper attention. There is a hypothetical correlation between the number of control points used and the map accuracy in a way that more control points means higher quality regarding all metric information extracted. However, we should always bare in mind that control points cost, and as a result there is a thin line between accuracy and efficiency.

Another equally significant factor is the precise location of the control points utilized. If our control points are not uniformly distributed then some areas of our projection's surface are depicted with a lot more precision regarding detail. This is one of the main reason why random distributions of the control points are never selected and Cartographers tend to use classified control points in order to achieve the same levels of accuracy across the entire map surface. This element of isarithmic mapping induces the generation of simple errors, the magnitude of which is defined by the structure of the surface used for the collection of raw data.

Last but not least, the model of the interpolation process followed for the estimation of values that refer to all points situated between control points is of huge importance as well, and therefore it can definitely affect the accuracy of an isarithmic map. Digital Cartography provides Cartographers with tools and options that enable them to use non-linear methods of interpolation in order to achieve higher accuracy, however let's keep in mind that the utilization of most of those tools requires knowledge regarding the available distribution. This can be a real problem, as Cartographers often have to overcome the obstacles induced by the unavailability of information regarding the surface variation of certain geographic entities. Of course there's always a solution, which in this case lies in the ability of a Cartographer to define the most appropriate interpolation method. Quality measurements and estimations help. Let's see why.

<center>![615px-Poland_topo.jpg](https://cdn.steemitimages.com/DQmaZHnz8syp5VRqXVsT2Tm4eFJNLMzxkNmHpv74dZETGvi/615px-Poland_topo.jpg)</center>
<center>*Topography of Poland - Hue used as a primary visual variable in order to represent qualitative variations, linear symbols used to depict borders./ Image Source: [commons.wikimedia.org](https://commons.wikimedia.org/wiki/File:Poland_topo.jpg) ([Creative Commons](https://en.wikipedia.org/wiki/Creative_Commons) [Attribution-Share Alike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/deed.en) Author: [Captain Blood](https://en.wikipedia.org/wiki/User:Captain_Blood))*</center>


# Quality Estimations
Any errors associated with all calculations regarding the values of the particular spatial characteristics that are being observed, measured, classified and symbolized can directly affect the (**x**,**y**) values of our control points. Even in cases where the (**z**) value is correct, wrong (**x**,**y**) values mean that our isarithmic lines won't be placed properly on the map's surface. This works the other way around as well, meaning that correct (**x**,**y**) values combined with wrong (**z**) values would still result in false placement for our isarithmic lines. Everything mentioned above is making it obvious that the level of accuracy regarding the position of control points, as well as the accuracy concerning the value (**z**) which refers to the spatial characteristic being studied affect the level of accuracy that can be achieved on a given statistical surface.

There are three different categories of method errors that affect the credibility of any metric information that can be represented and extracted from our map and is directly related to the spatial characteristics (**z**) depicted on an isarithmic map. Observation errors refer to the method of observation followed in order to gather measurements and affect the map's geometry, whereas sampling errors stem from the utilization of inappropriate methods of collecting data. Communication errors depend on the efficiency of the symbolization process and depends a lot on the Cartographer's personal decisions. I will share broad information regarding all categories of method errors in the next blog, coming out within the next few days. Thank you for being here.

![800px-LithuaniaPhysicalMap-Detailed.png](https://cdn.steemitimages.com/DQmSh3bxLFRsrzbvL8QvgYDGhPfXoRwLX9vpSNryTQRzXHC/800px-LithuaniaPhysicalMap-Detailed.png)
<center>*Topography of Lithuania - Hue used as a primary visual variable in order to represent qualitative variations, saturation used as a secondary visual variable in order to depict quantitative variations described by an ordinal scale./ Image Source: [commons.wikimedia.org](https://commons.wikimedia.org/wiki/File:LithuaniaPhysicalMap-Detailed.png) ([Creative Commons](https://en.wikipedia.org/wiki/Creative_Commons) [Attribution-Share Alike 3.0 Unported](https://creativecommons.org/licenses/by-sa/3.0/deed.en) Author: [Knutux](https://commons.wikimedia.org/wiki/User:Knutux))*</center>

**Alright people, that's it for now. We have officially reached the end of another part of my beloved *Introduction to Cartography* series. There's still plenty of room to have some fun and plenty of time to discuss more about Cartography. Please bare with me and follow me for more, I promise this is going to be fun. Oh, in case there are any questions regarding these blogs, please do 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](https://steemit.com/engineering/@lordneroo/introduction-to-cartography)

2. [CLASSIFICATION & BROAD CATEGORIES](https://steemit.com/engineering/@lordneroo/introduction-to-cartography-classification-and-broad-categories)

3. [CARTOGRAPHIC TRANSFORMATIONS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-cartographic-transformations)

4. [CONFORMAL PROJECTIONS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-conformal-projections)

5. [EQUAL AREA PROJECTIONS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-equal-area-projections)

6. [AZIMUTHAL PROJECTIONS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-azimuthal-projections)

7. [SCALE FACTOR & TRANSFORMATION](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-scale-factor-and-transformation)

8. [CARTESIAN COORDINATES & GRID SYSTEMS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-cartesian-coordinates-and-grid-systems)

9. [3D COLOR SPACE](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-3d-color-space)

10. [COLOR SYSTEMS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-color-systems)

11. [COLOR & PATTERN IN SYMBOLIZATION (Part I)](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-color-and-pattern-in-symbolism-part-i)

12. [COLOR & PATTERN IN SYMBOLIZATION (Part II)](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-color-and-pattern-in-symbolism-part-ii)

13. [CARTOGRAPHIC SYMBOLIZATION & VISUAL VARIABLES](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-cartographic-symbolization-and-visual-variables)

14. [SYMBOLIZING POINT DATA](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-symbolizing-point-data)

15. [SYMBOLIZING LINEAR DATA](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-symbolizing-linear-data)

16. [SYMBOLIZING SURFACE DATA](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-symbolizing-surface-data)

17. [SYMBOLIZING VOLUMETRIC DATA](https://steemit.com/steemstem/@lordneroo/introduction-to-cartgoraphy-symbolizing-volumetric-data)

18. [STATISTICAL SURFACE & POINT SYMBOLS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-statistical-surface-and-point-symbols)

19. [STATISTICAL SURFACE & LINEAR SYMBOLS](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-statistical-surface-and-linear-symbols)

20. [ISARITHMIC MAPPING](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-isarithmic-mapping)

21. [ELEMENTS OF ISARITHMIC MAPPING](https://steemit.com/steemstem/@lordneroo/introduction-to-cartography-elements-of-isarithmic-mapping)

![DQmQPRBLmhWag36FBxYVNJNKZRUsBbpPeZvm7FVixnWVXhk_1680x8400.png](https://steemitimages.com/DQmTjUEWdmEvtQcJMueV3YTGnHVNweSGbfDAAEVnCRGPJm4/DQmQPRBLmhWag36FBxYVNJNKZRUsBbpPeZvm7FVixnWVXhk_1680x8400.png)

**IMAGE SOURCES:**

- [pixabay.com](www.pixabay.com)

- [commons.wikimedia.org](https://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:***

- http://www.geog.ucsb.edu/~kclarke/Geography183/Lecture11.pdf

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

- https://www.researchgate.net/figure/A-Isarithmic-and-B-contour-line-maps-produced-by-the-study_fig4_257820422

- https://help.rockware.com/rockworks17/WebHelp/maps_2d_symbols.htm

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



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<sub><center>**Video Credits @gtg**</center>
<center>**GIF by @katerinaramm**</center></sub>

![DQmQPRBLmhWag36FBxYVNJNKZRUsBbpPeZvm7FVixnWVXhk_1680x8400.png](https://steemitimages.com/DQmTjUEWdmEvtQcJMueV3YTGnHVNweSGbfDAAEVnCRGPJm4/DQmQPRBLmhWag36FBxYVNJNKZRUsBbpPeZvm7FVixnWVXhk_1680x8400.png)

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

<center>**Highest Regards**</center>
<center>**@lordneroo**</center>