In the advancing digital world where statistical information and data are the backbone of decision-making processes, tools that visually represent these data become vital.
Understanding these tools, like map charts, is a prerequisite to using them effectively in our analysis. In this article, you will learn about the meaning and applications of map charts.
Understanding The Concept Of Map Charts
Before going further into the various applications of map charts, it is important to first establish an understanding of what they are.
By definition, map charts are types of data visualization tools that show the correlation of geographic areas to a certain dataset.
They present data on geographical maps utilizing colors, patterns, symbols, or proportional markers to represent the numerical values of each region.
The concept of visual data representation is not new. It has been prevalent since ancient times, with the use of maps for geographical and topographical representations.
The difference with map charts, however, is the value-added information that offers more than a mere geographical overview—a quality made possible by the advancement in technology.
Map charts have found usage in various fields such as geography, statistics, business, and health sciences amongst others.
To comprehend the usage of map charts in these fields, it is essential to understand what are map charts in greater depth.
The Elements And Features Of A Map Chart
Just as with other types of charts, a map chart comprises different elements and features that help convey the intended information. Some of these features include the legend, the value scale, the geography, and the plot area.
The geographical area depicted in the map chart often represents distinct categories of data, using color variations, data labels, or size variations, depending on the data type and the representation technique chosen.
The plotted area portrays the main data which can be further drilled down for deeper understanding via interaction capabilities offered by advanced software.
A unique feature of a map chart is its legend. This is a key that explains the symbols, colors, or patterns used in the chart.
The legend plays a significant role in ‘decoding’ the map chart, hence it must be designed clearly and articulately.
How To Create A Map Chart
Creating a map chart can be easily done using various software tools. However, the general process involves selecting the geographical area, retrieving the relevant data, choosing an appropriate chart type, and finally plotting the data.
You must ensure that you have the proper geographical divisions defined correctly.
If not, the geographic data should first be converted into sharable formats (like KML files) which can then be imported.
Once the geographical area has been fixed, the next step is to retrieve the relevant data to be represented on the chart.
Picking the right type of chart to use depends on the data and the information you want to communicate.
After deciding on the chart type, you then plot the data on the chart accurately.
This may involve setting color scales for choropleth charts, determining symbol sizes for proportional symbol charts, or plotting the directions for the flow maps.
Interpreting Map Chart Data
Map charts can be misinterpreted or misrepresented if not analyzed correctly. Hence, pay attention to the scale and legend.
You must become sensitive to the nuances of color hues, symbol sizes, or dot densities.
Moreover, one should also be aware of the potential influence of geographical size—an effect known as the area size effect.
Take an example of choropleth maps. Darker shades often represent higher numbers.
However, if the color gradient is not calibrated well, it could misrepresent the data. The same goes for the size of symbols or the number of dots in proportional or dot-density maps. The scale becomes important to make accurate interpretations.
Overall, map charts stand as potent tools in visual data representation, effectively conveying patterns, trends, and relationships in the data to aid in better decision-making.
However, they should be used responsibly, keeping in mind their limitations to avoid misinterpretation and bias, and enhancing the quality and accuracy of the data represented.
