Moduel 5 - Choropleth and Proportional Symbol Mapping

The primary objectives for this week's module include understanding choropleth map classifications, selecting appropriate color schemes, employing SQL Query language, utilizing proportional or graduated symbols, and creating thematic picture symbols. Choropleth maps are a type of thematic map that employs various colors or shading to represent data visually. It is crucial to normalize data in choropleth maps to ensure accuracy in the results.

In this lab exercise, we will utilize the provided data to develop a choropleth map that illustrates the varying population densities across European countries, as well as the wine consumption levels in each country.

Mapping population density rather than raw population counts on a choropleth map is key to understanding how people are distributed. Raw counts can mislead comparisons between different-sized areas, as larger regions may appear more populated even if they are not dense. Normalizing population data provides a clearer picture and meaningful comparisons.

Analyzing European population data shows that Russia has the most people at 104,687,534, but its density is only 26.47 individuals per square kilometer, given its large area. In contrast, the Netherlands has a population of 16,852,340, with a much higher density of 487 individuals per square kilometer, demonstrating that it has a higher population density despite its smaller size.

The initial step involves incorporating the EuropePopulation dataset into a new ArcGIS Pro project. Upon reviewing the data table, we identified four countries; Monaco, Gibraltar, Malta, and Jersey, characterized by high population density but notably low wine consumption, which can be excluded from our analysis. Additionally, these countries occupy a relatively small geographical area. Given the potential for these countries may skew the results, we have opted to exclude them from our analysis. To facilitate this exclusion, we employed the SQL expression feature available within the advanced symbology options.

The population density field POP_DENSIT, which represents population normalized by area, was utilized to classify the data into four distinct categories: equal interval, quantile, standard deviation, and natural break. Upon analyzing four data classifications, I found that quantile classification best represents population density in European countries. It provides an even data distribution and a clear legend. In comparison, equal interval and standard deviation classifications showed uneven distributions, with some classes having few countries and others many.

Initially, I thought natural breaks would be the best approach because it groups data effectively, minimizing within-class variance. However, it resulted in some classes with very low counts. Thus, quantile classification proved to be the most appropriate choice, ensuring fair country distribution and clarity in the legend. Furthermore, I used a continuous gradient from light to dark, where lighter shades represent low population density and darker shades show high density. This method allows for quick comparisons of population densities, making it easy to identify countries with high or low population density.

Subsequently, I utilized the labeling properties to configure the labels for all countries, deliberately excluding those with very small land areas. In order to maintain control over the labels, I exported them as annotations, which enables the adjustment and repositioning of the labels as necessary.

Next, I duplicated the EuropePopulation layer and renamed it to WineProportional. This involved reclassifying the layer using the Wine_Cnsmp field and applying proportional symbols as the symbology. Additionally, I incorporated another layer and applied graduated symbols for its visualization. I used the graduated symbol method to show wine consumption data for each country. This method divides data into classes, each with a specific symbol size. I created five unique symbols for the five classification classes. This approach helped control symbol sizes and reduced overlap issues. However, size variations might be hard for some people to understand. To improve clarity, I designed custom symbols with different sizes and images to better represent the levels of wine consumption.

I would like to share that I utilized the Feature to Point tools to convert symbols into point data, which proved to be extremely beneficial. This functionality enabled me to move or delete symbology, allowing for greater flexibility in repositioning symbols on the map. Moreover, I utilized the site Color blind test simulator/ to check the PDF map for color blindness accessibility.

 

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