4 - Color Choropleth

 For this week's module we focused on the color choropleth and classification schemes. Understanding this concept lets the author improve on his/her map's impact and map legibility to the map audience. This concept is sometimes overlooked especially when the author wants to convey a message to his/her map audience. The use of different colors sometimes have different meaning especially in other cultures like the use of red and green (HuffPost, 2016).

Linear Progression

Adjusted Progression

ColorBrewer

The linear progression color ramp has an equal interval of 20 for R, G and B. Then for the adjusted progression color ramp, I increased R values by 1/3 and decreased G values by 1/3 but remained B at constant value of 20.  Their color differences are quite minimal from dark to light due to a constant value progression amongst R, G and B values. Though I see that linear progression color has more green saturation/contrast that adjusted progression who has a darker contrast making its green progression harder to depict. While for ColorBrewer ramp, wherein I selected a green multi-hue, resulted in varying intervals, and has no definite interval value. The contrast is also higher in the ColorBrewer map which makes it easier to depict lighter color classes as it goes from green to almost white. In addition, its color difference from each color is more visual compared to the other 2 color ramps.

Then for Part 4, our task was to create different hues for a set of colors for a land use map which has 5 categories (urban, agriculture, forest, water and vegetation). Having a set of color with different hues that fall into a certain  categories will help the map viewer to define different urban category types or different vegetation types.

I used colors that are close to its real colors according to the reading “Using Color in Maps” that says using green for vegetation, red for roads/cities/buildings and blue for water (Strode, et. al). Then from each category I kept the same hue but changing the saturation from dark to light color. Saturation helps visualize the color’s vividness and influences each color scheme’s intensity and colorfulness (Brewer, 2016). Also, Brewer (2016) stated it is logical to keep the color scheme within the same category because using those relationships with related colors improves the map.

For Part 5, we examined different classification symbology methods in showing the Hispanic percentage over the population of each counties in Texas in 2000. 

Natural Breaks – creates the most optimal class range in the dataset and splits it into classes to maximize differences between classes. Also, it divides it where there are big differences in data values. The histogram shows that it is slightly skewed to the right with class 1 and 2 counts are close as well as 3 and 4.

Equal Interval – from the name itself is it divides the data into classes of equal-sized subranges. This helps you create a specific number or intervals based on the value range you want. The histogram shows that it is skewed to the right as the count of class 1 are far higher than of the other classes.

Quantile – It categorized the data into groups where it contains the same count per % classification. You can see in the table above that it only has 50-52 counties per class. Therefore, the histogram is almost equal or rather not skewed as each has the same number of counties.

I think the best would be Natural Breaks as it optimizes the classification of values, minimizing variation within classes and maximizing variation between classes, distributing the error uniformly (Kimerling, 2012). Equal interval is not good to use in population density values as it doesn’t communicate the right variation of values while quantile can be misleading by putting similar features in adjacent classes or widely range values in the same class.

Then for the last part, we created a population percentage map using choropleth mapping on our desired county. I chose North Dakota as it is  quite interesting that is has an multi-purpose coordinate system knowing that it belongs to two UTM Zones (Zone 13 and 14) and two State Planes. I happened to picked NAD 1983 StatePlane North Dakota N  FIPS 3301 (US Feet). 

Then with attributes 2010 Population and 2014 Population I calculated a  new field for population change = ((Population 2014 - Population 2010) / Population 2010) * 100 or in Python terms:

CH_POP_00_14 = ((!POP2014! - !POP2010!)/!POP2010!)*100 

I used natural breaks method with a 5-class symbology then changed the interval for the negative change so that the red-color would represent the negative and the middle would represent an off-white color. I used the natural break method to maximize the variation between classes. When I tried choosing the quantile method the highest classification range was too high from 17 to 72% which would create a class that has too wide range of values. Using natural breaks method, helps me visualize the significant changes in percentage values rather than being too skewed. Colors or classes are more distributed across counties, and we can determine which counties has a higher increase in population change from 2010 to 2014.