Model 4: Data Classification

 

    This week's lab was to learn basic techniques used to classify data for choropleth mapping. We were to create maps focusing on the Senior Population of Miami Dade County and classify them in different ways. The four classifications were; Equal Interval, Quantile, Standard Deviation, and Natural Breaks. We had to use cartographic design to make the maps look good while analyzing the different classification methods to determine which is the most accurate way to present the data.

    My map is divided into the four classification methods, the first is Equal Interval. In this classification, the created classes will all have equal ranges in the data. The range values are calculated by dividing the total data range by the amount of classes. This map is very general with its information, it doesn't get too specific with its classes.

    The second classification is Quantile; This classification distributes the number of observations into equal numbers by fractionating them. In the map, this classification makes the data seem more distributed, and each class looks proportionate in it.

    The Third is Natural Breaks; This method derives from algorithms that try to have all values within a class as similar to each other as possible, and at the same time as different as possible to values in other classes. In the map, this classification as well in the quantile method(it actually looks kind of similar to it) makes seem the data more distributed, and each class looks proportionate in it.

    Last is Standard Deviation; Classification based divisions of the standard deviation such that the area under the graphed curve is divided into equal sections. This will put most observations in one class surrounding the average value, while the other classes will have fewer observations as they get further from the mean. This does a great job at highlighting the data that we are looking for.