Cartographic Skills: Week 6 Data Classification

In this week's lab, we learned about the four different data classification methods which in turn had different ways of presenting the data that was given. To make sure that we understand the material that we read, we had to used ArcMap to present the four data classification methods using the Miami-Dade Census dataset. We placed each classification method in its own data frames which helped us compare and contrast the different methods being used in this lab. By visually examining the dataset through each classification, we were able to determine which method would be most effective with presenting the dataset that was given. The map that we first created from this dataset was based on  the percent above 65 data presentation which we then later used to created another map utilizing the population count normalized by area data presentation. This allowed us see what presentation method was more accurate in representing the data. 

The map that I have posted above is the Miami-Dade Census of people who are over the age of 65 utilizing the percent above 65 data presentation method to present this data. This map could be use for Miami-Dade County Commissioners to examine the population of the senior citizens and by selecting the right data presentation will make the map more effective. The data presentation that seems to depict the distribution of senior citizens accurately would the percent above 65. This presentation allows the commissioners to have a general idea what areas more heavily inhabited by senior citizens within Miami-Dade and it gives a much greater visual impact of the population of senior citizens. The commissioners might be able to implement more informed planning to come up with projects to provide better resources to assist senior citizens within the heavier populated areas. The population count normalized by area presentation isn’t the most effective in presenting the data since it is not as visually impacting as the percent above 65 presentation. The population count normalized by area  type of presentation seems to show a reduce presence of the senior citizen community in Miami-Dade which might lead the commissioners to not know the extent of the need by not having the true percentages and this all caused by not using the most apt presentation. The map shows the four data frames with different classification methods in each one. These data classifications are called equal interval, standard deviation, quantile and natural breaks.

• Equal Interval: This classification makes the data have equal ranges within the classes that are created. To create these equal ranges, the total data ranges are divided by the number of classes which create the range values. With this classification, the data can be easily interpreted by anyone viewing the map. The down side is there being no limit for what data is being distributed along the number line within these classes.
• Quantile: In this classification, the data distribution is divided by an equal number of observations. The data values are then put into classes which are arranged in numeric order. With this method, the equal number of observation and the percentage of each class are the same which helps condense the need to farther discuss the mapped data. The down side is that this method has the same issue as equal interval; there being no limit for what data is being distributed along the number line within the created classes.
• Standard Deviation: In this classification, the area beneath the graphed curve is separated out into equal sections. Most of the observations will be placed into one class surrounding the average value; meanwhile the other classes will have less and less data markers as they moved farther out from the mean. This method of classification is very useful because the mean can be used as a dividing point which allows the viewer to see the contrast of values that are above or below the mean value. The down side of this method is that it can only work within the data that is normally distributed.
• Natural Break: This classification uses algorithms which are trying to make all the values within a class equivalent to one another as much as possible; however, it also creates the opposite as well by creating the equal difference in the class values.. In other words, the variation of in-class is reduced and the variation of the inter-class is expanded upon. In this method, the data breaks are visually selected in a logical process. The down side is that this type of classification is subjective which makes the data presentation vary between different mapmakers and their take on the data.