Module 4 - Data Classification

    This week, in the forth Computer Cartography lab, was about Data Classification. 

    In this module we used data classifications to differentiate our data. This involved using Equal Interval, Natural breaks, Quantile, and Standard Deviation classifications. With these classifications we are able to see the class distribution change with each map as well as learn what each one does.

Equal Interval: groups data into equal grouped ranges that are uniform, however this provides errors due to masking clusters. 

Natural breaks: groups data into natural groups where the data looks to have similarities in their clusters.

Quantile: groups data similar to equal interval but equally distributes data into their category tracts, however this also conceals the data's true nature. 

Standard Deviation: groups data on the deviation from the mean point of all of the data, this provides more errors if not normalized and also reads a bit harder.

    In the first map, we used percentage of the people over the age of 65. With the classifications, you can see that each one is very varied with some similarities between them all. I believe the distribution is a bit more sporadic and since it is not normalized, may not be the best use for finding areas where there are people over the age of 65.

    In the second map, we used the raw normalized data of people over the age of 65. With the classification in these maps, you can see a more uniform distribution between all the maps. I believe the distribution is more correct as the areas line up more between each map. That is my opinion, however.

    In closing, this week’s lab was highly informative on how to display data, and I would love to try using more of it in the future.

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