GIS4007 - Module 4 - Data Classification

For our Module 4 assignment for Computer Cartography, we explored how data classification methods influence spatial data interpretation in GIS. This lab introduced us to four types of classification schemes: Equal Interval, Quantile, Standard Deviation, and Natural Breaks. Our task was to apply them in two contexts: percent-based and area-normalized data of seniors aged 65 and older in Miami-Dade County, Florida using ArcGIS Pro. 

Our dataset was specifically focused on the 2010 U.S. Census tracts in Miami-Dade County and included both the percentage of population aged 65 and up, and the number of seniors normalized by square mileage. We created two map, showing the four classification methods for each context (percent and area-normalized).

This map shows the percentage of seniors in each census tract. Equal Interval was easy to understand but hides variation in areas with similar percentages. Quantile made the map look balanced, but was misleading by placing very different values in the same class. Standard Deviation highlighted how far values stray from the average which was very good for telling outliers, but this assumes a normal distribution, and if the data is skewed due to many outliers, it might miss smaller but widespread differences. Natural Breaks was the best at revealing true patterns in the data by grouping similar values naturally.

This map normalizes the number of seniors by area to outline where they are most concentrated. Equal Interval was showing value range pretty well but was masking important patterns within the urban areas. Quantile was good at showing visual balance, but was downplay the areas with extremely high densities. Standard Deviation was useful for pinpointing the unusually high or low densities compared to the average (the outliers), but was having similar issues as the percentage map with outliers effecting class groupings. Natural Breaks again clearly showed the clusters of high-density senior populations which would be ideal for planning resources and services.

For the classification methods we explored, Natural Breaks was the most effective for both percent and area-normalized data as it revealed natural groupings in the data making it suited for targeting senior services or outreach.

In terms of data presentation, the percentage based map was much more effective than the area-normalized one when conveying where services for elderly might be needed most. The percent map clearly shows where seniors make up a higher proportion of the population (like in the upper northeastern part of the county), which is useful for policy planning like healthcare, public transit, housing etc. While the normalized map could be useful for knowing population density, for this data in particular, parts of the county with larger tracts being low in population were visually dominating the map, even if very few seniors live there, giving map readers the false sense of priority to those areas.