M4 Lab: Data Classification

This weeks module was about the different methods of data classification. The lab was designed to take these methods and apply them to a choropleth map, or more specifically two presentations of four maps each. Each presentation containing one map of Natural Breaks, Equal Breaks, Quantile, and the Standard Deviation classification methods. The above maps display these methods by analyzing the senior citizen distribution in Miami Dade County, FL. The top map was created in ArcGIS Pro first, it displays the percentage of senior citizens in each census tract and shows how the data is displayed differently using each of the classification methods. The bottom map was created by saving a copy of the top map and changing each frame to show the total population of senior citizens per square mile. Once these were finished we were tasks with determining what each method hides or reveals and which presentation was most accurate. I believe the total population per square mile is the least misleading and should be used since the percentage doesn't take the total population of each tract into account. This means that a tract could have a higher percentage of senior citizens but a smaller total number of seniors and still show up as darker on the map. I believe the standard deviation method is the most reliable since classes don't get watered down and are purely displayed by how far from the average each tract is.

Comments

Module 5 - Unsupervised and Supervised Image Classification

This weeks module focused on classifying images using multispectral signatures. Above you can see the completed classified land cover of Germantown, Maryland. To create this image above signatures were collected that correlated to each required feature. Then bands were chosen (R:4 G:6 B:5) that contained the largest separation amongst features to minimize spectral confusion. In the above image roads and urban areas were often confused leading to a much larger area of roads than actually exist. The inset map contains a classification distance map which displays the distance each cell is (spectrally) from the sample points with brighter pixels being further than darker pixels. This indicates that brighter areas have a higher chance of error.

Lab 3: M 1.3 Assessment

Lab 2: M1.2 Standards

The task for this module was to perform an accuracy assessment on two versions of the same road network in Albuquerque. One was mapped by the city of Albuquerque (yellow) and one by Street Map USA (red). In order to perform the accuracy assessment twenty points per network needed created at matching intersections on each network. Then a reference point was digitized based on satellite imagery to represent the "actual" location for each intersection. XY coordinates were assigned to all sixty of the created points and the values were exported to an excel spreadsheet. From there the RMSE for each of the networks were calculated using the reference points. With this the accuracy assessment was completed resulting in the following: ABQ_Streets Positional Accuracy: Tested 14.84 feet horizontal accuracy at 95% confidence level. StreetMapUSA Positional Accuracy: Tested 158.56 feet horizontal accuracy at 95% confidence level.

GIS Portfolio - Michael Malone

Search This Blog