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.

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Lab 5: M 2.2 Interpolation

This weeks module focused on identifying the best interpolation method for modeling the air quality over Tampa Bay. Four methods were tested using the same set of sample points Thiessen, Inverse Weighted Distance (IDW), Tensioned Spline (seen above), Regularized Spline. Thiessen Interpolation assigns all cells in the raster with the value of the nearest sample point. IDW calculates the value of all cells by considered multiple sample points nearby and giving closer points a higher weight than further points. Both Spline methods create a smooth surface over the sample points but the regularized version creates a smooth curvature regardless of the range of values in the sample meaning cell values can end up both above and below the minimum and maximum values found in the sample. The tension model attempts to fix this by constricted the curvature of values to the ranges found in the sample points.

Module 2 Lab: Land Use / Land Cover Classification, Ground Truthing and Accuracy Assessment

The above map shows digitized land use/land cover (LULC) classifications for Pascagoula, MS. A set of 30 random sampling points were created post creation of the LULC layer and they were verified in google street view to determine accuracy. Based on these points the LULC model was found to be 86.6% accurate.

Applications in GIS Module 2

This weeks module focused on using LiDAR data to determine the height and density of a section of Shenandoah National Forest in Virginia. Mostly this involved converting the LiDAR data to a raster and running various tools to achieve the desired datasets. New tools included LAS Dataset To Raster, LAS to MultiPoint, Con, Plus, Float, and Divide. Additionally a histogram was created to display the heights of cells in the forest. Density and DSM: Height with Graph: LiDAR:

GIS Portfolio - Michael Malone

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