M5 Lab: Choropleth Mapping

This weeks lab required the creation of a choropleth map visualizing the population density and wine consumption in Europe. Population density has been symbolized by using a graduated color scheme from light blue to purple, and wine consumption per capita has been symbolized using graduated symbols (small to large and light to dark green). We were tasked with choosing between graduated or proportional symbols, I chose graduate as the proportional symbols blocked many of the smaller European countries from view. To isolate the specific countries I wanted to symbolize and label, which varied between the primary and inset map, the data exclusion section of the symbology window was used and a query was written to omit unnecessary or redundant data. The graduated symbols used to represent wine consumption were sent through the Feature to Point tool which allowed my to move their individual positions. Similarly the country labels were converted to annotation so they could be positioned. Smaller countries required the addition of leader lines to their labels. After adding all essential map elements the legend was converted into a graphic so I could position each element, including the population density choropleth symbols being aligned for continuity of data. Everything was designed using ArcGIS Pro.

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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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