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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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 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.

GIS Portfolio - Michael Malone

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