Applications in GIS Module 4

This weeks module focused on running flooding and damage analysis in New Jersey and in Florida (seen above) This required turning LAS files into TIN and manipulation with the Raster calculator. The Region Group tool was a new tool used to exclude the low lying areas not attached to flood zone immediately on the coast. The most difficult part for me was setting up the symbology for the structures seen above. I ended up setting the primary symbology to Unique Values and then using the expression builder to designate the above classes that searched for the query fields created earlier in the assignment.

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GIS Programming Module 4

The below images are the output of the script for module 4 followed by the flowchart for the script. This module covered manipulating and editing data in feature class attribute tables. What you see below is the result of creating a new geodatabase, copying all shapefiles saved to the module 4 data folder to the new geodatabase, a search cursor identify the name, feature, and population of all New Mexico county seat cities populations. Finally it prints out a dictionary that has been populated with the county seat names and populations. Output: Flowchart:

Module 1 Lab: Visual Interpretations

The focus of this lab was to identify features in aerial images using various techniques. The top map used shows features that were identified using one of four methods, pattern, shape and size, association, and shadows. The bottom identified areas using texture and tone from fine to course and light to dark.

GIS 6005 - Module 6

Because the data provided for the above map contained positive and negative values which ArcMap does not support for proportional symbols, the data had to be modified. All states with positive values were exported to a new shapefile and the same was done with states with negative values. This was all that needed done with the positive valued states. However, for the negative valued states a new field needed added to the attribute table and it had to be populated with the absolute value of number of jobs lost to convert them to positive integers. To prepare the data for bivariate visualization classes needed to be made. To do this, three class quantification was applied to each desired variable to divide each into three relatively equal groups. Then all values in the first group of the first variable were classified with an 'A', then the second group was given a 'B', and the third was given a 'C'. the same thing was done with the second variable however 1, 2 ,...

GIS Portfolio - Michael Malone

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