Lab 1: M1.1 Fundamentals

The above layout shows the precision of the data collected by comparing the data to the average point (the star) It was found that the data was precise within 4.5 meters of this location (the yellow area). The data was also found to be accurate within 9.3 meters. Precision was determined by comparing the test waypoints to the average location and determining the distance where 68% of the points fell were within the distance. Accuracy was determined using the same method but rather than using an average location it was determined where 68% of the data was within range of a benchmark location known to be the 'correct' location. With this it was determined that the data is more precise than accurate since the data is clustered more around the average than the reference point.

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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:

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 ,...

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 mi...

GIS Portfolio - Michael Malone

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