GIS 5935 Module 1 - Data Precision and Accuracy

Module 1 of Special Topics in GIS dealt with the precision and accuracy of gathered waypoints from a GPS data collection unit. International Organization for Standardization defines precision as 'the closeness of agreement between independent test results obtained under stipulated conditions' [ISO, 2006]. With regard towards the lab assignment, precision would be determining the proximity of fifty gathered waypoints from a single location using a Garmin GPSMAP 76 data collection unit. As shown in the map below, many of the waypoints are in close proximity while others deviate from the majority. For this part of the lab, the mathematical mean was calculated for the x-, y-, and z- location for all fifty waypoints; this 'average' location is denoted on the map as a red 'X'. Once this average location was calculated, an analysis could be performed on the distance between each waypoint and the calculated average location. This precision analysis concludes that 50% of all gathered waypoints fall within 3.1 meters of the average location, 68% of waypoints fall within 4.5 meters of the average location, and 95% of all gathered waypoints fall within 14.8 meters from the calculated average location. Whether these precision analysis results would suffice varies widely between applications. These percentile distances may be acceptable and appropriate for one scenario and widely unacceptable in a different scenario; precision, therefore, is relative and must be determined at the beginning of each synopsis.

The second part of the lab assignment dealt with accuracy, and different tools that can be employed to determine the extent of accuracy within a dataset. According to GIS Fundamentals: A First Text on Geographic Information Systems, an accurate observation 'reflects the true shape, locations, or characteristics of the phenomena represented in a GIS', meaning that accuracy is a 'measure of how often or by how much our data values are in error' [Bolstad & Manson, 2022, p. 609]. For this portion of the lab, a dataset was provided and completely analyzed using Microsoft Excel. This was very beneficial, as it provided an excellent opportunity to deviate from the comfort of ArcGIS Pro, and into a program that is not so familiar. The first tool used to calculate the dataset's accuracy was a series of manual formulas, including minimum, maximum, mean, median and Root Square Mean Error. The second method used to display the accuracy of the dataset was a Cumulative Distributive Function graph, which is displayed below. 

For additional information regarding Root Mean Square Error, click here. For additional information regarding Cumulative Distribution Function, click here. Both sources are published by Science Direct and provide an extensive overview of their respective topics.

Sources:

Bolstad, Paul & Manson, Steven. (2022). GIS Fundamentals: A First Text on Geographic        Information Systems (7^th Edition). Eider Press.

International Organization for Standardization. (2006). Statistics - Vocabulary and Symbols.    Part 1: General Statistical Terms and Terms Used in Probability. International Organization for Standardization.

Science Direct. (2024). Cumulative Distribution Function.                            https://www.sciencedirect.com/topics/mathematics/cumulative-distribution-function.

Science Direct. (2024). Root Mean Square Error.  https://www.sciencedirect.com/topics/engineering/root-mean-square-error.

GIS 5100 Module 6 - Part II: Least Cost Path and CooridorAnalysis

In the second half of Module 6, we built upon the knowledge obtained in Scenario's One and Two: reclassifying raster datasets to generate a Suitability Map. From there, we were able to continue analyzing the datasets to obtain a Least Cost Path. A Least Cost Path analysis is a geoprocessing workflow that determines the lowest cost path from a source location throughout an entire dataset. As stated in the first post for Module 6, 'cost' is a relative term that does not necessarily refer to monetary units; however, in the map below, a Least Cost Path analysis was performed for an oil company wishing to install a pipeline through a southwestern portion of the state of Oregon. Once the datasets have been reclassified and combined using the Weighted Overlay tool, a Cost Distance function can be run to determine the cost from a set source location. As shown in the map below, the Least Cost Path is derived from the Cost Distance output, and the dashed line represents the lowest-cost route through the study area. Like the Suitability Map, factors can be weighted to give priority to some criteria over others. For instance, the bottom-left map in the image below provides a Least Cost Path based on slope alone. However, if cost is added for river crossings, the Least Cost Path is drastically altered to minimize the number of times the pipeline crosses the rivers in the area [see bottom-right map below.]

Another type of map that provides least cost information is a Corridor Analysis map; a Corridor Analysis map determines an area, or corridor, of least cost between two points; the Corridor analysis does not take direction into consideration. As illustrated in the Corridor analysis map, the Least Cost Path falls within the lowest classification of the corridor area. This would provide the oil company of an area to build the pipeline within, allowing them to make final decisions regarding pipeline installation while maintaining cost efficiency.

 Lastly, in Scenario 4, all of these processes were applied to create a Corridor analysis on travelling bears throughout Coronado National Forest. While no new processes were added to the workflow involved in creating this map, it was a great opportunity to apply all tools / functions in a cumulative effort to produce the deliverable below. I was quite happy with this deliverable and believe it to be an outstanding representation of my comprehensive understanding of Suitability Mapping, Least Cost Path Mapping, and Corridor Analysis Mapping.

GIS 5100 Module 6 - Part I: Suitability Mapping

Module 6 of Applications in GIS was very extensive and loaded with information; the Module was divided into two parts, with two scenarios for each part. This first portion of the module focused on Suitability Mapping; a suitability map identifies areas [within a study area] that meet some, or all, criteria for a given problem. For example, in the map below, regions within the study area are highlighted that are optimal for cougars to live in; these areas were determined by meeting four criteria: distance from roadways, proximity to rivers, areas with increased slope [mountainous or canyon], and forested areas. To determine which areas meet all [or none] of these criteria, a reclassification process had to be run on the provided datasets. For instance, the Digital Elevation Model was processed using the Slope Calculator function and reclassified into two distinct classes: areas with < 9° slope and areas with > 9° slope. Next, a reclassification was employed on the landcover dataset, distinguishing forested areas from every other landcover type. The same process was completed for proximity to roadways and rivers [a Euclidian Distance raster dataset was created prior to the reclassification process.]

After all datasets had been reclassified, the values for each cell could be added together using the Raster Calculator too, and the analysis could begin. Referencing the map above, three variations on the analysis are displayed. The two bottom maps illustrate regions within the study area that meet ALL four requirements, thus being determined as the likeliest places for cougars to dwell; the only difference between the two bottom maps is one is vector-based [comprised of polygonal geometries], while the bottom-right is raster-based [a grid array of pixel values]. The top map, however, displays the study area using a graduated symbology; this tells the map viewer how many criteria are met for the entire study area. A visual comparison of the top map with the bottom two clearly illustrates all three maps highlight the same optimal areas for a cougar to live.

The second scenario of Part 1 was a similar analysis, only determining which areas within the study area would be best for a future development. The criteria used in this analysis were: proximity to roadways, proximity to water, current land classification / land use, and the slope of the land. While there were some variations within the criteria, and what was determined to be optimal for development, the workflow for Scenario 2 was the same. As displayed in the map below, a graduated symbology was utilized to show how many criteria were met for each pixel within the study area.

The difference between the two maps in Scenario 2 is weighted value. The map on the left gives an equal weighted value for each criterion, while the map on the right does not. This is significant because some factors are going to affect the suitability of potential sites more than others. For instance, while proximity to water will have some effect on the location of a future development, it will not be the same effect as not having any available roadways to access the potential site. 

As outlined in this post, the applications of Suitability Mapping are endless, and can be used in a wide variety of ways. Although this lab was very time-consuming and covered a tremendous wealth of information, it was a great experience continuing to learn about the potential applications of Geographic Information Systems.