Special Topics in GIS (GIS5935): Lab 3

The goal for this week's lab was to show the percentage difference of accuracy between two road networks of Jackson County, Oregon. To get the results of how many polygons were complete depending on Street dataset and the TIGER Roads dataset, I exported each polygon grid into its own separate shapefile. Once all of the polygons were separated, I clipped the two datasets to the each of the polygon by using the batch option on the Clip tool. After the datasets were clipped to the polygon, I recalculated the geometry of the data to kilometers and used the ‘Statistics’ option to gather the sum of all of the polyline features’ length.

Photo Interpretation and Remote Sensing (GIS4035): Lab 4

In this week's lab, we had to use the previous data that we created in Lab 3. The data that was used was the aerial photograph of Pascagoula, MS and the LULC shapefile. We had to create a point shapefile called Truthing. This shapefile has 30 sample points to show the accuracy of the land use cover map. To identify if the points are accuracy or not, two categories were created and these categories are Accurate YES and Accurate NO.

Photo Interpretation and Remote Sensing (GIS4035): Lab 3

In this week's lab, we looked at an aerial photograph of Pascagoula, MS and determine the land use of each part of the town. After determining that land use, a code was assigned to each type of land use. ArcMap was utilized for this assignment which allow us to create a landcover of Pascagoula. We used the LULC land classification system and the level that was primarily used for the map was level II and there were some level III codes used as well.

Special Topics in GIS (GIS5935): Lab 2

In this week's lab, we had to determine the horizontal positional accuracy of road network according to the NSSDA. 20 random points were selected from the City shapefile and these points had good intersections and straight or close-straight angles. Using these points, the matching 20 points were found on the StreetMap shapefile. Once these points were found, a reference points were placed according to visual sense of where the true location of the points were. The XY Coordinates tool was used to all three point shapefiles so that each of the shapefiles' XY coordinates would be applied to the accuracy statistics chart. The attribute table of the shapefiles were exported as Excel files which I was able to copy and paste each of the X and Y data into the accuracy statistics.

1.       The City Results’ Horizontal Positional Accuracy: Tested 88.85 feet horizontal accuracy of 95% confidence level.

The StreetMap Results’ Horizontal Positional Accuracy: Tested 517.6 feet horizontal accuracy at 95% confidence level.

Photo Interpretation and Remote Sensing (GIS4035): Lab 2

In this week's lab 2, the first exercise looked at identifying various tones and textures in an aerial photograph which shown in the map 1. In the second exercise, features had to be identified in the aerial photograph which is shown in map 2. The features that we had to identify were shape-size, shadow, pattern, and association. The last exercise, we had to select five features and note the color changes between a true color photograph and a false color photograph. This lab was very interesting since we were applying the knowledge of the recognition elements to our examination of these aerial photographs.

Special Topics In GIS (GIS5935): Lab 1

In this week's lab, we learned how to calculate metrics for spatial data quality. The first image shows the precision and accuracy of the GPS Unit's results. The number of waypoints that were analyzed were 50. The data had to be projected from GCS WGS 1984 to UTM N17 NAD 1983. This allows us to be able to measure these points using metrics instead of degrees. We had to find out the accuracy and precision of the data. The result of the horizontal accuracy is 3.2 meters. The horizontal precision is 4.5 meters. The both of the results are 0.7 meters off from each other. The vertical accuracy is 5.96 meters and the vertical precision is 0.9 meters. The difference between the two vertical results is substantial. The measured difference between the two is 5.06 meters.  The second image is a CDF scatterplot chart of the error of x and y coordinates from 200 points. 

The horizontal accuracy was measured by measuring the reference point and the average location point. The Measure tool was used to measure the distance between the point which give the resulted number of what the horizontal accuracy is. The precision was found by sorting the distance field in the attribute table of the spatial joined data sets and assuming that the first number that was listed was 2% and count all the way to 68%.