Geocoding of Frac Mines

Lab #6: Geocoding Frac Sand Mines in Wisconsin

Introduction:

The purpose of this lab activity is to utilize the geocoding toolset to place active and inactive frac sand mines across the state of Wisconsin and compare the geocoded dataset to the actual locations of the mines. Each student was given a grouping of roughly 16 random mines with the purpose of using the broken information given, and creating a more accurate dataset with geocoding. The original Excel file can be seen below in figure 1, clearly not all mines have a complete usable address. The geocoded addresses will then be compared to the real accurate locations of the mines to see how accurate the geocoding was.

Figure 1: The provided Excel file before normalization.

Methods:

In order to geocode the original data and analyse accuracy several steps had to be done. The first step is to normalize the data.

1) Data normalization was done with the mine addresses so that the needed information was more easily accessed and the mines could be found with what information was given. A normalized table can be seen in figure 2. When comparing the original and normalized file it is clear how sporadic and inconsistent the provided data is.

Figure 2: A screenshot of the normalized data. The amount of fields containing "n/a" show how inconsistent the provided data was.

2) Geocoding begins with the step of accessing the geocoding toolbar. From the toolbar “geocoding addresses” is selected and the normalized sheet is added into the address field. “Address Inspector” is used to see which addresses in the table match to locations, my personal file for geocoding had 12 matched addresses and 4 unmatched. The unmatched addresses were found with the public land survey system, or PLSS, and “Pick Address from Map” was used to match a point to the address.

3) Merging shapefiles is the next step. The members of the class who geocoded the same mine locations as were contained in my file were combined into one shapefile to be compared to the mines I personally geocoded. This way the accuracy of geocoding among peers could be tested.

4) Distancing measuring is possible with several tools. The one used in this case was the “Generate near distance table”. This tool took the location of geocoded points and measured, in meters, the distance to a point that was supposed to be at the same point. The tool creates an output of a table containing the mine information and the distance between the points that is calculated. The result from this tool for the individually geocoded points and peer geocoded points is visible in figure 3. The same process is done for the real and accurate mine locations, a screenshot of the output is below as PLACE FIGURE. 

Figure 3: This table displays, in meters in the "near_dist" attribute, how far away from each other the mines were that were geocoded by me and by my peers.

Figure 4: This table displays, in meters in the "near_dist" attribute, how far away from each other the mines that are placed, by the DNR, at the correct locations.

5) Map. The final step was to take the geocoded files and compare accuracy visually in a map format. The first map, figure 5, is of the points I geocoded compared to others in the class who geocoded the same mines. The second map, figure 6, displays the mines I geocoded in relation to the actual position of those mines.

Figure 5: This map displays the mines that were geocoded by me personally and those done by classmates. Some mines are placed in the same location while others are clearly placed in wrong areas.

Figure 6: This map shows where the mines that were geocoded by me actually are. Again, this shows that some mines were placed by me in the right location while others clearly weren't. 

Discussion:

Of course this method of geocoding is not without its flaws. There are inherent and operational errors that often occur with geographical data in general. The errors came from issues in the data automation and compilation areas including geocoding which was done in this lab. The issue with geocoding is that the points will never be exact because they are manually placed and so they will never be in the exact location as where they actually area. Another error is attribute data entry. This error would not be visible through geocoding but is as simple as being provided with the wrong data in the provided Excel file.

Attribute accuracy, or closeness, is necessary to be able to see which points have been placed in the "right" area. The only way to truly know where exactly a location is is to rely on the longitude and latitude of the point. Only that coordinate can be referenced to to compare a geocoded points closeness.

Conclusion:

When the activity was finished the Excel file had been normalized, the points had been geocoded, mines with only PLSS addresses had been geocoded, and the accuracy of those points was tested using tools in ArcMap. This will be a good exercise to be able to refer to later in work as I will likely need to geocode points and they will not always be perfect. The near distance table tool allows me to see how close the points are to being accurate which is very valuable.

Sources:

Wisconsin Department of Natural Resources. (n.d.). Retrieved November 8, 2015, from http://dnr.wi.gov

PLSS - Legal Descriptions | PLSS. (n.d.). Retrieved November 8, 2015, from http://www.sco.wisc.edu/plss/legal-descriptions.html