This lab is an extension of the previous post and aims to show frac sand mining in Western Wisconsin through another lens. The goal of this lab was to download Trempealeau County data from the county website. The final result of this lab is a geocoded map created in ArcMap and an excel spreadsheet showing the distances of my points compared to other geocoded mines with the same address as mine.
1. Download Trempealeau County Land records data.
2. Download the updated list of mines from Wisconsin Watch.
3. Connect to the geocoding services provided by ESRI.
4. Geocode the downloaded mines to the street addresses that they area associated with.
5. Geocode the mines with the PLSS system manually.
6. Compare our results with our colleagues from class.
Methods
The initial part of this lab was to download Trempealeau County data from their geodatabase. The data was then utilized in our lab to concentrate on our region compared to the state. Trempealeau county has one of the highest concentrations of mines in Western Wisconsin and the data that was collected showed this. The next method used in this lab was to download data from our teacher's file which contained updated mines in Wisconsin. Each students in our course was assigned a unique number that corressponded with several mines. Then the mines were normalized in the excel file to make it plausible for them to be geocoded. The table was then put into ArcMap in a dbf file. Some of the addresses of the mines didn't match up correctly so you could either manually change the addresses or go with the suggested address of Arc. Once the geocoding was finished the class submitted their geocoded data in the form of a shapefile to the course folder. All of the data from the shapefiles were merged using ArcToolbox's merge tool. After geoprocessessing the mines were querried to find the mines with the same UNIQUE_ID as the mines that each individual originally geocoded. The querried mines were then created into their own feature. Finally, the point distance tool was used to find out how far our points were from our colleagues same mine points.
Results
In the figures below, you can see the excel files that were normalized, excel files with the finished product of distances and a map of the mines in Wisconsin. In Figure 1, the addresses were normalized to make an easier geocoding experience. The data given to us gave us some addresses or hints to addresses but were not in a form that Arc could geocode. Figure 2, shows the distances and how geocoding and manually normalizing can create differences in points that have almost the same data. Finally, figure 3 is a map of the mines that I did and the ones that were the same as mine.
Figure 1:
Figure 2:
Figure 3:
Discussion
Looking at Figure 3, it is very apparent of how manually normalizing data between individuals can create a distance divide in geocoded data. Because of the manual placing of addresses to mine sites when geocoding the ability to get perfectly correlated points between colleagues is nearly impossible. In the future I now have the ability to know more effective ways of normalizing data, especially when working with others. This lab also gave me the knowledge of how to work with others and finding a best way to normalize data where it will work with everyone's data. Both inherent and operational errors occur commonly according to Lo. Inherent errors are those in which occur during the creation of your data, while operational errors are those in which are created during work on your data. Because of these errors your data may not be fully correct and should be analyzed very closely. One example of an operational error is when I placed my mine inside of a certain county while my colleagues placed them elsewhere. Inherent errors would occur in my data if I didn't project my data in right coordinate system.
Conclusion
In our investigation of frac sand mines in Wisconsin we have so far learned about why frac sand is used, how to download data from government sites, and how to geocode addresses. Other skills that I have acquired from our labs include the ability to normalize data, how to navigate dbf files, how to convert dbf files into exel files, utilizing useful online source, comparing data to images, etc. As we continue discovering more about the sustainability of frac sand mines there will be more extensive data added to this blog.
Thank you for reading.
References:
Lo, Chor Pang and Albert K.W. Yeung. "Concepts and Techniques of Geographic Information Systems." 2 (2006): 108
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