The goal of exercise six is to geocode the locations of sixteen different mines from excel spreadsheet that was received from the Wisconsin DNR. First the data had to be normalized in order to work with the data effectively. From there comparisons are made between the locations I found and the locations other classmates recorded and then from there compared to the actual locations of the mines that was provided by the instructor.
Objectives:
The objectives of this lab are to get the data for the sand mines in Western Wisconsin from the DNR and connect to the geocoding service from ESRI online and to then normalize the table. Next using the street addresses, the mines are geocoded to there location on a map. If a street address was not given, the mine was found by using the PLSS and google maps. From there the locations found for the mines is compared to other classmates.
Methods:
Using the Unique mine ID field in Microsoft Excel, we were assigned sixteen mines in Western Wisconsin. The first step of the exercise was to try and open the data in ArcMap to find the locations, only to find that there was obvious errors in opening them immediately. Closer inspection of the table reveals that the data is not uniform, some mines did not have street addresses and others were just a jumble of PLSS( Public Land Survey System) data and other did not have full street addresses. The process of normalizing the data was essential to success in this exercise. To normalize the data the data was broken down into different categories as described in the list below.
- PLSS
- City
- State
- Zip Code
- Address
- Town/ City/ Village
- County
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| Figure 1: WI DNR Raw Data Table |
Figure 1 above is an example table of the data that was received from the WI DNR. The actual table obviously contained much more data but for lab purposes only a sample portion was used to show what we had to work with in the beginning.
Other information that was given included the Operator, Facility Name, Site Status, Facility Type, Property Size, the Facilities Contact Information and finally the Mine Unique ID. Of the information listed above the most essential information in normalizing the table was the Mine Unique ID. Not all of the maps were easily found even after normalizing the data, many required the use of Google Maps. Mines are growing in popularity so there are mines popping up all over, hence why the use of google maps was essential because it is more up to date than ArcMap.
The zip code and city were used to help get close to the location and then from there the mines were found manually using satellite imagery.
Below Figure 2 illustrates the final product of the table that was used to geocode the mines. The headings near the top show all the columns that had to be added and all the information that had to be separated to make the Excel sheet user friendly.
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| Figure 2: WI DNR Normalized Data Table |
After the data was normalized, it was then brought into ArcMap, immediately upon bringing the data into Arc, there was a few errors still with the data. A few of my columns had missing fields because there was no data for those mines, to remedy this a blank field was inserted. Next, a base map of world imagery was added, this gave us the first real sense of where the mines were that we were to geocode. Of the sixteen mines to be geocoded, only six of the mines had the correct address, the rest had to be found manually. Following this step, the other classmates data was added as well. From here using the select by attributes feature in ArcMap, to select by Unique Mine ID, the other students sixteen mines were selected and placed into the same geodatabase. Only one copy of each mine was used for reference, the reason there was multiple people assigned to each mine was in case someone did not get it done or placed a point in the wrong area. After the data was in the same geodatabase the projection was changed to UTM Zone 15 North. After the three separate shapefiles being worked with from other students was merged together, this make the process of finding distances between mines easier.
Results/Discussion:
The map below is a map that shows how my mines geocoded related spatially to the other students who also had to geocode the mines. The red circles are the mines that I worked with, while the green are my classmates. It is easy to see that some mines are not near any other mines, this can be attributed to the fact that there are so many mines in western Wisconsin. When having to hunt and peck for a mine on google maps, it was easy to select mines that were not the ones you intended to geocode.
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| Figure 3: My Mine Locations Compared to Classmates |
Along with the error mentioned in the above paragraph of simply selecting the wrong mine, there were many other opportunities for error to occur in this exercise. The map projections used when geocoding, entering data from one mine into another, selecting the wrong unique mine ID, using a gravel pit rather than a mining site, also if the user got frustrated and decided to just drop the location somewhere in the right area and give up. Finally there was no real description on where the geocoded point should be placed, meaning that some people may have dropped the location inside the mine, some may have been on the road in, while other could have done it like myself, placing the point on the road where the mine meets blacktop.
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| Figure 4: My Mines Compared to Actual Mine Locations |
Above, Figure 4 illustrates my geocoded mine locations in comparison to the actual mine location that was found using the latitude and longitude. In order for the map viewer to see how my mines related to the actual location, I made my points slightly larger and added a little transparency to the actual mine locations. Some of the locations are very close while other differ considerably.
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| Figure 5: Comparison of Mine Locations |
Above is a map that really gives a good idea into how the locations of mines can differ based on who is doing the geocoding. The red circle at the bottom of the map is the location that I selected as the location of the mine, the way I thought about it was that, that would be where the trucks would be going in and out and often Google Maps takes you to the driveway rather than the center of your destination. My classmate selected the road on the way in and the actual location of the mine is indicated by the yellow circle at the top of the map.
Conclusion:
In conclusion, this assignment tested patience to say the least. At times it was challenging to get the data normalized and then to merge the data, and from there figure out the best way to figure out the distances apart. I used a spatial join to calculate the distances. The tables below show the distances away from where I plotted my mine. I was within twelve meters of one of my classmates points, but in contrast I was over seventy kilometers from the actual location of one mine. That is attributed to some of the user errors listed above. This exercise was tough, but served as a good problem solving and skill building lab.
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| Figure 6: Distance From my Mine Locations Compared to Classmates |
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| Figure 7: Distance From my Mine Locations Compared to the Actual Location |
Sources:
Wisconsin DNR- http://dnr.wi.gov/
Google Maps- https://www.google.com/maps
Public Land Survey System- http://nationalmap.gov/small_scale/a_plss.html
ESRI Online- http://www.esri.com/software/arcgis/arcgisonline
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