Final Blog: Suitability Model

Goal and Objective
The goal of this lab is to find a suitable location for a new sand mine to be established, using factors such as geology features, land cover, slope, water table levels, and railroad proximity.

Methods

Figure 1: The model used in the suitability model.

As can be seen in Figure 1 above, the suitability model was a lengthy process. First, I created a Boolean raster of the geology features, the sand mostly being found in Jordan and Wonewoc formations (those features received a value of 1, all else was 0). A second Boolean raster was created of land cover features, giving developed land a value of 0, and everything else a value of 1. I then created ranks for suitable land-covers (giving barren and shrubby land a rank of 3, pastures a rank of 2, and fields and light forests a rank of 1), ranked slope to visualize high and low slopes, and ranked the water table data so that tables closest to the surface were highlighted. These three rasters were then added together using the raster calculator tool, and this raster was then multiplied with the two Boolean rasters from earlier, so that all that remained was the ranked values, while the unsuitable locations are excluded.

Results

 Figure 2: Map of the final suitability model.

The model above displays the areas that are likely candidates for the location of a new mine. The best locations are the dark green and the gradual transition to yellow and eventually orange display the less optimal locations (though they would still work). The gray areas are places were it wouldn't be efficient or logical to place a sand mine. 

Conclusion

It seems Trempealeau County has plenty of areas that would be great for sand mines. Had I had more time and better organized instructions and data, I think I could have done a much better job completing this assignment.

Network Analysis - Truck Routes

Goal and Objectives
The goal of this exercise was to use network analysis to create trucking routes from sand mines in southwest Wisconsin to the railroad terminal nearest to each mine. This data was then used to predict yearly costs to maintain county roads with the additional truck traffic in mind. The objectives of this exercise were as follows:

1. Use the Network Analysis extension in ArcMap to create a closest facility network.
2. Build a model to calculate the same network.
3. Calculate the cost of trucking travel on the roads (by county)

Methods

Using our mine data from the previous exercise and railroad terminal data provided by our professor, it was a simple task to create a routing network for the sand mine trucks. First though, it was required to query out the sand mines that were close enough to a railroad track that it could utilize a spur to transport it, instead of using trucks to move the sand to the nearest terminal. To do this, I made a Select By Attribute query to select all sand mines within 100 meters of a railroad, and then used aerial imagery to confirm that these mines were not to be included. I then made a feature class of the rest of the mines to be used in the network analysis. The closest facility route creates routes from a collection of instances (in this case, the mines), to a collection of facilities (the railroad terminals). We used ESRI street map USA as the Network Dataset, or the streets to be used in the routing of the trucks.

Figure 1: A model for the creation of the closest facility network. The blue ovals are the features that were used with the tools, the yellow boxes are the tools used, and the green ovals are the feature classes produced with the tools. It is easy to see the flow from the creation of the layer, addition of locations, and then creation and exporting of the feature class to a database.

Once the route was created, I had to organize the segments of the route based on the counties that they go through. To do this, I simply used the Intersect tool with a Wisconsin Counties feature class, and the routesCopy feature class itself. This produces a table of every route segment that runs through each county, which was quite a lengthy list. I then summarized the total length of routing through each county, creating a much smaller table that is easier to read and interpret. This was also an opportune time to add another field to the table, to display the total cost per county. Our professor gave us the value of 2.2 cents per mile, with each mine sending 50 trucks on the route per year. The equation for this field was then ([SUM_totalLength]*.022)*50. Figure 2 below displays the model and flow of this entire process.

Figure 2: A model for the creation of the summary table. I used the Intersect tool to join the routes with the counties, then summarized the route data by county, and then created another field to display the yearly cost per county.

Results

Figure 3: The summarized table, sorted with the most expense at the top.

As can be seen in Figure 3 above, the growth in sand mines within Wisconsin could have a potentially large effect on several of the state's counties. Eau Claire County, Chippewa County, and Trempealeau County all stand out, with additional costs above $5,000 per year. Looking at Figure 4 below, it is easy to tell why these counties are so heavily impacted. It looks as though nearly half of the total mines rely on the terminal in Eau Claire, with several of the routes running through Chippewa County as well. The multitude of mines in Trempealeau County leave no surprise to this result either.

Figure 4: A mines, terminals, and truck routes in western Wisconsin.

Conclusion

I was surprised to discover how much all these mines would affect the individual counties of Wisconsin. It is easy to see where all this additional traffic will be, and it is interesting to note the significant clustering of the mines in Wisconsin. Perhaps with this new need for more railroad terminals, another significant rise will occur in this part of the state. It is clear that Western Wisconsin has far less terminals than Eastern Wisconsin. With all of this new activity it wouldn't surprise me if we started to see a new pattern take form, to minimize the trucking traffic through each of these counties.

Data Gathering

Goal and Objectives
The main goal of this assignment is to familiarize ourselves with downloading data from several sources on the internet, as well as importing this data to ArcGIS by building a geodatabase. We completed the following objectives: Downloading the data from several websites, importing the data and joining tables, and finally building a geodatabase while projecting all of the data.

General Methods
First we downloaded all of the necessary data. In total, we downloaded data for railroads, land cover, elevation, cropland land cover, and SSURGO data. Along with the SSURGO data, we used a master drainage index table to analyze the drainage levels in Trempealeau County.  We downloaded the data from resources such as the USDA NRCS Web Soil Survey, the USGS National Map Viewer, the USDA National Geospatial Data Gateway, and the US National Atlas. A File Geodatabase was then created with ArcMap, and each feature was imported into the database. I had to project each feature so that they the coordinate system was universal for each feature class. For this exercise, I chose the NAD 1983 Wisconsin Transverse Mercator projection. Figure 1 below displays all the data that we gathered.

Figure 1. Clockwise from upper left: Cropland data and Railroad data, NLCD land cover data, Digital Elevation Model, and the soil data with Drainage Index being displayed.

We then had to look through t he metadata for each piece of data that we used and find the following information: Scale, Effective Resolution, Minimum Mapping Unit, Planimetric Coordinate Accuracy, Lineage, Temporal Accuracy, and Attribute Accuracy. Figure 2 displays a table with this information.

Figure 2. As can be seen in this table, there wasn't much to work with, in respect to the data's metadata.

Conclusion

I thought that this exercise was very important, because it revealed to us valuable resources of acquirable data. With these sources at our finger tips it should be a lot easier for us to access any necessary data for further research and mapping.

Geocoding Sand Mines

Goals and Objectives
The goal of this exercise is to explore the basics of downloading data, geocoding with ArcGIS, and analyzing the results for error. The exercise consisted of the following objectives:

1. Download list of mines from the WisconsinWatch website.
2. Geocode the mines using the ESRI address locator.
3. Geocode the mines with PLSS if need be.
4. Compare results with classmates.

Methods
The first step in this exercise was to obtain the locational data of mines in Wisconsin. Professor Hupy directed us to the Investigative Journalism website called WisconsinWatch. This website provided an Excel file with address records for over 130 sand mines in Wisconsin. The data was split up among the students in the class so that each student had 14 mines to geocode, with overlap among the students so that we could check for potential errors. Figure 1 below displays the data that I was required to geocode.

Figure 1. Raw location data for 14 sand mines in Wisconsin. Notice how inconsistent the Facility Address column is.

Geocoding is using address data to pinpoint data to a geographic location. However, ArcGIS cannot accurately geocode a mess of data that is displayed in the Facility Address column in Figure 1. Geocoding requires consistent and organized data. To prove this point I first geocoded the data above as is, and I wasn't surprised by the outcome. Only two points were in Wisconsin, several were in Canada, and points even appeared in Italy, South America, and Australia. Because the GIS software didn't have good data to work with, it placed the points using keywords that it did manage to find.

Using Google Earth and ESRI address locator, I used the address descriptions that were given to manually locate and update the addresses. I first located the mine by analyzing aerial images with Google Earth, then I used ESRI to select an address that was nearest to the mine's location. I then updated the information in a new Excel spreadsheet to be geocoded once all the addresses were normalized. It was necessary to use Google Earth because it has much more recent aerial images, and revealed the locations of mines that weren't even present in ESRI's aerial images.

Some of the mines only had PLSS information for the Facility Address (see the last row in Figure 1). The PLSS system breaks the state up into lots of squares, organized by Township (34N in the above example), Range (11W in the above example), and Section (28 in the above example). In that example, the mine is located in the Southwestern quarter of the Southwestern quarter of Section 28 of Township 34 North Range 11 West. Given this information, and by using a PLSS grid that was supplied from the department's online database, it was easy to find the mine's location on the aerial image. After this process, I then normalized the data in the new Excel spreadsheet by adding a City, Address, and Postal column to the spreadsheet and updating the new addresses as I located them with ESRI. Figure 2 shows the completed Excel table.

Figure 2. Normalized address data for the same 14 mines. The updated addresses are much more clean and easy to locate than the previously supplied location information.

After updating the addresses, the software was able to geocode the mines with no problems. Our entire class then shared each of our geocoded shapefiles so that we could analyze our results and find potential errors. I first merged all the shapefiles that were not created by me, in order to access the information that I didn't create. From this new shapefile of my classmate's mines, I selected each of the mines that I also geocoded, creating another new shapefile. With this shapefile and the shapefile of my geocoded mines, I used the Point Distance tool in ArcMap to create a table of the distance from my mines to each of the mines that my classmates mapped. Figure 3 shows a portion of this table. The data from this table will then be used to check for errors among my classmates and I.

Figure 3. The results of the Point Distance tool. There were 224 rows
(16 mines for each of my 14 mines). The table is unnecessarily redundant.

Because most of the data in this table was useless, I had to yet again organize data within the Excel table. First I sorted the Distance and INPUT columns from lowest to highest so that mines closer together would be at the top, with the mines still being in order according to the input mines. It was then easy to pick which of my classmates mines were closest to my mines. I created a new Excel table with this information (see Figure 4), adding a Kilometers column for easier reading.

Figure 4. Table of my mines (INPUT_FID) paired with the nearest
of my classmates' mines (NEAR_FID), with the distance between the two
in meters and kilometers.

Results

Overall, my classmates and I were fairly consistent with the mines that we mapped. Figure 5 shows the completed map of my mines compared to those of my classmates. This map, paired with Figure 4 above both support this claim. The nearest two mines were only 8 meters apart, while the furthest were 19 km apart. However, it is easy to see this outlier by viewing the map below. This could have happened either by myself identifying an incorrect mine, or by my classmates failing to identify a correct mine. 

Figure 5. A map of our assigned sand mines. 

Discussion

After all this work, it is easy to imagine the sources of error between my mines and those of my classmates. It is clear that each of us were fairly precise in locating our mines, as it appears that there are clear pairs for each mine. However, the accuracy for these mines vary depending on the operator. Any error in this activity is most likely an operational error, or an error that is created due to inevitable variation or mistakes caused by the operator of the software or hardware. I would say that our errors are due to inevitable variations in feature classification, image analysis, and attribute data input. There will always be errors in image analysis; what one person may identify as a small mining operation, another person may identify as a large farm. When it comes to aerial imagery, it isn't always clear what an object is, and sometimes it causes errors. During the geocoding process, errors due to variation in feature classification and attribute data input are also unavoidable. Sometimes there are several addresses to decide from, and it is up to the analyst to make a decision; not everyone will make the same decision. It is unavoidable to prevent errors like this, it is just up to the analyst to be aware of their presence and either fix them, or take them into account when using the data.

Conclusion

It was no easy hike getting from that first sloppy address table to a complete map of geocoded mines. This being said, I feel like my classmates and I did a good job with the mines that we were assigned. The map shows that each mine has a clear partner (they aren't just scattered about), and aside from the 19 km outlier, each mine is within 6 km of the other mine in the pair. I was surprised to see such precise results after considering the unavoidable errors.

Wisconsin and Frac Sand Mining

A recent development in the petroleum industry has lead to a significant increase in the sand mining industry in Wisconsin. The new process is called hydraulic fracturing (or simply 'fracking' for short), and Wisconsin's sand plays a pivotal role in the new development. Fracking uses fluid pressure to create fractures into rock deep below earth's surface, giving access to the oil and natural gas reserves within the formations. The sand is necessary to prop open the fractures in order to extract the gas. However, the type of sand must be very specific; It needs to be very well rounded, extremely hard, and of uniform size in order to perform efficiently. Wisconsin's sand meets all of these requirements, and the fact that west Wisconsin has abundant sand reserves near Earth's surface makes it an ideal place to mine the sand. Figure 1 below highlights the areas in Wisconsin where the ideal frac sand is located (in red).

Figure 1. Quartz sandstone formations highlighted in red. Source: http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

As can be seen in Figure 1, western Wisconsin has a significant frac sand capacity. Sand has been mined in Wisconsin for hundreds of years, being used for manufacturing glass, bedding sand, and even sandtraps on golf courses. The petroleum industry had also made use of the sand, but it wasn't until recently that the demand for frac sand skyrocketed to such spectacular (some may even say alarming) heights.

This new development doesn't come without its share of problems and issues, however. The rapid growth of the sand mining industry in Wisconsin has drawn attention from legislators, local government, and even the general public. Operations with such a grand scale are bound to encounter environmental impacts, especially when the process involves the environment itself. The notable issues in west Wisconsin are air hazards and road and traffic problems. At the mining site itself, heavy duty equipment is used to extract, transport, and prepare the sand. This machinery is going to contribute to emissions in the local area. The extraction process also produces crystalline silica emissions (very small particles that are similar to the frac sand itself). These emissions may contribute to an Inhalation Risk, though it is not currently a regulated Hazardous Air Pollutant. Furthermore, the transportation of sand from the mining site to where it will be used is a growing issue. Many of the local roads around the sand mining locations weren't built with heavy transport trucks in mind. The increase in traffic from this development will inevitably create problems with local road quality and maintenance.

Throughout this course, we will be using GIS tools and practice to analyze and predict the effects of this growing industry. We will be mapping the mining sites and railroad depots and analyzing the trucking routes to find out where there will be significant traffic. From this information we can estimate road degradation as an outcome of frac sand mining. Other possibilities include mapping ground water for potential well polluting, displaying areas at risk for air hazards, and much more.

Sources
"Frac Sand in Wisconsin." Wisconsin Geological and Natural History Survey. UW Extension, 2013. Web. 07 Oct. 2013. http://wisconsingeologicalsurvey.org/pdfs/frac-sand-factsheet.pdf

Silica Sand Mining in Wisconsin. Rep. Wisconsin DNR, Jan. 2012. Web. 7 Oct. 2013. http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf

"Wisconsin Department of Natural Resources." Silica Sand Mining. DNR, 26 June 2013. Web. 07 Oct. 2013. http://dnr.wi.gov/topic/mines/silica.html