Python

Our last and final exercise was to use Python and write a script for our risk and suitability map. Our objective for our exercise was to write a script that created 5 buffers off of our mine locations, with each buffer increasing by 1000 meters. The script written can be seen in figure 1, which is a screenshot of the script itself and the actual running of the script.
Figure 1 is a screenshot of the script used in the Python exercise

The next I did was to create a graphic of the actual map itself. Using ArcMap, I created a graphic that showcases what the script actually did. 

Figure 2 is a map of the mines with a buffer around them.

Using Python was a neat exercise for me. It showed a way that one can run another tool is ArcMap, with having to find the tool, import all the files, and in this case having to run a buffer 5 times. This script was effective in the sense that once we set the loop for the buffer to run 5 times while adding 1000 meters each time, it saved a bunch of time that would have been used to re run the buffer tool. Python would have been effective have been effective in running tool for our risk and suitability map. Instead of having to find and run all the tools, a simple script would have saved time because you don't have to find the tools you can just type them in in the script. The problem with Python is that it is very specific in what it needs and you can lose time by making a simple mistake by not putting in a quotation mark or a period. These mistakes would cause a person to have to re run a tool or the simple interface of a tool would have cleaned up this mistake

Suitable Sand Mine Locations in Trempealeau County

In this report, Trempealeau County was looked at extensively to see where the suitable sand mining locations were. The objective of the report was to take into the account of many variables within finding a suitable location for sand mines. Variables included in the report are land use and land cover, height of the water table, proximity to railroad terminals, elevation of the land, types of land formations situated within Trempealeau County. Withing each variables are sub-variables that will be explained throughout the rest of the report.

Data collected for this report are from multiple places. Trempealeau County has a database available for download located at http://www.tremplocounty.com/landrecords/ . This database included much of the information needed, like an elevation DEM, road networks, parcel data, school locations. A land use-land cover map was downloaded from the National Land Cover Data-set website. Our water table elevation map was brought in from the Wisconsin Geological Survey website. The cell size for each pixel in all the maps are 30x30 meters. The reason for this is that this is the lowest spatial resoultion for the maps, and in order to be accurate with all the analysis, I had to run the output cell size as 30 meter.
Land Suitability
The ranking systems used in our raster analysis is that the most suitable lands are given a value of 3, lands that are in the medium are given a 2, and lands that are the least suitable are given a 1. For elevation, it is only broken down into a 2 for the most suitable, and a 1 for the least suitable. After overlaying the 5 total rasters, the pieces of the lands will have a score between 5 and 14, with 14 being the most suitable pieces of land and the 5 being the least suitable pieces of land. Rivers and urban areas are removed and not eligible for the final raster calculation.
Figure 1, is the the master key for the following maps that were created. Using the master key, it will tell you all the values that were used in creation of this map. Justifications for why these values were used are in the following paragraphs. To see the image in greater detail, click on the image itself to see the full sized version.

For the first portion of our data analysis, multiple rasters were created based on an estimation of suitability. The first raster I created was an estimation of suitable elevation for sand mining. According to an unknown research project that was stated in the directions, suitable sand frac sand mining are located within the Jordan and Wonewoc formations. Using my estimations, I determined this formation to be between 250-400 feet in elevation. I used the reclassify tool on the Trempealeau County DEM, and gave values of 250-400 feet a 2 while values less the 250 and greater than 400 a 1.

Figure 1 is the master key for both Land Suitability and Risk Assessment Maps

                                                                                     
  Figure 2 is the raster reclassification of the elevation of Trempealeau County. 

The second raster analysis used was to determine suitable mine based on land use- land cover. I sorted through the categories of land use- land cover and gave each category a value 1 through 3 as shown in figure 1. I reclassified the raster based on these values. For the land use land cover, I gave lands that were farmland, barron, or shrub scrub a value of 3, lightly vegetated areas a value of 2, densely vegetated areas like a forest a value of 1, and areas that were wetlands or water a "no data" so they were excluded from the map. I based these land classifications on ease of access to clear the land to produce a sand mine.

Figure 3 is the raster reclassification of the land use-land cover in Trempealeau County

The third objective was to create a raster on proximity to railroads. I used Euclidean distance to the nearest railroads terminals. As shown in figure 3, all the sand mines located in Trempealeau County go to one of two railroad terminals. I reclassified each of these Euclidean distances by using equal intervals and gave each zone a value between 1 through 3. I based these values on a equal interval of three classes. Refer back to the master key to know the value ranges.

Figure 4 is the raster reclassification of the distance to the nearest railroad terminals. 

The fourth objective was to the original DEM of Trempealeau County and to run the slope tool to determine the slope located throughout the county. After running the slope tool, I reclassified the newly created raster into more suitable lands based on the slope, I again assigned values between 1 through 3 for the suitable lands. I gave these values a natural breaks interval for the slope grade.

Figure 5 is a raster reclassification of the slope ratings in Trempealeau County

The fifth objected was to take the water elevation contour lines and create a raster for suitability  Sand mines use lots of water, so water that is situated closer to the surface was rated higher than water that is not. First I had to take the contour lines and create a raster by using the topo to raster tool. After creating the raster, I then reclassified the raster with by assigning values between 1 through 3. I based these values on the height of the water table. I used an natural breaks interval for this sand mine because of the random heights of the water table.

Figure 6 is the water table height for Trempealeau County

After the creation of these rasters, I then used the raster calculator tool to find the most suitable land for sand mining in the Trempealeau County. Values range from 5, which is the lowest suitableness possible, to 14 which the highest, most suitable location possible. As seen below, you can see the different values represented by different colors in the map.

Figure 7 is the final raster overlay, which showcases the most suitable and least suitable locations in Trempealeau County

Figure 8 is the workflow model for the sustainability portion 

Risk Assessment

The second part of this report is a risk assessment of lands in Trempealeau County. This is where we are going to take important environmental, and public factors into account. Risk assessment factors that we are looking at are lands near residential neighborhoods, lands near public schools parcels of land, lands located near streams, lands located near wildlife areas, and lands situated on prime farmland.

The ranking scale for Risk Assessment is that the lower the total score, the least suitable that piece of land is. The higher the score, the more suitable that piece of land is. Please refer back to the master key for details on the value meanings

The first risk assessment factor I am looking at is Residential Zoned areas. Here I took all the residential zoned areas located within the Trempealeau County database and ran a Euclidean Distance tool. The reason for running the Euclidean Distance tool is because sand mines can not be located within 640 meters of residential areas. After running the tool, I then reclassified the the Euclidean distance to 1 class, and gave it a rating of 3 for highest risk assessment possible.

 

Figure 9 is a Residential Zone Risk Assessment map for Trempealeau County 

The second risk assessment is the placing of mines within 640 meters of school parcels. Using the same process as before, a Euclidean distance was ran based on parcel data that was queried to find all school parcel data within Trempealeau County. I than reclassified the data to give the areas not impacted a 3. 

 

Figure 10 is a Risk Assessment map of school parcel data in Trempealeau County

The third map created was a risk assessment map of sand mines located near streams. Using the same process before, I created a Euclidean Distance  The reason being is that I believe a mile is a fair amount a distance where an industry should need to be aware of potential stream pollution, and the primary and secondary streams are the larger streams located within the county. Again, a reclass was created to give areas that fall within certain distance as seen in the master key.  A 1 was given for high risk assessment while a 3 was given for a low risk assessment. 

 

Figure 11 is a Risk Assessment map of locations of  streams

The fourth map is a risk assessment of lands near a wildlife area within the county. Using the master key as a breakdown, The estimated breakdown in meters should provide enough of a buffer if an accident or pollution would occur and not affect the area right away before it could be contained. Using the Euclidean Distance tool and then reclassisfying it, a scale  was created around the wildlife area and  a 1 was given for areas of high risk assessment and a value of 3 for a low risk assessment

 

Figure 12 is a Risk Assessment map of locations located near a wildlife area

Another assessment is a map of all the prime farmland located within the county. Prime farmland is a rarity and once a sand mine is put on it, the farm land would lose its value. Here I reclassified the prime farmland and gave it a value of a 1 for highest risk assessment, and a value of 3 for the lowest risk assessment

 

Figure 13 is a Risk Assessment map of locations located on prime farmland in Trempealeau County

Two viewsheds were created also in this risk assessment. Using parks and trails, viewsheds were used to create a map of viewable locations from these parks and trails. As to not disturb the natural beauty Trempealeau County has to offer, I eliminated the parcels of land that were viewable from the viewshed analysis, and gave the non seeable locations a value of 3 for low risk assessment.

Figure 14 and 15 are the viewshed analysis of the parks and trails located in Trempealeau County


The final step is to run a raster calculator and combine these maps. Values are range from 12 to 18.  I ran the raster calculator and figure 16 shows the results of the calculations and the areas of highest risk assessment. Again as previously stated the lower the value, the least suitable that piece of land is and has a greater risk assessment. The higher the value, the lower the risk assessment is and the more suitable that piece of land is.

Figure 16 is a Risk Assessment map of Trempealeau County

 

 

 

Figure 17 is the model builder image of the all the tools used in this assessment

Results and Conclusion

Looking at each map individually, you can see how random the values are. You can find 30 by 30 meter squares with a value of 14 next to 30 by 30 meter square with a value of 5 in the Land Suitability maps. the same can be said with Risk Assessment maps, you can find a 30 by 30 meter square with a value of 12 next to a 30 by 30 meter square with a value of 18.
Sand mine companies are going to have to weight choices. Will it be best to put a mine on a parcel with a score of 14 next to a parcel that scores a 5 or is it better to put a mine on parcel with a score 10, with all the surrounding parcels a 10 also. These are the kinds of decisions the companies will face. Maps like these can help shift a decsision one way or another.
While looking at the overall outputs of both the Risk Assessment and Land Suitability maps, we can the there are many different locations within Trempealeau County that have a wide range of values. Until we overlay the two maps and find out a difference between land suitability and risk assessment, we do not have an accurate value for each 30 by 30 square.
Overall this report challenged a GIS professional under many differnt levels. The ability to maintain and organized geodatabase was the toughest issue I had because of all the differnt tools we were running. The next toughest challenge was being able to maintain out 30 by 30 meter spatial resolution and maintaining the integrity of map accuracy. Without map accuracy, credibility of the map is completely gone.

Notes
These maps are created using data not collected by myself. I am not liable for  all maps and assessments based of these maps. Use these maps at your own risk.

Frac Sand Mining Project

Previously, I have geocoded all the mines and now its time to take the next steps; setting and running network analysis.     
After going through the set up of the project, now is the time we actually run our data and analysis it to see what kind of costs counties can incur from sand mine trucks running loads on the roads itself.

To run our project properly, gathering a few shapefiles was necessary. From ESRI folder, the Wisconsin counties, roads, and railroad terminals shapefiles, were collected for use. Next I imported the merged mines shapefile that was created in the previous steps.

Figure 1

 

     The first step in the process was to create a closet facility analysis. This is found in the network analysis toolbox. It will ask you what your incidents and facilities are. For the incidents I imported the merged mine shapefile, and for the facilities I imported the railroad terminal. One option that needed to be checked was to make sure that the process was taking the mines to the railroad terminals and not from the railroad terminals to the mines. This network analysis created the routes the trucks take from the mines to the facilities based on time or distance, in this case it is based on time. Next, taking this route, using the identity tool was needed to give the routes/roads a spatial identity it needed so the proper analysis can be given to the roads in each county. What the identity tool doing is it is taking each route and breaking it down into what county it is located in. If the route crossed counties, it separates the route at the county boundary and calculates the mileage for each individual county. Using figure 1, we can see the data model that is ran. You can see each individual inputs and outputs for each of the steps that is needed to create this model. The final step in the model is the identity step. Using this identity output, I then was able to get the total county mileage by using a summarize function. Now the calculation can be preformed to see the cost of the the sand mines trucks have on county roads. Using the estimated cost of 2.2 cents a mile and 50 trips, which is there and back so 100 was the multiplier, taking the total mileage per county at 2.2 cents a mile at 100 for the trips, the estimated cost was calculated.

Figure 2

 

     Figure 2 has the map and graphs of the calculated data for the project. Looking at figure 2, La Crosse, Trempealeau, Eau Claire, and Chippewa Counties were in the top 4 by a long shot. La Crosse's estimated cost is a little over $2,250 per 50 trips.

     The overall impact of the sand mines is unknown at this point, for every action there is a reaction. This project is looking at one of the potential impacts that sand mine companies can have on the public sector. Living next to a sand mine, there is more than 50 trips in a day. So using this equation  there would need to be another multiplier for the number of days in operation to get the true cost. The county government knows the cost of the replacement of the road or improvement of the road so it can hold the loaded sand mine trucks accountable. This estimate can help factor in the true cost of the damage to the roads that is from the sand mine companies. 

     On a personal note, as a supporter of the sand mine and a taxpayer, I believe the sand mine companies should be held responsible for the deterioration  of the roads that are not rated to hold a fully loaded sand mine truck. I do not think it is fair to the taxpayer that they have to pay for the road that is damaged because a vehicle that is overweight for the road is using it. 

*Due to blogger not being able to accept PDF's, I am unable to put the figures on the website until I am back in Eau Claire and able to convert the PDF to a JPEG.

** It is now updated with the appropriate images

Data Gathering

    Now  we continue building on our Frac Sand Mining project by collecting the necessary data needed to run a complex model.

     For our project, there was a few steps we had to take before we could geocode our data. First we had to gather our data from a wide variety of sources and put them into a single database table. We gathered data for Trempealeau country off of the county website. Luckily for us Trempealeau county keep track of all the sand mines located within its boundaries. You can see the data and website at the link listed here.  http://www.tremplocounty.com/landrecords/  Next we found more data at
http://www.wisconsinwatch.org/2012/07/22/map-frac-sand-july-2012/
Here is where the main body of our data came from. They had collected mine locations throughout all of west central Wisconsin. These two websites is where our wide range of data came from.
The problem with our data is that there was no sort of organization to it. We had to normalize our data in some way, shape, or form. We formed groups and each took a chunk of data. The major problem was the address where in multiple forms, so by using aerial photographs and trying to identify locations of mines off of aerial photographs, we went through our data to see if it was accurate and cleaned up some of the records. This cleaning process elimated some mines that were listed twice, but also removed some of the mines that were not identifable because they had no address.

     We then combined all of our data and connected to a GIS server to run our geocoder. We had to have common fields so we broke our data down into addresses, city, state, and zip code to help the geocoding process out. After entering in the necessary data, the geocoder used entered data to place a point on the map.We ran into some problems because we did not have addresses for all mines so the geocoder place the points in the center of the city that was listed. This is where using our aerial photographs helped because we are able to search to see if we can identify our mines. Another problem were some of the mines were listed twice so we had to remove some of the mines that were redundant.

     Are results were as followed: we were able to id 74 mines, and we were unable to id 25 mines. This is not including the Trempealeau county data which added some more mines. We still were able to showcase a wide range of mines in our data and had high number of mines found so we can proceed to our next step. There still could be some slight error because not all mines are opertaional at the time of the aerial photographs, so we have to rely heavily on the fact that the addresses on the websites were accruate to begin with.

Here is a map of our mines

Frac Sand Mining Overview

Frac sand mining has and is becoming an issue in Wisconsin. According to the Wisconsin DNR, Wisconsin has high amounts of the necessary sand needed for frac sand mining. Frac sand mining is the process in which the required sand is taken out of the ground (mining) and it is ship to the natural gas facilities that uses the sand in their natural gas operations.

According to this map from Wisconsinwatch.org most of the frac sand mining is taking place in west central Wisconsin.

Issues with frac sand mining are pollution based. How harmful is the sand that is being pulled out of the ground. Are the the airborne particulates a cause of concern? Is the extra pollution being produced by the mining facilities and transportation vehicles needed to ship a concern also? There are also environmental concerns because frac sand mining requires the destruction of habitats to get at the sand.
For this project, I am going to use GIS to help geocode the location of the mines to help further identify problems related to frac sand mining. One issue we are going to look as is the transportation infrastructure health to see if there is deterioration of roads along the frac sand mining transportation routes.

http://dnr.wi.gov/topic/mines/silica.html
http://www.huffingtonpost.com/2012/12/07/frac-sand-mining-wisconsin-health_n_2256753.html

http://www.wisconsinwatch.org/2011/07/31/sand-mining-surges-in-wisconsin/