Raster Modeling

Goal and Objective: The goal of this exercise was to become more familiar with the geoprocessing tools that are more associated with raster modeling. Such tools include reclassify, raster calculator, and viewshed. With these tools I was able to create a suitability model using influences that include landcover, slope, water-table depth, distance to railroads, and the geology of the county of Trempealeau County, WI. I was also able to create a risk model using the factors of streams, prime farmland, residential/populated areas, schools, and parks.

Methods: Creating these maps I used model building to help myself see the process of where I was getting my information and where the new data was going. The ranking for my models were 3 to1. 3 being the highest suitability to 1 being the lowest. The scale for my risk model is 1 being the highest risk and 3 being the lowest risk.

- Geologic Criteria



Figure 1: Map showing suitability based on Geology



 

This may be the most important factor for a sand mining company because they need to find ground where the grain size will make their mining work. The high suitability areas in this map contain Jordan and Wonecon Formations. I decided to set all the other formations as low suitability.

- Landcover Criteria

Figure 2: Map showing suitability of Landcover

The amount of vegetation in a certain area can effect whether or not a company will want to take the time to clear it for mining. The areas with a rank of 1 are developed and shrub areas. Areas with forest vegetation are set at 2. The most suitable areas, ranked at 3, are barren lands, pastures, and herbaceous lands.

- Railroads Criteria

Figure 3: Map showing distance to rail terminal

 This map shows the range in distances it is to the nearest rail terminal. Sand mining companies will want to pick an area where it will be closest to the terminal so that they will use less money for transportation. Using the Euclidean distance tool I was able to find ranges where 3 is the most suitable and 1 is the least suitable.


- Slope Criteria

Figure 4: Map showing slope suitability

It is much easier to mine on a slope that is more flat than uphill. Using the slope tool and a DEM a.o.i I created this model. Slope with less than 10% is ranked 3. 10% to 25% is ranked 2. Anything beyond 25% is 1.

- Water-Table Depth Criteria

Figure 5: Map showing water-table depth suitability

This data had to be downloaded from the Wisconsin Geological Survey website. With this data I had to convert it into a raster so that it could be used further in creating my suitability model. It is important for sand mine companies to know what the water-table depth is because the more water in the area the more pumping they have to do. The highest suitability here is 3 while the lowest is 1.

- Suitability Model

Figure 6: Map of Suitability in Trempealeau County, WI

After creating all the suitability models I added them all together using the raster calculator so that I could come up with this model. The areas with the highest suitability are ranked 3 and the areas that would have the lowest for sand mine companies are 1.

Figure 7: Model Builder used to make suitability maps

- Stream Risk Model

Figure 8: Distances created to show how close the risk is to streams

It is important for mining companies to know how close they are to streams because they can effect the environmental health of the stream. The Euclidean distance tool helped me rank the distances where the risk will be high or low. Areas with a risk of 1 are high, 2 are medium, and 3 is low.

- Prime Farmland Risk Model

Figure 9: Map showing prime farmland at risk

Since farming is one of the state's economical advantages a company would not want to damage any useful farmland in the area. The highest ranking is 1 while the lowest is 3.

- Population Risk Model

Figure 10: Areas where population would be most effected.

 

It is important for sand mines not to set up their mines close to neighborhoods and populated towns. Drilling can cause contaminated water and there is much noise with transportation. The ranking of 1 is where there is a good amount of population. The lower rankings are areas that are scarcely populated.

- Schools Risk Model

Figure 11: Areas that show the risk for schools

You could say the importance for keeping away mines away from populated areas is the same for schools/school districts. To get the schools I created a layer feature from the sites feature class and added it to my geodatabase. I then used the Euclidean distance tool to buffer and create this model. The areas ranked 1 are where the schools are. The majority of the area around the schools have a medium risk.

- Parks Risk Model

Figure 12: Risk for parks in the county

 For a factor of my own I chose parks that are within Trempealeau County. I took the Euclidean distance tool to make a buffer and then reclassified the rankings to create this model. The ranking of 1 has the parks in it. The lowest rank is 3.

- Risk Model

Figure 13: Risk Model for Trempealeau County, WI 

After combining all of the risk models together I came up with this final risk model. The areas with red to yellow have a lower risk and areas with green to yellow have a higher risk. the low risk areas would be better for the sand mines.

Figure 14: Model Builder used to create the risk models

Results and Discussion:

- Overlay Model



Figure 15: The final map showing a final suitability model 

After using the raster calculator to add the final suitability and risk models I came up with this final model. The areas with high suitability are more green and areas with low suitability are more red. There is a good amount of space where a company could choose their next mine. It is interesting to see how many factors can go into creating an area that is going to do its best to make the sand mine companies happy and the residents living in the county happy.


Conclusion: Creating models using ArcMap geoprocessing tools gives someone an opportunity to answer questions that effect the surrounding communities. These models could help the people who live in Trempealeau County understand more about what factors go in choosing a location for a sand mine. This model helps get information across that shows potential areas in which sand mines could pop up.



Sources:

http://wgnhs.uwex.edu/maps-data/gis-data/







Network Analysis

Goals and Objectives: The goal of this exercise is to get a better grip on how network analysis is used in a real world scenario. The scenario we had to find the shortest transportation routes for sand mine trucks to the nearest railroad terminal. The constant back and forth driving of these trucks damages the road and it cost the residents of that county's money to fix those roads. Using the network analysis tools we can find the shortest distance and how much the damages cost. be sure to note the number of trips and cost is hypothetical.

Methods: The first operation was to create a python script that would select sand mines that were active, were not connected to a railroad terminal, and were 1.5 km away from a railroad. Once this was completed it was saved to the created geodatabase for this exercise. Next we added the street network dataset to our ArcMap and made sure to turn on the Network Analyst Extension. The results from the script and all the rail terminals were then added to the map. Next using the network analysis toolbar we created routes that were connected to the closest facilities. The closest facility tool helped determine the closest rail terminal to each sand mine. The next step was to use Model Builder to create a workflow that would help generate the results we would need to find the shortest route and cost. First we needed to add the streets to the closest facility tool. The result then had locations added using the mines_norail_final and trucks_rails_terms2. Once solved the routes are then selected and the copy features tool is used to save the selected features to the geodatabase in my work folder. I projected the routes along with the Wisconsin County Boundaries using the Wisconsin NAD_1983_HARN. After intersecting the two projected results I did a summary statistics to find the distance of route within the counties. I then converted the meters into miles using the equation [SUM_Shape_Length] *.00062137. To find the cost I took the distance result from the newly created field and used the equation [Distance] *2.2 *100. The result ends up showing the distance in meters, miles, and the cost per county.



Figure 1: The model builder used to calculate distance and cost for the counties

Results: The results shown below are the python script that was used to generate the mines. The table is what was created using the Model Builder. The map below shows the shortest distances to the rail terminals and the cost to each county. The results show that Chippewa, Dunn, and Wood County are the most effected by the cost. It is interesting to see Eau Claire has no sand mines but with routes going through it there is a nice price to pay for the trucks transporting the sand.

Figure 2: The python script used to select certain mines.

 

 

Figure 3: The resulting table from model builder showing the distance and cost.

 

 

Figure 4: The map showing the shortest routes and cost per county.





Conclusion: Working with Network Analysis was new and exciting. I was able to take the mine locations and find routes that would get the trucks to the closest rail terminal. This would help keep the trucks off other roads and make it cost less for the counties to repair. It is still unfair for the counties that would have to pay for the trucks damage to the road even though the county has no sand mines in them (i.e. Eau Claire, La Crosse). As I said before these results are hypothetical so we can not be sure that they are accurate to a T. 

Sources: WI DNR, ESRI Street Map USA

















Data Normalization, Geocoding, & Error Assessment

Goals and Objectives: The main goal for this lab was to take the data provided by the Wisconsin DNR and locate all the sand mines using geocoding in ArcGIS. Geocoding is needed because we have to normalize the data that is incorrect. These errors make finding the locations harder than it needs to be for whoever is using the data. We also needed to compare our normalized data with the actual locations of the sand mines so that we could see how far off we were on the distances between the two points.

Methods: Our first step was to normalize the data that was given to us. Some sand mines had an address and some used the PLSS for their locations. There were even mines that had both. To help normalize the table I had to separate and create new fields such as PLSS, Street Address, City, State, and Zip Code. Some fields that didn't have an address or PLSS I just left blank. Using the Geocoding Tool in ArcGIS the addresses where placed on the map. The problem is most of these addresses was that they were in the wrong spot. With the PLSS the address won't even map, I had to use the PLSS quarter-quarter layer to pinpoint an address for the mine. Once I figured out where all the mines were I used the Merge Tool to combine the mine locations that my classmates had to geocode. Now the actual mine locations were to be added the map so that we could see how close the geocoded locations to the actual locations. For this I used the Point Distance Tool which showed the distance between each of the mines.




Figure 1: (Table 1) This is a picture of the WI DNR data that we received. This table is an example of data that is not normalized

Figure 2: (Table 2) This is a picture of that same data normalized and using the new fields such as PLSS, County, etc.

Results: Here are the results comparing where the geocoded mines are in relation to the distance of the actual mine locations. The purple dots represent the sand mines the class located and the green dots show where the mine is. The table shows the distances between the actual and geocoded mines locations.



Figure 3: A map showing the geocoded and actual sand mines across WI.

Figure 4: This is the table that shows the distance between the two locations

Discussion: There are always going to be people out there that cut corners and don't provide the best work. With gross, systematic, and random errors it can mess up data that really shouldn't be too hard to mess up in the first place. The problem with the table we got from the WI DNR was that there was operational error. The information placed in the table was disorganized and this made some of the attribute data input confusing.

Conclusion: This lab taught me that fixing other peoples mistakes can take up a lot of your own time. From normalizing the data to locating it on a map it can get a little hectic. The main point is to check for data accuracy because even though you may geocode you might not get the address that matches up on the map.

Sources:

Wisconsin DNR

Lo, C. P., Albert K. W. Yeung, and C. P. Lo. "Chapter 4." Concepts and Techniques in Geographic Information Systems. Upper Saddle River, NJ: Pearson Prentice Hall, 2003. N. pag. Print

Data Gathering

Goals and Objectives: The goal of this lab was to download data from different sources on the web and to use this data in ArcGIS for creating a geodatabase. Also we learned more about using the Python Script which we used in projecting the data. The data we had to collect was for Trempealeau County, WI. Frac sand mining is a hot topic in this county, so I downloaded data that would help show how the mining is effecting the environment surrounding the mining sites.

General Methods: To obtain this data I went on five different websites that had information on the elevation, crop cover, land cover, railroad network, and the geodatabase for Trempealeau County, WI. The data downloaded off the site was delivered as a .zip file. I needed to extract the zip files to my working folder in order to view them properly. The websites that were used were the US Department of Transportation, the USGS National Map Viewer, the USDA Geospatial Data Gateway, the Trempealeau County Land Records, and the USDA NRCS Web Soil Survey. The links to the websites can be found below this paragraph.

Links

US Department of Transportation- http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/2014/polyline

USGS National Map Viewer- http://nationalmap.gov/viewer.html

USDA Geospatial Data Gateway- http://datagateway.nrcs.usda.gov/

Trempealeau County Land Records- http://www.tremplocounty.com/landrecords/

 

 

USDA NRCS Web Soil Survey- http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

 

 

 

Map Layers:

 

Cropland Cover from NASS raster

Land Cover from NLCD raster

Elevation from DEM raster




 Data Accuracy:

 

 

 

Conclusion: Some datasets are easier to find than others. Sometimes you can't find them at all and it makes the process more difficult for people who want to know if the data they are using is legitimate. This data could throw off the project and lead to many errors along the way. More websites need to start completing their metadata so that others can count on them as being a reliable source.  

Python Scripts

Python Script is a from of computer code that can be used to translate commands onto ArcGIS. It can be difficult to learn, but it is another language that can be useful in working with GIS. With it you can buffer, erase, and even intersect just by writing a simple script. In this instance, for Ex.5, I used Python to project, clip, and put different rasters into a geodatabase. The geodatabase is for Trempealeau Country and the purpose for the whole project is to figure out how sand mining in that area is effecting the environment. The letters in green tell the steps and the rows of words below them are the actual commands.

 

 

LAB 7

 

 

For this script we needed to take all the sand mines and separate the ones that don't have a rail line attached to the mine. I had to set up variables and a couple SQLs to help create a new feature layer. Using the script I was able to create a feature layer that shows the mines with no rail lines. Some problems I ran into were the silly errors like misspelling words or not capitalizing certain letters. After I fixed these errors the script ran fine. I went into ArcMap to make sure the layer worked and it did perfectly. The number of mines I had was 41 mines.

 

 

 

 

 

 

 

Lab 8

 

 





 

 

 

 

 

This script takes in all of the risk models created in ArcMap and creates a weighted risk model. The figure below shows the completed script when is done running.

 



 

 

 

 

 

 

 

 

 

 

 



Sand Frac Mining in Western Wisconsin

What is Sand Frac Mining?

Sand Frac Mining, or hydrofracking, is a process where sand is used to extort oil and gas that is locked thousands of feet underground. A hole is drilled down deep to where the resources lay and then frac sand, chemicals, and water are forced into the cracks. This is also known as blasting. The high pressure also creates new cracks. Once this step is completed the water and chemicals are removed which just leaves the sand. With the sand now holding open the cracks, or fissures, the gas and oil can now be pulled out and brought back to the surface. The frac sand used in this process has to have strict qualifications. It needs to be almost pure quartz, very well rounded, extremely hard, and of uniform size. The pressure the sand needs to withstand is between 6,000 psi to 14,000 psi. The majority of the sand used is Wisconsin’s silica sand because of how well it meets these qualifications. The process can be seen in figure 1.

Figure 1
The Process of How Fracking Works

 

 

 

Sand Frac Mining in Wisconsin

            Right now Western Wisconsin is the most popular site for sand frac mining. Even though people have been sand mining in Wisconsin for over 100 years permits for sand mining sites have increased significantly. There are currently over 60 mining operations open in the badger state. The sites stretch from the most southern Columbia County to all the way up north in Burnett County. In figure 2 you can see on the map the correlation of why the mining sites are where they are.  

            With the booming of these sand mining sites a lot of environmental impacting questions are raised. The Wisconsin DNR states that there are two types of air emissions come from these operations. “The first is from dust that may be emitted during the mining and handling of sand. The second is from various pollutants emitted from equipment used to mine, handle, and/or process the sand.”(dnr.wi.gov). In the article “Mining Companies Invade Wisconsin for Frac-Sand” from http://ecowatch.com/2012/04/27/mining-companies-invade-wisconsin-for-frac-sand/   it talks about how these mining sites are taking away the beautiful, hometown feeling landscapes of the state of Wisconsin. Water pollution and Oil spill contamination are also a few of the potential problems that some nearby citizens have faced or might face.

 

Figure 2
This Map Shows How Wisconsin is Prime Real Estate for Fracking.

GIS In Sand Frac Mining

            I believe the use of GIS in this field would be very favorable. You could keep data on which routes the transportation truck are going, see how the landscape is changing after a certain amount of mining, or have it keep an eye on the different environmental issues that surround the controversial topic. In creating maps with all this information the public can have a better chance of gaining more knowledge on what is really going on in their area.

 

Sources:

http://wcwrpc.org/frac-sand-factsheet.pdf

http://dnr.wi.gov/topic/Mines/Deposit.html

http://dnr.wi.gov/topic/mines/silica.html

http://dnr.wi.gov/topic/Mines/documents/SilicaSandMiningFinal.pdf