Raster Analysis

 

Goals

The point of this exercise is to bring together everything we have learned this semester into a final project. We have looking at Frac sand mining in Western Wisconsin and this is the final part of that subject. We have looked at the current sand mines that are in operation or will be soon and the effect they are having on the roads around them. Now we are looking for a place to put a new mine in Trempealeau. This is all hypothetical but we learned how you could go about this in the real world. We did this through the use of various raster reprocessing tools which helped use build a model for both sand mining suitability and environmental risk in Trempealeau County Wisconsin. After we made each of those models we combine them to find the best locations for a new mine.

 

Methods

Making the Suitability Model

A suitability model is made up of multiple factors we will explore those as we go through this step. The first factor is looking for the areas where the frac sand is present, without the right geologic makeup mining isn't possible. The two geologic formations we are interested in are the Jordan and Wonewoc formations. To find these areas we brought in a geologic map of Trempealeau County and symbolized it by geologic unit. We then want to convert this feature to a raster with the feature to raster tool so that we can run raster analysis on it. Once it is a raster the next step is to run a reclass and put the two formations we want as a 3 and the rest as 0 so that only those formations are on our map.

 

The next factor in the suitability model is finding a suitable land type where setting up the mining will be less expensive based on how hard it is to clear the land. The less vegetation on the land the less money it takes to clear so I picked bare land, hay/pasture, and shrub/scrub all of which have small amounts or no vegetation on them. I also made another reclass for all the other land types and made sure they are all zeros to make sure they do not end up in the final map.

 

Next we found where the rail depots are in Trempealeau county are because shipping the sand by rail is a big part of sand mining. I brought in the rail depot feature class and ran the Euclidean distance tool on it. I classified this into 3 distances with the closest being the best to put the mine in, to cut down on transportation costs.

 

Flat land is ideal for a location of a mine because of the need for a large area to place machinery and piles of sand after they are extracted from the ground. It is also cheaper to mine if you aren't digging into the side of a mountain. I brought in a raster of the county and ran the raster surface tool called slope . This shows the slope of the entire area which I then reclassified into 3 levels. 3 being the flattest land or least slope and 1 being the steepest.

 

The final suitability factor is the depth of the water table in the county. Frac mining takes a lot of water to do so the closer to the surface and more accessible the water is the better. The is no raster that shows the water table heights so I had to go online and download a arc info coverage. Basically this is a bunch of contour lines showing the elevation of the water table. I then ran a tool that takes those lines and produces a raster out of them so I can do further analysis. Once I have the water info in raster form I can reclass it into ranks where 3 are the areas that the water table is closer to the surface and 1 where the table is far below the surface.

 

The map below shows all of the above criteria in map form with all the ranks that I assigned to them. The final step for the suitability part is to take all of these criteria and do a raster calculator where they are added together into one suitability map. Below there is also the model I made in model builder that shows all of the rasters and tools I used in this part of the exercise.

 

Suitability Factors


 

Suitability Model

Making the Risk Model

The second part of the exercise is the risk model. This model takes into consideration many factors that would be considered for safety and healthy purposes when placing a new mine. The first factor to take into consideration is the effect these mines will have streams. They generate a lot of water and waste water that can get into streams and pollute them. I brought in the stream feature class and then did a reclassify to select the kind of stream I am interested in. I chose perennial streams that flow over land because these have the best chance of being effected by the mines. I then ran a Euclidean distance on these streams and did a reclass to rank them as 3 being the closest to the streams and highest risk and the furthest 1 and the lowest risk.

 

Another area to consider is prime farmland. Wisconsin has a lot of land used for agriculture and it is a large part of our economy so we do not want this mine to interfere with that. I brought in the farmland feature class and did a reclass so that the farmland is a 3 or high risk area and the rest of the lands are set to zero.

 

 Noise is a big concern when it comes to mining as well. Air quality concerns from dust in the air is also a reason why these mines should not be too close to highly populated residential areas. To make sure this isn't the case in my model I brought in the zoning feature class which give you information on what land is set aside and should be used for. I then selected all the residential areas in that feature and did a Euclidean distance. I then reclassified and ranked the closest areas as 3 and highest risk and the furthest as 1 and least risk. 

 

A similar procedure was done for schools. We do not have a feature class with the school locations so I brought in the land parcel feature class for Trempealeau county and did a query to select all the parcels that are owned by the school districts. One I had the areas where the schools are I ran a Euclidean distance and ranked just like above.

 

For my factor of choice I chose lakes. I chose lakes that are there year round because if the mines are too close to these they have a chance of being polluted just like the streams. I ran a Euclidean distance and ranked the distances just like the previous two examples.

 

These mines are eyesores in some peoples opinions and we want to make sure that you can't see them from scenic areas in the county. I brought in the prime recreational area feature class and from which I chose horse trails and ran a tool named view shed. This makes a map of the all the areas that can be seen from the horse trail in all directions. I reclassed this so that the visible areas are 3 or high risk so that the new mine does not go in this area.

 

Just like with the suitability model I took all of these factors and added them together in raster calculator to get the overall risk model. The maps below are all of those factors individually mapped out. The model showing all the tools in there as well.

 



Rick Factors

 

 

Risk Model

 Overlay

The final step to this project is too take the suitability model and risk model and bring them together in raster calculator. This will give you the final places for the new mine. I ran a reclass on this as well and ranked the areas as highest, medium and low desirability for the new location. Below are maps of the raster calculators. The entire model start to finish is there as well.

Final areas

 

Results/Conclusion

As you can see there not too many large areas after you combine the models. The majority of the ideal area is up in the northwestern part of the state. In real life these areas would be looked at and evaluated further before starting a mine but this exercise gives valuable skills that can be used in the first stages of planning a new mine. The amount of things you can do with raster analysis is incredible. It give you the ability to take pretty much any kind of data you can think of and do some kind of analysis on it that can be helpful in many situations. 

 

Data Table With Ranks



Entire Model



 

 

 

 

 

 

 



 

 

 

 

Network Analysis

Goals and Objectives

The goal of this exercise was to learn how and perform network analysis in a real life scenario. The example scenario we are considering the transportation of frac sand. We routed the sand from the mines to the nearest railroad terminal. Once that is done we learned how to estimate the number of trips the trucks will take and the cost this traffic will incur on the local roads. We then figured how much each county in Western Wisconsin would have to pay to maintain these roads. This is a relevant example because in the real world frac sand trucking has a significant impact on the local roads. 

Methods 

     The first step in this part of the lab was to bring in the streets network data set for the whole U.S. The next step was to bring in the rail terminals data set. Since we are only interested in the mines that move their sand by truck and rail I did a select by MODE_TYPE and did truck and rail mines. I then took that selection and made a feature class that only has the truck and rail terminals.
     The next step was to do some network analysis. I opened the network analyst window and added a new closest facility layer. I loaded my rail_terminals as the facilities and my mines as the incidents. I then ran a solve this gave the routes from my mines to the closest rail terminal. It appears on the map that there is only one truck on each route but if you select the route there are many records of trucks on each route.
     Next I began to build my model in model builder. I started by recreating the above described routes through use of the make closest facility layer and add locations tools. Just like above I add the solve tool to my model and then run the model. I then used the select data tool to extract my routes from the closest facility solver and save them in my goedatabase. To save them I used the copy features tool.
      Once I had all the routes to the closest rail terminals could then begin to find how much the truck are going to cost the county in road damage. The first thing I did was to find the distance the trucks are traveling. I added a field and called it Miles then I did a calculate field and divided the shape_length field in the route table by 1600. This gives the distance in Miles. Next I added another field called Cost. I took my Miles field and took it times 100 because the trucks make 50 trips. I multiply that by 2.2 cents and then divide that whole equation by 100 because there are 100 cents in a dollar.(See calculations below.) I now had totals for how much each trip cost. The next step is to find out how much each county spent. In order to do this I did a field join between the Wisconsin counties class and my routes class. Once those are joined the final step was to run a statistics summary on the Cost field. I summarized it by county names so it took all the costs for each county name and summed them. The chart in the results section in what that table looks like. Directly below is the model I made and ran to get my desired results.



Model Builder

[SHAPE_LENGTH]/1600 = (x)mi   Then I did.  ((x)mi *100)2.2)/100= cost per trip

Results

After the model is run the map below is the result I got. It shows all the mines we are concerned with in Western Wisconsin as well as the rail terminals. The map also shows all of the routes the trucks are taking from a mine to the nearest rail terminal. 





Final Results Map

The chart below shows the hypothetical cost that sand trucking would cost by county, These number are all made up for a an example, however you can see that there is a cost associated with the transportation of the sand. I would venture to guess that the true numbers for the cost to each county is in the thousands of dollars every year for road maintenance. We only figured the cost for 50 truck trips from each mine in a year when in the real world that can be done in a day. Who should pay to fix the roads? Should the county have to pay for this cost because they are in charge of maintaining the roads or should the sand companies pay for it because they are the reason the road is deteriorating faster than with normal traffic. If the mines have to pay for it, is that fare because there are mines that put the sand directly onto rail and wouldn't have to pay this "road tax". This is a question that has been raised and discussed in depth. It is one of many possible draw backs that people see about frac sand mining. Air quality concerns along these roads is also a topic of discussion. 

Cost by County

Conclusion

The point of this lab was not to look at pros and cons of frac sand mining it was to apply network analysis to a real life situation and understand the results. This is a valuable skill that is widely used in many situations. At a city, county, state, and world scale for emergency response, package delivery, every day use of your GPS to get from point A to point B and countless other situations. I really enjoyed learning this technique. It  was more of a hands on technique and seeing how it can be applied in the real world makes it more interesting and realistic to me instead of just crunching data numbers like some of the other techniques. 

Sources: ESRI Street Map USA

Geocoding

Goals and Objectives

The goal of this lab was for us to connect to one of the databases on the departmental server and geocode some data that was in an Excel file. Once did that another goal of this lab was to learn how to gather, normalize and geocode data we obtained from the Wisconsin DNR. This is one of the first parts of a larger project we will be building on throughout the semester involving sand mining in Western Wisconsin. Our study area will consist of Trempealeau county and several others.

Methods

The first part of this lab was to connect to a geodatabase on the geography department’s server. This geodatabase included an Excel sheet with information about a couple of hundred Frac Sand Mines in Western Wisconsin. This data was given to us by the Wisconsin DNR. This Excel did not have all the data or was not organized in a way that we could geocode right away so we had to normalize the table. In other words I created 5 new columns to the Excel file, State, City, Street, Zip Code, and PLSS. By adding these fields you make the table readable by the geocode tool. So once we had the table normalized we could geocode the data or find the addresses and locations of these mines. We did this because not all the mines had an address with them. There are two ways of geocoding, manual and automatic. For the automatic way you put the normalized table into the geocoder and it will give you the street address for the mine if it can find it. The automatic was is obviously easier but most of my mines had to be manually geocoded. This is done using a wide variety of tools. I used Google Maps and Google Earth along with the PLSS finder. The Public Land Survey System is a grid system that is used to keep track of land parcel ownership. If I knew the PLSS address for a mine I would put it into the PLSS finder and it would take me to the grid square where that address is. This narrowed done my search area. I would then switch to Google Earth or Maps and look around that area for a sand mine with similar dimensions to what it is described as in my Excel sheet. On some occasions I could find the mine and get a street address off of Google Earth or Maps but a lot of the time the imagery is not up to date enough to show these mines because most of them are fairly new. Another method I used to find the mines was to just Google the name of the mine or the county it was in. I found a couple of my mines this way because some counties have all the mines in a document with addresses and owner information and the mine name so those made this easier. This was a challenging and pretty time consuming process. I was only responsible for 16 mines and of those I had to manually find 5. This took me a couple of hours.

Results

Below are a sample of the non-normalized table and the table after I cleaned it up. You can see in the non-normalized table that all the address information in just in one column. The goecoder cannot differentiate between street addresses and zip code and things like that in that format so I normalized the table. I took that address information and spread it out into separate columns which the goecoder could read.

Non-Normalized Table

Normalized Table

After we had all of our mines geocoded and placed into a shapefile in ArcMap we compared how much variation there was between the places each of us placed our mines with 4 other people who also placed the same mines. We did this by using the point distance tool which selects the mine closest to each of my mines and measures the distance between them. My table is measured in meters and as you can see below there was a lot of variation as to where the mine was placed by other people compared to where I put mine.

Distance Table in Meters

Discussion

During this lab there were a couple of different kinds of errors that could occur. During the geocoding process the major and most obvious error that occurred is an operational error. It is operational because it is a user error or caused by human mistake. When everyone had their mines geocoded and we brought them all together into one map the same mine was placed in multiple different places. In a perfect world each geocoded mine should have been in the same spot as the others with the same ID. This is caused by a mistake in image analysis or just by the opinion of the person where the mine should be.

Another type of error is called inherent error. This error occurred when we measured the distance between the mines and got different measurements. This is caused by the limitations of the computer or technology you are using to make the measurements. Technology is only so good. So operator accuracy is limited by machine technology and also by the resolution of the data. The lower the resolution the less accurate the measurements will be.

With these errors occurring the location of these mines is not very accurate in most cases. If we were in need of a very accurate location we could take the locations we got and compare them to a higher resolution data set that has more accurate locations of the mines and calculate the root mean square error of planimetry. 

Conclusion

Data that is not normalized is hard to work with. This lab showed the importance of normalized data and how if it isn't, preforming other tasks with it such as geocoding is nearly impossible. Knowing how to normalize data is a good skill to have because you will get data in all kinds of formats and from all kinds of places that can not be used until it is normalized. Geocoding is also a very valuable skill to know. It can be applied in many practical situations. The results of this process are what you see below. This is my map showing all the geocoded mines in an easy to understand way. It includes the location of my 16 mines as well as the locations of those same mines as located by other students in the class. The differences in the locations illustrate the errors that can occur during the geocoding process. 

Final Map of Mines

Data Source: Wisconsin DNR

Data Downloading

Introduction

The goal of this exercise was to become familiar with the whole process of downloading and obtaining from different internet sources. Then we imported the data to ArcGIS where we joined it. All the data was also projected into one coordinate system and stored in a geodatabase that we designed and built from scratch.  This exercise is the beginning step to a larger project. We will be making a suitability abd risk model for frac sand mining in Western Wisconsin. Before we can do that this first step of gathering and exploring the data we will use is crucial.

Methods

     The first data that we downloaded was a map of the rail system in and around Trempealeau Wisconsin. On the Bureau of Transportation Statistics website there is a Railway Network Zip file that we downloaded and extracted the shape file for to use in our final map.

      The next data we downloaded was from the USGS Map Viewer. In the viewer we zoomed into Wisconsin and clicked on Trempealeau County. Then in a drop down of data available we selected the land cover data from 2011. This data was emailed to us in zip form which we then saved and unzipped to use in the exercise. From this website we also downloaded the elevation data set for Trempealeau County.
      Another website we used for land cover was the USDA Geospatial Data Gateway. This land cover data was all about cropland, what kind of crops are planted where. We also downloaded an entire geodatabase from the Trempealeau County Land Records site. This database included a wide variety of datasets which would be used later in the exercise.
     We went to the USDA NRCS Web Soil Survey website to download all the soil data about Trempealeau County. Like we did with previous data sets we unzipped it into our folders because it is downloaded as a zip.
     After we had of this data downloaded we did a series of joins and created some relationship classes to bring together similar data sets and make the working environment easier and more organized for us.
     The next step of the exercise was to make a Python Script that would take our downloaded data turn them into rasters that can be mapped for the purpose of project. Our script took the Cropland, elevation, and Land cover data that we downloaded and ran some tools on them that you can also do in the ArcMap environment. It projected each of the data sets into the same projection, ran an extract or clip so that we were only left with Trempealeau County which is our AOI, and finally saved the modified rasters into a geodatabase. Below is that script.

After the script was done running we were left with the 3 maps below. One for elevation, cropland cover, and land cover of our AOI only.

When looking downloading and working with any data there can always be errors and that is why we need to look at the metadata for each data set to try and determine if there are errors or things that could cause errors in our data. The table below shows some of the things we check in metadata.

This exercise was a lot to wrap your head around. There are many many steps to produce the map above in this post. The skills that we learned however are very valuable and will be helpful in the workplace when dealing with data downloading.

Sources:

http://nationalmap.gov\viewer.html

http://soils.usda.gov\survey\geography\ssurgo\description.html

http://nationalmap.gov/viewer.html

http://datagateway.nrcs.usda.gov/

Wisconsin Frac Mining

Frac Sand mining has become increasingly popular over the last couple of years as new petroleum extraction methods have been developed. All over the United States companies have begun to extract a very special kind of sand and sell it to the big oil companies for a huge profit.  It just so happens that Wisconsin has become the leading producer of this sand,and the industry is continuing to grow at an astonishing rate. What is so special about Wisconsin? Well, we have the largest deposits of this frac sand anywhere in the United States, and it is not very far below the surface making Wisconsin an ideal place to extract this resource.

            How does frac sand tie into petroleum companies? Petroleum companies use this sand to do a method of petroleum extraction called hydraulic fracturing. The sand is pumped down in to the earth in a mixture of water. This mixture gets into the cracks where oil and natural gas are located under the earth’s surface. The water is then pumped back out if the ground leaving the sand to dry in those cracks which expands them making it easier to get at the resources. In order for this to work the sand has to be very hard round grains of quartz with very few impurities in it.     The majority of the mining in Wisconsin is taking place in west central Wisconsin where there are huge deposits of this almost pure quartz sand. There are also mines to the south and east but that sand is not nearly as pure making it more expensive to produce. The sand is taken out of the ground and then washed, purified, dried and shipped, usually by rail, to big oil companies.

            Frac mining has created a lot of jobs and brought an unbelievable amount of money to the communities where these mines are going up, but not everyone is happy. There are many concerns that have been voiced about this industry. These mines destroy the landscape that is on top of them which disturbs the wildlife in the area. In many people’s opinions this creates an eye sore in the community. Another concern that has been voiced by people in the past is the reduction of air quality around these mines. By the plants themselves when the sand has been cleaned and dried and is being put into storage piles very fine dust particles are released into the air around the plant. Along railroad tracks the same thing is happening. The wind from the train moving stirs up the sand dust and puts it into the air. Water and stream pollution is another concern that has been brought up. The runoff from the massive strip mines and processing plants can end up in nearby creeks and rivers making the water murky on occasion.

            How is GIS connected to this industry? GIS can be used in many ways for the good of this industry as well as for those who may oppose these frac mines. When you apply for a permit to do this mining on your property or any piece of property there are zoning issues and land use restrictions. Through GIS you can determine if the land you are interested in is an area where mining is an allowable land use. Once the companies have large pieces of land purchased they can keep track of property boundaries and mining progress though GIS. Planning the most effective and efficient trucking and rail routes is also a part of GIS that can and is exploited by the mining companies. For people who are opposed to frac mining and may be concerned about air quality and water quality they can use GIS to support their concerns. For example they can create maps to track water runoff from the plants or show how far the diminished air quality ratings reach from the plants and rail lines where the sand is being moved.

People wonder whether Frac sand mining is a good industry or practice and I can see both sides of the argument. I can’t say if it is good or not but this argument will continue to produce many opportunities to use GIS both in the industry and outside of it. This industry creates so many jobs and opportunities which are good for the economy, but it also leaves its mark on the world. Finding a way for the industry to exist and limit the effect on the earth is the ideal situation and I think that GIS can play a big role in finding that happy medium.

Source: U.S. Geological Survery Silica Yearbooks

Works Cited

"Industrial Sand Mining." Wisconsin Department of Natural Rescources. N.p., 22 Sept. 2014. Web. 05 Oct. 2014. <http://dnr.wi.gov/topic/mines/silica.html>.

Robertson, James M. "Frac Sand in Wisconsin." Wisconsin Geological and Natural History Survey (2012): n. pag. 2012. Web. 06 Sept. 2014. <http://wcwrpc.org/frac-sand-factsheet.pdf>.

"Town of Colfax Residents Concerned about Proposed Mine's Proximity : Dunn County News." The Dunn County News. Ed. Barbara Lyon. The Chippewa Herald, 03 June 2014. Web. 06 Oct.                 2014.<http://chippewa.com/dunnconnect/news/local/town-of-colfax-residents-concerned-about-proposed-mine-s-proximity/article_c4bc5f44-e279-57c0-b743-fb12022648d7.html>.