Exercise 8: Raster Modeling Part 2 - Raster Analysis Python

The final blog post of the year had the goal of: writing a python script to generate a weighted index model.  This was a simple python script to write, as not a lot went into to the script.  The task was generate a model which identified areas of the county that are high of risk.  I choose to use the residential raster because I thought keeping a sand mine away from high population areas is the most important when considering the different elements.

Figure 1: Python Script

Figure 2: Risk Model

The areas in white are considered to be most at "risk" compared to the areas least at risk in black.  I found the python script very easy to run and the results were clean.  Much of the county is at risk except for the sliver or room in the central extending out towards the Mississippi River.

Exercise 8: Raster Modeling - Part 1: Raster Analysis

Introduction

The goal of the final exercise of the year was to build multiple suitability  models of Trempealeau County which represented the best and worst locations for sand mines to be built.  Multiple factors were used when determining the location of the mines including: geology, land use/land cover, distance to railroads, slope, water table, proximity to streams, impact on farmland, impact to populated areas/schools, visibility of recreational areas, and one area of our choice that we thought should be included in the analysis.  Many of the tools used to calculate prime areas or areas of risk were in the spatial analysis tools used on raster.  The main tools I used when running the analysis included: euclidean distance, reclassify, and raster calculator.  The objectives of the exercise are listed below:

1. Generate a spatial data layer to meet geologic criteria
2. Generate a spatial data layer to meet land use land cover criteria
3. Generate a spatial data layer to meet distance to railroads criteria
4. Generate a spatial data layer to meet the slope criteria
5. Generate a spatial data layer to meet the water-table depth criteria
6. Combine the five criteria into a suitability index model
7. Exclude the non-suitable land cover types
8. Generate a spatial data layer to measure impact to streams
9. Generate a spatial data layer to measure impact to prime farmland
10. Generate a spatial data layer to measure impact to residential or populated areas
11. Generate a spatial data layer to measure impact to schools
12. Generate a spatial data layer to measure impact on one variable of your choice
13. Combine the factors into a risk model
14. Examine the results in proximity to prime recreational areas

Methods

As I mentioned above the area of study was Trempealeau County located in Western Wisconsin, some of which borders the the Mississippi River.  To speed up the process of running all of these tools in ArcMap, Dr. Hupy, assigned to only use about a half or two thirds of the county as seen in figure 2 below.  Some of these tools used in ArcMap take a very long time to run, therefore making the area of interest smaller, the process will speed up.  Under Geoprocessing > environments > and processing extent > it was set to the boundary so all processes would output to the same area.  One huge error that occurred while I ran my process was my output not being correct.  As you can see in some figures below, the output of some tools I ran would stay in a rectangle and not be clipped to Trempealeau County.  I found this error only to happen when running euclidean distance, any of the others tools I would run would result in the correct boundaries.  I have no idea why this was happening, my processing extent was set and I tried and tried again to change around things and fix this problem, but nothing was working.  I ended up just keeping the results and placing the boundary line over the rectangle.  I think it defiantly affected my results but not too seriously as I compared with other students in the class. 

Figure 1: Trempealeau County

Figure 2: Processing Extent

Geology:

The first objective was to find suitable rock or land formations.  This was done by adding the geology feature class from the Trempealeau County database that was provided to us.  It was found that the most desirable geologic formations for sand frac mining are the Jordan and Wonewoc formations.  After converting the feature to a raster a re-class was run and ranked to determine where the Jordan and Wonewoc formations occurred.  I gave Jordan and Wonewoc a 3 and the rest of the geologic formations a 1.  In the first half of the model, I awarded the best locations to mine with a 3, and the worst locations with a 1.  Some were even given a 0 because mining was virtually impossible there. 

Suitable Land:

The next objective was to find and rank the best locations for a sand mine.  Using the land cover raster downloaded from exercise 5 I ran a reclassify tool on the the different types of land uses.  Determining which land uses are most suitable for sand mining was the hardest part.  When ranking the certain land uses it is important to remember the amount of money it would cost to run a mine in the area.  Therefore, picking an area with little forest or shrub cover would most suitable.  I determined that barren land and agriculture were the most suitable and awarded a 3 to both of them.  They were followed by shrub and herbaceous land, forest/wetlands, and developed land, and finally areas of water, snow or ice.  These groups were given a 2, 1, and 0 respectively.  After determining the ranks I ran a reclassify tool to sort the suitable land covers.  

Proximity to Railroads:

Objective three was the task of finding lands closest to rail terminals.  Being closer to a rail terminal means that it will be more cost efficient when transporting the sand.  Using the rail terminals from exercise 7 I ran a euclidean distance to find areas closest to the one rail terminal in the county of Trempealeau.  I ranked the classified the ranks based on natural breaks and found that the most cost efficient area was within 10,000 meters of the rail terminal. 

Suitable Slope:

The best location for a mine is to find flat line, the next objective was to calculate slope to find the least sloped areas.  I used the Trempealeau County digital elevation model from exercise 5 to calculate the slope of the area.  First a raster calculator was used to convert feet to meters and then the slope tool was run.  When running the tool it is common to find a "salt and pepper" effect, therefore running another tool, block statistics, to hide the "salt and pepper" effect was necessary.  I used a 3 x 3 cell filter and ranked the lowest slopes the highest.  

Water Table:

A sand mine uses a lot of water to cool and compact the sand.  Therefore being close to a water table may make an area more valuable.  For objective five, the task was to find the areas where the water table was closest to the surface.  To do this data was downloaded from Trempealeau County to our folder.  Because the data came in an exported coverage (.e00) it had to be converted to a raster.  The topo to raster tool was used as it converted the data to be used in ArcMap.  After this process was done I used the re class tool and ranked using natural breaks.  

Figure 3: Model 1

Figure 4: Ranking system for model #1

The next model had a different angle is it looked at how the sand mining impacted the environment and people surrounding.  The ranking system followed this model: For each criteria you want three ranks for impact (3 = High, 2 = Medium, 1 = Low). High impact will be locations where mines should not be located because of high environmental and/or community impact.

Proximity to Streams:

Sand mines have the potential to produce a lot of pollution either by erosion which can cause sediment to run off into streams or by the blowing of sand which can also end up in streams.  Because almost the entire county is covered in streams running a euclidean distance would not logically make sense.  I ran a select by attribute to grab the most important streams, or perennial streams, to use in my analysis.  I then used a raster calculator to convert to meters and a feature to raster to transform the streams to a raster.  Then a euclidean distance was run and the re class tool used natural breaks to rank the data.  Again in this model 3 is the most influential to the environmental.

Impact to Prime Farmland:

Keeping farmland that is critical to farmers to area is very important, therefore, we do not want to destroy this land by building a sand mine in the area.  The prime farmland feature class was obtained from the Trempealeau County database and it was converted to a raster.  From her I used the re class tool to rank the types of farmland.  I ranked my farmland accordingly: prime farmland had a value of 3, prime farmland if had a value of 2, and not prime farmland had a value of 1.

Impact to Populated Areas:


Sand mines can cause a lot of noise and dust traveling in the air.  For this objective the task was find areas of highly populated areas to find areas where it is not okay to mine.  The distance a mine needs to be away from a highly populated area is 640 meters.  I used the zoning feature class and queried out all residential areas because these areas will have high populations.  Then converting it to a raster and running euclidean distance.  I then used the re class tool and ranked my data.  0-640 was given the number 3 and numbers 2 and 1 were broken up using natural breaks.  Instead of using meters I left the data in feet and converted the 640 meters to 2,100 feet. I did this for schools and parks as well.

Impact to Schools:

Again because sand mines can cause a lot of air pollution and noise it is important to keep them away from schools.  Querying out the schools from the parcels feature class was the first step.  Then converting the feature class to a raster and running a euclidean distance to find the distances from the schools.  Again using the, 640 meters away, I ranked my re class based on that assumption.  3 being most vulnerable and 1 being least vulnerable.

Impact to Parks:

One factor of the sustainability model was of our choice.  I choose to look at parks in Trempealeau County.  Using the parks feature class I converted it to a raster, using the feature to raster tool.  Then running the euclidean distance tool followed again by the re class tool.  AGAIN I followed the model of being 640 meters away from a sand mine and ranked my re class accordingly.

Visibility from Prime Recreation Areas:

The last objective of the model, was to run a viewshed analysis of a prime recreational area.  A veiwshed analysis is the the area that can be see from the human eye when walking or standing in the area.  Being in a special recreational area a person is not going to want to be able to see or hear a huge sand mining in the area.  I choose the recreational area of Lakes Coulee Wildlife Area, located in the northeast area of Trempealeau County.  It is home to many hunting and hiking trails, and also has a high population of trout.  It is monitored by the State and an area of high importance to the county.  The first step of the process was to query out lakes coulee from the recreation feature dataset.  Then convert the polygon to a point because viewshed can only be run with a point or polygon feature class.  The viewshed was then run using the digital elevation model used above to calculate slope along with the lakes coulee point.  The re class tool was then used giving the areas visible from lakes coulee a 3 and every where else a 2.

Figure 5: Model 2

Figure 6: Ranks for model 2 

Results

First I want to explain some errors that occurred while making the suitability models.  As you can see below in the images not all of maps intersected within the boundary desired.  This happened when I ran the Euclidean distance tool.  As I mentioned earlier, I tried and tried again to fix this and nothing worked, therefore I left it and placed the boundary line over the map.  This did have a minor impact on my final products but nothing to dramatically change the outcome.  Two other errors that occurred when mapping had to with my ranking system.  For the first models I ranked 3 as the best locations and 1 as the worst but then reading later in the model for the 2nd part, I read that 3 is the worst and 1 is the best.  It made my ranking system flip.  Also I messed up the rankings for slope and proximity to streams.  Giving a 3 where a 1 should be and a 1 where a 3 should be.  I only noticed the mistake when mapping and it did have a an impact on my final models.  I switched the rankings for figures 10 and 13 to what they should be.  

Figure 8: Land Cover Suitability

Figure 7: Geology Suitability

Figure 10: Slope Suitability

Figure 9: Proximity to Rail Terminals Suitablity

Figure 12: Suitability Model 1

Figure 11: Water Table Suitability

The first suitability map, figure 12, gathered and ranked data based on what sand mines need.  Figure 12 represents the best locations with a highest number in red.  Most locations are in the North West part of the state and border a river.  Because you cannot have a sand mine on a river these ranks were given a 0 as you can see in the dark blue above.  It makes sense that the best locations of the first model would be along streams and rivers because they are close to water, the water, table and flat land. However being close to rivers can also lead to environmental problems. 

Figure 13: Proximity to Streams Suitability

Figure 14: Prime Farmland  

Figure 15: Populated Areas Suitabilit

Figure 16: Impact to Schools Suitability

Figure 18: Lakes Coulee Suitability

Figure 17: Parks Suitability

Figure 19: Model 2 Suitability

The second model represents the worst locations to place a map base on environmental impacts.  These locations are represented with high numbers in purple.  Most of these locations are in the south to south east.  The best locations for a sand mine with little environmental impacts are represented with low numbers and a green color.  Looking at figure 19 it seems that the best locations are in the north corners and one line striping in the middle of the county.  If I were to combine both models I think the best locations for a sand mine would be in the North East South East portions of the county because both models represent great locations with easy access to resources but not too environmental degrading. 

Conclusion

Completing the final exercise of the year was very rewarding.  Using all the information and tools run from the beginning of the semester to now was cool to see how they were all used.  I think I am now an expert on certain aspects of sand mining and especially Trempealeau County.  This exercise in particular was the most difficult I have ever encountered.  I ran across many errors but used my knowledge and classmates to pass through them to get a results.  However, it did teach a lot about the spatial analysis tools used with rasters that will be very valuable next semester and the rest of my career. 

Sources: 

http://wgnhs.uwex.edu/maps-data/gis-data/ 
http://www.mrlc.gov/nlcd01_leg.php
http://dnr.wi.gov/topic/lands/WildlifeAreas/lakescoulee.html

Exercise 7: Part 2 - Network Analysis

Introduction

Part 2 of Exercise 7 dealt with a new method of GIS I have never dealt with before, titled Network Analysis.  In this case network analysis will be used to find the shortest route between active sand mines in Wisconsin and the nearest rail line.  The end result will feature a map representing the routes between sand mines and rail lines and also a table showing the amount of money each route will cost for commuting purposes.  After completing a python script in part 1, the objectives of exercise 7 include:

Load features into the Network Analysis Window
- Calculate a route
- Calculate a closest facility and route
- Build a model to calculate the closest facility route.
- Calculate the cost of sand truck travel on roads by county.

Methods

The first step in starting the Network Analysis was to become familiar with the tool.  In class we tested some of the functions, new route and closest facility to see how the tool worked.  We then added the main components to begin the process, including the mines from part 1, rail terminals provided by our professor, Christina Hupy, and streets from an Esri database.  We had to select the rail terminals to fit our need by selecting ones that are located in Wisconsin (one in Winona, MN), and rail terminals to only shipped using rail lines and not by air.  The next step was to find the closet facility by loading the mines into the incidents and the rail terminals into the facilities.  This will calculate the closest facility by using the streets from Esri, you can see the results in figure 1.

Figure 1: Result after the closest facility tool was run.
Purple squares - mines  black circles - rail terminals  Orange lines - routes

Now the next step of the process is to build a model in ArcMap using model building to find the total cost it takes to drive from the sand mine to a rail terminal for each active sand mine in Wisconsin.  A couple of things we assumed is to come up with the cost is that each sand mine takes 50 truck trips per year to the rail terminal and the truck has to return and also the hypothetical cost per truck mile is 2.2 cents.  These hypothetical numbers were created by our professor because we simply could not calculate the correct numbers.  In model builder the same step was taken by adding the closest facility layer and then the mines as incidents and rail terminals as facilities.  Then after that was solved the next task was to take the routes, project them and add fields to calculate the cost.  The first field added was length, and calculated it to route length divided by 1609, because there are 1609 meters in a mile.  Next we added a cost field, to calculate the cost field we multiplied it by our length * 100 (because of the round trip) and *.022 ( 2.2 center per trip).  This gave us an output table after summarizing by county to find the total cost of sand trip per truck.

Equations used in my field calculator:
Length = route length / 1609
Cost = length * 100 * .022

Figure 2: Completed Model

Results

The completed model gave me mixed results.  As you can see in figure 3 below the map I created to show the routes taken between sand mines and rail terminals.  As you can see, when comparing it to figure 1, there is no route between the northwest mine in Wisconsin and a rail terminal.  I could not figure out how to fix this problem after trying and retrying again to find a solution.  When I looked closely I could see a route going to a non existing mine on the border of Minnesota.  I am not sure why that mines route was going to a mine I had selected out many steps before.  However, the rest of the map shows that many mines are traveling to Chippewa County, Wood County, Trempealeau County, and also to Winona Minnesota on the border of Trempealeau County. 

Figure 3: Map showing the routes between mines and rail terminals

After calculating the cost field I found that Chippewa County experienced the most cost of sand truck travel on roads with 462 dollars.  This was followed by Dunn, and Wood County which totaled 353 and 311 dollars.  Looking at the map above you can see a lot of routes traveling through Eau Claire, Chippewa, and Dunn County which makes sense when comparing it the table below because all three counties rank in the top 3.  Wood county is in the middle of the state where a good number of mines are all traveling in a short distance to one rail terminal.  Shown by the table below Wood county experiences the highest frequency of travels on roads towards rail terminals, which would equal high cost.

Figure 4: Table showing cost of travels on roads per county

Conclusion

Running a network analysis on the sand mines to rail terminals turned out to be a new challenge because this was the first time I have worked with it and also the first time working with model builder in over a year.  Model builder can be a tool of great efficiency, and clarity, but it can also be a challenge because if one step is wrong the tool will not run.  It WILL take multiple times becoming more fluent with both applications as it furthers the knowledge in ArcMap. The results I got could have been much cleaner especially since I missed one mine in the north part of the state.  However, I was happy on how my final map and table turned out, as they both represented my results in the way I wanted. 

Sources:

Esri Geodatabase

Exercise 7: Part 1 - Python Script, Network Analysis - Data Preparation

Part 1 prepared the data for network analysis by writing a python script the select the sand mines in Wisconsin to be used.  The criteria we used to select certain mines are: the mines must be active, the mines must not also be a long a rail loading station, and if the mine is within a 1.5 km of a railroad it will be eliminated.  The reason we want to eliminate sand mines near railroads is because we will be doing a network analysis of the sand mines to the nearest rail roads, and estimate the number of trips the trucks will take and the cost of this traffic on local roads in part 2.

The python script resulted in a point feature class containing information of 41 mines that will be used for network analysis in part 2 of exercise 7.

Figure 1: Python Script Completed

Exercise 6: Data Normalization, Geocoding, and Error Assessment

Introduction

The goal of this lab was to develop skills in data normalization, geocoding, and then assessing the errors after the processes have been run.  For this exercise the class was giving a excel spread sheet containing the locations of different sand mines across Wisconsin.  The table included addresses, facility names, operator, city name, county, and more: see figure below 2.  The only problem was that that some of the addresses were not normalized and contained only the PLSS information.  For example: NE SW Sec 2, 7N, 3W, our task was to fix these addresses and other information so the data could be used in ArcMap for geocoding.  The last step of the the exercise involved querying out the mines assigned to us and assessing the location of the mines using ArcMap. 

Methods

As I stated above each student was given an excel sheet containing information regarding sand mines in Wisconsin, figure .  The task involving the spreadsheet was to normalize the data so the correct faculty address, community (city), zip code, state, and the mine unique ID field, were correctly entered into a new personal excel sheet for future use.  Each student was responsible for at least 16 mines, I ended up normalizing 22 mines.  Dr. Christina Hupy, course instructor, developed a system where each student was assigned a number and each number was assigned to a different mine.  Therefore four students attempted to normalize each mine to test the accuracy of our geocoding, which can be seen in the results tab below.  To find the correct address for each mine I used google maps, Google search, and the PLSS shape file our geospatial technician, Martin Goetll provided for us. Finding the correct addresses was a large task, but searching the web by using some of the addresses provided and the facility name made it easier to find.  In or order to geocode it is critical to normalize the data.  If you were to try and geocode the mines before normalizing it would not work, therefore normalizing the table with the correct information is a critical step in the process.

After normalizing the data the next step is to geocode the mines in ArcMap using the geocode tool.  This involves signing into Esri's ArGIS online, adding the excel sheet of the mines normalized, filling out the correct parameters, and finally running the tool.  After geocoding the address a table like figure 1 will appear on the screen.  In my case all 22 of my mines matched with a score of 90 or higher, therefore I did not have to manually match any of my mines.  In fact all but six had a score of 100 showing that I did a good job normalizing my table for geocoding.

Figure 1: Results showing how many addresses matched after geocoding

After geocoding the mines the next step is to merge all of my classmates mines into one feature class, to then query out the mines that I used to compare accuracy.  Querying out all of the mines that were the same as mine to test the accuracy was a somewhat difficult task because some classmates did not correctly normalize the data.  All of the mine id's were not located in the same field therefore searching through the attribute table to find all the mine ids that matched mine was necessary.  After querying out the mines that matched mine the process is complete and the point distance too was run to give a result of the accuracy between the mines.

Results

Figure 2: Mine table before they were normalized  

Figure 3: Part of my normalized table

Figure 1 shows the table provided for us giving the information of the mines and figure 3 shows my part of my normalized table.  Figure 4 below shows the location of my mines in purple and then the the queried/matched mines in black.  As you can see some of my mines and classmates mines matched up perfectly and others were not matched but decently close.  One thing to note is that there can be the same mine twice or three times in black as each mine was normalized four times by our class.  Resulting in more black squares than purple triangles. 

Figure 4: Map showing my mines purple and queried mines in black

The table below shows my mines (Input) and the queried mines (near) and the distance, in meters, between them.  As you can see about two thirds of my mines matched very accurately with the queried mines but some of them did not match well at all.

Figure 5: Table showing the distance in meters between my mines and
the queried mines

Discussion

I experience many errors while working through this exercise, but I think this assignment was meant to produce errors and challenge us in handling these errors.  Using the normalized tables of my classmates caused error when querying out the mines I needed.  Some people from the class did not use the correct mine id field when normalizing making it difficult to find the mines I needed.  Also another source of error came when trying to find the correct address, zip code, and city of the mine.  Because some of the mines only contained PLSS information finding the correct address of mine became difficult.  Also because some of the mines do not even exist yet and are proposed or inactive finding an existing address was tough.  After running the point distance tool to check for accuracy of my mines and the classmates mines table errors were common.  In figure 5, from input 2 down you can see the accuracy is not precise at all.  This was because of a data entry error when normalizing the tables.  Either that is on me or one of classmates, I did notice for one of my entries I did not put an s in front of the address causing it to not be in the same place as my classmates who did include an s.  Not including the s caused the geocode process to not match it accurately to the right address therefore causing an error. 

How can we know which points are actually correct and which ones are not? We can tell which mines are accurate by looking at the match address field after geocoding.  If the matched address field contains the same address I have and my classmate has then that is the correct location of the mine.  You can also look at the distance field, figure 5, for support and if it is very close to zero then it is the correct location.  We can also tell it is a correct location if the score is 100 and the status is M, which means matched.  By looking at these three things in both my mines table and my classmates mines table I can tell which mines contain the actual correct location and which mines do not. 

Conclusion

This exercise developed skills in normalizing tables, geocoding, and then assessing errors.  This helped developed new skills when dealing with the location of a building.  It shows how accurate and clean you need to be when working with geocoding in ArcMap. I was pretty happy with the results I got, I think I normalized my table well and the accuracy of my mines compared to my classmates was average.  Doing another assignment like this I am sure we will be more efficient and smooth when normalizing the tables and finding the correct address. 

Sources:

Mines provided by Christina Hupy

US census bureau

Exercise 5: Part 2 - Data Gathering

Goals and Objectives

The goal of this assignment, part 1, was to become familiar with the process of downloading data from different sources on Internet, importing the data to ArcGIS, projecting the data from the different sources into one coordinate system, then building and designing a geodatabase to store the data.  The lab also has the goal of working with metadata to check the accuracy of the data and their sources.  This blog post will cover how and where the data was downloaded, some maps to show the some of the data and raster's converted in pyscripter with maps. Then the blog will go over the data accuracy by collecting the metadata from each source.  

General Methods

Data was obtained from multiple websites and was stored in our class folder.  However because this website data contains so much storage the data, in the form of a zip file, was first saved in a temporary folder that will delete files after 30 days.  The zip file was then extracted into our personal class folder for future use.  The data from these sites the class is interested in is strictly in Trempealeau county, clipping the data and projecting it so it can be used in the future was a critical step as can be seen in my previous post 2.

US Department of Transportation, 2014 rail lines were downloaded.  This data shows the rail lines located in Trempealeau county.  

http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html 

UGSG National Map Viewer, Trempealeau county had to be selected and land cover 2011 3x3 Extent was downloaded.  Also, another data set was downloaded, National Elevation Dataset 45 (1/3).

http://viewer.nationalmap.gov/viewer/ 

USDA Geospatial Data Gateway, much naviation was needed here to find Trempealeau County and download land cover - cropland data.  

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

Trempealeau County Land Records, a Trempealeau geodatabase was downloaded from the counties website itself.

http://www.tremplocounty.com/landrecords/

USDA NRCS Web Soil Survey, Soil data was downloaded from this site, which I found to the most difficult, creating an AOI of trempeauleau county was critical in downloading the soil data.  

http://websoilsurvey.sc.egov.usda.gov/App/WebSoilSurvey.aspx

After all the data was downloaded and stored in the correct folders the python script and data were ready to be edited.

Maps

High Value: Green   Low Value: White

High Value: White   Low Value: Black

The maps above represent the process I have taken to download, project, and clip the data into Trempealeau county.  The first three maps are products of projecting and clipping them into Trempealeau county through pyscripter. As you can see all three were successful and will be used in future labs when analyzing frac sand mining.  

Data Accuracy

Numbers in red were calculated by myself from lecture
and Lo Chapter 4

Finding all of the metadata for each data set was very difficult because the metadata did not include all of the necessary information.  Some of the data accuracy fields had to be calculated by hand from lecture notes and from readings.  

Attribute accuracy is defined as the closeness of the descriptive data in the geographic database to the true or assumed values of the real-world features that they represent.  For the metadata of the lab I could not find any information on the attribute accuracy, however the minimum level of accuracy in identifying land-use and land cover categories should be at least 85 percent. 

Minimal Mapping Unit is the smallest polygon or feature mapped on the raster or vector.  When working with a small scale mapping a tiny park area inside a urban is not necessary because of the size of the scale. 

Conclusion

I found this lab to the most challenging of my college career, from downloading the data, writing the python script, and finding the metadata took a great amount of time.  However, I feel like I learned a lot about the challenging things above especially navigating the data to keep it one place and writing the python script to specify the rasters. When using the data sets in future labs I want to be able to understand the attribute tables better especially the soils data.  The soils attribute table help a lot of information after joining and the map it created was confusing.  Metadata is something of great importance and I feel I did not do a great job in finding all of the necessary steps.  Improving on this is a goal before I graduate.   

Exercise 5: Part 1 - Python Script

Introduction

Post 2 will cover the python script created for the 2nd part of the lab.  The first part of the lab will be discussed in post 3.  The objectives of this lap were to write a Python script to project, clip, and load all the data into a geodatabase.  

Python is a open source programming language that uses a scripting language to run functions of programs.  In our case (GIS II) we write python scripts in for ArcGIS especially ArcMap.  Python is a common application used in the work field and will be viable for our future in working and finding a job.  For this assignment the python script will project, clip, and store the rasters that were downloaded, see part 3, into the Trempealeau county database.  We will use these newly projected rasters in futures labs when analyzing sand mining in Trempealeau county.  For more about Python you can click here or here. 

Python Script

Figure 1: Completed Python Script

Above you can see the completed python script and the results of the new rasters can be seen in part 3 of my blog.  They script took a while to complete and a one error that gave me trouble was I had name my folder raster and the script would not run, giving me errors.  Because there were so many raster words in the script I changed the folder name to part 2 and the script was able to run clean.  As I stated earlier the completed raster's can be seen in part 3 of the lab.

Exercise 4: Sand Mining in Western Wisconsin Overview

Introduction

Frac Sand Mining in Wisconsin has been occurring for over 100 years, but recently their has been an increase in the mining for the use of hydraulic fracturing.  The sand mining is primarily located in western Wisconsin as you can see in figure 2 below, because of the abundant amount of sandstone that fits the needs of the hydraulic fracturing mines.  Throughout the semester our GIS (Geographic Information Systems) II class will analyze and investigate the impacts of sand mining in western Wisconsin. This post will be an introduction to sand mining with the following post being more detailed into the project.  

Hydraulic Fracturing 

Figure 1: This image shows the drill process and how
the sand is used to keep the cracks open.
http://www.h2odistributors.com/contaminant-fracking.asp 

First a background on what hydraulic fracturing is will be given because the frac sand's primary use is for hydraulic fracturing.  This process involves drilling thousands of feet beneath the earth's surface to create cracks in rock.  This process is used to extract oil or natural gas in places like Texas, North Dakota, and others.  After the the cracks in the rocks are created the frac sand, along with water and chemicals, is pumped below to keep the cracks open so the oil and natural gas can be extracted.  With recent technological advancements, horizontal drilling, it is possible to extract oil and natural gas that was previously not available along with need for fuel is causing the demand for frac sand from Wisconsin to rise.

Frac Sanding Mining in Wisconsin

Figure 2: Locations of Frac Sand Mines and Sandstone in Wisconsin
http://curiousterrain.wordpress.com/2012/10/30/frac-sand-in-wisconsin/

Wisconsin has an a great amount of resources of sand and has been mined in Wisconsin since the 19th century for many different uses.  Frac sand is silica sand or silicon dioxide SiO2, quartz.  It has many uses including: paving roads, filtering drinking water, and used in the hydraulic fracturing process.  Most sand is mined and recent demand is used for the of hydraulic fracturing process  The sand deposits and mines are primarily located in western Wisconsin, as seen if figure 2 below.  Because this mining is taking place so close to home our GIS project and the mining being a 'hot topic' it will have a significance.

Figure 3: http://dnr.wi.gov/topic/Mines/documents/
SilicaSandMiningFinal.pdf

However, not all silica sand can be used for hydraulic fracturing, it has to meet a specific need.  Wisconsin is lucky enough to have an abundant amount of sand that can meet this needs as illustrated in red in figure 3 below. The removal of this sand from landscapes involves the destruction of land and along with all mines cause environmental problems and hazards.

Impacts of Frac Sand Minding 

Although sand mining  can be very beneficial it has many environment impacts which will be a study through out the semester.

Air Impacts: dust can be released into the air from handling the sand and also pollutants from the equipment used to mine will be released into the air.  This can effect near by farmers or communities which live near the farm.

Water Resources: the mine has the chance of impacting ground water, rivers, and streams by causing them to change path or pollution.  Wells used by near by farmers have the potential to decrease because the ground water has shifted from the mining.  The mine also can impact the runoff of water because of the removal of land, the streams and rivers can pick up sediment when running through a mine, contaminating the water source.  The sand mines also need a lot of water to wash the sand before it is sent to the hydraulic fracturing mine.  This cause the depletion of lakes, rivers, streams and ground water greatly impacting the surrounding area.

Deforestation: to get the sand forest cover must be removed for the mining to take place.  This reduced wildlife habitat and causes them to find a new home.

Mine's location: The location of the mine impacts near by farmers.  Because almost all mines are located in the agricultural fields of western Wisconsin, this cause the mines to buy land and sometimes cause families to be moved.  The noise that also comes from a mine can effect communities wild life and hunting areas.

How GIS will be used in the project

GIS can be used in this project to evaluate the locations of specific mines.  We can also use GIS to detect where the right places or wrong places to build mines may be.  By using land cover data it these places can be predicted.  The following posts will describe how GIS is used when analyzing this project. 

Sources

http://www.h2odistributors.com/contaminant-fracking.asp

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

http://curiousterrain.wordpress.com/2012/10/30/frac-sand-in-wisconsin/

http://www.jsonline.com/news/wisconsin/frac-sand-mining-splits-communities-b9962665z1-217312971.html