Raster Modeling

Introduction

The final exercise in my GIS 2 class will have me creating a suitability and impact model for a subset section of Trempealeau County, Wisconsin.  I will be utilizing multiple raster layers including geology, land cover, water table height, proximity to rail terminals and slope for my suitability model.  To construct my impact model I will be using raster layers containing streams, farm land, residential areas, schools, and wildlife areas.  To classify and create suitability and impact areas I will be using a number of raster tools in ArcMap including Reclassify, Euclidean Distance, and Raster Calculator.  After creating both of the models I will be able to combine the results to calculate the areas which would be highly suitable to frac sand mining with the lowest impact to the county.  The last step in this process will be to create a Python Script which will created a weighted impact model as a variation of the original impact model.

Methods

All of the anylysis will be done with in a subset portion of the southern portion of Trempealeau County, Wisconsin (Fig. 1)

(Fig. 1) Analysis boundary for raster tools in ArcMap.

Suitability Model

To create my suitability model I will be using the following list of variables:
1. Geology
2. Land Cover
3. Water Table Height
4. DEM (Slope)
5. Rail terminals

Each of the variables will be classified and then ranked in order from 1-3 with 1 being the least suitable and 3 being the most suitable.  The following section will explain the classifications and the rank for each of the variables.

(Fig. 2) Breakdown of the categories and rankings for the suitability model.

Geology was separated into 2 categories with the Wonewoc and Jordan formations in one category with a rank of 3 and all the other geology categories in the second category with a rank of 1. The Wonewoc and Jordan formations are the two most desirable geologic units when searching for locations to mine silica sand.


Land Cover was separated into 4 categories based on the ease of removing overburden material from the landscape to access the sand.  Based on this criteria barren land, herbaceous, hay/pasture, cultivated crops were placed in a category with a rank of 3.  Shrubland was placed in a category by itself and given a rank of 2. The third category contained the mixed, evergreen, and deciduous forests.  The fourth and final category contained residential/urban areas, wetlands, emergent herbaceous wetlands, and open water areas and was given a rank of 0.  The rank of 0 will be used in a seperate raster feature class where all of the 3 & 2 values will be given a rank of 1.  With the ranking set up this way I will be able to use multiplication in the Raster Calculator to exclude the areas with a 0 rank from being considered as suitable.

Rail depot locations were simply broken down by an equal interval distances.

Slope was broken down in to three different categories based on the slope of the land.  Having a low slope to the land simplifies the process to extract and transport the sand.

Water table height was broken down into three separate categories based on the distance the water table is from the surface.  Drilling wells to access the water are priced based on the distance they have to drill to access the water.  So having water closer to the surface will keep the cost down for the mining company.

Before ranking and categorizing the rail terminals I utilized the Euclidean Distance tool to create a raster distance model from the rail depots point feature class.  Additionally, I had to convert the Digital Elevation Model (DEM) to display the slope using the Slope tool in ArcMap prior to ranking or classifying.  I utilized the Reclassify tool to categorize and rank all of the variables for the suitability model according to the table I created (Fig. 2).

Once all of the raster feature classes had been reclassified I utilized the Raster Calculator to add all of the values together and multiply the addition results by the Exclusion Raster.

(Fig. 3) Data flow model of steps taken to produce the suitability model.

Impact Model

For the creation of the impact model I will be utilizing the following variables.
1. Streams
2. Farm Land
3. Residential Areas
4. Schools
5. Wildlife Areas

Each of the variables were taken from vector feature classes and converted to raster feature classes utilizing the Euclidean Distance tool in ArcMap.

All of the criteria for the impact model was ranked from 1-3 with 1 being the high potential of impact, and 3 being low potential for impact.  This is opposite of the suitability model which will allow me to combine the results from the suitability model and the impact model at the end to determine the most appropriate locations for mines with in my study area.  All of the criteria was based on distance away from the feature to be impacted.  To achieve a ranking of 1 (high potential) a distance of 640 meters or ~2100 ft was applied. To be ranked with in the 2 category the area must be 2101-4200 ft away from the selected feature.  Anything farther than 4200 ft away was given a rank of 3 as there would be little impact on the area.

(Fig. 4) Category and rank breakdown of Impact Model criteria.

The streams were selected from a feature class of hydrologic flow of streams from the Wisconsin Department of Natural Resources.  These streams are ranked according the Strahler stream order which orders the number of streams from the bottom (1) to the top (9 in this case).  I choose to include streams with a ranking of 5-9.  My reasoning with larger streams feed into smaller streams the potential is there to contaminate a large stream which would have an impact on the entire system. To fully assess this situation and apply proper rankings further research would need to be completed.

Residential areas were selected from the Trempealeau County Zoning Districts feature class.  I selected Rural Residential, Residential-8 (R-8), Residential-20 (R-20), Residential Public Utilities, and Incorporated to create a feature class of only residential parcels.  After this feature class was created I ran the Euclidean Distance tool and Reclassify tool to rank and categorize the feature distances.

I used the Parcels feature class from Trempealeau County to select school locations.  I created the Query: LastName LIKE 'SCHOOL%'  to select all of the school locations which I used to create a new feature class of the school locations.  I then created the raster feature class and applied the distance ranks in the same manner as previous classes.

The Wildlife Area feature class was found in the Trempealeau County database which had vector polygons defining the outline of the established wildlife areas with in the county.  I proceed to convert the feature class to a raster and apply the ranks according to the decided distances.

Again once all of the raster feature classes had been created and properly ranked I utilized the Raster Calculator add up the values to calculate the impact to areas across the study area.

(Fig. 5) Data flow model of steps taken to create the impact model.

The next step in my process was to combine the suitability model and the impact model to create a map of the best locations for sand mining with minimal environmental and community impact.  I reclassified both models to reduce the number of output ranks.  I utilized the raster calculator again to combine the models and develop my output model.

(Fig. 6) Data flow model for the creation of the best location map.

Viewshed Tool

The Viewshed tool is a powerful tool with in ArcMap which could have been used in the impact model.  The processing power required to run the tool across large areas made it not feasible to use for the entire project.

The Viewshed tool utilizes a DEM to calculate the what areas are visible from specific points with in the extent of the DEM.  This calculation could have been use to determine if a mine would have been visible from recreation areas or other locations which would have been negatively impacted by the mine being visible.

The objective for this section of the assignment was to gain basic understanding of how the tool works and the results you will receive.  I was instructed to utilize just a few points to keep the processing time to a minimum.  I chose to use the Trails feature from the Trempealeau County database.  I selected the horse trails from Trails and then I converted the line to start points.  This left me with 4 points to run the Viewshed tool from.

The Trail View legend in Fig. 10 displays the number of times and area can see from the Horse Trail points.  These results could have been overlayed with the previous results to eliminate areas where sand mines would have been visible from recreational areas or any other type of feature.

Results

(Fig. 7)  Display of raster feature classes used and the resulting suitability model.

(Fig. 8)  Display of raster feature classes used and the resulting impact model.

(Fig. 9) Ranked display for trying to determine the best location for sand mines.

(Fig 10) Display of the results from the Viewshed tool in ArcMap.

Discussion

When analyzing Fig. 9 the majority of highly suitable and low impact land resides in the northwestern portion of the study area.  To protect people, and the environment I would suggest mining in locations which had a value of 7 or higher.  Constructing a mine site in a location of a value less than 7 stands could have potentially large impacts on multiple areas of the community. Even if an area has a value of 7 or higher further investigation should be completed to assure the mine will not be impacting an important environmental or residential area.  

The majority of the feature classes used to build these model were related to environment/land and very few were related to people or animals.  The final product is one of many tools which should be used in determining the best location for a frac sand mine.

I believe it is important to understand the majority of these raster feature classes are fairly accurate generalizations of an area.  Knowing these areas are generalized means they still need to be physically inspected to determine if mining the area was appropriate or not.

Conclusion

Combining multiple raster layers through the raster calculator is an effective method to achieve a display of all the variables with weights on one map.  Creating the suitability model separate from the impact model allowed me to be able to sort features to the appropriate model and rank them properly. The final result is an effective display of locations which should or shouldn't be explored for sand mine locations. 

Sources

Cropland Cover. In United States Department of Agriculture. Retrieved October 14, 2015. http://datagateway.nrcs.usda.gov/

Trempealeau County Land Records. Retrieved October 14, 2015, from http://www.tremplocounty.com/tchome/landrecords/


United States Department of Transportation. Retrieved from http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

United States Geological Survey. Retrieved October 12, 2015, from http://nationalmap.gov/about.html

Web Soil Survey. In United States Department of Agriculture. Retrieved October 14, 2015, from http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

Network Analysis

Introduction

The goal of this lab is to introduce the basics functions of Network Analysis in ArcMap.  Sand mines transport their sand in a multitude of ways.  Generally all of the sand produced in Wisconsin will have to travel by raiload to leave the state.  Many mine sites have a direct access to rail transportation and do not truck the sand via public roadways.  However, many of the mines in Wisconsin have to transport their sand a fair distance to reach the railroad terminal.

The Wisconsin Department of Transportation NW Region Planning Staff estimated when the sand mining industry hit full stride it could have the ability to haul approximately 40 million tons of sand a year out of the state of Wisconsin (Hart).  The transportation of 40 million tons of sand to the terminals could have a significant impact on the local roadways.

Through the use of the Network Analysis function in ArcMap, I will use hypothetical value of 2.2 cents per mile to calculate the cost of additional maintenance to the roadways by county from sand transportation.  We will also be using a arbitrary number of 50 trips per mine to the railway facility.  Thus my findings will not be a true reflection of the cost but my methods could be used to calculate the true impact if the proper cost was available.



Methods

Preparation of the feature classes is needed before utilizing the Network Analysis tool.  I will be using the mine data received from the Wisconsin DNR which was utilized in the previous lab.  Not all of the mines in the data are actively producing or transporting sand.  Many of the mines have rail loading stations directly at the mine site and will not be trucking any sand.  Additionally, it is highly likely mines within 1.5 km of a railway will have had a spur rail built to transport their sand.  I wrote a python script to select all of the active mines, without a rail loading station, and not withing 1.5 km of a railway.  In the end I was left with 41 mine which fit my criteria.

I was provided a geodatabase for the lab exercise which contained a feature class of the rail terminals in Wisconsin I was instructed to use for the analysis.  I added a street network dataset from ESRI street map USA which was also provided for me.

Utilizing model builder I created a model to calculate the cost of maintaining the roadways from sand transportation (Fig. 1).  First I used the Make Closest Facility Layer tool  and Add Locations with the mines as the incidents and the rail terminals as the facilities to set up my the network analysis.  To actually run the analysis I added the Solve tool to determine the rail station with the shortest drive time from each mine.  The next step was to use the Select Data and Copy Features tool to create a feature class from the calculated routes from the network analysis.  The calculated route was in a GCS coordinate system which cannot be used accurately for measurement purposes.  I brought in the Project tool to project the feature class in to NAD 1983 HARN Transverse Mercator feet to let me achieve accurate measurements and calculations.  The next step was to use the Intersect tool to break the routes distance down by county.  Since some counties had multiple routes I used the Summary Statistics tool to create a table with the total route distance broken down by each county. With the use of the Add Field and Calculate Field tools I created 2 new fields within the table.  The first field I created converted the measurement of the distance from feet to miles.  I multiplied the foot distance by 0.00018939 to give me the distance in miles.  The second field was the dollar amount calculation of the impact cost of the trucking.  The cost of maintaining the road networks was calculated by multiplying the number of trips to the railway station by 2 to account for the return trip to the mine, multiply that result by the miles of the route and finally multiply that figure by .022 (hypothetical cost of maintenance).  The equation was displayed like the following in the tool: "2 * 50 * [Dist_Miles] *.022".

(Fig. 1) Model within ArcMap Model Builder for the creation of the network analysis tool and calculations.

Results

To better display the results I exported the final table with all of the calculations to an Excel file using the Table to Excel conversion tool within ArcMap.  With the table in Excel I was able to create a graph to display the results.

(Fig. 2) Graphic display of increased maintenance cost of roads due to sand mine truck traffic.

(Fig. 3) Additional roadway maintenance cost by county from sand transportation.

Discussion

The total amount of money is a lot lower than I originally figured.  I feel this has to do with the dollar figure we used to complete our calculations and the number of trips per mine.  Even if the dollar figure was correct I can almost guarantee the number of trips is higher than 50 trips per year.  I would venture to guess that on a good day the number of trips would be 50 per day.  Even doubling the number of trips would greatly increase the dollar figure calculated.

The transportation model only used the railroad stations within Wisconsin.  Due to an error in my methods early on I found a couple of the mines in the Western part of Wisconsin would save time if they took their sand to railroad stations in Minnesota.  Had we utilized the stations in Minnesota this would have change the impact to specific counties in those areas.

Trempealeau county has the highest number of sand mines but does not have high maintenance cost.  The cause is a centrally located rail facility which keeps the distance trucks have to travel down.  I believe this is one reason why there are so many mines located in Trempealeau county.

The two counties in Northwestern Wisconsin show a cost but there is not a route displayed on the map.  The network analysis took the fastest route to get to the rail facility.  The majority of the route was in the state of Minnesota.  The trucks only traveled a short distance to get out of the state, thus low maintenance cost for the county.

The analysis tool choose the fastest route to from the mine to the railroad.  Just because the tool choose this route does not meant the trucks use the determined path.  An actual route track from the mining companies would need to be obtained to calculate the true impact to the roads, along with the true dollar figures and number of trips per year.

Conclusion

The network analysis tool has a vast number of uses, and is fairly simple to use if you have a basic understanding of ArcMap.  Calculating the shortest or fastest distance is useful but may not always be the route chosen by actual people or businesses.  Businesses use this tool to everyday to save money by keeping the miles down on company vehicles thus saving on maintenance and fuel costs.  Being the numerical values are hypothetical I cannot derive and true conclusion about the impacts sand mine transportation has on local roadways.  There is no doubt in my mine semi transportation has a impact on the longevity of the roadways all across the planet.

Sources

Hart, M. V., Adams, T., & Schwartz, A. (2013). Transportation Impacts of Frac Sand Mining in the MAFC Region: Chippewa County Case Study. In Mid-America Freight Coalition. Retrieved November 11, 2015, from  http://midamericafreight.org/wp-content/uploads/FracSandWhitePaperDRAFT.pdf

Data Normalization, Geocoding, and Error Assessment

Goals and Background 

The objective of this exercise is to further our understanding of geocoding.  Proper geocoding requires the analyst to normalize the input data.  Normalizing the data is required for the geocoding tool with in ArcMap to "properly" function.  After geocoding the analyst must examine the errors inherently associated with geocoding.

This exercise had me geocoding the location of 20 of 129 sand mines in Wisconsin given to us from the Wisconsin Department of Natural Resources (DNR).  The entire list of mines was split between all of the members in our class, allowing for the mines to be geocoded by 3 different students.  Having multiple students geocode the locations will display if there was an error in the process by one or more of the students.  Our professor kindly removed the x,y coordinates from the file to simulate real world situations of acquired data.  In the following Methods section I will discuss the variety of ways I utilized to geocode the list of addresses I was given.

Methods
The first step in the process was to copy the information of my 20 mines from the original Excel file into a separate Excel file. You can see in (Fig. 1) there is multiple addresses columns and multiple formats within those address columns. 

(Fig. 1) Portion of the Excel file received from the Wisconsin DNR.

After copying my 20 mines into my own Excel file I created new columns to separate out the addresses from the city, zip codes, state, and the Public Land Survey System (PLSS) information.  Many of mines in my group had no address and only had the PLSS information.  I also eliminated the fields not pertinent to geocoding. Looking at (Fig. 2) you can see I created a separate field for each portion of the address.

(Fig. 2) Portion of the table I normalized from the original data.

After normalizing my data I ran the geocoding tool with in ArcMap.  My instructions were to use the World Geocode Service (ArcGIS Online) when geocoding in ArcMap.  After the tool ran I had results of 15 (75%) matched, and 5 (25%) unmatched addresses.  Now these results are a little misleading.  After using the Zoom to Candidates in the Geocoding Review/Rematch Address screen, I found only one of my mine locations was correctly depicted on the map with the actual location.  The meant I would have to locate all of the mines on the map manually using the Pick Address from Map feature.

Due to the approximation of the tool many of the locations which had actual addresses in the table were in the ball park area but needed to be adjusted for precise location.  The precise location is desired for the most accurate analysis possible when we use this data in later exercises.

Additionally, I was verifying the locations using a ArcMap basemap which was not as current as I would prefer.  Many of the mines were not actually depicted on the basemap.  I utilized Google Earth which has updated images to see if the mine was depicted for comparison against my basemap.  I also used Google Earth to check addresses which were not found using the geocoding tool.  The majority of the address I input into Google Earth brought me to the direct location I was looking for.  Using Google Earth for reference, I would locate the same area on my basemap and adjust the geocoded point to the correct location using the Pick Address from Map feature.

For the mines with no address, I was provided PLSS data.  Using feature classes of the Townships, Sections, Quarter Sections, and Quarter Quarter Sections, from the DNR geodatabase on our campus servers, I located the approximate locations of the mines.  After narrowing the location down using the PLSS system, I used the basemap image to find the actual location of the mine.  Being the basemap was not current not all of the mines were depicted.  I used Google Earth as I did before to locate the mines I could.

After manually checking and locating the 20 mines I was assigned my results table was finally 100% (Fig. 3).  This does not mean all of the mine locations are depicted in the correct location.  A few of the mines were not visible on the basemap or Google Earth.  Using a combination of address (if available) and the PLSS information (if available) I chose the location for the mine on the map.

(Fig. 3) Geocoding Result chart from ArcMap.

Results

The final task for this assignment was to compare my results against my classmates who geocoded the same mines I did.  However, 2 of the 4 class members did not turn in their shapefile for me compare against.  Using the mines from the other 2 people I was only able to compare the accuracy of 9 mines.  Being able to only compare 7 of 20 mines doesn't give a good assessment of the accuracy between my points and the other peoples points.  Even with only 7 mines I could see something was off with one of the mine locations (Fig. 4).  All of the points from my classmates were very close to exactly where my points were located except for one.  You can see in (Fig. 4) the points along the west edge of Wisconsin do not line up.  After further investigate it was my point which was incorrect and my classmates was correct.

(Fig. 4) Comparison between my geocoded locations (green triangles) and the 7 mines from other classmates (blue dots)

Due to a lack of information to compare to I didn't calculate an average of distance error but I utilized the Near tool in ArcMap to complete a quick calculation of the distance between my points and those from my classmates (Fig. 5).  The row in the chart with Maiden Rock is the point location which I made a mistake on, and you can see by the last column the distance error is the largest.

(Fig. 5) Error chart for the distances between my points and my classmates points.

After geocoding we were given the shapefile with the actual locations of the mines to compare to our points.  Since I had point locations for all of the mines in Wisconsin I selected out my mines by their unique id and created a new feature class of just those mines.  I created a map comparing my geocoded point locations and point locations of the actual mines (Fig. 5).  Looking at the map you can see the same point from my previous analysis is off verifying my point is the one which is wrong. As you peruse the map you will find a few other of my points which are not exactly where they should be.  The scale of the map does not precisely show the distance variation between the points.  I will examine the reason for errors in the discussion section.

(Fig. 6) Comparison of my mine locations with the actual locations of the mines.

After plotting the points on the map I again used the Near tool in ArcMap to calculate the distance between my points and the actual locations of the mines.  After the calculations were made I added the values to a new column in Excel to the corresponding mine.  Then I calculated the average distance of error between all of the mines.  As you can see from (Fig. 7) the average distance of error was ~1713 meters.

(Fig. 7) Error distance of each mine along with the average calculation.

Discussion 

There are a number of factors which contribute to the error of the mine locations.  Digitization of the location was an inherent and operational cause of error.  The points from the DNR were centrally located within the mine area.  I was instructed to locate my point at the driveway entrance of the mine for future roadway analysis.  You can see the variance in location from the driveway entrance and the central point of the mine (Fig. 8).

(Fig. 8) Large scale image of a mine location and the variance of my point and the DNR location.

Inherent errors are very typical in geographic data.  How you represent a location on the map with a small point when the actual object (in this case a sand mine) is not point shaped or the same size. Also, each person creating the map with choose a different point style and choose to locate that point based on their own purpose.  If the map designer needed highly accurate locations geocoding is definitely not the proper way to locate points on a map.

The locations in which were a great distance off was an error on my part.  Somewhere along the process I either missed relocating the point or never even looked at it.  I felt I went through the complete list more than once but I was obviously wrong.  I never went back through to double check all of my locations were correct.  This would have been a simple error to catch had I gone back through the list.

I would have liked to compare my location with those of my classmates further.  However, their failure to complete their task left me unable to fully complete mine.  I fell this is a good reminder lesson of how depending on other can go wrong.  In the real world there will be people who don't get their information in on time which could jeopardize the entire job.  I feel this should be taken into account when planning the time frame for completing a job.

Conclusion

Overall this lab was a great learning experience of many aspects.  Data organization from outside sources may not always be in the best format for your use.  Understanding your platform allows you to best prepare your data for analysis.  Geocoding (and maps in general) are a generalization and no two people will map the same locations the same.  There will always be a variance in locations on a map unless you use an accurate GPS location of your desired point.  Preparing the locations per the task is the best way to achieve accurate results.

Data Gathering, Interoperability and Projections

Introduction

This is the first step in our analysis of sand mines in Wisconsin.  For this portion of the assignment I will be gathering and preparing data for sand mine analysis.  To accomplish this task, I first will become proficient downloading data from a multitude of sources across the Internet.  Secondly, I will become efficient at importing and compiling the data into a well organized geodatabase.  The final step will have me projecting data from different sources into the same projection and clipping the data to my area of analysis.

Trempealeau County, Wisconsin (Fig. 1) is the the main focus area for our analysis.  All of the data I will download and prepare will be for Trempealeau County.

(Fig. 1) Trempealeau County is located in the west central portion of Wisconsin (Map Source)

Methods

The initial objective to my assignment was to download data from the following 5 different state and federal agencies:

• US Department of Transportation (USDT).
• Railway Network Data

• US Geological Survey (USGS)
• National Land Cover Data 2011
• National Elevation Dataset

• US Department of Agriculture (USDA)
• Cropland Cover Data 

• Trempealeau County Land Records
• Entire Geodatabase from Trempealeau County

• Natural Resources Conservation Service (NRCS) sub-organization of USDA
• SSURGO Web Soil Survey Data

I navigated to the appropriate websites using links provided to me by my professor (see sources below).  The focus of this study is with in Trempealeau County, so when possible I downloaded only the data for the county.  Those images or feature classes downloaded will be clipped or extracted by mask (raster images) to the Trempealeau County boundary.

Each data set with the exception of the USDA was downloaded directly from the website.  The USDA data was sent to me in a email attachment.  All of the data sets were delivered in a .zip file format.  From the .zip files I extracted the actual data and placed the data in organized individual folders.

Steps in preparation for each dataset

USDT

From the USDT I downloaded the polyline, Railway Network 2015 data  for the entire United States.  After I downloaded the entire geodatabase from Trempealeau County, I clipped the railroad network to only those rail lines which fell within Trempealeau county.

USGS

From the USGS I downloaded the following 3 different datasets: National Land Cover Database 2011 (NCL),  National Elevation Dataset (2 different coverage areas).  Using the box/point tool within the National Map Viewer I made a box which enclosed all of Trempealeau County.  After setting the box as the area of interest (AOI) I selected each data set separately and downloaded them to my computer.

USDA

From the USDA I downloaded the Cropland Land Cover data set for the state of Wisconsin.  As stated before the data has to be processed and emailed to you for downloading.

Treampealeau County Land Records

From Treamplealeau County I downloaded the entire geodatabase from their land records divsion.

NRCS

From the NRCS I downloaded the SSURGO web soil survey database for Trempealeau County.  The SSURGO data is a very comprehesive dataset which takes a little more work to properly display need information.  I will not be going through the step by step tutuorial, just understand it is more than just a drop in shapefile/feature class with all the information.

Organization & Transformations

Before I organized everything into one nice neat package I had to combine my Digital Elevation Models (DEM) which I downloaded from the USGS.  The southern tip of Trempealeau County was in one image and the majority of the county was in another.  Using the Mosaic to New Raster tool within ArcMap I combined these two images to form one new raster image.

The next step in the process is to create a location to store all of our data.  For this instance I will be using the Trempealeau County Database as my location.  The database is very well organized already and will serve as a great base to build from.

However, I cannot just throw all of my data in the geodatabase.  All of the data I have retrieved is in varying projections and coverage areas.  I want to focus all of this data to the extent of Trempealeau County.  To assure accurate results in my analysis I want to have all of the data in the same projection.

After analyzing the Trempealeau County database I found they were using, NAD 1983 HARN WISCRS Trempealeau County_Feet. (NHWTCF)  This projection is the most accurate when analyzing data across Trempealeau county.

There are multiple approaches to accomplishing the projection and clipping of the data.  Our class had to use Python scripting to write a geoprocessing script to achieve the desired end result.  For this portion we were only dealing with the raster images we have extracted from our data.  To preform these objectives I will be using the Project tool and Extract by Mask (Clip) tool with in ArcMap via PyScripter.  The benefit of using a script, is it allows us to run the same tool across the 3 raster images at once.  If I was to process this by hand in ArcMap I would have to run the Project tool and the Extract by Mask (Clip) 3 times individually.  Though it may not seem like a great time savings in this instance, if I had 100 images to project and clip, it would save me a ton of time.  The exact Python script can be seen via my Python tab at the top of my blog or by click here.

The final step in this exercise was to make a map of all three of the raster images from the previous steps. (Fig. 2)

(Fig. 2) Comparison of the raster images with locator map.

Data Accuracy

Data accuracy is important to the quality and accuracy of your analysis.  Evaluating data accuracy can be a challenge.  Information about the data accuracy should be stored in the Metadata file which is usually provided when you download the data.  However, as Dr. C. Hupy has stated there is no "data police" to assure the information is provided.  Even if the information is provided, the chances of it being in the proper format is even less likely.  Industry standards have started to improve this issue with Metadata but it will take time for all of the available data to catch up and revise or create proper Metadata.

For our assignment we were asked to look at 7 different aspects of data accuracy.

• Scale
• Effective resolution
• Minimum mapping unit
• Planimetric Coordinate Accuracy
• Lineage
• Temporal Accuracy
• Attribute accuracy

I examined the metadata for hours in addition to searching the websites the data was retrieved from for additional information.  In the end, I could not find many of the items I was requested to retrieve.  I created a table with the information I found to display the accuracy differences between datasets (Fig. 3).

(Fig. 3) Compilation of data accuracy components for downloaded data.

 
Conclusion

This exercise was a great introduction to the available data sources across different governmental agencies.  Being able to see the different formats the data is delivered in gives me a additional list of things to prepare for when starting a project.  Our introduction to Python adds another way to run geoprocessing tools within ArcMap while saving time.  The data accuracy examination is a great eye opener to understand that you really don't always know everything about your data.  I would not make a life or death decision based on this data due to lack of accuracy information available.


Sources

Cropland Cover. In United States Department of Agriculture. Retrieved October 14, 2015. http://datagateway.nrcs.usda.gov/

Trempealeau County Land Records. Retrieved October 14, 2015, from http://www.tremplocounty.com/tchome/landrecords/


United States Department of Transportation. Retrieved from http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

United States Geological Survey. Retrieved October 12, 2015, from http://nationalmap.gov/about.html

Web Soil Survey. In United States Department of Agriculture. Retrieved October 14, 2015, from http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

Frac Sand Mining in Western Wisconsin: Project Overview

Introduction 

Sand mining is not new to Wisconsin, this type of mining has occurred in Wisconsin for over 100 years.  The sand mined in Wisconsin is used in a multitude of way such as, glass manufacturing, foundry molds, and golf course sand traps.  Due to recent surges in hydrofracking the state of Wisconsin has seen an increase in mining permit applications and proposals.

What is Hydrofracking?

Hydrofracking is a technique used by the oil and natural gas companies to extract resources thousands of feet below the earth's surface (Figure 1)  The technique involves using explosives to create cracks in the surrounding rocks.  Following the creation of the cracks, then frac sand, water, and various chemicals are pumped into the well to expand the cracks while also holding them open.  With the rocks cracked and held open it is easier to extract the product which was previously contained by the rock.  Though hydrofracking is not new, advances in the industry have allowed previously non-extractable gas and oil to be extracted.  The same advances have also made the process cost effective.

(Fig. 1)  Illustration of hydrofracking also referred to as hydraulic fracturing. (http://en.skifergas.dk/technical-guide/what-is-hydraulic-fracturing.aspx)

Frac sand is the product which the majority of the new mining companies are extracting.  Frac sand is silica sand, commonly called quartz.  Not all silica sands meet the standards to being used for hydrofracking.  The sand must be almost entirely quartz, very well rounded, uniform grain size, and have high compressive strength (Figure 2).  Large deposits of this type of sand are located in sandstone formations within the state of Wisconsin (Figure 3).

(Fig. 2) Frac Sand with a penny for size correlation. (http://www.disclosurenewsonline.com/wp-content/uploads/2013/07/frac-sand.jpg)

(Fig. 3) Frac sand mines and sandstone formations located in Wisconsin. (http://wcwrpc.org/frac-sand-factsheet.pdf)

Sand Mining Process

Sand mining operations vary from mine to mine.  The following 6 step process illustrates a typical sand mine and sand processing plant.

1. Overburden removal/excavation-- The removal of topsoil and subsoil to expose the underlying sand.  Often the overburden is used on the perimeter of the site to create a berm.
2. Excavation--  Removal of the sand.  Typical mines use large excavating equipment such as excavators or front end loaders for sand removal.  Certain situations require blasting to release the sand from the geological formation.  The excavated materials is then stacked for storage or hauled to the processing plant.  Hauling is done via a semi-truck or trains.
3. Crushing--  The sand deposits which require blasting often require crushing to reduce the size of the particles.
4.  Processing--  The sand must go through additional steps to be used for hydrofracking.  The sand will be washed, dried, sorted, to achieve the desired uniformity.
5.  Transportation-- Through the entire process the sand is transported using a variety of methods.  The preferred method to haul sand is currently the railroad though in some areas dump trucks, gondola compartmentalized trucks, and barges are being used.
Reclamation--  After exhausting the supply of sand from the site the owner/permittee must reclaim the mine area.  There is variation on the requirements for mine reclamation from county to county.  The general rules for reclamation is no steep slopes, no vertical walls though some may be authorized with approval from the county.  After the grading is complete the surface must receive topsoil to allow plants to grow.  Once the topsoil is places, seeding and mulching can occur.

Issues with frac sand mining is Western Wisconsin

The list of issues associated with frac sand mining is quite long as described by the Wisconsin Department of Natural Resources (WIDNR).  I will highlight a few of the issues from the list I feel are important.  If you would like to read more about additional issues please refer to Silica Sand Mining (WIDNR).

During the entire mining process machinery which is burning fossil fuels are being used, thus adding pollutants to the air.  The machinery which is being driven on the public roadway is causing additional degradation to the roadways.  Dust particles are released throughout the entire process.  The sand is often watered in an attempt to reduce the dust.

(Fig. 4) Pin heads displaying air monitoring stations for particulate matter in Wisconsin. For more information see the following link. (http://dnr.wi.gov/topic/Mines/AQSandMap.html)

The processing of the sand requires a large amount of water.  Depending on the plant the average water use is expected to range from 420-2 million gallons per day.  Some processing plants have a closed-loop processing system and others have an open-loop system.  Acrylamide may be present in the wash water from the processing facility.  The US Enviornmental Protection Agency (EPA) set the the maximum allowable level of acrylamides at 0 in public drinking water.  This equates to the possibility of contaminating the ground water with the wash water from the processing facilities.

Class Overview

Frac sand mining is very controversial especially since the great increase in the number of mines across the state.  Many people rejoice with all of the job opportunities which become available when a new mine is created in their area.  The environmental effects, landscape changes, and risk for water, or air contamination are argued from the opposing side.

The main focus for our GIS II class will be the suitability and risk of mining within Trempeleau County and several other counties in Western Wisconsin. Our class will be downloading data from multiple federal and state agencies for analysis from which our suitability and risking modeling with be derived.  The progression of this work will be displayed in later blogs.

Sources

Industrial Sand Mining (n.d.). In Wisconsin Department of Natural Resources. Retrieved October 7,  2015, from http://dnr.wi.gov/topic/Mines/Sand.html