Raster Modeling

Goal and Objectives:

The goal of this assignment was to find a suitable location for a frac sand mine in Trempealeau County Wisconsin.  To do this many raster tools and data sets were used and manipulated.  Two models were created, one finding suitable locations and the other finding areas with the lowest impact.  These two models were combined to find the best location for a frac sand mine.


Methods:

Suitability Model

In order to find the most suitable location for a new land mine in Trempealeau County, soil geology, landcover, distance to rail, slope suitability, and water table elevation were taken into account.  These layers were created into rasters by specific criteria and ranked accordingly.  Areas that were more suitable were given the rank of 3, and the least suitable areas were given the rank of 1.  This was done for all the layers then the layers were added up using a raster calculator to find the most suitable location.

Figure 1

The first step to finding a suitable location for a sand mine was to find a location with the right geologic layer.  To do this a Trempealeau County geology layer was used from the Trempealeau County Geodatabase downloaded from their website.  The geology layer was converted to raster based on the geologic unit field and was then reclassified.  Since the Jordan and Wonewoc formations are most suitable for sand mining, those formations were grouped together and there rest were put in a different group.  This allowed for a map (Suitable Geology: figure 1) to be made with suitable geology and not suitable.

A sand mine located in the middle of a heavily wooded forest would be more expensive to log out, in order to find land with the proper landcover, landcover data from the USGS was reclassified and ranked to suitable and not suitable.  Specific landcover classifications are located in figure 3 below.  The suitable landcover map can be viewed in figure 1.

It is important for a sand mine to be located close to a railway.  This allows for sand to be transported to the rail at a cheaper cost.  The Distance to Rail map in figure 1 shows the areas located closest to a rail terminal. The areas closest to rail terminals were given the highest rank

The Slope map in figure shows the areas that have the best slope for sand mining.  A gradual slope or no slope at all is ideal for a sand mining operation. The areas with a gradual slope were ranked the highest

The fifth map shows Water Table elevation in Trempealeau county.  It is important for a sand mine to have access to water for the mining process.  Higher water table elevation was given a higher rank because it allows for easier access to water.

The final map shows the Calculated Suitability of the best location for a sand mine.  This model added the ranks of all the other maps together and reclassified them into 5 categories.  Areas that had the highest number were the most suitable and areas with the lowest number were label the least suitable.  This is demonstrated by the model below.

Figure 2

The figure below is a chart of how each of the layers were categorized and ranked.

Figure 3

Impact Model

Just because the land found in the model above was the most suitable for a sand mine, does not mean it's the best spot.  In order to find the best location, an impact model was created to find areas where a sand mine would have the least impact on the people and environment.  This was done by using distance to streams, distance to farmland, distance to schools, distance to residential area, and distance to wildlife areas.  These were categorized and given ranks of 1-3 with 1 having the least impact and 3 having the most impact.  These were then all added together to create a map of the areas that had the least and most impact.

Figure 4

The layers for the impact model were categorized by the following:

Figure 5

Figure 6

Each of the maps in figure 6 were created by running the euclidean distance tool on their shape file then reclassifying and ranking them based on the table in figure 5 above.   These were then added together using raster calculator to come up with the calculated impact map that can be seen in the lower right hand corner of figure 6.  In this model the areas with lower numbers from the raster calculator were shown as low impact and the high numbers were high impact.

In order to find the best possible location for a sand mine, the Impact model and the Suitability model were combined to get the best areas.  This was done by using the raster calculator on the results of both of the models.  To get the best areas, the impact model was subtracted from the suitability model.  This was because the suitability model had high numbers for the best areas and the impact model had low numbers for the best area.  When the impact model was subtracted from the suitability model, the higher numbers were the best areas and the lower numbers were the worse areas.  Figure 7 below shows the model that was used to combine both models.  Figure 8 below that is the final suitability map.

Figure 7

Figure 8

Figure 8 shows areas in red that have an optimal location for a sand mine in Trempealeau County.

Viewshed Model

Trempealeau County is known for its beauty and nature.  One of the many outdoor recreation activities they help provide access to is bike trails.  They have a large number of paved and unpaved trails that go all throughout the county.  It would be a shame for a sand mine to be located within sight of one of these trails.  ArcMap provides a tool that can show all the land that is visible from a from a specific spot.  The Viewshed tool was used to find all the land that was visible from the Beauty and Diversity Abound trail on the eastern portion of the county.  This allows the user to find areas that the sand mine would be visible from.

Figure 9

 
Discussion:

Without the use of raster analysis none of this high quality information for the location of a new sand mine in Trempealeau County could have been created.  Raster analysis is a powerful tool that when used properly can help in almost any situation.  Only five variables were used in both models.  If doing this for a real life situation more variables could have been used and they could have been categorized and re-classed into more classes.  This would have resulted in a map that was more specific.  The viewshed map was not even utilized into the analysis.  Viewsheds could have been created for a slew of other variables and those results could have been created into a variable to go into the raster calculation.

I found the process of finding distances from specific inputs then classifying those and ranking them an efficient way to perform raster analysis in this situation.  It ended up being a more streamlined process than using shapefiles and data in vector format.

Overall the final risk and suitability map effectively displayed what it was trying to display. It would be a great map to use for people looking to for a new spot to put a sand mine.  Raster modeling and the process in this lab could be used for many situations and shows the power of GIS and the importance it has and will have in the world of business.  













 

Sand Mine Road Network Analysis

Introduction:

The road networks from sand mine to railway get used frequently by heavy trucks that are hauling sand from the mines to the railway loading stations to get shipped away.  This project uses GIS to calculate the amount of roads in each county and the cost of the trucks using the roads.  The data for the amount of trips the trucks are making is hypothetical and the figures are made up for the purpose of this lab.  The goal of this lab is to get introduced to network analysis and its features on ArcMap.

Methods:

To begin this lab a python script was made to filter out the mines and railway terminals to get railways with loading stations that are not located at a mine and to get mines that actually extract sand and do not have a railway terminal located at the facility.  This code can be found in the Python Scripts blog post.

In ArcMap the network analysis toolbar was used to complete steps in this lab. The first step was to make a layer in the network analyst tools. This was done by using the Make Closest Facility tool and called Closest Facility.   The next step was to find the closest railway loading station to the mines.  To do this the add locations tool was used to add the mines with no railway terminal at the facility and the railways terminals not at a mine.  When setting up the add location tools the mines were set as the incidents and the railways were set as the terminals.  Then the solve tool was used to create the best street routes from the mines to the railways.  The next step was to make this routes layer a feature class.  In the model builder, a model only tool of select data was used to select the routes.  Then the copy features tool was used to make those selected features into a feature class.  In order for the routes to be the appropriate units, the data from this point in the model had to be projected.  The Project tool was used to project the routes into NAD 83 Wisconsin TM feet.  This is important for later.  The next step in the process was to find the amount of road that is used for sand transportation in each county and to calculate the amount of money it costs each county.  This was done by using the intersect tool to find the routes in each county.  Then the summary tool was used to create a table with each county and the amount of road used for transportation in feet.  Next, the add field tool was used to add a cost field to that table. The calculate field tool was used to convert the routes distance in feet to miles and multiply each mile by 2.2 cents to get the amount of money each county was owed in road damage.  That number was then multiplied by 100 for a theoretical 50 truck trips to the railway and 50 back to the mine. The results from this equation were the cost in dollars for each county. The following image is the model showing all the tools and the results from the tools.

The following image shows the equation used to calculate the cost of transportation for each railway. 

Results:

The routes in the map above are the best routes from the mines to the railway terminals.  The table above shows how much road, in feet, is in each county and the corresponding cost of transportation in each county for the trucks moving sand from the mines to the railway and back to the mines.  This cost is from the county to the mine company for wear and tear on the roads.  Over a long period of time this cost can add up. For a theoretical 50 trips per year the cost is very manageable.  If a mine has to make 50 trips a day the cost would go up significantly.  Now that the model is made if a mine company wanted to know how much it would cost them for X amount of trips a year a simple modification of the cost equation could output the cost for each county the mine company has to drive through.

Conclusion:

This lab was a great way to get experience using the network analyst features of ArcMap and to get a little more experience using the model builder in ArcMap.  The model builder allowed for the whole process to be laid out and ran more then once if changes needed to be made.  It allowed for the network analyst tools to be utilized to make routes in a quick easy fashion and for costs to be calculated with those routes pain free.  The network analyst features in ArcMap are can be very important for future projects and is a great set of tools to be familiar with.  

Data Normalization, Geocoding, and Error Assessment: Sand Mining Suitability Project

Goals and Objectives
The purpose of this lab is to be introduced to geocoding and the challenges that come with it.  In this lab 19 sand mines in western Wisconsin were geocoded then compared to the actual location and classmates locations.  This lab was designed to make the user explore different tools that ArcMap has to offer, and to make the user creatively think how to solve a problem.

Methods
The mine location data file came from the Wisconsin DNR.  It contained a variety of information including facility name, contact address, address, PLSS location, facility type, etc. This information was not put together in a useful manner.  The address field contained the PLSS location as well as the actual location. The first step was to create a new table with just the mines that were to be geocoded and to normalize that table. The original table contained 129 mines, 19 were assigned to each student to geocode.  The 19 mines were put into a new Excel table and new categories were created to normalize the data.  PLSS location, street, city, and state categories were made and the actual facility address was put into these new categories. In some cases the mines did not have an address only a PLSS location. Once the table was normalized it was brought into arcmap and used to geocode the locations.  The address locator tools was used from the geocoding toolbar to find the location of the mines. The fields that were added were used as the fields to locate the mines by.

The initial matching report revealed that 100% of the mines were matched. This was not true.  A lot of the points were not accurate and needed to be found manually and matched by hand, this was done using the interactive rematch tool.  

This was done for all 19 mines.  For four of the mines no address was included.  Two of them had PLSS locations that were used to find them.  PLSS location quadrants were imported and used to locate these mines.  For the other two a google search was used.  One mine only had an intersection as its location.  For this one google maps was used to locate it. At the end all 19 mines were located and a  point feature class was created of the results. 

The next step was to compare my mines to the actual location, and the location of classmates who had the same mines.  The first step was to use a query to get the same mines from other classmates data and the actual location data set.  

This query selected only specific mines that were to be compared.  Once the mines were selected a new feature class was created for each of classmates and the actual locations data set.  The next step was to compare my points to the others.  To do this the near tool was used. This tool finds the nearest point to one of mine and gives the distance to that point in meters. To compare my points to classmates, the classmates data was the input feature and my points were the near features. This was done because they didn't have all 19 of the same points and the near tool would have just found a random point to fill in for that mine. For comparing my locations to the actual locations, the actual locations was the input features class and my locations was the near features class.

  This tool creates a new column in the input table with the distance to the nearest feature. The four tables were then exported as a text file and brought into Microsoft Excel.  The average distance to mines was calculated by using the average equation in Excel.

Results

The data from the DNR came in a format that was not ready to be used.  Four new fields were created to normalize the data.

Not Normalized

Normalized

Originally the address field was a mess.  The table was normalized to get the address in a more organized fashion.  This allowed the geocoding process to go quicker by finding the address more accurately.

After all the mine locations for the classmates were sorted out a map was created of all the different locations mines were geocoded at. 

The map below shows one mine and the four locations that were geocoded for that mine.  This map shows how some of the variance occurs.  One person put the location at the mine entrance on the road, down the road, in the mine, and behind the mine.  This variance results in the distance between locations being different for each person and for each mine. 

The table below shows the distance from the actual mine location that the DNR provided and where my mine locations were.  The average distance was 1027 meters.  This high number is the result of a few mines being very far off.  

The error that occurred was positional error .  This is the result of the points not being accurately placed at the mine location.  This has to do with different understandings of how to locate a place and how addresses work.  An address is read from the end of the driveway and not the actual spot of the house or building on a property.  The most accurate place to put a address location would be where the driveway meets the road.  This is not where the DNR placed the points and that is why there is fluctuation from the actual points and the authors point locations. 

Conclusion:

Geocoding can be very accurate and points can be placed where the user deems them most appropriate.  This freedom to place points where they please also leads to the resulting locations being different.  This must be kept in mind whenever geocoded locations are being used.  They might not be in the right spot for the application the user wishes to use them for.  

Data Gathering

Introduction:
The goal of this lab was to gather a variety of data on Trempealeau County from different sources to develop file management and data acquisition skills.  This data was downloaded from five different sources then compiled into a database that could be used for further analysis.  Pyscripter was used to project, clip, and load rasters into the geodatabase.

Methods:
-Railway data was collected from the US Department of Transportations website.
https://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_atlas_database/index.html

-Landcover/Landuse and elevation data was collected from the USGS National Map Viewer.
http://nationalmap.gov/about.html

-Landcover Crop Land data was collected from the USDA Geospatial Data Gateway.
http://datagateway.nrcs.usda.gov/

-A Trempealeau County Geodatabase was collected from the Trempealeau County Land Records.
http://www.tremplocounty.com/landrecords/

-Soil data was collected from the USDA NRCS Web Soil Survey.
http://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm

Data Accuracy:
Each data set comes with meta data that provides information about the data.  This information includes:
Scale-

Effective Resolution- Resolution or scale that the data was taken in.  This has to do with camera height and lens focal length.

Minimum Mapping Unit- Smallest depictable or plotable object on a map.

Planimetric Coordinate Accuracy- How close the objects in the data set are to the real world position.

Lineage- Documentation of source materials, whats been done to the data by who and who collected it.

Temporal Accuracy- How relevant the data is today.

Attribute Accuracy- Closeness of descriptive data to real world.  Magnitude of gross error.

The table below shows how each of the data sets fall into the categories above.  This data was taken from the meta data for each of the data sets.

The following maps were created to display the data collected:

Conclusions:
Based on just the metadata provided with the data sets lots of important information was not included.  Resolution and scale should be two things that every data set clearly provide.  It is important for using the data with any confidence.  If I was using this data for a project I would have to keep in mind that some of the data may not be temporally accurate.  The earth is constantly changing and up to date data is important for doing accurate analysis.

Python Scripts

Python is a programming language that is user friendly and easy to learn.  The use of python in this class is aimed at gaining an understanding of what is happening behind the scenes in programs like ArcMap when running tools and other things that require using scripted programs.

Script 1:

In Lab 5 a script was created to project, clip, and load rasters into a geodatabase.  This code uses a for loop to go through the rasters and establish their datums.  The code then decides if it needs to be changed and will project it into the  projection of the database selected.

Script 2:
This python script is query out the mines that are active, are actually the facility type of mine, and do not have a railroad on site.  It then takes these results and creates a new layer called mines_norail_final.

Script 3:
This script was designed to make one class have more weight than the others for the raster modeling lab.

Sand Mining in Western Wisconsin

Introduction:
Fracking has been used for extracting oil and gas in the United States for past 75 years.  Fracking is short for the term hydraulic fracturing.  Fracking is the process of creating fractures in the ground by injecting a combination of liquid and sand mixtures into the earth at a very high pressure.  These cracks allow for easier extraction of oil and gas.  The sand used for fracking is a quartz sand with a very specific grain size and shape.  Western and  central Wisconsin is home to some of the very best sand for this process.  The geologic structure of western and central Wisconsin allows for the sand to be almost perfect size and structure for fracking.  Frac sand is then taken to a refinery that washes, sorts for uniformity, and dries it out.  The send is then ready to use for fracking all over the county.

The new development of horizontal drilling for natural gas in the U.S. has created a mad rush to get as much frac sand as possible. 

Problem:

The mad rush for frac sand has created issues in the local area. With low amount of regulation people have been selling their land and mines have been put up all over the state.  This created economic booms in the surrounding areas.  The problem is that frac sand mining has slowed down over the recent years and employment and economies have fallen because of the lack of money going into these areas. There is also the issue of pollutants that are emitted during the process of mining and refining the sand.  There are permits and regulations that need to be obtained from the state to continue operations.  Since there are environmental concerns around frac sand mining, the state and DNR are hesitant to give these permissions.  

Roles of GIS:

During this course I will be utilizing my skills in GIS to figure out ways to solve some problems in the frac sand business.  I will be using my skills to determine ways that frac sand mining is effecting the environment and ways that can be stopped.  Importing data into GIS platforms will allow for data manipulation, database creation, and a variety of other data implementation to allow for successful analysis. 

Sources:

WDNR. 2012. Silica sand mining in Wisconsin. Retrieved February 22, 2016.

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

USGS. (2012). Frac sand in WI. Retrieved February 22, 2016.

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

King, P. (2015, June 3). Wisconsin towns worry frac sand boom will dry up. EnergyWire. Retrieved February 22, 2016.

http://midwestenergynews.com/2015/06/03/wisconsin-towns-worry-frac-sand-boom-will-dry-up/