Python Scripting

Introduction
Python scripting can be used to perform mapping functions in a more timely and efficient manner.  For the purpose of this project, python script was used to create a "while loop" for buffering.  This function was used to create a 1,000 meter buffer around each frac sand mine location in Wisconsin five times.

Methods
To create a "while loop" buffer for mines in Wisconsin, the following script was created:

Python Script:

>>> import arcpy

... from arcpy import env

... env.workspace = "W:\geog\CHupy\geog491_s13\HANSONLA\EX10"

... bufdist = 1000

... i = 1

... while i <= 5:

... arcpy.Buffer_analysis("Mines_Project","mines_buff" + str(bufdist) + ".shp", str(bufdist),"FULL","ROUND","ALL")

... bufdist += 1000

... i += 1

...

>>>


Results

The following figures show the results of the buffer loop created for the frac sand mine Northern Frac Sand.  The different colored rings show the five separate buffers of 1,000 meters (purple), 2,000 meters (grey), 3,000 meters (blue), 4,000 meters (green) and 5,000 meters (orange).

Figure 1: Buffer loops of 1,000 feet
(Created from python script)

Figure 2: All buffers created through python script

Figure 3: Frac sand facilities buffers & site location index
Trempealeau County, Wisconsin

The following figure displays frac sand facilities that are located within a 1,000 meter radius of a body of water.

Figure 4: Frac sand facilities within 1,000 meters of a waterbody

The following figure shows the 5,000 meter buffer around frac sand facilities in Wisconsin as well as railways.  It can be seen that many frac sand facilities are located within 5,000 meters of a railway.

Figure 5: 5,000 meter buffer, frac sand facilities & railways

Discussion/Conclusion
Python script is very useful when mapping functions require a tool to be ran multiple times for different outputs.  The python script created for this activity provided five 1,000 buffers around frac sand facilities in Wisconsin.  By writing a script, it wasn't necessary to manually run the buffer tool 5 times.  The python script took approximately five minutes to perform and output the results; this was much less time than would be required for individual tools.

Frac Sand Suitability Index

Goal and Objectives

Suitable locations and environmental risks for sand mining in Trempealeau County, Wisconsin, were determined using a variety of raster geoprocessing tools to build an index model.  Two index models were created.  The first was an index model of site locations and the second was an index model of environmental risk.  The criteria was hypothetical, derived from Kent Severson and students at the University of Wisconsin-Eau Claire.

Data for this analysis was provided by Trempealeau County, Wisconsin DNR, NLCD and USGS.  Professor Christina Hupy provided a geologic map for the purpose of this project as well.

Methods

Site Location Suitability

Site Criteria included Jordan and Wonewoc geologic features, elevation, slope, Land Use Land Cover, Distance to a rail terminal, and depth to water table.  Non-suitable land cover types were excluded from possible sand mining sites.  Site criteria was ranked from 1-3 with 1 being low suitability, 2 being medium suitability and 3 being high suitability.  The following table exhibits the ranked values for each site location criteria.  The ranks were added together using Raster Calculator.  The output of this tool was multiplied by the binary data for land use land cover to exclude residential areas.

Figure 1: Frac Sand Mining Site Location
Suitability Criteria Table
Trempealeau County, Wisconsin

Figure 2: Site Location Suitability Model

The slope tool was used to determine slope of the DEM for Trempealeau County.  Block statistics were used on the output feature class to avoid the "salt and pepper effect" that is commonly exhibited in slope output data.  The results were reclassified for the elevation and slope output feature classes.  By reclassifying the data, the values could be ranked as shown in Figure 1.  Areas with a steep slope were considered low suitability because mining companies prefer low sloped areas for sand extraction.

Frac Sand occurs mostly in Jordan and Wonewoc geologic formations.  These formations occur at general elevation levels.  The Trempealeau County DEM was reclassified and Jordan formations elevations were given high suitability values, Wonewoc formations were given a medium suitability and all other formations were given a low suitability value.

The National Land Cover Database Product Legend was referenced to determine suitable Land Use Land Cover.  The input raster was reclassified; the output feature class considered Open Water, Developed Open Space, Developed Low Intensity, Developed Medium Intensity, Developed High Density, Woody Wetlands and Emergent Herbaceous Wetlands as low suitability.  Barren Land, Shrub/Scrub and Grassland/Herbaceous were considered high suitability.  Deciduous Forest, Evergreen Forest, Mixed Forest, Pasture/Hay and Cultivated Crops were considered medium suitability.  Residential and wetland land classifications were excluded from the final suitability map.

Frac sand mining facilities must transport the extracted sand via railways.  The distance from a rail terminal was used as criteria for suitable site locations for sand mining facilities.  This was done using the Euclidean Distance tool.  The output of this tool is a raster feature class that accounts for the distance from a rail terminal.  The output was reclassified and the furthest distances were given a low suitability value, medium distances a medium suitability value and short distances were given a high suitability value.

Water is needed for mining processes in order to clean the extracted frac sand.  Depth to the water table was used as criteria for site location suitability.  A water table elevation feature class was converted to raster using the Topo to Raster tool.  The data was then reclassified.  Large values were given a low suitability value, medium values were given a medium suitability value and small values were given a high suitability value.  The closer a location lies to the water table, the easier it is to reach water.

The ranked rasters were added using Raster Calculator.  The excluded LULC feature class was added to the output using Raster Calculator, so these areas would be accounted for in the final product.

Environmental Risk Suitability

Environmental risks criteria included proximity to streams, prime farmland, proximity to residential areas, proximity to schools and visibility from prime recreational areas.  Areas located within a wildlife area were excluded from locations resulting from these factors.  Environmental risk criteria was ranked from 1-3 with 1 being high suitability, 2 being medium suitability and 3 being low suitability.  The following table exhibits the ranked values for each environmental risk criteria.

Figure 3: Frac Sand Mining Environmental Risk
Suitability Criteria Table
Trempealeau County, Wisconsin

Figure 4: Environmental Risk Suitability Model

The first suitability factor for environmental risk was proximity to streams.  Perennial springs were selected from a WDNR hydro line feature class.  The Euclidean Distance tool was used to determine proximity.  The output was reclassed.  Small values were given low suitability ranks, large values were given high suitability ranks.  Water contamination is an environmental risk, so the further a location is from a stream, the better.

Because agriculture is important to Wisconsin's economy, it would not be ideal for mines to be located on prime farmland.  To determine areas of prime farmland in Trempealeau County, a Prime Farmland feature class was used.  The vector feature class was converted to raster using the "farmland code."  This allowed the raster values to account for the type of prime farmland.  The values were then reclassed to indicate suitability ranks.  Areas of prime farmland or farmland of statewide importance were ranked as a 3, low suitability.  Farmland that would be prime if it was drained and protected from flooding was ranked as medium suitability.  Non-prime farmland was ranked as high suitability.

To determine locations near residential or highly populated places, residential districts were selected from a zoning district feature class.  The selected data was then converted to raster.  The Euclidean Distance tool was used to calculate the distance from each residential zone.  Areas within 640 meters were ranked as low suitability; the longest distances were ranked as high suitability.

A parcel data set was used to select parcels that were owned by school districts.  Euclidean Distance was used to calculate the distance from schools.  The output was reclassed; long distances were considered high suitability and short distances were considered low suitability. 

Trempealeau County is home to many well-known recreational areas.  These areas are important for tourism in the area.  Suitable sites for sand mining facilities should be in locations that are not visible from recreational areas.  To determine visibility, a parks feature class was converted to a point feature class.  The Viewshed tool was ran.  This tool results in a raster with either "Visible" or "Non-Visible" values.  The raster was then reclassified as 0 for Visible and 1 for Non-Visible.

Proximity to wildlife areas was also used as criteria for suitable locations for frac sand mining facilities.  Euclidean Distance was used to calculate the distance from a Wildlife Areas feature class.  The resulting values were reclassed as high suitability for long distances, medium suitability for medium distances and low suitability for short distances.

Using raster calculator, all of the output raster were multiplied to create a suitability raster.  The values ranged from 7-18.  A 7 value corresponds with high suitability and 18 with low suitability.

Results and discussion

Figure 5: Locations of perennial streams & prime farmland
Frac Sand Suitability
Trempealeau County, Wisconsin

Figure 6: Suitability and Frac Sand Facility Locations
Trempealeau County, Wisconsin

Figure 7: Residential and School Suitable Distances
Trempealeau County, Wisconsin

Figure 8: Visibility from Recreational Areas
Trempealeau County, Wisconsin

Figure 9: Frac Sand Suitability Index Maps
Trempealeau County, Wisconsin

Conclusions
The results of the site location index model show that areas of northern Trempealeau County are best suited for frac sand mining facilities.  The environmental risk index model shows west central and northern Trempealeau County are the best locations for frac sand mining facilities.  Overall, areas near the Chippewa and Mississippi rivers are poorly suited for frac sand mining. Proximity to residential areas is a major factor in the suitability of frac sand mining site locations.

Sand Sustainability Project Part II

The goal of this Project was to study the transportation routes from frac sand from the mines to a railroad terminal.  These transportation routes could result in significant impacts on local roads.  A network analysis was performed to determine the distance travelled on local roads and the cost of the impacts for each county in Wisconsin.

Data
The addresses of frac sand facilities were used in the network analysis.  I, along with three other class members, located these addresses through geocoding as well as aerial imagery.  Each class member created a feature class of their located mines;these feature classes were then merged into one feature class in ArcGIS.  X and Y coordinates could not be found for two mines.  These mines had to be removed in order to correctly perform the network analysis.  A railroad terminal dataset was provided by our professor, Christina Hupy.  This dataset represents locations within United States for transportation terminals such as bus stations, train stations, marine terminals, and other significant transportation nodes.  Street and county data was supplied by ESRI.  Each of these datasets were crucial to the success of the network analysis.

For this analysis, it was assumed that trucks travelled between each frac sand location and rail terminal 50 times (one-way) per year (100 trips total).  It was also assumed that the cost per truck mile was 2.2 cents.

Methods
A network analysis can be performed using dialog boxes in ArcMap, but this process can be tedious and time consuming.  To perform this network analysis, a model was built in ModelBuilder, an ArcGIS application.  ModelBuilder allows the user to input feature classes and tools into a model.  When all aspects of the model have been added and defined, the model can be performed.  The use of a model presents many benefits.  The process is performed in a single step, yet is editable in order to fix any issues or to change the output of the model.

In order to produce the desired results of this project, the "Closest Facility" network analysis tool was used.  Closest Facility network analysis created a route between a frac sand location and the nearest terminal.  It was used because it most closely resembles the interaction between these features in the real-world.  The model below represents the steps taken to perform the network analysis (Figure 1).

Figure 1- Workflow created in ModelBuilder to perform network analysis

Before the distance travelled per county could be calculated, the SUM Shape Length values had to be converted from meters to miles.  This was done through the field calculator tool using the equation "SUM Shape Length / 5,280."  Once the feature lengths were in miles, the field calculator was used again to calculate the cost per county using the equation "Miles * 100 * .022."

Results
The model resulted not only in a network analysis of the frac sand transportation routes, but also the cost and travel distance per county.  These results are displayed below in Figure 2.

Figure 2-Final results of Network Analysis

Conclusions
The counties that exhibited the most distance travelled were Chippewa, Eau Claire, La Crosse and Trempealeau.  These counties exhibited the highest cost as well.  It can be concluded through these findings that as the travel distances increase between the frac sand facilities and rail terminals, the cost also increases.  Although other factors can contribute to deteriorated road conditions, it can be implied through the results of this analysis that there is a significant relationship between the frac sand transportation, road deterioration and cost per county.

Discussion
The calculations of the network analysis outputs were equally as important as the network analysis. The results of the calculations and the network analysis can be used separately to provide information on the impact of frac sand mining in Wisconsin, but the combination of the results exponentially increase the value of the research.

The real-world replication of network analysis make this tool invaluable to geospatial technology.  It is a tool that not only replicated real-world networks, but also allows the users to define parameters to expand the capabilities of these results.  Many types of network analysis are available and can be used to produce different results.  It is important to know the ramifications of the type of network analysis used; if not used correctly, results may be flawed or inaccurate.

Silica Sand Mining Project Data Gathering

Introduction
The goal of this aspect of the Frac Sand Suitability model for Trempealeau County, Wisconsin was to collect and develop data.  The data includes environmental, transportation and demographic features as well as geocoded locations of frac sand facilities in Wisconsin..  A variety of agencies were used to import this data into a geodatabase in ArcGIS.

Methods

The National Atlas provided a line feature class for railroads in Wisconsin.  Frac sand processes often use railways to transport silica sand from the mine to processing plants and oil wells.  This feature class was downloaded from National Atlas data and imported into the geodatabase.  The United States Geologic Survey (USGS) provides a wealth of geographic data.  For the purposes of this model, elevation and land cover data was acquired from this agency.  USGS provided land cover data from 2006 in raster format.  The MRLC provided a description of the data and a legend for the raster codes.  A National Elevation Dataset was also in raster format.  The DEMs (Digital Elevation Models) had to be shared as two separate tiles because of the large storage capacities of the files.  In ArcGIS, the “Mosaic to New Raster” tool was used to merge the two tiles.  Both sets of data were imported as raster datasets in the geodatabase.

Figure 2-Land Cover Data
(Source: USGS)

Figure 3-Elevation Data
(Source: USGS)

Cropland data was acquired through the United States Department of Agriculture (USDA) Geospatial Data Gateway.  This information will be used to determine land cover in the suitability/risk model.

Figure 1- Cropland Data
(Source: USDA)

The National Resources Conservation Service (NRCS) provided soil data for this model through the Soil Survey Geographic Database (SSURGO).  This data was imported to the geodatabase as a feature class, the component table for soil data also had to be imported.  These features were joined through a simple relationship class using the primary key “MUKEY."

Figure 4- Soil & Railroad Data
(Source: NRCS, National Atlas)

Coordinate systems varied between each dataset.  In order for accurate representation and analysis of the data, a common coordinate system had to be set.  The datum of each dataset was inspected and a coordinate system was chosen based on two factors: common datum and area of interest.  The datum of each dataset was NAD 1983 and the area of interest was Western Wisconsin.  The NAD 1983 UTM Zone 15N coordinate system was chosen because it fit within the datum of all data and it was the best fit for the area of interest.

Geocoding
A spreadsheet from Wisconsinwatch.org (http://www.wisconsinwatch.org/viz/fracmap) was used to create the reference data for geocoding.  The spreadsheet had to be normalized before it could be used in the ESRI ArcGIS geocoder.  To normalize the data, the state, county, community and address had to be separated into individual columns.  Many of the addresses were formatted in PLSS description.  This format is not compatible with the ESRI ArcGIS geocoder.

Figure 5: Normalized Spreadsheet; Source: Wisconsinwatch.org

Once the spreadsheet was normalized, it was uploaded to ArcMap and loaded into the address locater.  The figure below shows the success

Sand Mining Suitability GIS Project Overview

Wisconsin’s rich glacial history has provided abundant sandstone deposits upon its geologic landscape.  This glacial history has created an expansive array of Jordan, Wonewoc and Mt. Simon sandstone formations containing high-quality silica frac sand (Figure 1).  Although sand mining has occurred in the state for hundreds of years, the practice of hydrofracking has rapidly increased the demand for sand mining in recent years.  Hydrofracking pumps a combination of water, frac sand and chemicals under high pressure into underground oil or natural gas wells to open natural fractures.  This allows natural gas or crude oil to be more easily extracted from a well.  It is important to note that no oil or gas wells exist in Wisconsin, but the state’s abundant frac sand resources are sought to meet the demands of the oil industry.

Figure 1: Wisconsin bedrock types; data provided by WDNR

Western Wisconsin has become a hotbed for frac sand mining.  According to the Wisconsin Department of Natural Resources, “Wisconsin has approximately 60 mining operation and 30 processing facilities operating or under construction” and as of January, 2012 “20 new mining operation proposals” (WDNR, Silica Sand Mining in Wisconsin, P. 3).  The extraction of frac sand can potentially cause an array of issues.  Mining may cause dust particles and pollutants to be emitted into the air; the extent of emissions is considered minor, but is a concern for air quality purposes.  Frac sand facilities may be located near rivers or streams; it is possible for run-off from these facilities to reach bodies of water and cause contamination.  Contamination can include increased siltation or erosion. (DNR, P. 24)  Water resource impacts can extend into loss of habitat and ecology, especially in regards to Wisconsin’s fisheries.  Transportation infrastructure can also be impacted by the processes of frac sand mining.  The sand must be transported for the extraction site to the processing site.  The amount of weight asserted onto existing roadways coinciding with increased traffic may cause road deterioration.  These factors can cause a decrease of property values in areas near frac sand facilities.  The potential implications of sand mining, while numerous, are highly contested.

Figure 2: Frac sand processing in Wisconsin
Photo provided by BanksPhotos, iStockphoto

Figure 3: Aerial view of a frac sand mining operation in Wisconsin
Photo provided by BanksPhotos, iStockphoto

The use of a geospatial information system (GIS) can be used to assess these possible implications through a suitability/risk model.  A geospatial information system is used to represent real-world features in a spatial manner.  Spatial information can be used to analyze geographic patterns and determine relationships between features.    A suitability/risk model will use geographic data pertaining to frac sand mining to determine spatial frequencies, analyze network associations and consider the relationships between environmental impacts and frac sand mining.  This model will focus on Western Wisconsin, specifically Trempealeau County.

Sources:

Silica Sand Mining in Wisconsin

Wisconsin Department of Resources, 2012