Post 6: Raster Analysis for Sand Mine Locations

Goals

• Build a raster suitability model
• Build a sand mining risk  model
• Overlay the models to find the find the best locations for sand mining with as little impact as possible on the environment and surrounding communities

Objectives

I. Suitability for Mining
Generate a spatial data layer to:

1. Meet geologic criteria
2. Meet land cover use criteria
3. Meet distance to railroad terminal criteria
4. Meet slope criteria
5. Meet water-table depth criteria
6. Combine the five criteria into a suitability index model
7. Exclude non-suitable land cover types

II. Risk for Mining
Generate a spatial data layer to:
    
     8. Measure impact to streams
     9. Measure impact to prime farmland
   10. Measure impact to residential/populated areas
   11. Measure impact to schools
   12. Measure impact to parks and recreational areas
   13. Combine the factors into a risk model
   14. Examine results in proximity to prime recreational area

Data Sources

Data for raster analysis in this lab came from numerous sources and are summarized in the following table (Table 1):

 



Table 1. Summary of data used for the raster analysis portion of the Sand Mine project and their associated sources.

Methods

Tools Used

• Spatial Analyst: Block Statistics, Euclidean Distance, Extract by Mask, Raster Calculator, Reclassify*, Viewshed
• Conversion: Polygon to Raster, Topo to Raster
• Data Management: Project

*The reclassify tool was used to determine a location's suitability for sand mining and were based on the following ranks: 1 = unsuitable; 2 = Moderately Suitable; 3 = Highly Suitable. More details on how the ranks were assigned for each criterion will addressed in the Results section of the report.

Objective

1. Sand mines need to be located in areas where the geology is suitable for such processes. In Wisconsin, the Cambrian Jordan (Ej) and Wonewac (Ew) sandstone units are ideal for frac sand mining due to the size of grains within the units and the degree to which they are sorted. The TMP_Geology shapefile was converted to a raster (Tmp_geologyPolygonToaRaster). The new raster file was reclassified so that areas consisting of two groups: one where Ew and Ej were grouped together (tmpgeology_reclass1) and another that contained all other geologic units. The reclassified raster was reclassified again so that numerical values could be assigned to each group: 3 equal to the Ew/Ej group and 0 equal to all other units (Figure1).

Figure 1. Geologic units in Trempealeau County were rasterized and reclassified twice.

 
2. Land cover suitability was sorted out in terms of suitability in a manner similar to the geologic units in  objective 1. Data from the NCDL2011 raster  file were fleshed out in terms of its suitability for sand mining. NCDL20011 data were reclassified twice to produce the suitability model (Figure 2).

Figure 2. Land cover raster data were reclassified twice in order to create a suitability model for sand mines in Trempealeau County, Wisconsin.

 

3. A future sand mine's proximity to a rail terminal was a consideration in terms of developing a suitability model. Rail terminal data were projected into an appropriate coordinate system (e.g. to NAD_1983_HARN_Wisconsin_TM). The Euclidian distance tool was applied to determine the general distances from each rail terminal.  Once the Euclidean distance tool ran, the resulting terminal_distance raster was reclassified in terms of its suitability for sand mining (Figure 3).

Figure 3. The Euclidean distance and reclassify tools were used to determine suitability of locations in Trempealeau County in terms of their proximity to railroad terminals.

4. Slope suitability models were created for Trempealeau County from the USGS DEM (DEM_Out), which had been reprojected into the Trempealeau County coordinate system. Block statistics were applied to the resulting raster output (slope1) in order to smooth out the coarse "salt and pepper" effects produced by the slope tool. Steep slopes were deemed unsuitable for sand mining and were classified as such for the suitability index (Figure 4).

Figure 4. Slope suitability model that was created to help determine suitable areas for sand mining in Trempealeau County.

5. Water table elevation contours were converted to raster format based on Wt_ELEV field. The resulting raster dataset (topo2raster4) was then reclassified based on suitability for frac sand mining (Figure 5).

Figure 5. The Topo to Raster and Reclassify tools were used to determine the suitability of locations within Trempealeau County, WI that are suitable for frac sand mining location.

6. A suitability index was created using the Raster Calculator (RC) tool. The RC tool produced a raster dataset where cell values (ranks) of reclassified datasets were multiplied by one another. The higher the resulting cell values; the more suitable the location was for frac sand mining, the higher the value associated with each cell. The suitability index (suit_indexuse) was then reclassified itself based on the 3,2,1 ranking system that was used to reclass all raster datasets in objectives 1-5 (Figure 6).

 

 

Figure 6. Raster calculator was used to create a mining suitability index for Trempealeau County.

 

 

7. Lands determined to be unsuitable for sand mining location were excluded by reclassifying the suitindex-reclass1 raster dataset produced in step 6. All lands in Trempealeau County deemed unsuitable were given a cell value of '0.' When such values are multiplied with risk suitability model in step 14, such areas will be given a value of zero (Figure 7).

 

Figure 7. Land deemed unsuitable for mining in steps 1-6 were excluded from the final model by reclassifying such areas with a value of zero.

 

 

8. The Euclidean distance tool was used to determine the proximity of streams in Trempealeau County in order to assess how frac sand mine locations might affect such areas. The Euclidean distance tool was applied to a feature layer that contained only named, primary river systems (hydro_primary). The resulting feature class (riverseuclideandist1) was constricted to the Trempealeau County boundary by using the extract by mask tool. The resulting feature class (extract_eucluse1) was reclassified (streamsreclassuse1) into a 3,2,1 ranking system (Figure 8).

 

Figure 8. Suitability model used to determine the impacts that sand mines may have on streams in Trempealeau County.

 

9. A suitability model was produced to identify areas that are highly suitable for farmland. The primefarm feature class was first converted to a raster. The resulting raster (farmlanduse) was reclassified based on the 3,2,1 ranking system (Figure 9).

 

 

Figure 9. A risk assessment index was created based on whether or not land was highly suitable for farming.

 

10. A suitability assessment model was created base on distances from residential areas within Trempealeau County. A feature layer (Zoning_Districtsselection) was created in ArcMap that contained only areas zoned for residential purposes by the county. Euclidean distance was calculated for the zoes in the feature layer (zoning_reseuclidean1). The Trempealeau County boundary was used as a mask to ensure that all distances were constrained within the county (zoning_extract1). Data from the extracted raster dataset were reclassified based on the 3,2,1 ranking (Figure 10).

 

Figure 10. A suitability model was created based on distances from areas within Trempealeau County zoned as residential.

 

11. A suitability assessment model was created base on distances from schools located within Trempealeau County. A new feature layer  (ParcelSchool) was created using select by attributes to locate parcels within Trempealeau County that were owned by school districts. Euclidean distances were calculated for the ParcelSchool feature layer. Extract by mask was applied the Eucliean distances raster (school_eucliean1) so that the values were constrained within Trempealeau County resulting in the Extract_school1 dataset. The extracted dataset was reclassified in terms of suitability based on proximity to schools with Trempealeau County using the 3,2,1 ranking system (Figure 11).

 

 Figure 11. A suitability model was created based on Euclidean distances from schools located in Trempealeau County. Suitability was ranked on the 3,2,1 system.

 

12. An assement suitability model was created for wildlife areas and parks within Trempealeau County. Parks and Wildlife feature classes were joined with a union based on their mutually common GlobalID fields creating the Wildlife_Areas_Union feature class. Euclidean distances were calculated for the Wildlife-parks union resulting in the parkseuclidean1 raster dataset. The parkseuclidean dataset was extracted in order to constrain distances within Trempealeau County (parkextractuse1). The extracted  feature class of Euclidean distances was reclassified according to the 3,2,1 classification system.

 

Figure 12. A suitability model was created for parks and wildlife areas located in Trempealeau County.

 

13. The five assessment models created in steps 8-12 (streamsreclassuse1, parkreclassuse1, school_reclass1,  farmlandreclassuse,  zoning_reclassuse1)    were combined using the raster calculator tool. The raster tool multiplied the raster values of each cell (3 or 2 or 1) from the input feature classes, with higher output values indicating areas with low risk. The resulting raster (risk_ass1) was reclassified in the 3,2,1 system producing the riskmodel_reclass raster suitability model (Figure 13).

 

Figure 13. Five risk assessment models were multiplied in and reclassified in order to create an overall risk assessment.

 

14. The viewshed analysis tool was used to figure out what areas in Trempealeau County would be visible from the park. The park was represented as a point in ArcMap (FarmDays Prg Prk) which was input into the viewshed tool along with a DEM for Treampealeau County (dem_out). Unlike the extract_dem used to calculate slopes in step 4, elevation values in dem_out were still in meters. To fix this issue, the 'Z-factor' parameter within the viewshed tool was changed from its default value of 1 to the meter-foot conversion factor of 3.28 (Figure 14).

 

Figure 14. The viewshed tool was used to determine what areas of Trempealeau County are visible from the Farm Days Progress Park.

 

15. The final model for sand  mine land use assessment was created by combining raster values from the suitability index created in step 6 (suit_final1) and the risk assessment model created in step 13 (rskmodel_reclass1). The two rasters were combined by multiplying their cell values together and reclassifying the results (Figure 15).

 

 

 

Figure 15. Raster calculator tool was used to combine the cell values in the suitability model with those from the risk assessment model. The resulting values were then reclassified.

 

 

Results

Suitability

Geology
Geologic units suitable for franc sand mining include those from the Cambrian Wonewac (Ew) and Jordan formations (Ej). Ew and Ej are highly suitable for frac sand for a variety of reasons including grain-size, composition, and the roundness of the sand grains. In contrast, geologic units in Wisconsin, such as the Ordovician Prairie du Chien (Op) group which is a carbonate rock instead of a quartz sandstone like Ew and Ej.

Because Ew and Ej are so suitable for sand mining, the units were assigned a ranking of 3 (Table 2; Figure1) when the geologic suitability was created (Figure 16).

\

Figure 16. Geologic suitability map for Trempealeau County Wisconsin. Suitable geologic units equal 3, while unsuitable units equal 0 (legend). 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

Table 2. Summary of geologic uni9ts in Trempealeau County and the associated rank of each. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 

Land Cover

  Land cover in Trempealeau County was classified based on how suitable areas with such coverage were to frac sand mining companies on the 3,2,1 classification scheme. For example,  areas with open water and wetlands were classified as 1, since it is unlikely mining companies will or can build in such areas; developed areas with high density populations were given a low suitability value (1) as well since sand mines will not likely build close to densely populated towns and cities (Table 3; Figure 17).

 

Developed areas classified as medium, low, or open space were given a value of 2 since such areas may  be difficult to set up mining operations due to strict zoning regulations. Similarly, areas of Trempealeau County which had any sort of forest (e.g. deciduous, mixed, evergreen) were classified with a suitability value of 2. The reason forested areas in Trempealeau County were classified as such is due to the fact that that the area would be difficult to clear since trees would need to be removed (Table 3; Figure 17).

 

 The areas with land cover in Trempealeau County  that is preferential to sand mining sites were the areas located on barren land, herbaceous, hay/pasture, cultivated crops, and scrub/shrub land; all such coverages were assigned a value of 3. The most difficult assignment for land cover suitability were those classified as scrub/brush. While it could be argued that clearly brush/scrub for mining sites is nearly as strenuous as clearing forest in terms of energy output (e.g. physical, fuel, etc.), clearing land of scrub would likely be easier to get permitted for than logging out large swathes of land with tree coverage, which may require foresters and perhaps regulatory agencies to examine prior to cutting timber in such areas (Table 3; Figure 17).

 

Due to the fact that it is not likely that any mine will be located very near densely populated areas, e.g. in the middle of a city or town, or in a lake or wetland, all such values were re-ranked from a value of 1 to a value of 0 (Table 3, Figure 17).



 

Figure 17. Land cover suitability map for Trempealeau County, Wisconsin. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 



Table 3. Classification scheme for land cover data. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 

Rail Terminal Distance

 

  Euclidean distance was used to determine the distance from rail terminals in Trempealeau County. With average diesel prices in Trempealeau County  about $2.70gallon (Automotive, 2015) , areas closest to rail terminals are prime locations for sand mine sites that do not have on-site rail terminals. Only one rail terminal is located in Trempealeau County (Arcadia), however, terminals in adjacent counties were also factored into the Euclidean distance calculation performed in the model. One thing to consider as far as Euclidean distances to rail terminals is concerned is that such distances are "as the crow flies" and are not based on road (Manhattan) distances.

 

  Area in Trempealeau County  closest to rail terminals (0-20,000 m) were assigned a rank of 3, areas  a moderate distance from the terminals (20-40 km) were assigned a value of 2, and areas far from rail terminals (40 km>) were assigned a rank of 1 (Table 4; Figure 18).  

 

 

Figure 18. Euclidean distances of rail terminals in Trempealeau County, Wisconsin. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 



Table 4. Summary of ranked Euclidean distances of areas in Trempealeau County in terms of distance from rail terminals. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 

Slope

 

  Slope is another important consideration when it comes to selecting land suitable for mining frac sand. For example, if the slope is too great, it will be difficult to maneuver machinery around. Also, steep slopes could pose a hazard.

 

  It was difficult to determine what a good slope might be for sand mining purposes, but a slope of 1-5 degrees was determined to be safe for mining purposes and were assigned a rank of 3. Although slopes greater than 5 degrees dangerous for highway travel (USDOT, Grade), such slopes will likely not be an issue for heavy excavation equipment (Table 5; Figure 19).

 

  Considering a sand mine does not have the same restrictions as highway traffic due to  the machinery such sites would use to excavate sand, a ran of 2 was assigned to slopes in Trempealeau County that ranged from 5-15 degrees (Table 5; Figure 19).

 

Slopes greater than 15 in Trempealeau County were assigned a rank of 1. Such slopes may be difficult to operate heavy equipment on and may prove to be unstable, and thus unsafe for excavation (Table 5; Figure 19).

 

 

Figure 19. Slope suitability map for Trempealeau County, Wisconsin. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 



Table 5. Slopes calculated for Trempealeau County, Wisconsin and their associated ranks. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 

Water Table Depth

 

  Easily accessible water is also important to the sand mining process, especially when it comes to removing fine particles from the useable sand. As such, a suitability index was created for Trempealeau County based on water table depth.

 

  While water closest to the surface is the easiest to pump in terms of the energy needed to do so, the intermediary water table was assigned a rank of 3. The reason the intermediary depth was ranked highest is that if the mining operations were to go below the water table, the mine would be drowned out and the location made useless; mining to a depth of 700 feet (about mean sea level) would be less likely than mining to a depth of 566 feet (Table 6; Figure 20).

 

  Water table depths of 566-700 feet were assigned a rank of 2. As mentioned above, such a designation was made due to the fact that if the aquifer were too shallow, mining operations could be halted if mining were performed to such a depth (Table 6; Figure 20).

 

Water tables at depths greater than 900 feet below the surface were ranked 1. Such water would be expensive to  drill a well to. Also, if such a depth is required to reach the water table, such an aquifer may be near exhaustion and/or difficult to recharge with new rainwater than an  aquifer located at a higher elevation (Table 6; Figure 20).

Figure 20. Water table suitability classification for Trempealeau County, Wisconsin. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 



Table 6. Water table depths for Trempealeau County, Wisconsin and their associated ranks. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 

Overall Suitability

 

  Suitability models generated for geology, land cover, terminal distance, slope, and water table depth were combined in  order to produce an overall suitability index. 

 

  The five suitability models had each of their cell values multiplied together using the raster calculator tool in ArcMap. The multiplication tool multiplies the values of two or more rasters. The result is that the most suitable land in terms of use for sand mining operations would have the highest raster values after the multiplication process was completed.

 

  Areas in Trempealeau County with the highest raster values (175-244) were deemed most suitable for sand mining, intermediate vales (20-175) were deemed to be moderately suitable, and areas with low raster values (0-20) were deemed unsuitable for sand mining (Table 7; Figure 21). 

 

  A large amount of the area in Trempealeau County is unsuitable for frac sand mining (~1700 sqr. km). Most of the county is moderately suitable for frac sand mining (~2.4 sqr. km), while very little land is highly suitable for frac sand mining sites (~8sqr. km) based on the index model created for this project (Figure 21).

 

 

Figure 21. Suitability index for frac sand mine sites in Trempealeau County, Wisconsin. 3 = Highly Suitable; 2 = Moderately Suitable; 1 = Unsuitable.

 

 



 

Table 7. Summary of suitability  values, numerical rank, and overall rank for the sand mine suitability index.

 

 

Risk Assessment

 

Stream Risk Assessment

 

  Euclidean distances from streams were assessed for Trempealeau  County. If  a sand mine is located too close to a river or stream, fine particles from the mining processes (e.g. silts/clays) may increase the turbidity of the water flowing through them. Such changes could have an effect on local fish populations or riparian organisms.

 

  Areas of Trempealeau County that ranged from 0-500 feet from a primary river or stream were deemed to have the highest risk for contamination and ranked 1 (e.g. unsuitable for frac mine sites). Areas 1000-10000 feet from a river were deemed to be moderately risky in terms of contamination from frac sand mines and ranked 2. finally, Areas located 10000-20000 feet from a primary river would likely have the least impact on sand mining locations in terms of environmental risk (Table 8; Figure 22).

Figure 22. Euclidean distances from streams in Trempealeau County, Wisconsin. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

 

 

Table 8. Euclidean distances from primary rivers and streams in Trempealeau County and their associated ranks. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

Prime Farmland Risk Assessment

 

  While prime farmland in Wisconsin is likely good for frac sand mining, e.g. it probably has shallow slopes, is not covered by trees, and is located close to the water table for irrigation purposes, such land should be conserved for farmers. For example, if topsoil that isuseful as farmland is removed in order to harvest underlying frac sand, the area that was striped of such soils may not recover for future use as cropland.

 

  Areas described as 'not prime' were ranked 3 because such locations are most suitable for frac mine locations. Areas labeled as 'prime farmland if drained,' 'prime farmland if drained and protected from flooding,' 'prime farmland if protected from flooding or not frequently flooded,' were ranked 2 since some effort would likely be required to make such areas suitable for frac sand mining locations. Areas in Trempealeau County as 'all area prime farmland' or 'farmland of statewide importance' were ranked 1 since such locations would be good for agricultural purposes (Table 9; Figure 23).

 

Figure 23. Areas in Trempealeau County that are useful for or serve as farmland. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

 



Table 9. Summary of prime farmland classification in Trempealeau County and associated ranks for such areas in terms of impact from future frac sand mine locations. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

Residential Risk Assessment

 

  A minimum of 640 meters (~2100 feet) is required as a buffer zone between sand mines and residential areas for noise purposes; the same distance will serve for a wind shed for fine particle transport. Risk of sand mine locations in terms of their proximity to residential areas were also assessed. Euclidean distances were determined from areas zoned as R-20 (high density rural),  R-8 (medium density rural), and RR (low density/rural) by Trempealeau County (Trempealeau County Zoning Center). 

 

  Areas of Trempealeau County located 0-10500 feet of a residential zone were ranked 1. Such a ranking ranged to 10500 feet because such a distance is about five times the minimum buffer required by law for noise buffer, however, such areas may still have some noise, especially if trucks drive by the homes (Table 10; Figure 24).

 

 Residential areas located 10500-21000 feet of a potential sand mine were ranked 2. Such locations may still be affected by truck traffic coming and going to sand mines (Table 10; Figure 24).

 

Residential areas located 21000 feet> (10 times the legal limitations) will likely be unaffected by noise or particulate matter from the mines and were ranked 3 (Table 10; Figure 24).

 

Figure 24. Euclidean distances from areas in Trempealeau County zoned for moderate residential densities. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.



Table 10. Summary of Euclidean distances from residential zones in Trempealeau County and their associated ranks. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

School Risk Assessment

 

  Sand mine risk was assessed in terms of proximity to schools for Trempealeau County in a manner similar to how such risk was assessed in terms of Euclidean distances from areas zoned as residential. For example, risk was assessed in terms of noise pollution and particulate matter deposition. 

 

 Areas of the county closest to the school (0-10500 feet) were ranked 1. Areas located moderate distances from schools (1500-21000  feet) in the county were ranked 2, while areas of the county with distances greater than 21000 feet were ranked lowest (3) in terms of risk from sand mining operations (Table 11; Figure 25).

 

Figure 25. Areas of Trempealeau County and their Euclidean distances from schools. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

 



Table 11. Summary of Euclidean distances from school zones in Trempealeau County. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

 

Park and Wildlife Area Risk Assessment

 

  The risk of sand mining to parks and wildlife management areas was assessed for Trempealeau County. Due to the fact that humans spend less time in parks or wildlife refuges, the risk areas, in terms of Euclidean distance, were reduced (approx. -6900 feet) for such areas in Trempealeau County. however, since parks and wildlife areas also draw in tourism to the area (e.g. Perrot Sate Park), such areas should still maintain a buffer between them and potential frac sand mining sites. Also, another reason to place a buffer on recreational and wildlife areas in terms of their proximity to potential sand mine sites is that such operations may stress or otherwise adversely affect the flora nad fauna of such areas.

 

  Distances close to parks and wildlife management areas (0-3600 feet) were ranked 1 in terms of risk to such locations from sand mining operations; distances of 3600 feet are still greater than the minimum distance required for noise/wind buffers for school and residential zones. Areas located a moderate distance (3600-10500 feet) from parks and wildlife areas were ranked 2 in terms of risk assessment, while recreational areas are located more at distances greater than 10500 feet from recreation areas in Trempealeau County, or 5 times greater than minimum residential and school noise/wind barriers  (Table 12; Figure 26).

 

Figure 26. Euclidean distances to park and wildlife management areas in Trempealeau County, Wisconsin. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

 

 

 





Table 12. Summary of Euclidean distances (feet) from parks and wildlife areas in Trempealeau County and their associated ranks. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

 

Overall Risk Assessment

 

  Overall risk assessment in terms of social and environmental impacts in Trempealeau County were combined by multiplying cell values for the raster stream, prime farmland, residential areas, school zones, and wildlife areas. Locations in the county that are least affected by potential sand mine locations are located mostly in the west-central portion of the county. Most of the county was comprised of area (~1900 sqr. km) that would have a significant social and/or environmental impact on the county (Figure 27).

 

Areas with low values (1) are most at risk in terms of proximity to potential mining locations, areas with moderate risk were ranked 2, and areas with low risk are ranked 3. As such, after the multiplication process areas likely to be least affect by potential mining sites will have the highest cell values (150-250). Areas with moderate and high risk will have cell values ranging from 75-150 and from 0-75, respectively (Table 13; Figure 27).

 

Figure 27. Risk Assessment model created for Trempealeau County, Wisconsin. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.



Table 13. Overall (multiplied) risk assessment values for Trempealeau County and their associated ranks.  3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

Viewshed

 

A viewshed model was created for a single park in Trempealeau County. Some people may find that a sand mine's location to a park or recreation area interferes with the serenity of such a location. Viewshed analysis was conducted on Farmer Progress Days Park in the central portion of Trempealeau County. 

 

Farm Days Progress Park is relatively small (< 1sqr. km) and has  small  area from which it can be viewed in the county; likely due to the fact that the park lies near the bottom of a small river valley. Alos, it is important to remember that the viewshed analysis tool only takes into account the topography as it would appear on a bare earth, i.e. one void of vegetation. As such, it is likely that potential  sand mine locations may be less visible from areas such as Farm Progress Park (Figure 28).

 

 

Figure 28. Viewshed analysis for Farm Progress Days Park in Trempealeau County in terms of potential sand mine locations.

 

 

Combined Suitability and Risk Assessment Models

 

  Raster multiplication was used to combine cell values from both the suitability index model of Trempealeau County and the risk assessment model. Areas with the most risk/suitability concerns were assigned the lowest values (0-2), while those with the least amount of concern were assigned the  highest cell values (4-6); middle cell values fell between 2 and 4 ((Table 14; Figure 29).

 

The majority of the land in Trempealeau County (~1000 sqr. km) is unsuitable for establishing a frac sand mining operation in terms of the suitability-risk assessment index generated for the county. Only a relatively small portion of the county (<130 sqr. km) is highly suitable for frac sand mining purposes, while the remainder falls  in the middle grounds I terms of combined risk assessment and suitability (Table 14; Figure 29).

 

 

Figure 29. Combined suitability-risk assessment index for Trempealeau County in terms of potential frac sand mine location.

 





Table 14. Summary of the  cell values, numerical hierarchy, and overall rank assigned to the combined suitability-risk assessment index for Trempealeau County, Wisconsin. 3 = Low Risk; 2 = Moderate Risk; 1 = High Risk.

 

Conclusions

 While suitability and risk assessment indices like those produced in this portion of the frac sand mine project provide a general guidance as to what areas of the county are best suited for potential mine sites, they cannot predict every variable. For example, such models cannot predict whether or not the owners of prime locations for frac sand mines will sell such land to companies for the purpose of sand mining. Also, such models cannot predict whether or not townships within a county will be receptive to sand mining operations in their locales; this is a very real issue for sand mining companies due to the controversial nature of viewpoints currently held by residents of the state with regards to frac sand mining in Wisconsin.

 

  Another important issue to  consider with regards to frac sand mining in Trempealeau County, or any other county for that matter, is whether or not there should be a cap on active mines within a county or within a certain distance of one another. Having a large cluster mines in one general area may provide a slight increase in jobs, but may destroy some of the aesthetics of such locations. Finally, sand mining was a booming industry in Wisconsin when fuel prices were fairly high, but as the prices associated with petroleum products decreases, so too will the need for unconventional recovery methods such as frac sand mining.

 

Sources

 

 

Automotive.com, 2015, Trempealeau Gas Prices: http://tools.automotive.com/gas-prices/33/

          wisconsin/trempealeau/trempealeau/ (accessed May 2015).

 

Trempealeau County Government Center, Trempealeau County Comprehensive Zoning Ordinance:

            http://www.tremplocounty.com/landmanagement/zoning/RevisedOrdinance/CHAPTER_2.pdf

            (accessed May 2015).

 

USDOT, Grade: http://www.safety.fhwa.dot.gov/geometric/pubs/mitigtionstrategies/chapter3

            /3_grade.cfm (accessed May 2015).

 

 

 



 

 

 

 

 

 

 

 

 

 

 

 

 

Post 5: Network Analysis

                                             Goals and Objectives

The purpose of this assignment is to perform network analysis (NA) on frac sand mines in Wisconsin. Feature class data were prepared for NA in a Python scripting exercise (Pyscripting 2 Link).

 Using the 'Model Builder (MB)' program in ArcGIS, the objectives for the exercise are as follows:

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

                                                          Notes

 The following table summarizes the data that were used for the NA exercise as well as the sources associated with the datasets:
 

Table 1. Summary of datasets and sources used for network analysis.

 

 



 

  It should be noted that dollar-value used to calculate the travel of sand trucks on roads by county is a hypothetical value.

 

                                                         Methods

  Network Analyst is a set of tools and functions within ArcGIS. NA tools allow for the calculation of logistical problems, such as routing sand from sand mines to rail terminals in using the most efficient pathways. Once such pathways are determined, other ArcMap tools can be used to assess the costs associated with using such routes. The NA load locations interface (fig.1) was first used to generate a route in ArcMap before using the Model Builder function of the program. Generating a route in the familiar ArcMap program (fig. 2) allowed for a comparison of the results when MB (fig. 3) was used to perform the same function.

Figure 1. NA interface to load locations for network analysis. The interface above is for loading incidents (mine facilities). A similar interface was used to load facility (rail terminal locations).

 

Load Features onto NA Interface.   ESRI street features were uploaded into ArcMap (fig. 1), along with the rail terminal features and sand mine locations. Sand mine location data were previously adapted for the current exercise using Python scripting (see link in introduction). Adaption of the data included creating a feature class for sand mines that did not have a rail terminal within 1.5 km of them. Such a feature class was made because it is unlikely that sand will need to be hauled across roads if the facility it originates from  has its own rail terminal.
 

Calculate Route. When all the data were loaded into the NA interface properly a route was generated between the sand mines and rail terminals (fig. 2). The closest facility route was generated by selecting the solve button on the interface.

Figure 2. Closest routes generated between facilities (rail terminals) and incidents (sand mine facilities) using the NA tool in the ArcMap interface. 

Build a Model. Model builder was used to determine the closest route between rail terminal facilities and sand mine incidents (fig. 3). Model Builder was also used to create a layer from the routes using 'select data' and 'copy features' tools. Once a route was generated, a batch projection tool was used to project the mine, terminal, and rote datasets into the NAD_1983_HARN_Transverse_Mercator coordinate system so that it would match the coordinate system used for Wisconsin county data (fig. 3).

 

 

Figure 3. Model of the tools (rectangles), input feature classes (blue and green ellipses) and outputs datsets (blue ellipses) used for network analysis used to find the closest routs between sand mines and the nearest rail terminals.
 

Calculate Cost. In order to calculate the annual cost of sand truck traffic by county, the road length  needed to be determined for each county (in miles). The tabulate intersection tool was used to determine the length of sand truck routes through each county (Brost, 2014). The tabulate intersection tool was useful because the parameters of the tool automatically converted the lengths of roads in each county into miles. MB was used to generate the table that contained the distance calculated by the tabulate intersection tool (fig. 4). Additionally, the "County_FIP" field was preserved when the new table was generated so that it could be joined to the county feature class in ArcMap for data comparison (table 2).

Table 2. The table generated by the Tabulate Intersection tool was joined with the attribute table for Wisconsin county boundaries via the County_FIP field.
 

  Once the length of road that ran through each county was determined, an annual cost per county was generated based on a dollar-amount per mile. The hypothetical dollar-amount used to generate the cost was $0.022/mile. The assumption is that sand each mine facility will require haul 50 truck loads per year. Calculations also accounted for the fact that each truck would have return to the sand mine facility after bringing material to the rail depot (eqn. 1). MB was used to calculate the annual calculate the cost of road use per county by adding a field to the table generated by the tabulate intersection tool and applying the field calculator to the new field.

 

 

 

 

 

Figure 4. Model used to determine the miles of road found in each county and to apply the equation to determine the annual cost incurred per county due to the transportation of frac sand.

 

Annual Road Use Cost Per County = (0.022) x 2 x 50                                                     Equation 1.

 

                                                       Results

The routes between sand mine locations and rail terminals is shown in figure 5. Some of the original routes entered Minnesota from the east due to the fact that the closest rail depots to Wisconsin sand mines were in that state. However, such routes through Minnesota were clipped as the project is meant to determine costs incurred by Wisconsin counties for the transportation across their roads.

 

  The tabulate intersection tool used to determine the mileage of routes through each county seemed to be fairly accurate. For example, the 'measure' tool was used to crudely estimate (at 1: 1,000,000 scale)  the distance of routes in four counties and compared to the results generated by the tabulate intersection tool (table 3). While three of the counties, Clark, Outagamie, and Dunn had distances fairly close to the distances generated by the Tabulate Intersection tool, the estimate (measure tool) for Eau Claire county was off by a considerable amount (~50%; Table 3).
 

 

Figure 5. Map showing routes between sand mine facilities and rail terminal depots. The route feature class was generated using MB.

 

 

Table 3. Comparison of lengths generated by the Tabulate Intersection tool and estimated using the Measure line tool on the ArcMap interface.

  The county with the highest cost associated with frac sand trucking is Chippewa at around $690/year (tables 3 and 4; figs. 6 and 7). Chippewa county's annual cost for frac sand transportation is more than double second and third highest, which are Monroe and Wood Counties at $331 and $329, respectively.

The counties with the least amount of frac sand through traffic were Winnebago and Burnett counties at $2 and $3, respectively (tables 3 and 4; figs. 6 and 7).

  Five counties incurred costs due to frac sand transportation through across their borders, although neither has a mine nor a rail depot (figs. 6and 7). The counties were Eau Claire, Vernon, and La Crosse, Dane, Dodge, and Winnebago at $103, $73, $237, $14, $62, and $2,  respectively. Of course, as mentioned previously, mileage across Eau Claire County appears to be erroneous.

Six counties would route their sand through Minnesota they choose to do so via the shortest route distance-wise (figs. 6 and 7).  Four counties, Burnett, St. Croix, Pierce, and Pepin would route their sand exclusively through Minnesota, while the remaining counties, Trempealeau and Buffalo have routes through both Minnesota and Wisconsin.

Table 4. Attribute data from the joined tables (table 3) were cleaned up and organized into Microsoft Excel.
 

Figure 6. Graph illustrating the annual cost to transport sand across the county based on mileages generated for each with the tabulate intersection tool.
 

Figure 7. Map showing annual estimated costs incurred by counties affected by sand mine traffic to and from rail depots.

 

                                                          Conclusions

 Network Analysis and Model Builder generally provided good tools for calculating the closest routes between sand mines and rail terminals. However, although a comparison of the distances generated by the Tabulate Intersection tool and the measure tool in the ArcMap interface were close to one anther in general (table 3), Eau Claire county's distances were off from one another by about 50%. There is no real good explanation for the gross error between the two distances generated for Eau Claire county, especially when considering the fact that Eau Claire has only one route (segment) transecting it while others with two (~4.6%) or even six segments (~2.5%) were closer in terms of value.

 

  Due to such disagreement in the distance values generated by the Tabulate Intersection tool and those estimated manually with measure tool, accuracy of the rest of the distance data from untested counties is questionable as well. In the future it might be interesting to generate route distances by county using two (or more) methods and then perform a more in-depth analysis on the error generated between the methods used.

 

                                                Works Cited

 

 Brost, S., 2014, Network analysis, Fall 2014 Geog 337:
         http://fracsandgeog337.blogspot.com/2014/11/network-analysis.html (accessed April, 2015).