Network Analysis

Background:

Sand mining is done throughout a large portion of the state of Wisconsin. Most of the sand that is mined is transported around the country using rail facilities. However, many of the areas that are being mined are not located at loading facilities. To overcome this, the sand is hauled by truck to local roads to the nearest loading facility. The process of repeated heavy truck traffic may have a negative impact on the local roads increasing the cost of maintenance. Much of the funding for road maintenance comes from local tax payers who drive lightweight vehicles which do not cause much damage, it may be beneficial to know how much of an impact the transportation is having on the roads and be able to assess this cost properly.

Objectives:

The objectives for this exercise were to first, use the sand mine locations we previously geocoded, to gather data through network analysis on the fastest routes from the mines to the nearest railroad terminal. Then, using the distance traveled during transportation from the mines to the facilities, we will be able to project the cost per county associated with the sand transport.

Data:

The data used for the network analysis were, the mines locations, which were geocoded in arcmap from a Google fusion table (https://www.google.com/fusiontables/DataSource?docid= 17nDFI4iUP OdyDOEWU7Vu1ONMiVofa3aWR_Gs-Zk#map:id=3).  Road network data were obtained from an ESRI source (W:\Geog\ESRI\streetmap_na). The data with the location of the railroad terminals was gathered from a shape file located (W:\geog\CHupy\geog491_s13\ex\ex7\railterminals_shp). Other data used in the mapping process were gathered from ESRI online.   

Methods:

To begin, we opened a new arcmap document and saved it to our geodatabase. We then added the street feature classes from the ESRI file, and imported the rail terminal feature class from the department folder to our geodatabase and added it to the map. We also added the geocoded mines to the map. We then had to turn on the network analysis tool and add the toolbar. Using network analysis (NA) we started a new route.  We right click on stops and then click load locations, then loaded our mine locations, we did not change any of the defaults and solved. The program solved for one continuous route to all of the mine sites with a stop at each (figure 1). This is not what we were looking for.  Without closest facilities this output does not give us any of the usable information we need to complete the task. So we removed this from the display.

Fig.1.The results of our first attempt, using stops, yielded
a map with a continuous route to all of the mines, this is
not the result we were looking for so it was not used further.
The routes are overlaying the streets_na layer.

We then added a new closest facilities layer. In this layer we loaded the mines as facilities and the rail terminals as incidents and solved. This gave us an output containing a route from each of the mines to the nearest rail facility (figure 2). Some students may have had issues with this running each facility to the nearest mine but mine did not. This output gives us the information we need to complete the analysis.

Fig.2.The results of our second attempt using closest facility
gave us the results we were looking for. we now have a route
from each of the sand mines to the closest rail facility. The
routes are overlaying the streets_na layer.

If we want to store the steps we used to generate the data needed we can use model builder (MB) (figure 3). So we opened model builder saved our model in a new toolbox within our geodatabase. We had to open the NA tool set  Then we added the closest facility to the model, by editing the tool we added the streets layer and left the defaults. We did set the model to run as travel to facility. Then we had to add the add locations tool to the model. We connected this to the closest facility output and edited it to use our mine locations as input locations, sub-layer incidents. We then added a second add locations tool and used the rail terminals and facilities, we connected this tool to the first add locations tool output. We added the solve tool to the model and ran the model. The model ran all possible routes from the incidents (mines) to the facilities (terminals) using the road network, the model was set to look for the fastest time. Although some of the outputs may appear to go out of the way these were the fastest routes, this was not done using distance.  We could then add the results to the display and we had the output we needed to continue. We exported our results as a feature class to our geodatabase. To do this we added the select data tool to the model and connected it to the previous output, within the tool there is a child data option, we chose route and ran the tool. Then we added the copy features tool and connected it to the select data output and ran the tool. Now we have the routes from each of the mines to the nearest terminal for the state. In order to get the data into each county we need to connect the data to a counties layer. First we added a counties layer to the model and the project tool; we also added the project tool for the sand route data. We ran the project tools to project each data set into NAD 83 UTM Zone 15N. Then, we added the intersect tool to the model and connected the last two outputs and ran the tool. Now we have a dataset with all the roads used for transport within each county. By adding the summary statistics tool and connecting it to the output, selecting shape length and sum as the fields to summarize and name for what to summarize to we were able to get an output of the total length, in meters, of roads in each county that are traveled by sand trucks.

To complete the analysis we opened the table and added a field converted meters to miles using the total distance in meters, for each county, and dividing it by 1609.34 (the number of meters per mile). We then added another field and using the parameters given, each mine sends 50 trucks per year to a facility and returns, at a cost of 2.2 cents per mile of road, built a statement to finish the calculations and give us the total cost per county for roads traveled during sand transportation.  The equation used was – total miles per county * (50 * 2) * $0.022 per mile = cost ($) per county.     

Fig.3.Using modal builder to due network analysis. The layers we began
with were not all projectable, we projected our output and the counties layer
before intersecting them giving our final output and results the proper
projection and units. 

Results:

The total mileage of roads covered in the state during transportation (figure 4) is approximately 3,951 miles. If each mine is sending 50 trucks to rail facilities each year that becomes a total of 395,100 miles of travel distance on the states roads. The total costs statewide associated with this amount of travel are $8,689 (table 1).  The mining and transportation activities are highly concentrated in west-central Wisconsin, part of an area known as the sand belt. So it is no surprise that this area would have the highest amount of transportation traffic and thus the highest costs. Four counties, Chippewa, Eau Claire, Lacrosse and Trempealeau, carry $5,158, 59.4% of the total cost (table 2).

Fig.4. This map show the locations of sand mines in
Wisconsin and the routes to the nearest rail facility.
Notice the high concentration of activity in the west-
central part of the state.

County Name	Total Distance (meters)	Total Distance (miles)	Accumulated distance (miles)	Cost per year
Chippewa	1,049,247.59	652	65,200	$1,434.00
Eau Claire	928,135.41	577	57,700	$1,269.00
La Crosse	908,911.90	565	56,500	$1,243.00
Trempealeau	886,714.39	551	55,100	$1,212.00
Barron	467,491.93	290	29,000	$638.00
Dunn	389,993.73	242	24,200	$532.00
Monroe	356,913.82	222	22,200	$488.00
Wood	244,360.28	152	15,200	$334.00
Pierce	167,732.86	104	10,400	$229.00
Jackson	151,475.88	94	9,400	$207.00
St. Croix	128,717.86	80	8,000	$176.00
Winona	123,287.84	77	7,700	$169.00
Buffalo	91,918.33	57	5,700	$125.00
Clark	67,266.12	42	4,200	$92.00
Burnett	64,376.05	40	4,000	$88.00
Pepin	57,433.79	36	3,600	$79.00
Juneau	47,807.17	30	3,000	$66.00
Columbia	40,857.46	25	2,500	$55.00
Washburn	35,336.54	22	2,200	$48.00
Dodge	29,407.27	18	1,800	$40.00
Waupaca	28,487.68	18	1,800	$40.00
Green Lake	24,833.73	15	1,500	$33.00
Portage	24,603.21	15	1,500	$33.00
Houston	15,920.78	10	1,000	$22.00
Outagamie	15,084.95	9	900	$20.00
Rock	7,682.11	5	500	$11.00
Walworth	2,566.38	2	200	$4.00
Marathon	1,496.53	1	100	$2.00
Totals	6,358,061.59	3,951	395,100	$8,689.00

  Table.1. These are the results of the analysis showing the distance (roads covered)

   per county in meters. The meters have been converted to miles and then the total

   distance traveled per county and the total costs per county per year.  

County Name	Total Distance (meters)	Total Distance (miles)	Accumulated distance (miles)	Cost per year
Chippewa	1,049,247.59	652.00	65,200	$1,434.00
Eau Claire	928,135.41	577.00	57,700	$1,269.00
La Crosse	908,911.90	565.00	56,500	$1,243.00
Trempealeau	886,714.39	551.00	55,100	$1,212.00
Totals (top four)	3,773,009.29	2,345.00	234,500	$5,158.00
		Percentage of total		59.36241

  Table.2. These are the top four counties by total cost per year. These four counties

   make up about 59% of the total road cost in the state.

Conclusion:

These costs are estimated costs of road maintenance that are passed on to local taxpayers. This is an important issue because many of the taxpayers are driving lightweight vehicles that do not cause much damage to the roads. However, the damage being caused by the increased amount of traffic associated with mining operations and also the extreme weight of those vehicles may be increasing the costs of maintaining the roads. The money for the repairs is not being gathered proportional to whom or what causes the damage. The results of this study could help by assessing those costs back to the mining operations and relieving some of the burden from the local taxpayers.