Exercise 7: Network Analysis

Exercise 7: Network Analysis

Goal/Objective

The most controversial aspect of sand mining in West Central Wisconsin is the effects large trucks will have on local roads. The cost of road construction falls directly on the taxpayers in each county and not on the sand mining companies. Therefore, it is important to calculate to total cost of road maintenance that each county will have to pay for road upkeep. This exercise will help become familiar with some of the network analyst tools ArcMap has to offer and to give a rough estimate of the total cost each county will incur due to the transportation of sand.

Methods

The first step in the process was to write a python script to select mines that meet certain criteria. In this case we want mines that are active and are more than 1.5 km away from any railroad. After these mines were selected they were exported into the geodatabase. Next, we ran the network analyst tool to calculate routes, using ESRI Street Maps USA, from the sand mine to the closest rail terminal facility using the "New Closest Facility" function. After the routes were calculated we exported them into the geodatabase. Exporting the routes created a shapelength field which will be used to calculate distance. Next, we intersected the new routes feature class with county boundary feature class to get the total distance traveled per county. Finally, we used the predefined criteria of 100 trips per mine at 2.2 centers per mile to calculate the total cost each county would incur. Figure 1 below is the data flow model that describes the steps to calculate total cost per county.

Figure 1 Data flow model that shows the steps used to calculate total distance traveled and total cost per county.

Results

Figure 2 shows the routes that were created using the "New Closest Facility" function in the network analyst toolbar. Each mine was routed to the closest rail terminal.

Figure 2 Routes used to transport sand from mines to the closest rail terminal

Table 1 and Figure 3 show the total cost each county would incur if there were 50 truckloads of sand to the rail terminal at 2.2 centers per mile traveled. As you can see Chippewa County has the largest cost at 452.59 dollars. It is interesting to note that although Eau Claire County does not contain any sand mines they will still have a cost of 205.06 dollars.

Table 1 The total distance traveled by trucks in each and the total cost associated.

 

Figure 3 Map showing the total cost each county will incur due to sand transportation.

 

Conclusion

In conclusion, the total amount each county would have to spend to maintain roads is significantly lower than I originally thought it would be. However, this is a rough estimate because not every mine will be using the routes as specified. Some mines will ship their sand to other processing facilities in the area. Another factor that could lead to error is that not every mine will only ship 50 truckloads of sand. Some mines are significantly larger than other and will need more trucks to transport all the sand to the rail terminals. Also, the 2.2 cents per mile traveled may not be accurate. If this price per mile traveled is determined to be higher the cost per county will increase dramatically. Therefore, this lab was interesting to learn how to use different tools ArcMap has to offer, but the results may not necessarily be very accurate.

 

Exercise 6: Data Normalization, Geocoding, and Error Assessment

Exercise 6: Data Normalization, Geocoding, and Error Assessment

 

Goals and Objectives

     The goal of Exercise 6 is to become familiar with the process of table normalization the different methods of geocoding addresses. To accomplish this goal we will be examining a table containing the most up-to-date sand mine locations in West Central Wisconsin provided by wisconsinwatch.org.

Methods

     The first step in any geocoding task is table normalization. When downloading tables from online sources the tables frequently contain errors in the address field which prevents it from being geocoded properly. Table 1 shows an example of the sand mine table that was downloaded from wisconsinwatch.com. Notice several of the address fields contain street addresses and Public Land Survey System coordinates, while others only contain PLSS coordinates. Since the geocoding tool will not be able to process addresses based on PLSS we need to normalize the table. To do this we create a new address field, city field, state field, and zip field in the table to fill in the information correctly. Entries that only contain the PLSS information will be left blank at this point and the location will be manually entered later. An example of the normalized sand mine table is found in Table 2.

 

Table 1 Table of sand mine locations that was downloaded from wisconsinwatch.org. Notice how the address field contains a large variety of address types from street addresses to PLSS.

 

 

Table 2 Table of sand mine locations after normalization. Notice how the address fields have been split up into address, city, state, and zip fields.

 

 

     After the table was normalized to include fields for Address, City, State, and Zip we were able to input the data into the geocoding tool in ArcMap10.2.2. This tool selects the address that best matches the input address and places the point in that location. Mines that only contain PLSS information are manually inputted. 

 

Results

      Figure 1 shows the result of my geocoded mine locations against comparison mines that were geocoded by my classmates. As you can see only a small fraction of the total geocoded mines were coincident with the comparison mines. Mine locations differ anywhere from 177 feet to 11 miles (Table 3)depending on the geocoding method used.  

Figure 1 Map showing the location of my geocoded mines compared to the same mines geocoded by classmates.

Figure 2 A closer look at the error associated with geocoding. In theory, the comparison mines should be coincident to the geocoded mines.


 

Table 3 The distance between mines with the same unique ID. Only 5 of the mines were coincident with the comparison mines, while the rest have a large range of distances between them.

 

Discussion

     With any GIS project there is going to be inherent error and operational error. In this case the inherent error comes from projecting complex real world features onto a 2-dimensional surface. Another important source of inherent error is caused by the geocoding software itself. The geocoding tool breaks down street segments and divides the segment by however many parcels are on that street. This gives every parcel along that segment the same size, which is rarely the case. The geocoding tool places points based on the average of the parcel size. Therefore, addresses may be geocoded to neighboring parcels.

 

     The largest source of error in this exercise comes from operational error. Operational error consists of incorrect data entry into the table or incorrectly digitizing the location for mines that only had PLSS or a range of addresses available. Incorrect digitization led to large differences in the location of geocoded mines and comparison mines. Many of the mine locations were not visible on basemaps so the actual location was unknown.

 

Conclusion

     In conclusion, the process of downloading data from online sources can be very frustrating if the data is not entered correctly. Tables must be normalized before address data can be geocoded. Even after normalization there are going to be some points that must be entered manually. Even if the geocoding tool can automatically select an address it may not select the correct address. Therefore, it is important to check you data after geocoding to see whether each point is located in the correct spot.

Sources

Lo, C.P., Yeung, A.K.W. (2003) Concepts and Techniques in Geographic Information Systems. Pearson Prentice Hall.