Lab 5: Vehicle Routing Problem

In this lab I had to solve a vehicle routing problem (VRP) for a trucking company that had the goal of providing continuity between drivers, customers, and service areas. Using GIS to solve a VRP is fast easy way to come to a solution.
I created a total of 16 routes that covered every order while keeping them in one service area to provide continuity. The screenshot above shows the solution that ArcMap's network analyst calculated after I tweaked the settings. It provides a nice balance between cost effectiveness and customer service.

Lab 4: Network Datasets

This week I learned about how to create and add functionality to a network. The first iteration of was just a basic network that could build a route between certain locations.
The second iteration had me connect the RestrictedTurns feature class to the network. This provided a more accurate picture of the route because not all intersections allow every type of turn. The route ended up taking a little longer than the first iteration. At least now the driver won't be violating traffic laws.
The third and final functionality added was historical traffic data. Essentially the earlier models let the driver see how long their route would take without any other vehicles on the road which is not that realistic. This functionality gives the driver a better ability to anticipate their route and the time it will take to complete it. This route took the longest of the three.

Lab 3: Determining the Quality of Road Networks

The goal of this lab was to get more experience with accuracy assessment. The TIGER 2000 data had 258 grid squares that were more complete than Jackson County GIS. Jackson County GIS had 38.
The map above shows the percent difference in completeness between a set of road data from Jackson County GIS and one from TIGER 2000. The blue areas show grid sections that differ by less than 100%. White to brown are the more extreme values. One grid section had a difference of over 1700%! The negatives show percent difference in favor of TIGER 2000 having more data in that grid section. The positives favor Jackson County GIS.

Lab 2: Quality of Road Network Data In Albuquerque, New Mexico

This lab was an exercise in testing data quality. I received two sets of street map data. One was from the City of Albuquerque and the other from StreetMaps USA. I first used ArcCatalog to create a rough network dataset of both street maps. This program predicts where streets form intersections and places points where the intersections are. I then used a sampling tool to randomly select one hundred points that I could use for the project. I had to find points that were present in both datasets, examples of good intersections, and met sampling rules. This caused me to drop from one hundred points to twenty-nine, displayed in the screenshot above. I matched up all the points on both data sets and created a reference set of points based on orthophotos of the study area. I then used the National Standard for Spatial Data Accuracy (NSSDA) to calculate how accurate both sets of data are when compared to the reference set. Using the standard reporting statements presented in the Positional Accuracy Handbook¹ I got two statements. For the City of Albuquerque data, I got:

Using the National Standard for Spatial Data Accuracy, the data set tested 26.7 feet

horizontal accuracy at 95% confidence level.

For the StreetMaps USA data, I got:

Using the National Standard for Spatial Data Accuracy, the data set tested 360.6 feet horizontal accuracy at 95% confidence level.

1 Positional Accuracy Handbook. 1999. Minnesota Planning, Land Management Information Center, St. Paul,

MN.