GIS5935 - Lab 8 - Surface Interpolation

Hello,

In the 2nd part of this lab we used different surface interpolation methods to estimate water quality in Tampa Bay.  We were provided data from 41 observation points where water quality samples were taken.  It is important to understand your data; where is it sparse or dense, where are there irregularities, and redundancies and use this knowledge to explain trends.  You may need to remove outliers and normalize data prior to applying an interpolation method.

To estimate the water quality of Tampa Bay we compared four different techniques, Thiessen, IDW (Inverse Distance Weighted), Spline Regularized and Spline Tension.

The Thiessen interpolation method provided more information about the data you are working with.  Any location within in a Thiessen polygon is “closer to its associated point than to any other point input feature.”  Using Thiessen interpolation I think is similar to creating a Voronoi Map in Geostatistical Analyst.  The disadvantage to using this technique is that when you assign each location within the polygon the same value as the nearest point, you could over generalize your data and not account for differences despite them being closely located together.   

The IDW method estimates cell values by averaging the values of the sample data in the neighborhood of each cell.  The closer a point is to the center of the cell being estimated the more influence or weight it has in the averaging calculation.

There are two Spline methods: Regularized and Tension.  The regularized method creates a smooth, gradually changing surface but may have values that lie outside the range of the sample/input data.  The tension method controls the stiffness of the surface based on the input data but creates a less smooth surface with values constrained by the range of the sample/input data.

I've chosen to display my Spline with Tension Interpolation of the water quality in Tampa Bay as I felt it was the best method to estimate water quality based on the data we were provided via the 41 sample locations.

Spline with Tension Interpolation Method

GIS 5395 - Lab 7 - TINs and DEMs

This week's lab had us learn about the differences between TINs (Triangular Irregular Networks) and DEMs (Digital Elevation Models).  A TIN is a "vector version of a 3D surface).  DEMs are rasters that describe elevation.  We used both ArcMap and ArcScene to view and modify TINs and DEMs in the various lab exercises.  We learned how to modify vertical exaggeration, use lighting or illumination to display the image more clearly.  In ArcMap and ArcScene you can alter the symbology of a TIN to display the slope, aspect, nodes, and elevation of the TIN.  A DEM raster would require us to use tools to create a slope and aspect rasters.  We also compared the contour lines of DEMs versus TINs for a particular study area.  Depending on your application a TIN may be more useful than a DEM if your study area is narrower and more detail is needed.  Below is a screen capture of a TIN with nodes, edges, and contour lines.

TIN Symbology

I enjoyed learning how to create, edit, and change the symbology of a TIN.  I have a better understanding of how TINs and DEMs are different.

GIS 5935 - Lab 6 - Location-Allocation Modeling

This weeks lab was our final assignment under the Networks topic.  I really have enjoyed this topic's assignments and have a much better appreciation for these analyses and their broad uses.  As with previous assignments we familiarized ourselves with the topic by completing Exercise 9 of the Network Analyst Tutorial in Part A of the assignment.  Part B had us apply this newly acquired knowledge to use a location-allocation analysis to optimize the reassignment of market areas to distribution centers.  First we used the Network Analyst location-allocation model to determine which distribution center best served each customer.

Location-Allocation Network Analysis - Which Distribution Center Best Serves Each Customer?

Inspecting the above map we could see that a number of customers would be better served by distribution centers that did not fall in their market area grouping.  In order to further analyze which market centers would be better served by a different distribution center we performed a series of table manipulations by using spatial and table joins and using the Summary Statistics tool as well as the Summarize menu option in the opened Attribute Table.  These statistical calculations enabled us to determine which distribution center was responsible for the majority of customers in each market area.  Using this criteria we reassigned 13 market areas to different distribution centers.  There were some weaknesses in this analysis.  Some market areas could be served by two distribution centers because they both served an equal number of customers.  Another issue was at least one market area was bifurcated and the reassignment didn't make sense for the entire market area but rather a portion of it.  Ideally, this market should have been separated into two distinct market areas.

Location-Allocation Analysis of Market Area Assignments to Distribution Centers

This was probably my most favorite lab assignment of this class thus far.  While the table manipulations were cumbersome and confusing at first, the overall capabilities of this network analysis method were impressive.

GIS5935 - Lab 5 - Vehicle Routing Problem

I really enjoyed this weeks assignment as I could relate it to several real-world situations.  Part A of the assignment had us continue to increase our knowledge of Network Analysis by executing two more tutorials.  These practice runs enabled us to have the background to perform the tasks in Part B.  The focus of the second task was to use the Vehicle Routing Problem (VRP) solver to determine routes for a trucking company with a distribution center in South Florida.  Optimized routes needed to align with "the company's goal of providing continuity between drivers, customers, and service areas."

Initially, we did not allow flexibility with our route zones.  If a route, assigned to a zone, passed an order in a different zone, the route was not allowed to pickup that order even if it was not serviced.  This hard route zone setting caused several orders to not be assigned to a route and caused some orders to have time violations.  For our second run of the VRP solver we preserved the routes for the orders that were serviced and enable two more trucks to be included in routes.  After making the change, all orders were assigned a route and only one order had a time violation.   Overall, the cost for adding the two additional trucks was a net loss but customer service was dramatically improved by ensuring that all orders were serviced.

After performing the tasks in this assignment I have a new appreciation for how complex vehicle routing can be and how important attribute data is in your network analysis.

Network Analysis for Trucking Company with Depot in South Florida