Lab 8 - Surface Interpolation

This week we covered various interpolation methods, as well as the best uses for each type of interpolator. 

Part of the lab involved comparing two DEMs that we created from elevation point data.  The two interpolation methods used were IDW (Inverse Distance Weighted) and Spline (regularized).  To compare the differences between the two methods the Raster Calculator tool was used to subtract the elevation values between the two DEM grids.  A map of the final result is shown below.

Map showing the differences between two DEM grids.

Lab 6 - Spatial Enhancements

View of the spatially enhanced image.

This week we utilized our newly acquired ERDAS Imagine skills to apply visual enhancements to raster imagery.  The image above originally had these thick black diagonal lines crossing through it - with the spatial enhancement measures applied these lines are not quite as noticeable.

The enhancements were applied using both the ERDAS Imagine software and ArcGIS.  The processing began with ERDAS Imagine by applying a Fourier transformation with a low pass filter.  Then the image was further processed by applying a 3x3 sharpening effect (the 3x3 refers to the number of cells used as the kernel for the enhancements).

The image was then opened in ArcGIS, since this program has a Focal Statistics tool (and ERDAS Imagine doesn't quite have these statistics in its arsenal of enhancements).  After setting the Focal Statistics tool to run a rectangular 3x3 mean pass the image was considered to be ready for the final map layout.

While the diagonal lines haven't disappeared completely at least it is now possible to figure out what the features in the image are.  Apparently it is possible to remove those diagonal lines, but that requires some additional (advanced) techniques... trying to get the Fourier transformation correct for this exercise was difficult as it was.

Lab 7 - TINs and DEMs

This week's lab explored the differences between a TIN surface and a DEM. 

TIN stands for 'Triangulated Irregular Network' and DEM stands for 'Digital Elevation Model'.  Both surfaces represent landforms based on elevation data, and both can be used to show slope, aspect, and contours.  A major difference between the two is the file type - a TIN surface is vector based whereas a DEM is raster based. 

Detail view of a TIN surface, showing contours, nodes, and edges.

A bonus to a TIN surface is that aspect, contours, and slope can all be depicted and stored within the same file.  A DEM raster cannot do this - different files would need to be created to show slope, aspect, and contours... and this could potentially involve multiple steps (for example, a slope raster may need to be converted to polygons if one wanted to show it as vector data).

Another bonus to a TIN surface is that breaklines (essentially, a linear feature) can be used to modify the original topography.  This is not possible with a DEM, thus making the TIN surface ideal for engineering applications... or detailed archaeological site maps.

Why aren't we using more TIN surfaces  if they're so great?  Well, they require a lot of computer memory, and therefore shouldn't be larger than 1,000,000 nodes.  That may sound like a lot, but these things tend to be quite detailed and so can reach that threshold easily.  TINs also are not the best surfaces to use when one wants to model continuous data over large areas - that is where DEMs shine.

Lab 6 - Location-Allocation Modeling

This week's lab saw us using Network Analyst to perform a basic location-allocation analysis.  The goal was to show where change may be needed in a fictional company's distribution center service areas.  Since the distribution centers were built during the company's national growth, the areas being served may not actually be the best fit in terms of time and resources.  The service areas, or market areas, were reassessed using the location-allocation function.

Comparison of the market areas before and after completing the location-allocation analysis.

Technical Notes

The location-allocation analysis was run with the distribution centers as a required facility type.  This meant that the solver had to choose all 22 of the distribution centers when computing the final service areas.

The analysis also utilized advanced settings and set the analysis type to minimize impedance.  This meant that it solves what ESRI calls the 'warehouse location' problem - it minimized all of the total impedances set into an analysis.  Our impedances utilized a roads network, which allowed U-turns and had the origination of the routes be at the facility (as opposed to the demand points - a.k.a. customers). 

If you were wondering, an impedance is a cost built into the analysis - meaning something the algorithm has to factor in.  Since the location-allocation analysis is built into the Network Analyst extension, this means that the algorithm essentially calculates the best routes from a facility to a customer.  No impedance cut-off was set (which usually refers to a set mileage from a facility or a drive time), so theoretically a customer located hundreds of miles away from a distribution center could be included within the market area for that center... provided that no other facility was located closer.

Lab 5a - Intro to ERDAS Imagine and Digital Data 1

This week's lab was mostly a tutorial on how to navigate within ERDAS Imagine.

Detail view of land cover data set within Washington State.

The functionality of ERDAS Imagine is great - it makes sense on the user's end, and one doesn't really need to drill down into various properties just to change one little thing.  Being able to mess with each color band was neat, and helped to make certain aspects of remotely sensed imagery processing a little less abstract.  However some drawbacks to the program do seem to be its map making capabilities (there are known bug issues).  The above map was finished using ArcGIS.

The view above is an inset of a much larger classed raster image.  It took some tries to get the correct view extent to transfer over to ArcGIS... even though this step was spelled out in the directions I suppose I still needed to do my own trial and error to learn this part! 

Lab 5 - Vehicle Routing Problem

Screen capture of a solved vehicle routing problem.

This week's lab focused on the vehicle routing problem, and how ArcGIS Network Analyst extension solves for this problem.  The screenshot above shows the results of the analysis, which is a series of 22 routes with various delivery stops.

Solving for any vehicle routing problem (VRP) can become a bit complex as behind-the-scenes work just to build up the truck and employee cost information, customer information, and depot information can be very extensive.  This does not even begin to include the use of a suitable road network on which to model the routes, or the definitions of where each truck/route can go... happily the great majority of this work was completed for us prior to beginning this lab.

The solved routes in the screenshot above utilize all 22 trucks in a distribution company's fleet.  Each truck was assigned to a certain "route zone" meaning that they ideally do not leave their zones to make deliveries in other areas.  However a few trucks were allowed to stray outside of their routine service zones in order to be part of a more profitable solution for this particular company.  Why have a service zone?  In order to provide continuity for the customer by having the same delivery man, instead of random unknowns doing the drop-offs. 

The one big issue with making service zones overly strict is this: the VRP solver can miss the obvious 'common-sense' solution in favor of an optimal solution that adheres to strict parameters.  Prior to the screenshot above our initial VRP route had been solved with such strictly defined service zones.  As operating costs are also an important factor, the VRP solver had provided only 14 routes (meaning 14 trucks) to shoulder the burden of delivering 128 orders across southern Florida.  This meant a lot of overtime, and in the end there were 6 orders left unfulfilled and several others that would have been delivered outside of regular business hours.

By tweaking a few items (mainly allowing a few trucks to make deliveries in adjacent delivery zones) the VRP solver was able to assign a truck route to deliver all of the orders, with only 1 order (out of 128) being made after normal business hours.  The overall revenue generated with the modified route also went up ... along the with cost to operate, but the operating costs were comparable to the rise in revenue.  Assuming that a satisfied customer is one that does repeat business, then choosing the optimal route solely on the lowest operating cost simply does not make sense - one needs to also make sure that the customer's needs are being met.  This was accomplished with the modified VRP route.

Module 4 - Ground Truthing & Accuracy Assessment

This week was all about 'ground truthing' classification maps and evaluating the overall accuracy of the classes assigned.  Using the Land Use / Land Cover map created for Module 3 we 'ground truthed' our own classifications.  Overall my map from last week was 67% accurate... ouch!

The above map had been 'ground truthed' for accuracy - overall accuracy was found to be 67%.

Since physically visiting Pascagoula, Mississippi was not an option for me (or most of my classmates) we used Google Maps as our higher grade dataset, then visually compared pre-selected sample locations against Google Maps. 

The sample locations were derived using a stratified random scheme, with at least 2 points per class type.  Most classes had 3 points, and two classes (which did not have very large representation on the map) had only 1 point.  The largest class contained 4 sample points.  Sample locations were plotted using a program called the Sampling Design Tool.  The program is available as a download through ESRI and was created by NOAA's Biogeography Branch.

After checking Google Earth against the sample point locations on my original LULC map, I have some basic observations:

1. My original cropland designation apparently was just grass mowed with a distinctive pattern... I'm still not buying it, and believe instead that the land use has changed over the years.
2. My beach was apparently someone's home - they live right on the water and seem to have little to no landscaping.  That was not readily apparent at my MMU of 1:4,000.
3. I should have set 3 sample points per class, and then manually messed with their location.  Many of the sample points were bunched up for some reason.  Also, classifications that did not cover as much map space should have had at least 1 sample point taken away.  But the largest classes didn't really need to absorb those 'excess' sample points.