GIS4035 Module 7: Multispectral Analysis

Map 2: Snow identified with band

combination Red: 5, Green: 4, Blue: 3

Map 1: Water feature identified with band

combination Red: 4, Green: 3, Blue: 2

Map 3: Lake identified with band
combination Red: 7, Green: 5, Blue: 3

This lab explored different ways to identify features using ERDAS. After reviewing histograms to identify patterns and shapes in the data, the imagery was viewed in grayscale to look for dark and light shapes as well as patterns. EMR bands were manipulated to emphasize different features in the imagery. Trying to find the right band combination to help make a feature noticeable was a challenge, but the band combination references posted on the discussion board were helpful. Exact pixel values of specific areas were obtained by using the Inquire Cursor. Map 1 shows the use of TM False Color IR to highlight bodies of water. Map 2 utilizes a Short Wave Infrared Color Composite of 5-4-3 to illustrate snow. The final map, Map 3, uses a band combination of 7-5-3 to make certain water features, such as this lake, stand out.

GIS 4102 – GIS Programming Module 7 – Working with Geometries

The assignment for this week's module involved creating a text file with data from a rivers shapefile. This turned out to be a greater challenge than creating a shapefile from a text file. Before committing my scripting results to the text file, I worked out the kinks with just a version printed to the interactive window. After it was printing the proper results, I went back and added the write command. The pseudocode for this assignment is shown below along with a portion of the resultant text file:

Start

Text File Created from a Shapefile

Import modules & classes

Set workspace

OverwriteOutput

Set output path

SearchCursor (OID, SHAPE, NAME)

Open text file

Processing statement

Row Loop

        Set vertex counter

        Point Loop (getPart)

        Increase counter

        Print row

        Write row to text file

Process complete statement

Close text file

Delete row

Delete cursor

End

This scripting turned out to be more challenging than I expected. Something that I found helpful was to comment out certain sections of code while I was making adjustments. That way not only did I preserve code that was working, but I also could alter other code to get the results I needed.

GIS4048 Module 7: Homeland Security - MEDS Protect

Three Mile Buffer Zone around Boston Marathon Finish Line
with Nearest Hospitals and Finish Line Perimeter Security Checkpoints

Lessons learned from the 2013 Boston Marathon bombing have prompted increased security measures by the Department of Homeland Security. These measures include increased security at ingress and egress points as well as improved surveillance around the event site for monitoring purposes. The first map created for this exercise shows a 3-mile buffer zone around the Boston Marathon finish line, a 500-foot buffer around the finish line, the locations of the ten nearest, currently operational hospitals with emergency rooms, and their 500-foot radius protective buffer zones within which increased security measures prior to, during, and following the marathon can be planned. Fifteen security checkpoints on local and secondary roads leading to the finish line are shown at the outer limit of the 500-foot buffer zone around the finish line as shown on the smaller inset map. Identification verification and backpack checks could take place at those locations.

The second map focused on specific locations suggested for placement of surveillance equipment within the immediate vicinity of the finish line itself. LAS Dataset 3D View and the orthoimagery layer were used to determine where to place the 15 potential surveillance points. Prior to point selection, hillshade was generated for 2:30 pm on April 15, 2013. Shadows impact surveillance equipment's capabilities, and hillshade provided a baseline for the day of the marathon. A difficulty encountered during selection of surveillance locations was determining a set of locations that would provide complete coverage of the finish line vicinity as determined by using the Viewshed tool. Viewshed is an indicator of visibility from other vantage points, in this case, surveillance locations. Believing that cameras placed along roof lines or on the sides of buildings would provide that coverage, initial points were placed in that manner. However, to obtain fairly contiguous coverage, the points had to be adjusted not only vertically but also horizontally. Not knowing the heights of the buildings in the area was a disadvantage, but taking advantage of Google street view as well as researching commercial building heights in general provided some guidelines to estimate reasonable, attainable heights for surveillance cameras. Using 3D GIS techniques to determine locations of surveillance points is significantly more economical, effective, and efficient time-wise than physically selecting, inspecting, and adjusting potential surveillance points.

Suggested Locations for Surveillance Points near Boston Marathon Finish Line

The biggest roadblock that I encountered in trying to assemble a comprehensive security analysis for the marathon came with the inability to complete the line-of-sight portion of the lab. Even with meticulous attention paid to the line-by-line instructions and completing the work in one session (having been forewarned), I could not get a line of sight to show up between any pair of selected points (any surveillance point and the finish line). Redoing the map from the very beginning several times, including completely re-downloading the data again did not improve the situation. This resulted in an incomplete map as a profile graph could not be made for a nonexistent line of sight. Having a profile graph would have enabled a surveillance team to further evaluate potential surveillance points. For instance, the horizontal location of the obstruction can be determined by the aerial view, but how the obstruction possibly could be lessened by the vertical adjustment of the surveillance camera can only be determined by a side view shown with a profile graph.

A 3D version of the map was created in ArcScene using the finish line raster as a surface layer. The orthoimagery layer was draped over it as well. Because I did not have any lines of sight to put in the ArcScene portion of the lab, I added the finishline and suggested surveillance points, hoping that the surveillance points would be placed at their offset heights. That did not happen. Again, the actual line of sights are an essential part of a security analysis like this.

Even though I was unable to complete the lab as intended, the experience was extremely worthwhile. The numerous repetitions of certain steps have helped reinforce key aspects of the lab.

Cartographic Skills Module 7: Choropleth Mapping

Module 7 lab continued the data classification experience started in Module 6 and involved the completion of two choropleth maps.  Both ArcMap and Adobe Illustrator were used to complete the maps, one of which was in color (Change in US Population by State) while the other was in gray scale (Change in US Population by Census Division).  The exercise included using Excel for making calculations and utilizing that information to determine appropriate classification method for the data, selecting appropriate color scheme and grays for the different classes of the choropleth maps, labeling states and divisions, and creating legends.  Being quite fond of Excel for numerous purposes both personally and professionally, I used it beyond the lab requirements to determine additional information for different classification methods which I then used to select a data classification method for the second map.

Percent Change in State Populations (1990 to 2000)

Practicing projections in Introduction to GIS (GIS4043) provided the experience needed to change the projections of Alaska and Hawaii to something which would yield more realistic graphics, specifically for Hawaii.  The nearly North-South orientation of the Aloha State was a bit disconcerting, but reprojection took care of that.

Labeling divisions for the second map was an exercise in experimenting effort.  Several font sizes, types, and effects were attempted in trying to achieve the best, most uniform appearance for the division labels.  The appearance of masking was not appealing, and trying to label enough of the states in each division to distinguish between adjacent divisions which were in the same class (West North Central/East North Central and Middle Atlantic/New England) was a challenge.  Finally I settled on allowing labels to straddle some state lines, but shifted the labels so that the lines were generally between letters or words in the label.  Using Transform > Move allowed very fine movement to do this.

Percent Change in Census Division Populations (1990 to 2000)

The results of the lab assignment are two easy-to-read-and-interpret maps, one for the change in US population for individual states as well as the District of Columbia and another for the change in US population by Census Divisions.