GIS 5100 - Lab 5 - Spatial Accessibility Modeling

These week we used GIS to model spatial access to facilities and services.  Our first task, Part A, was to complete some ESRI tutorials using the Network Analyst toolset.  Exercise 1 had us create a network dataset from existing transport-related spatial data.  Exercise 3 had us find routes with an existing network dataset.  In Exercise 4 we found the quickest routes from service providers to client locations within an existing network dataset.  Lastly, Exercise 5 had us calculate service areas with an existing network dataset.

In Part B we were tasked with performing a network analysis of access to psychiatric hospitals in the state of Georgia.  The tasks in this assignment section made me realize, once again, the power of GIS to inform the end user.  We used Spatial Join to create a data table that could then be further analyzed using Microsoft Excel.

The last section, Part C, had us perform a network analysis of spatial accessibility to branch campuses of the Austin Community College (ACC) system in Travis County, Texas.  We used a Spatial Join to combine census data to our network analysis routes.  We were comparing two scenarios.  In the first scenario all seven ACC campuses were open.  The second scenario showed accessibility after one campus, Crystal Creek, was closed.

Next, we examined how the closure of the one campus would affect residents who were aged 18-29.  The closure of one campus did result in a nine minute increase in the average travel time to the closest campus for residents in that age group.

I had to take my time working through this assignment and refresh some Excel and statistical skills.  This was a powerful "application" assignment.

Comparison of Accessibility to ACC Campuses by Residents in Travis County, Texas

GIS 5100 - Lab 4 - Visibility Analysis

SECURITY CAMERA PLACEMENT AT BOSTON MARATHON FINISH LINE

In Lab 4 we learned about several new tools in the Spatial Analyst and 3D Analyst toolboxes.  Using DEMs we focused on the two general types of visibility analysis: viewshed analysis and line-of-sight analysis.  The primary tools we used to perform the assigned tasks were viewshed, line of site, and visibility.   We also used the Extract Values to Points tool to perform additional analysis of one task.  

In Part C of the lab we were asked to view provided LiDAR data using the LAS Toolbar. Next we used the LAS Dataset to Raster tool to generate a DEM from the LiDAR data.  Our visibility analysis used this DEM along with a provided camera location to analyze visibility at the finish line of the Boston Marathon.   Using the DEM, an orthographic image of Boston, the study area boundary and the finish line, we were tasked to locate two additional cameras to provide the best possible coverage for the street segment where the finish line was located.  The visibility tool provides more options allowing for height offsets and view angle settings.

The original camera was located west of the finish line at a height of 100 feet and a viewing angle of 90 degrees.

I placed my 2^nd camera northeast of the finish line on top of a corner building with an offset of 100 ft using the identify tool and the elevation raster to determine the estimated height of the building.  The starting horizontal angle was 180 and the ending horizontal angle was 270 to create a viewing angle of 90 degrees.

I placed my 3^rd camera slightly northwest of the finish line on top of a building parallel with the finish line road segment.  Using the identify tool and elevation raster I estimated the building height at 165 ft and set my OFFSETA value.  The horizontal starting angle was 100 and the horizontal ending angle was 190. 

I also slightly changed the starting and ending angles for the 1^st camera to a horizontal starting angle of 350 and an ending angle of 80 for optimum coverage.

The assignment mentions using a combined set of three cameras for the analysis but I found it difficult to find starting and ending angles that would best combine coverage of the three cameras together.  It seemed best to perform a visibility analysis showing the coverage area from each camera location (and height) and display them individually and show the overlap areas as well as the individual coverage.

I also was unable to figure out how to remove the Not Visible classes from the Legend.  I tried using layer properties and properties within the Legend but was unsuccessful.

Visibility Analysis - Security Camera Placement at Boston Marathon Finish Line

I enjoyed this lab even though it was time consuming especially due to some travel commitments I had this week.   There were several great ideas posted in the discussion forum for a variety of applications for viewshed and line of site analyses.

GIS 5100 - Lab 3 - Watershed Analysis

In Lab 3 we learned about modeling streams and watersheds using a provided Digital Elevation Model (DEM) from USGS.  We used a variety of tools in the Hydrology toolset within Spatial Analyst Tools.  The first step was to make the DEM by hydrologically correct by filling in all the sinks using the Fill tool.  That output was then used to create a flow direction surface raster (fdr) which used the D8 algorithm to create cells that had values of the possible 8 flow directions and their corresponding values (E-1, SE-2, S-4, SW-8, W-16, NW-32, N-64, NE-128).  The fdr was used as input for a flow accumulation raster which represents the number of upstream cells that flow into each specific cell based on the flow direction.  It is possible for the flow accumulation value to be zero if there are no upstream cells flowing into a particular cell.  A stream raster was creating using the Conditional Con tool and the flow accumulation raster and setting an arbitrary threshold to a value of >= 200.  This threshold determines the number of cells it takes to create a stream channel.  Using the Stream to Feature tool we were able to convert the streams raster to a vector feature which maintains the logical flow direction.  Individual stream links and a stream order raster were created with the Stream Link and Stream Order tools.

To create watershed delineation we used both the Watershed tool and the Basin tool.  The Watershed tool uses a pour point field which in our assignment was the Stream Link output raster.  A watershed delineation was created for each stream segment.  The Basin tool uses the edges of the DEM as watershed outlets instead of pour points.   There were quite a variety of tools in the Hydrology toolset.  

For the final deliverable, I compared my modeled stream network and watershed results from the DEM raster with the actual (or observed) stream network and watershed features that were provided.  I chose to model a watershed that was on the east side of the island as it had a large outlet into the ocean.  Instead of just using one outlet point based on the stream raster, I chose five pour points because they were in close proximity to each other.  There were 2,649 observed streams with total stream network length = 2,623km and 3,606 modeled streams with a total stream network length = 2526km.   There was a difference of 957 streams or 1.3 times more modeled streams than observed streams.  Most of the 957 streams however are along the coastlines or edges of the island.   The 957 modeled streams are lower order streams (1^st order) that were probably created because of the flow accumulation threshold value of 200 and the flatness of the terrain.  I also changed the threshold value to see how it affected the number of modeled streams.  Increasing the Flow Accumulation threshold from 200 to 300 reduced the number of modeled streams from 3606 to 2414 or just a difference of 235 streams between the observed count of 2649.  Increasing the threshold to 500 drastically decreased the level of detail and reduced the number of modeled streams to just 1442. 

There were 22 observed watersheds and 1789 modeled basins.  During the modeling, a larger number of smaller watersheds/basins were created around the coastline edge which accounted for most of the comparison difference.  The pour points feature with 5 outlet points created 5 distinct modeled watersheds that fell inside the observed watershed but did not have the same coverage area as the observed watershed which was much larger.  The observed watershed covered 99 square kilometers.   The respective modeled watersheds had a cumulative watershed area of 65 square kilometers (1.7+51.8+9.5+.8+.9). 

I was not aware of the ability of ArcGIS to create so much information from one DEM raster.

Comparison of Streams & Watersheds - Observed vs. Modeled