The city of Houston had proposed the construction of 54 new microwave transmission towers to enhance emergency response efforts. The Enterprise GIS group within the Planning and Development Department was asked by the Information and Technology Department to develop a method for modeling and visualizing the proposed microwave transmission corridors in order to locate any possible obstructions to any of the transmission signals. Using LiDAR and transmission corridor elevation models, a sophisticated process was developed to identify existing obstructions, indicate future building height restrictions and efficiently illustrate the impacts of different planning scenarios. This article is directed at those who may be asked to undertake a similar project for modeling spatial networks in a 3D environment, and would like to see an example of progress from conception through completion. The discussion will explain the project's design, the variety of geoprocessing steps and visualization methods used, the results, and their implications in the planning process.
The city of Houston’s Information Technology Department wanted to locate and visualize potential signal obstructions between proposed microwave transmission sites across the city. The city’s Enterprise GIS group within the Planning and Development Department was asked to develop a process that would allow for the timely and efficient identification of possible obstructions and would eliminate the need to send a surveying crew to the proposed locations to physically verify probable signal impediments.
Many steps were needed to complete the task, and it required use of several software packages. Below are the steps followed to take a spreadsheet containing X, Y and Z coordinates all the way to a
completed 3D model.
- Format Excel spreadsheets with coordinate and elevation data
- Create AutoCAD scripts from Excel spreadsheets to automate transmission corridor creation
- Create and convert CAD files for use in GIS analysis
- DWG Polyline Feature Class TIN Raster
- Acquire and process full-feature LiDAR data
- LAS MultiPoint Feature Class TIN Raster
- Raster calculations to determine obstructions
The initial data delivery received from the IT department was simply an Excel file containing the proposed locations of dozens of microwave antennas, and how they connect. The data included the latitude, longitude and elevation above ground level for each transmitter and receiver with an indication of which location was the beginning or end point for the transmission corridor. A fair amount of data manipulation was needed to translate the initial spreadsheet into a format that was more compatible with the AutoCAD scripting process. These changes included reformatting the spatial coordinates from Degrees-Minutes-Seconds to NAD 1983 State Plane Texas South Central (ft.) and adding ground elevation derived from a digital elevation model (from 2 ft. contours provided by the Harris County Flood Control District) to the proposed transmitter/receiver elevations to establish height above sea level for each location. It was necessary to determine height above sea level as the LiDAR data used in subsequent steps provide elevations above sea level as well.
Once the data were appropriately formatted in Excel, another spreadsheet was created in the same workbook to construct an AutoCAD script for each individual corridor in order to automate the creation of the microwave transmission network. AutoCAD 2010 (or newer) is necessary because the new Mesh modeling functionality is critical to establishing interoperability between CAD and GIS software. Each corridor’s script, such as that in Figure 1, was appended to a larger AutoCAD script file (.scr) so that the entire network could be created in AutoCAD at one time, as partially shown in Figure 2. The resultant file was saved as an AutoCAD version 2004 .dwg. The .dwg had to be saved in 2004 format because saving the resulting file as a version 2010 .dwg created incompatibilities with ArcGIS.
CAD and GIS Interoperability
Due to the inherent interoperability functionality of ArcGIS, the resulting AutoCAD file was immediately viewable and useable. The point, polyline, polygon and multipatch features of the CAD file are compatible with GIS. Figure 3 illustrates the native CAD file in ArcScene. For analysis, the polyline features were critical. For visualization, the 3D polygons and multipatch cylinders were useful.
Two parallel paths were followed in GIS during the analysis phase of this project. The first involved the processing of full featured LiDAR to gather elevation data within a certain distance of the transmission corridors. The second path involved processing the elevation data from the corridors themselves. Ultimately, possible obstructions to the microwave transmission corridor network were determined by finding areas where the LiDAR elevation was greater than the corridor elevation. In other words, obstructions were located by locating areas where the full feature LiDAR surface protruded into the transmission cylinders, through subtraction of rasters.
The LiDAR data were initially processed using a third-party extension for ArcMap called LP360 by QCoherent. This extension quickly and efficiently handles and processes native LiDAR .las files and was an integral part of this project. Without full feature LiDAR elevation data it would have been impossible to determine obstructions to the transmission corridor network. The general process for the LiDAR data was to convert .las data to MultiPoint feature class, create a TIN from that feature class, then create a raster from the TIN and extract the corridor’s extent by mask. This was done to preserve the integrity of the elevation data and to have a data format that lent itself to analysis through raster calculations. The raster, as shown in Figure 4, was ultimately employed in the obstruction calculation analysis. ArcGIS Model Builder was used in the initial planning stages and a more thorough Python script was developed from the Model Builder framework to automate the “LiDAR to MultiPoint to TIN to raster” processing.
Similar to the LiDAR processing, each corridor went through a multistage transformation process. The polylineZ feature of the CAD file was exported into a file geodatabase. This initial polylineZ feature class contained the corridors for the entire network. Individual corridors were then created from the larger feature class to expedite processing. Each corridor was converted from a polylineZ to a TIN, and that TIN was converted into a raster. Figure 5 illustrates the resulting corridor raster. The gradient from left to right (shown above as a grey scale raster on top of the LiDAR raster) indicates an overall slope to the corridor. The subtle gradient from top to bottom indicates the elevation changes of the corridor due to its cylindrical nature.
With both the full feature LiDAR elevation and microwave transmission corridor elevation data processed and converted to rasters, it was simply a matter of subtracting the LiDAR elevation from the corridor elevation to determine areas where the full feature data could obstruct the proposed corridor. The results for one corridor are shown in red in Figure 6. Note that two microwave transmission corridor networks were created in AutoCAD. One network was created with full 360° cylinders for visualization purposes, and the other was created with the180° lower half-cylinders for analysis. Using half-cylinders was another critical component to the success of this project. Creating a TIN that would be converted to a raster from a full cylinder would have misrepresented the elevation data during the raster calculations because of the overlapping vertical elevations in the horizontal extent.
There were many options for visualizing obstructions to the microwave’s path, once the microwave cylinder rasters were subtracted from the full feature LiDAR rasters, and the obstruction locations were known. GIS provides the ability to examine all of the individual microwave shots and show what is encroaching into the microwave transmission (and by what extent). Volumes and areas for each obstruction can be calculated to determine if changes need to be made to minimize the impact to signal degradation. In some cases, a slight adjustment to the height of an antenna or a horizontal shift in the tower’s location was all that was needed to bring signal degradation ratios within acceptable tolerances.
Buildings that were found to be obstructions were especially interesting because unlike utility pylons and other towers or power lines, a building has the ability to completely block a signal from reaching the antenna. In Figure 7 and Figure 8, an upper edge of a building (in the full feature LiDAR TIN data) is shown intersecting the microwave cylinder hovering above it.
Another option was to align the viewing angle straight down the center of the microwave transmission and view the obstruction heights in relation to the full microwave cylinder walls.
Graphics like Figure 9 make invisible concepts visible by colorizing and realistically displaying the actual transmission corridor cylinder and showing what is inside, along with actual corresponding elevation values. In order to visualize microwave corridors as horizontal cylinders (having different elevations at their endpoints), many steps had to be performed. The interpolate line tool (in 3D Analyst) worked well to construct 3D lines that started and ended at differing elevations for each microwave corridor. Below is a summary of how the visualizations were achieved.
- AutoCAD 2010 mesh functionality was used to generate what would be considered a 3D radial buffer along each corridor’s interpolated 3D centerline (mentioned above). These polygons (or their multipatch counterparts) can be rendered and modeled in ArcScene as full volume cylinders with the LiDAR and tower location data as a backdrop.
- Then, a color ramp was assigned to the 3D polygon based on elevation values, and it was made partially transparent to see any areas where the full feature LiDAR TIN protruded through the transmission corridor.
- Selections were made for all actual LiDAR points that were inside the obstructions, and they were displayed as well (by Z value) to provide exact spot elevations along the TIN surface.
- Depending on the viewing angle and the amount of obstruction visible, a half-cylinder was used in lieu of a full cylinder to see the depth of the obstruction.
- The final models along with all final graphics were rendered in ArcScene.
The volumetric information was calculated by first identifying and isolating the section of the microwave cylinder where an obstruction occurred, and using the cylindrical volume formula to attain its volume in square feet. The Tin Polygon Volume tool (in 3D Analyst) was used to isolate and calculate the volume of the portion of the full feature LiDAR TIN that was inside the cylinder. This was the volume of the obstruction itself. For the example shown in Figure 10 a percentage of 7.72% was calculated as the final volume of the obstruction for that portion of the microwave corridor.
Area calculations are a little more complex. A signal can (theoretically) travel almost all the way though the corridor, only to be blocked at a single location within the pipe. Area fluctuates along the obstruction depending upon location and how far into the cylinder the obstruction protrudes at that exact point. Figure 11 below shows two paths through the cylinder that were unobstructed, while the other three paths were blocked.
We decided to slice the cylinder into 144 sheets (about 1 foot between slices) at 90º perpendicular to the cylinder axis. Each slice was analyzed to determine how much of the cylinder’s circle area was occupied by the obstruction. Afterwards, all 144 slices were flattened to a single 2D plane to get the total area. The three planes of obstruction shown in Figure 11 are broken down and examined in Figure 12. The final area obstruction of all cross section planes (after flattening) was 11.74%, which was quite substantial.
Results and Impacts
The city of Houston saves close to $100,000 in tax payer dollars for every obstruction that is caught and fixed without having to send out a crew to check for signal loss. Finding and fixing potential problems using the city’s GIS data also can save potentially thousands of dollars in the design phase involving new legs of the microwave transmission network. Several options exist for correcting the obstructions, including the following:
- Elevate or lower the microwave antennas to clear the obstruction.
- Relocate the tower upon which the microwave antennas are located to another position inside the same parcels.
- Relocate the tower upon which the microwave antennas are located to different parcels altogether.
The project has already been invaluable in making sure (double checking) claims of a vendor, when statements are made as to the severity of an obstruction. Since buildings can pop up anytime within the city of Houston, work is already underway to translate this model into the plats and permitting procedures that are already in place, making for faster identification of potential problems. Inclusion of the GIS obstruction model will also give landowners an opportunity to mitigate the path of the transmission. In addition to the $100,000 in tax payer dollars, there are hidden savings through avoidance of costly and time-consuming litigations.
As the model was refined, additional issues became apparent, including the need for greater redundancy. Visualizing the paths made it clear where new paths could go. We look forward to continuously refining the database and model to more accurately reflect the transmission 3D shapes and to the integration of the GIS data into the decision making process during plat review procedures.