GISG 110 Final Report


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This was the written report for my final project in my introduction to GIS class. From May 25, 2005.

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GISG 110 Final Report

  1. 1. GISG 110 Final Presentation: San Diego’s Red Light Enforcement Program 1 San Diego’s Red Light Enforcement Program Alvin Hernandez and Steve Ruge GISG 110 Final Presentation Casey Cook, Instructor Mesa College (Wednesdays 6:00 PM) May 25, 2005
  2. 2. GISG 110 Final Presentation: San Diego’s Red Light Enforcement Program 2 Project For our project we chose to evaluate San Diego’s Red Light Enforcement Program (hereinafter referred to as the RLEP) and to display our evaluations using custom generated maps created with ArcMap. We evaluated several different aspects of the program (number of days of accidents, the number of rear-end collisions pre and post camera, the number of red light violations pre and post camera, all accidents at each intersection pre and post camera and the number of citations issued at each camera intersection. Pre- camera refers to the period one year before the installation of the red light camera and post-camera refers to the period from which the intersections went live with the cameras to March 17, 2004 when the Manager’s Report was released to the San Diego City Council. All evaluations were based on the latest data released by the City of San Diego (2002) for the four intersections in the Figure 1: Red Light Camera (inset) program. Since the report was released two more intersections went on-line but have no data available for analysis at this time. The data was evaluated using ArcMap with Geocoding performed by Streetmaps USA to present the results of our evaluations to the class. Background In 1996, California enacted California Vehicle Code Section 21455.5, authorizing Government entities to utilize automated photo enforcement systems at intersections. Beginning on November 25, 2002, the San Diego City Council authorized the selection of a contractor to perform various tasks related to the RLEP. The first site went operational in June, 2003 at the corner of 10th Avenue and “A” Street in downtown (City Center) San Diego (figure 2, at left). The objective of the RLEP is to improve traffic safety at all signalized intersections by reducing the number of violations and collisions attributable to red light running. San Diego’s RLEP utilizes a technology that records serious traffic violations the moment they occur. Cameras are used only to photograph vehicles that run red lights in target areas. Officers review the film and a traffic citation is issued to the vehicle’s registered owner through the mail. Intersections are selected for inclusion in the program based on collision history and type. Other factors include violation history, citizen and Police Department input, traffic volume (both vehicle and pedestrian), traffic speed, potential gridlock and site distribution Figure 2:the community. Before an intersection is selected for photo enforcement, staff considers implementing engineering throughout Former Mayor Murphysuch as additional warning systems and pavement markings, adjustments to yellow light timing, and all-red phasing to solutions Activates the First Red Light Camera enhance intersection safety. If the countermeasures are not effective, the proposed intersection will be considered for photo enforcement. Once the system is operational at a site, data is analyzed as to the change in collision history, collision type, and citation history. Methodology We first obtained data on the four signalized intersections within the city limits through the internet and the city’s own website ( This data was only available in raw form, as part of a memo to the city council regarding the performance of the program. We took this data and entered it into a Microsoft Excel spreadsheet (See Figure 3, next page).
  3. 3. GISG 110 Final Presentation: San Diego’s Red Light Enforcement Program 3 Figure 3: Importing Data from Hard Copy into a Microsoft Excel Spreadsheet The spreadsheet was saved as a text file so it could be imported into ArcMap and used as a data source for when we were ready to begin Geocoding intersections onto our map. Next, we went into ArcMap and started the StreetMap program to begin Geocoding. StreetMap automatically generated the San Diego city layer and the street grid for our map, including major roads, freeways, lakes, and airports (the last two included as points of reference to give viewers a general idea of what neighborhood in the city they were viewing). We started up the Geocoding tool in ArcMap located under the Tools Menu > Geocoding > Geocode Address (figure 2). At this point we were presented with an option to use a Geocoding tool and we chose StreetMap. After StreetMap loaded we were instructed to select the row in our table that contained the information we wanted to be geocoded. In our table we have a column called Int_Street_Name which Figure 4: Geocode Addresses… contains the intersections that have cameras installed. The intersection names are separated by the ampersand (&) so we specified that in the box asking us how the data was separated. Finally, we clicked the box telling Streetmap to go ahead and perform the Geocoding. On the first attempt we successfully geocoded 1 address out of 4. We were puzzled as to why the Geocoding was not performed. On a hunch we decided to look up the zip code of each intersection in Google and use that as further defining criteria. We added a column called Int_Zip to our table and re-geocoded. Re-performing the Geocoding was a bit more successful this time, with a success rate of 75% (or 3 of the 4 intersections). We still could not figure out what was wrong. Realizing that Streetmap is a program that covers the entire United States, we decided to help it out by telling it that these four addresses were California addresses by adding an Int_State column to our data table and filling in CA for each of the four addresses. Success! All four addresses were successfully geocoded and their locations were placed on our map. Once the intersections were successfully plotted on the map we went through the available symbols in Transportation and as good luck would have it we found the traffic signal with the red light illuminated. It was a perfect representation of the red light camera locations and we chose that as our plot point. Next, we turned on labeling on the map and specified that labels should be created based on the Int_Street_Name field of our data. We decided to make the font large and include a halo effect so it would stand out on the map and clearly identify each intersection. In preparing the maps used in our presentation we basically stuck to a crisp, clear, simple format so that the design would not overwhelm the data being presented. An example of one of our maps is included as figure 5 for reference. Figure 5: Red Light Camera Installations We decided to place a title at the top of the map in a simple, bold, direct font. We used a styled, yet basic and clear “north arrow” and included a map legend along with a scale bar on the map. The overall goal with the maps was to show the location in relevance to the entire city of San Diego.
  4. 4. GISG 110 Final Presentation: San Diego’s Red Light Enforcement Program 4 There was one problem with the maps showing the locations of the cameras. They were designed to best be viewed at a scale of 1:250,000. Unfortunately, in order to have a legible map at that scale we had to omit major street names which would have helped the viewer to locate those intersections even better. The only solutions we could come up with were to include the freeway system (since most all San Diegans can reference anything by its reference to the nearest freeway) and to develop a series of smaller, more intricate “zoom” maps for each of the four intersections being studied. In the detail maps (made at a scale of 1:24,000) we were able to “zoom in” on the immediate area surrounding each intersection and show the local major street names and any area attractions. An example of this kind of map is included below as Figure 6. Ultimately we wanted to include both the intersection name and its related data on the accident and citation maps. We played around with various labeling techniques but could not find the appropriate technique that would preserve both the intersection name and its statistical data so we decided to show them separately. In this same regard we also chose to show data for “pre” versus “post” camera data in this method due to the same problems. See figures 8 and 9 on the next page. The final step in preparing the map was to select a background color of blue which set the ocean and bays in the map to be blue. An annotation was added over the ocean indicating it was the Pacific. See figure 7 below. That was the basis for the main map showing all locations of installed cameras. From that point we only had to modify what was being labeled – we went through the various other categories of data and chose to do a “before camera” versus “after camera” approach to illustrate any observed changes in accidents, red-light violations, and overall citations issued at each intersection, among other data. Once the “master” map was made, we simply had to go into the labeling dialog box and choose the next category to be labeled and Figure 6: Detail Map of “A” St & 10 Ave the map would be updated with the new data. We decided to continue with the same scale for the rest of our maps: 1:250,000 for the “big picture” maps and 1:24,000 for the “zoomed in” intersection detail maps. Conclusions This project taught us a great deal about using ArcMap to produce maps for distribution to others or for use in a presentation, as well as how to geocode addresses on a map. Through trial and error we were able to accomplish our goal of representing data on a map and sharing it with an audience. We found out the many tools ArcMap has available to construct maps and help analyze data. The project was also beneficial to us in that it allowed us to study a topic that we were both interested in, the red light camera enforcement program, in greater detail. Going into this project both Alvin and I made the assumption that the RLEP was useless and did not reduce the amount of accidents that occurred at the Figure 7: Annotation “Pacific Ocean” camera intersections. Upon completing this report our assumptions were challenged.
  5. 5. GISG 110 Final Presentation: San Diego’s Red Light Enforcement Program 5 Figure 9: Red Light Accidents Before Cameras Figure 8: Red Light Accidents “After” Cameras