Linear Referencing

10/16/14

Problem:

Linear referencing is a method by which geographic data and events are analyzed using distance. One way to process the relative positions of these events along a measured, linear feature is through dynamic segmentation. The events are maintained in an event table and the linear referencing system aids the display of these events on a map. This exercise was designed to explore the basics of understanding linear referencing. For example, combining graphic and linear attribute information can help answer questions such as the correlation between auto accidents and road conditions. The study area was “Pitt County, NC”, and the data used for these analyses were acquired from Dr. Perver Baran at NCSU. The material was based on information from Introducing Geographic Information Systems with ArcGIS: A Workbook Approach to Learning GIS data (2 Ed.) Wiley, pp. 547-556. The coordinate system used for these data was NAD_1983_StatePlane_North_Carolina_ FIPS_3200_Feet.

Analysis Procedure:

After formatting the ArcMap environment as required by the project and loading the pertinent data, tools specific to the Linear Referencing actions may be loaded as a customized toolbar. Some of the tools may include Make Route Event Layer and Identify Route Locations. The Make Route Event Layer tool can be used to produce event layers from data tables to be visualized on the map, such as an Accident Event and a Pavement Event, as for this exercise. Mile distances of various segments were calculated using the Field Calculator. Then, the accident point data were intersected with the linear pavement data using the Overlay Route Events tool. The Make Route Event Layer tool was used to produce an Accident_and_ Pavement Event, which was used to identify accident events and upon which pavement types they occurred. These data were used to calculate the number of accidents per mile for each of the road pavement ratings of greater than 75 or less than 75.

Figure 1. Workflow diagram for Linear Referencing

Results:

After making the Pavement event layer, a particular route was identified from the layer table (Figure 2). This route was the focus of the remainder of the exercise. Similarly, after making the Accident event layer, accident events along the identified route were highlighted (Figure 3). The primary focus was to correlate accident events with pavement types to see if road condition influenced the prevalence of accidents. To do so, individual segments of the chosen route were identified (Figure 4) and their corresponding road condition linked to the accident events. Based on the data, it appeared there was virtually no difference in accident rate dependence on pavement type; 1.6 accidents per mile occurred on high quality pavement while 1.7 accidents per mile occurred on other condition types.

Figure 2. Identifying the specific route of interest used in this exercise.

Figure 3. Identifying the locations of accidents on the specific route of interest

Figure 4. Identifying segment beginning and ending points

Application and Reflection:

The linear referencing method of data analysis appears to be a good means of evaluating relative positions of events or conditions or characteristics along a linear feature, such as a transect line or a trail system, that itself has conditions or characteristics. Having the ability to link these different data sets offers a strategy to draw conclusions about the relationships between them. For example, the mushroom mapping project my daughters and I are conducting (see Our Mushroom Hunting) involves mapping our mushroom sightings along the trail systems of the Eno River State Park. Each mushroom sighting is a point event with a set of attributes, such as soil moisture content. The trail segments are of known lengths and lie in different environments, such as along the river bank. Perhaps the linear referencing method will facilitate our ability to link our mushroom sighting attributes with trail location to be able to draw conclusions about where the different mushroom varieties are likely to emerge. The data I would like to have for this are soil moisture content, light level, temperature, relative humidity, barometric pressure, and GPS coordinates collected at the time of the mushroom sightings. I would also like to know the trail segment location, slope, aspect, elevation, and distance from the nearest stream, as derived from the map already generated. I envision using the procedures demonstrated in this segment to make and process event layers from the data table for each attribute generated by the weather shield instrument and link them to the data generated from the existing map document.