Trail User Intercept Study Data Model
Amy Anderson
Spatial Sciences Institute
12712 N. 84th
Peoria, AZ 85381
Figure 2 provides a Google Earth view of the study area in
relation to the rest of South Mountain Park. The study area is ...
And, another point to make on the interview routes is that some
interview subjects did not stick to the official trails. T...
In building the topology rules, reference was made to a National
Park Service report on building a dynamic trail inventory...
SSCI 582 Presentation
Trail User Intercept Study Data Model
Utilize trail user intercept study results to examine analysis and visual display 
Photoshop used to digitize information
Route map used in interviews
(showing one subject...
• Enhance digitization accuracy
• Show dynamic analysis in fewer sheets of paper
• Toggle and que...
Data Source Format Use in GIS
Trail Map City of Phoenix PDF Digitize trail segments & intersections
54 pages of hand completed interviews
Not all interviews provided a route 
Time Slider
Select by Feature & Relationship Class http://www....
• Data loss & reinterpretation
– Inconsistency of interviewers capturing data
– Incompatibility with domain rule...
• Add observation data as linear referenced events
• Add trail segment attributes to capture information su...
For questions contact:
Amy Anderson
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Trail user intercept study data model and results


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Research Paper and Results Presentation detailing the attempt to link interview survey data and traffic observation data to a trail system within a GIS in a meaningful way to better illustrate and analyze user behavior within South Mountain Park in Phoenix, Arizona.

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Trail user intercept study data model and results

  1. 1. Trail User Intercept Study Data Model Amy Anderson Spatial Sciences Institute 12712 N. 84th Lane Peoria, AZ 85381 480-363-8455 ABSTRACT Much of trail data available is related to the physical characteristics of the trail itself; less is available in regards to the user’s experience on trails, including routes they prefer and why they prefer them. This user data is of interest to trail designers as they plan maintenance, realignments, or build new trails, and should be used in conjunction with the physical knowledge to create and maintain experiences that will attract trail users without degrading the environment surrounding them. The key to doing this is to collect, analyze, and report trail user data in a dynamic way that effectively supplements all other known data about the trail in question. Drawing from transportation traffic models and trail inventory systems it may be possible to create an efficient and effective geodatabase that can spatially report on trail user behavior and preferences. This paper presents one approach taken to geographically analyze and understand trail users in an Arizona park. The techniques described require a modification of the market research study design to anticipate how the data will be used in the geodatabase after it is collected. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications – Spatial Databases and GIS Keywords Trail databases, intercept studies 1. INTRODUCTION Qualitative research methods such as observational and intercept studies can answer many questions about trail users including volume and patterns of use, as well as goals, motivations and preferences regarding trail activity. To enhance the understanding of trail users Volunteers of Outdoor Arizona (VOAz) used several of these qualitative techniques in January 2013 to study the trail usage patterns and behaviors of South Mountain Park and Preserve. The Park is managed by City of Phoenix who has a contract with VOAz to perform trail maintenance. Opinions, preferences, and trail usage patterns were collected via interviews and are the backbone of this data model project. The goal was to develop a geodatabase designed to further analyze and visualize the route data collected as well derive additional meaning and context from the other non-spatial data collected from interview subject. Although, it would be ideal to include analysis of the extensive on-site surveying of trail, tread, and vegetation conditions gathered over the past several years, the scope and intent of this geodatabase project was to experiment with the ability to analyze user behavior information from market research studies, as it relates to geographical elements. Success towards this goal would be measured by the ability to show fewer maps to display trail user patterns. As it stood at the beginning of this project, nearly every route was unique to the trial user; however, many routes overlapped each other. There were more than 20 pages of maps to illustrate the routes taken by interview subjects. And those pages did not provide contextual data to determine if patterns changed by age, by group size, by mode of travel, or by time of day or day of week. The geodatabase model would help condense and intertwine the data derived from the studies and allow additional dynamic and rich analysis to be conducted from the hard copy surveys. Figure 1 illustrates the workflow used for this project. The geodatabase model was expected to provide many benefits that would be harder to efficiently achieve via other methods and would provide a template for future similar projects. In addition, the geodatabase was expected to be able to grow with other attribute or spatial data related to the trails in this area. It could grow with subsequent surveys of land and people. Figure 1. Trail Interview Geodatabase Workflow 2. Technical Content 2.1 SOUTH MOUNTAIN PARK South Mountain Park and Preserve is one of the largest municipal parks in the nation, covering more than 16,000 acres and 51 miles of primary trails for non-motorized multi-use activities (City of Phoenix). The portion of the park VOAz is currently working in is within the easternmost boundary in an area bounded by Pima and Beverly Canyons.
  2. 2. Figure 2 provides a Google Earth view of the study area in relation to the rest of South Mountain Park. The study area is at the northeastern most point and is a small portion of the overall area. Figure 2. South Mountain Park with study area outlined in NE corner On Saturday, January 9, 2013, seven volunteers including the author intercepted 54 trail users as they exited the study area via a trail off the northern edge of Pima Canyon Trailhead (Anderson, 2013). These interviewed subjects provided information regarding how often they use the trails in that area, their preferences and opinions related to trail conditions and trails in general, and most importantly they provided us with a hand- drawn route map to document exactly where in the park they traveled on that day. This data is part of the public input process which informs future trail planning and rehabilitation needs in the park. Figure 3 provides a closer view of the study area. The yellow pushpin designates where the majority of interviews took place. Figure 3. Pima - Beverly Canyon area Although VOAz is working in this specific area, most trail users are using trails throughout the park and subsequently have trail patterns that go outside these boundaries. This is important when modifying trails in any area, as trail connections need to be maintained and addressed. The trails in South Mountain Park were primarily built by the Civilian Conservation Corps in the 1930s (City of Phoenix). Because of the time that has passed and environmental changes many of the original trails have been modified by users to include an array of unofficial trails. We used a trail map found on the City of Phoenix website to help gather the route information however, many of the trails no longer match these records and trail signage has become confusing. These problems came out in the interview data and are to be addressed by VOAz and City of Phoenix in the coming years. 2.2 DATA Referring to Figure 1, the data collected for use in the geodatabase included an area trail map from the City of Phoenix website, the interview route data (an example shown in Figure 4), and the non-spatial interview data collected from interview subjects. Figure 4. An example map collected from an interview subject Trail Map The trail map provided from City of Phoenix’s website was obtained as a PDF. This map needed to be set up as the base layer for querying and referencing the interview data. It had to be georeferenced to align properly in ArcMap, and trail segments and intersections were then freehand traced to digitize them as a base layer for querying against in the geodatabase A Colorado State Parks project from 2008 created a geodatabase with three feature types: trail segments (lines); trailheads (points); and miscellaneous points (page 8). Using this approach, the trail map was digitized to reflect trail segments (lines), and trail intersections (points). The point layer also includes a trailhead subtype in order to assign rules and symbology to the subtype. This may need to be separated in the future as technically a trailhead point is not an intersection per se. Interview Routes In reviewing the Colorado State Parks report, the description of routes and trail segments used on page 9 is very similar to what was intended with this project. Each interview subject had a route which could overlap another person’s route. Each route should be a line made up of trail segments from the base trail map. However, at page 23 it’s noted “the trail routes feature class, as it was originally designed, is not included in the final geodatabase. This is because trail routes are best calculated “on the fly” as a combination of one or more connecting trail segments.” So, the model did not execute on the most valuable piece that could be replicated in this project. Instead, for this project, trail routes were digitized as lines one by one from the drawn-on maps. The lines were traced from the base trail map layer to be precise matches. Unfortunately, many of the query options intended haven’t been realized. Summarizing the stats of which trail segments are used most often is not available via the frequency function as the route data is not based on attribute data. Spatially selecting the trail segment does allow the geodatabase user to view the interview routes that use that particular segment, but there’s no summary feature to roll all the segments up to see a count of interview routes for each segment. It’s possible to click on each segment and manually get that information, but if the database grows it will be less user-friendly to do this.
  3. 3. And, another point to make on the interview routes is that some interview subjects did not stick to the official trails. They used unofficial paths or cut their own paths up summits. This information could not be captured in the geodatabase as it was structured because all interview routes had to be traced and overlap an official trail segment from the base layer. This means some data was lost in the interpretation and import stages. Interview Data As all the data collected was in hard copy format it needed to be copied and interpreted into a format usable by ArcMap. Data entry was a large part of this process. In addition several questions asked on the interview sheets needed to reformatted to work in the geodatabase environment. For example, although a question may have asked for only one answer (eg. What is your primary #1 goal here today?), the interviewer may have accepted multiple answers. These data quality issues had to be addressed. For the purposes of this project and experimenting with user data, the multiple answers were reinterpreted into a “mixed” category. This creates some data interpretation inconsistencies that will need to be addressed in the future if VOAz is to undertake additional trail intercept studies such as this one. Several other questions from the interview worksheet were captured in Excel in a modified version. The question “I’m going to read a list of 5 activities to you, please say “yes” to any of those that are generally important to you when using trails.” was rearranged in Excel to show the options as categories and Yes or No as the corresponding data that could be collected. Again, in the future, if VOAz does studies like these we will need to modify the questions so that it will be easier to transfer to a database. Perhaps we can even skip the hardcopy part and use an iPad to collect the data, thus pushing it directly to the database at the capture point. Table 1. Summary of data used in geodatabase Data Source Format Use in GIS Trail Map City of Phoenix PDF Digitize trail segments & intersections Interview Routes VOAz Hand drawings Digitized routes Interview Data VOAz Hand written Excel import 2.3 METHODOLOGY The South Mountain geodatabase consists of Trail Segments, Trail Intersections, Interview Data, and Interview Routes. Additionally, relationships and a topology are included to enhance the integrity of the data. Figure 5. UML Diagram of South Mountain Geodatabase Geodatabase structure The geodatabase is named SouthMountain.gdb. The catalog image on the right shows that within the geodatabase there is one feature dataset (Trails), a raster file (Genericroutemap), a table (Interview_Import), and a relationship class (Routes2Records). Inside the feature dataset there are three feature classes and an overriding topology which provides rules to the feature classes. These items are described in the bottom right portion of the UML diagram, also shown on the left side of Figure 6. The feature dataset is contained within the pink box. The topology, in orange and yellow, indicates that it is connecting all three of the feature classes. The raster file and the table are shown as separate blue boxes. Domains, subtypes and relationship classes are not shown here. Figure 6 Geodatabase structure catalog view Feature Datasets and feature classes There is one feature dataset, Trails, in SouthMountain.gdb. The Trails dataset houses the topology between InterviewRoutes, TrailIntersections and TrailSegments. Trail Intersections is a point feature class showing all points of trailheads or where other trails meet. Between each intersection are TrailSegments which are represented by lines. InterviewRoutes are representing each trail users route by using a line which traces over the corresponding TrailSegments. Relationship Class There is a relationship class in the geodatabase which can be seen in Figure 6 and also in green in Figure 5. This relationship class, Routes2Records, describes the relationship between the InterviewRoutes line feature class and the Interview_Import data table. The UML diagram indicates there is a 1-1 relationship. There must be one interview route per interview data record. This relationship is based on the ID field which matches to the trail user interviewed. If it does not find an ID in either table that does not exist in the other, an error will occur when validating. Or, if it finds multiple routes or records for the same ID it will also error. This is important to maintain the data integrity and to minimize data entry errors. However, the problem with this setup is that some interview forms did not provide a drawn map, or provided a drawn map that used trails other than those digitized in the base layer (unofficial trails or off-path routes). This means that the sample of usable data shrank when moving it from the raw forms into the geodatabase. This can affect the margin of error or sample error of the data when analyzed. Topology A topology exists in the feature dataset in order to help align intersections, trail segments, and interview routes.
  4. 4. In building the topology rules, reference was made to a National Park Service report on building a dynamic trail inventory. In this report they reviewed the building of an inventory of trails in a 153,000 acre recreation area. The geodatabase they created included current trails, archived trails, and route systems which were created from the trails data (Belk, 2004). On page 5 of the report it states, “Creating a route-system only required dissolving the “UTAP-d” polylines from the GIS trail inventory, then assigning both length and directional values to each route.” (Belk, 2004) Unfortunately this approach would not work for this project because we were not creating routes but rather reporting on routes being used. But, the topology rules used in the NPS paper were similar to those used in this project:  InterviewRoutes must be covered by feature class of TrailSegments.  TrailSegment endpoints must be covered by TrailIntersections.  TrailSegments must not self-overlap or self-intersect. Domains Domains are used extensively in the SouthMountain.gdb to provide standards for entering information into the Interview_Import data. As more interviews are collected and data is captured in the database it’s important that all entries be as consistent as possible in order to query the data. Domains are set to limit the data entry errors which can be caused by simple things such as representing a Yes value as Y, Yes, yes, or 1. By setting a domain on a Yes or No question the values will be set and queried at the highest quality level. Sub-types Subtypes set coded values for particular elements within a feature class. It’s important to document what those coded values represent to data can be analyzed and assigned to the proper coded value. Subtypes also help increase the performance and integrity of the data by only associating a subtype to relevant fields so that space is used most efficiently. In the SouthMountain.gdb there are two subtypes, InterviewLocation and Type. InterviewLocation distinguishes between three different locations where interview data was collected. T1 is the main location which is the default. Type distinguishes between trail intersections allowing for different symbology and rules associated with trailheads, interview locations, trail continuations, and a standard trail intersection. 3. DISCUSSIONS AND FUTURE WORK In summary, exploring the use of a geodatabase to track and analyze trail intercept data and route patterns was a useful exercise in understanding the challenges and technical skills needed to execute this enhanced market research undertaking. Other approaches should be investigated to storing and manipulating the route data so that it’s more efficient to digitize. Updating the interviewing approach to use GIS technology rather than hard copy paper and pens could increase the consistency and quality of the data as well as lighten the data entry load and minimize data loss and data entry error. To expand on this geodatabase, observation data could be added to this as linear referenced line and point events along the routes or trail segments. Creating a user interface to allow multiple users or volunteers to enter the data through a user friendly form would be of great benefit. And lastly, another expansion would be to incorporate more trail attributes using the Federal Trail Data Standards set forth by the FGDC in 2011. Useful data that is collected by VOAz and standardized by the FGDC includes trail condition, surface, length, slope, elevation gain/loss, status, type, prohibited use, designed use, etc. These pieces of information can help plan realignments and future improvements and having them housed in the same database would be beneficial when reviewing the viability of any one trail segment. 4. REFERENCES [1] Anderson, Amy & Volunteers for Outdoor Arizona. (2013). Pima Canyon Trail Study. Available from: rojects_events/somo_pima_canyon/pcplan_study.htm [2] Beck, Melanie, & Gottschalk, Ralf. (2004). Using ArcGIS to Develop a Dynamic Trail Inventory presented at 24th Annual Esri International User Conference, August 9–13, 2004. Retrieved from: 1471.pdf [3] City of Phoenix. South Mountain printable tri-fold map (eastern area). Retrieved from: arks/@parks/@nrd/documents/web_content/southprinteast.p df [4] Colorado State Parks. (2008, Fall). Colorado Trails Mapping Project: Phase II: Date Inventory System, Web Prototype and Pilot Mapping Project. Retrieved from: /Trails%20Program/Phase%20II%20Report.pdf [5] Standards Development Group, Federal Geographic Data Committee. (2011, November). Federal Trail Data Standards. Retrieved from: projects/trail-data- standard/Federal_Trail_Data_Standards_FGDC-STD-017- 2011.pdf
  5. 5. 1 SSCI 582 Presentation Trail User Intercept Study Data Model AMY ANDERSON 05.10.2013
  6. 6. 2 FINAL PROJECT OVERVIEW PURPOSE: Utilize trail user intercept study results to examine analysis and visual display  opportunities of ArcMap geodatabases AUDIENCE:  VOAz Project Managers & Partners BACKGROUND:  Volunteers for Outdoor Arizona is a non‐profit  organization that builds, rehabilitates, and preserves  trails throughout Arizona.  For a project with the City of Phoenix, VOAz was required to obtain public  input regarding the trails in South Mountain Park. In January 2013 we  conducted observation studies and interviewed trail users as they exited  certain trails. This type of interview is known as an intercept study and the  results of these interviews became the focal point of my final project.  
  7. 7. 3 EXAMPLE OF ROUTE DATA COLLECTED Photoshop used to digitize information Route map used in interviews (showing one subject’s info)
  8. 8. 4 GOALS OF FINAL PROJECT • Enhance digitization accuracy • Show dynamic analysis in fewer sheets of paper • Toggle and query data efficiently, linking geographic elements to  attribute data • Allow for additions to the database (observations, physical  characteristics of trails) • Use time as an analysis element
  9. 9. 5 DATA REVIEW Data Source Format Use in GIS Trail Map City of Phoenix PDF Digitize trail segments & intersections Interview Routes VOAz Hand drawings Digitized routes Interview Data VOAz Hand written  Excel import Trail Map = map used in interview sessions Interview Routes = routes interview subjects indicated they took that day Interview Data = attribute data collected during interview sessions
  10. 10. SYSTEM WORKFLOW INTERVIEW DATA  (Non‐Spatial) 54 pages of hand completed interviews Not all interviews provided a route  drawing INTERVIEW ROUTES  (Spatial) 29 unique route drawings All route drawings can be linked to  interview data OFFICIAL TRAIL MAP (Spatial) Map used in interviews to capture  routes. Only a portion of park is shown  and no unofficial trails which are in use TRANSFER TO EXCEL READY FOR IMPORT RELATE & JOIN GEOREFERENCE DIGITIZE TRAIL SEGMENTS DIGITIZE TRAIL INTERSECTIONS CREATE TOPOLOGY DIGITIZE ROUTES VIA TRACING TRAIL SEGMENTS USED QUERY • Large groups only • Groups with dogs only • Groups traveling in morning only • Only runners SPATIAL SELECT • Select a trail segment to see stats of subjects who traveled there. Are they all old, young, bikers, dogs? • Select a trail intersection to see which trails join it. • Select a trail segment to count interviews, understand density of travel. TIME SLIDER Dynamically view subjects based on their interview time to see sequence of travel patterns. This will work best with observation data not interview data GEODATABASE SYMBOLOGY Show attributes of interest in different ways (graduated symbols based on age young to old, or mode of travel, etc.)
  11. 11. 7
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  16. 16. 12 FUNCTIONALITY DEMOS Time Slider Select by Feature & Relationship Class Spatial Select by Attributes
  17. 17. 13 ISSUES • Data loss & reinterpretation – Inconsistency of interviewers capturing data – Incompatibility with domain rules in geodatabase • Data visualization – Inability to effectively show overlapping routes (thick vs thin  based on segments used most) – Inability to statistically summarize spatial data (count)
  18. 18. 14 FUTURE WORK • Add observation data as linear referenced events • Add trail segment attributes to capture information such as trail  grade, trail condition, trail maintenance history, etc.  • Use this as a template for future studies of this kinds • Modify the intercept survey questions in order best translate into  a database • Train interviewers to be more consistent in data collection process  • Update data collection process to electronic to eliminate data  entry and human interpretation
  19. 19. 15 For questions contact: Amy Anderson 480‐363‐8455