GEOG 566






         Advanced spatial statistics and GIScience

April 6, 2018

Spatial and Temporal Patterns Among Multi-day, Overnight Wilderness Users

Filed under: My Spatial Problem @ 10:48 am
  1. A description of the research question that you are exploring.

Glacier Bay National Park and Preserve (GLBA), located in southeast Alaska, contains over 2.7 million acres of federally designated terrestrial and marine wilderness (National Park Service, 2015). Recreation users access GLBA Wilderness primarily by watercraft; the park lacks formal trail networks in its wilderness and terrestrial connectivity is fragmented by the park’s water resources. First designated as wilderness in 1980 through the Alaska National Interest Lands Conservation Act, management of the park’s wilderness has been guided by a 1989 Wilderness Management Plan (National Park Service, 1989). Much has changed in Alaska and GLBA since that time, including an increasing cruise ship tourism industry using the park’s waters and reductions in glacial ice, and the park is currently engaged in updating its Wilderness Management Plan to adapt its management practices to these modern contexts. Additionally, the Wilderness Act of 1964 includes explicit statements about how wilderness should be managed – these statements have been operationalized into the Wilderness Character framework (US Forest Service, 2008). This framework provides managers with benchmarks for understanding the degree to which the wilderness experiences of recreationists align with the characteristics of wilderness described in its enabling legislation. Of interest to the park in this wilderness management planning process is developing a better understanding the wilderness experiences of backcountry overnight users.

The advent of widely available access to Global Positioning System (GPS) technology has led to the ability to continuously track the movement of people through space and time (van der Spek et al., 2009). Using GPS tracking in recreation research expands on previous methods of data collection by providing a reliable way to continuously measure behavior and to generate precise estimates of the spatial and temporal components of visitor movement (van der Spek et al., 2009). Specifically, GPS provides researchers with exact locations and time stamps of visitor movement, whereas self-reported or researcher recorded methods of data collection are subject to estimation error and imprecision that can lead to misrepresentations of actual travel patterns (Hallo et al., 2012; van der Spek et al., 2009). Using GPS technology also removes the potential negative impacts to experience caused by more invasive methods of data collection such as physically following the visitor or observing visitor movements (Cole & Hall, 2012). Furthermore, as GPS technology has continued to advance, earlier obstacles to using GPS technology in visitor use studies, such as burden to the visitor and unit cost, have been resolved (Hallo et al., 2012).

In wilderness settings, GPS technology has been primarily employed as a tool for studying the behavior of day users – meaning those users that do not stay overnight in wilderness as part of their recreation experience. Previously, limits in GPS battery life have been the primary factor preventing the study of overnight wilderness users through GPS technology. Recently, Stamberger et al. (2018) used recreation-grade GPS units to track overnight users in Denali National Park and Preserve. While Stamberger et al. (2018) successfully collected 113 GPS tracks from multi-day users, success of the study was limited by GPS battery life, reliability of the units used, and challenges in data collection and management. Additionally, Stamberger et al.’s analyses focused primarily on the spatial distributions of users with primary results focusing on use density. This study seeks to continue to expand on the contributions of Stamberger et al. by overcoming the battery life and reliability limitations through use of a different, recreation-grade GPS unit with enhanced battery life and through implementing data collection methods in the field that reduce and address the reported logistical challenges. Moreover, this study seeks to explore the potential for new analyses for analyzing GPS data from overnight wilderness users through employing analyses that not only provide descriptions of the spatial component of use but that equally consider the temporal component of use. In this way, this study seeks to describe patterns in the behavior of multi-day, backcountry users through analysis of the spatial and temporal data collected.

Research Questions

Primary Focus: Behavior of Wilderness Users

  • What spatial and temporal use patterns emerge among overnight, multi-day wilderness users in Glacier Bay National Park and Preserve?
  • What differences or similarities emerge in spatial and temporal use among days in an overnight, multi-day wilderness trip (i.e., looking at the spatial and temporal use of all trips on day 1 do we see emerging characteristics)?

Secondary Focus: Intersection of Behavior and Location

  • What are the land cover characteristics of terrestrial wilderness use in Glacier Bay National Park and Preserve? Do relationships exist between the spatial and temporal characteristics of terrestrial use and land cover classes?
  • What are the bathymetry (or other?) characteristics of marine wilderness use in Glacier Bay National park and Preserve? Do relationships exist between the spatial and temporal characteristics of marine use and marine features? Note: I’m not sure what data I’d use to operationalize this at this time.

Practical/Data Analysis/Class Questions

  • How can the overnight, multiday tracks be meaningfully displayed and/or symbolized for reporting? I’d like to try to figure out a way that both space and time can be represented given that a central contribution of tracking overnight users is seeing their use of space through time (i.e. multiple days).
  • What analyses can be used that move beyond descriptive statistics (i.e., calculations of distance traveled, time spent)? Are there clustering analyses that take in to account both spatial and temporal characteristics rather than just spatial characteristics?
  • Are there standard diagnostic or exploratory data plots (outside of viewing the data in ArcGIS) that can be used to understand the GPS data and determine appropriate spatial statistics for analysis?
  • How can I “normalize” the data to ensure that observed differences are not a function of the number of GPS points dropped but a function of actual distances in behavior? Do I need to normalize? Note: This may not be relevant for this dataset, but I’m working with another spatial dataset for a publication where this is relevant.
  1. A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

Dataset: The dataset for analysis is a sample of 38 GPS tracks of multi-day trips in GLBA Wilderness (Figure 1). Recreation grade, personal GPS units were administered to a sample of wilderness visitors, June through August 2017, prior to the start of their multi-day wilderness trip. Study participants were asked to carry the GPS unit for the duration of their trip and return the unit at the end of their trip. GPS units tracked visitor movement continuously throughout the trip.

Figure 1. GPS track data collected from wilderness users in GLBA Wilderness during the summer 2017 use season.

Temporal Resolution and Extent: Units recorded a GPS point at various intervals, determined as a function of speed of travel. When speed was recorded at 0 miles per hour (MPH), the GPS units recorded an X,Y location point every 60 seconds. When speed was recorded at 1 MPH, the GPS units recorded an X,Y point every 15 seconds. When speed was 2 MPH or greater, the units recorded an X,Y GPS point every 8 seconds. Data collection began with the first GPS unit distributed on June, 17, 2017 and ended with the last GPS unit returned on August, 6, 2017. Most tracks recorded between two and four days of data. Some tracks are incomplete (i.e., the entire trip was not recorded) because the battery died or the unit malfunctioned prior to being returned at the end of the participant’s overnight trip.

Spatial Resolution and Extent: At each time interval (described above), the GPS units recorded X and Y coordinates. Coordinates were recorded in decimal degrees. The geographic coordinate system for the data is GCS_WGS_1984. The spatial extent for the dataset is the park boundary for GLBA.

  1. Hypotheses: predict the kinds of patterns you expect to see in your data, and the processes that produce or respond to these patterns.

At this point, I do not have any formal hypotheses about the spatial or temporal behavior of wilderness users in GLBA Wilderness. My analyses will be exploratory, and I hope to look at several different analyses and outputs to ultimately identify an approach/analysis that works well within the limits of the data and will be practically meaningful for Wilderness managers. Ultimately, I’d like to be able to describe hot spots in both space and time and to identify spatial and temporal trends among the days of each trip.

  1. Approaches: describe the kinds of analyses you ideally would like to undertake and learn about this term, using your data.

Spatial Descriptions: I would like to create a kernel density map and perform a hot spot analysis to get practice using those tools and to understand spatially where clustering is occurring in the data. These analytical outputs are common density outputs in the recreation literature and I’d like to make sure that I’m applying them appropriately. I am also interested in potentially using a nearest neighbor hierarchical cluster analysis to understand where spatially explicit clusters exist in the data. I’ve used this analysis before, but again I’d like to make sure that I’m applying it appropriately and interpreting the output appropriately. I am also interested in an analysis (maybe path analysis?) that identify statistical patterns in the sequence of the X,Y points rather than identifying statistical hot or cold spots among the points in the GPS tracks.

Spatiotemporal Analyses: I have read a paper that uses the Space-Time Cube in ArcGIS to understand hot and cold spots in space and time and thought that the output was interesting; I would be interested in using that tool, if appropriate, to try to analyze the space and time elements of the GPS tracks together. Generally, this next level of analysis is an area where I am looking for guidance, as I’m not really familiar with other spatiotemporal analyses. I’ve been doing some initial research, but need to keep working on this end to find out what analytical tools are available. At this point, I’d be looking for something that is descriptive, and data driven as I do not have formal hypotheses to test.

  1. Expected outcome: what do you want to produce — maps? statistical relationships? other?

Ideally, I’d like to be able to produce visualizations, whether it be maps or other, that represent statistically significant spatiotemporal behaviors in the data. In essence, when a manager looks at a map or visual output, I’d like to be able to show that what is displayed is statistically significant and doesn’t just look significant because of the symbology used.

  1. How is your spatial problem important to science? to resource managers?

Since the establishment of “wilderness” as a federal lands designation, recreation researchers have engaged in research to understand the unique experience of recreating in wilderness. To date, primary methods for conducting research to shed light on the quality of wilderness experiences has used qualitative and quantitative approaches to collect interview and/or survey data from wilderness recreationists. These studies have focused on understanding an individual’s perceptions of their experiences, with topics ranging from motivations, meaning and importance, aspects of the experience, preferences for management, the social and environmental impacts of wilderness use, and the emotional benefits of wilderness experiences (Dawson & Hendee, 2009). Researchers have also sought to understand wilderness behaviors through data collection techniques such as visitor-recorded trip itineraries, visitor-mapped travel trajectories, and visitor reports on such items as trip duration, activities, and encounters with other users to name a few items. A common characteristic of these data collection methods targeting measurement of behavior is that all measure visitor perceptions or recollections of wilderness experiences and behaviors rather than actual experiences and behaviors themselves. While approximations of actual behavior, these measures are limited in utility, creating uncertainty in the understanding of such basic questions as where do wilderness users go during their trips and how long do they stay in wilderness? In seeking to measure actual behavior, researchers have employed such methods as research observation to record occurrences of specific behaviors in which visitors engage or the use of sensor technology to record counts of visitors passing a location at one time. These data collection techniques provide measures of actual behavior; however, the measurements are made at one time, and rarely can be used to provide a continuous record of visitor behavior in wilderness. Through using GPS technology to track overnight wilderness users in this study, an increased level of data accuracy and resolution is available for analyzing and understanding patterns in overnight wilderness users than has been previously possible.

From a managerial perspective, the analysis of these data will provide Wilderness managers at GLBA with an increased understanding of the overnight wilderness visitor population for use in upcoming Wilderness management planning efforts This new information is notable, as the overnight wilderness visitor population is the primary user group in GLBA Wilderness.

  1. Your level of preparation: how much experience do you have with (a) Arc-Info, (b) Modelbuilder and/or GIS programming in Python, (c) R?

Arc-Info: My level of experience with Arc-Info is proficient – I can easily navigate my way around the software and work independently to problem solve. I would not consider myself an expert as there are several tool boxes within Arc-Info that I have never used. I work primarily with vector data (point, line, and polygon) and am much more familiar with tools built for these data types. I am somewhat familiar with the spatial analysis toolbox, but have not had much success using these tools in Arc-Info as my datasets have been too large in the past. I would consider myself an expert in navigating the online help available through ESRI.

Modelbuilder/Python: My level of experience with Modelbuilder is proficient, although I have not used Modelbuilder in recent years. I know that it is an available tool for linking processes, but in the past of have automated those processes using Python rather than model builder. In my master’s program I took a course specifically oriented around learning how to use Python for data management and to leverage Arc-Info tools. The course content focused on batch processing, data management, and calling tools from Arc-Info using Arcpy. It has been a little bit since I have used these skills directly, but I’ve tried to maintain those skills and could work through some code if needed. I consider myself a beginning Python programmer with much to learn. I did save my resources from my prior class and have a great textbook on Python programming in the Arc environment that I’d be happy to share with others.

R: My R experience is new, and gained through taking STAT 511 last term. I feel comfortable in the RStudio environment, and find many similarities between Python and R. I would like to learn how to work with and analysis my GPS data in R, and how to leverage any spatial visualization tools that R has to offer. My experience in R is novice, but not intimidated!

References

Cole, D. N., & Hall, T. E. (2012). Wilderness experience quality: Effects of use density depend on how experience is conceived. In Cole, D.N. (Ed.), Wilderness Visitor Experiences: Progress in Research and Management, 2011 April 4-7, Missoula, MT. Proc. RMRS-P-66 (pp. 96–109). Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station.

Hallo, J. C., Beeco, J. A., Goetcheus, C., McGee, J., McGehee, N. G., & Norman, W. C. (2012). GPS as a method for assessing spatial and temporal use distributions of nature-based tourists. Journal of Travel Research, 51(5), 591–606.

National Park Service. (1989). Wilderness Visitor Use Management Plan: Glacier Bay National Park and Preserve.

National Park Service. (2015). Glacier Bay: Wilderness Character Narrative. Available: https://www.nps.gov/glba/learn/news/wilderness-character-narrative-released.htm.

Stamberger, L., van Riper, C. J., Keller, R., Brownlee, M., & Rose, J. (2018). A GPS tracking study of recreationists in an Alaskan protected area. Applied Geography, (93), 92-102.

United States Department of Agriculture Forest Service. (2008). Wilderness character and characteristics: What is the difference and why does it matter? Available: https://www.wilderness.net/NWPS/documents/FS/FS_Wilderness_Character_Characteristics.pdf.

Van der Spek, S., van Schaick, J., de Bois, P., & de Haan, R. (2009). Sensing human activity: GPS tracking. Sensors, 9(4), 3033–3055.

Print Friendly, PDF & Email


1 Comment

  1.   jonesju — April 9, 2018 @ 7:54 am    

    hi Susie,
    Thanks for the complete and thorough spatial problem blog post. To get started, I suggest that you create GIS layers of the tracks (a subsample – not everything). The tracks would be represented as a set of segments attributed with times. That means you have to pick some minimum temporal resolution: for now, maybe use 30 minutes. Then see if you can run a spatial filter over your data to ask, for example, how many tracks were within XX distance of one another in the same 30-minute period? This would help get at the question of the quality of the wilderness experience (i.e., how much solitude or not visitors are getting).

RSS feed for comments on this post.

Sorry, the comment form is closed at this time.

© 2019 GEOG 566   Powered by WordPress MU    Hosted by blogs.oregonstate.edu