GEOG 566






         Advanced spatial statistics and GIScience

April 5, 2018

Exploring spatial and temporal behavior patterns of recreationists in Grand Teton National Park

Filed under: My Spatial Problem @ 12:33 pm

Overall context about my research and spatial problem:

For my Master’s thesis I will be exploring the spatial and temporal behavior patterns of water-based recreationists at a popular lake destination in Grand Teton National Park. More specifically, I will be examining if there are differences in the movements between three primary paddlesport user groups: canoers, kayakers, and stand-up paddleboarders. I will analyze the total distance people traveled, the amount of time people spent on the lake, the total distance traveled from shore, and if there are hot/cold spots of visitor use. This spatial analysis will be coupled with a survey that uses goal interference theory to explore perceptions of conflict between and among these user groups. Each person who receives a GPS unit will also participate in a survey about their experience and self-reported behavior during their visit. The analysis for the survey component of the research will be a bivariate regression analysis, examining the relationship between user group (independent variable) and perception of conflict (dependent variable). This research will be one of the first to combine survey data with spatial data to understand how people perceive and respond to conflict in time and space within water-based recreation settings. Further, this research will contribute to the dearth of knowledge about the spatial/temporal movements of water-based recreationists in parks and protected areas.

The caveat is that I do not have these data as I will be collecting them this summer. Therefore, for the purposes of this class, I will be using a mock dataset that will ideally allow me to use similar spatial analysis tools that can be applied towards my upcoming research. It is important to note that the dataset I’m using for this class is not water-based, but rather land-based hikers along a complex trail system. Because water-based recreation movement is typically more diffuse than trail-based recreation, and because I don’t have survey data, the research question for this class will be different from my actual thesis research question. However, the proposed research questions for this course aim to answer similar questions that I will be asking in my own research.

1. A description of the research question that you are exploring.

A. What spatial and temporal patterns emerge from of day-use hikers in Grand Teton National Park?

This research question seeks mostly descriptive answers about human movement and behavior within this trail system. How far are people going? How much time is spent recreating? Where is visitor movement clustered? Where is movement more diffuse?

B. To what extent does group size influence the spatial and temporal behaviors of people pausing or congregating in certain areas along a trail system in Grand Teton National Park?

This research question seeks to examine the relationship between two variables and allows me to explore temporal characteristics of visitor movement.

I imagine as I delve into the data other research questions will emerge.

2. A description of the dataset you will be analyzing, including the spatial and temporal resolution and extent.

The dataset I will be analyzing is a collection of 652 GPS tracks of day-use visitors at String and Leigh Lakes in Grand Teton National Park. These GPS units were distributed to a random sample of visitors between July 15 – September 8, 2017. Each intercepted visitor was asked to carry the GPS unit with them throughout the duration of their visit at String and Leigh Lakes. When deploying the units, study technicians also recorded the total number of people in the group, and the intended destination for their day visit. To maintain independence between samples, only one GPS unit was given to each group.

The GPS units used in this study were Garmin eTrex 10 units. These units collected point data every 5 seconds. The GPS tracks were saved as point features for analysis in ArcGIS so that each visitor’s hiking path can be represented by a series of points. The positional accuracy of these units can vary up to 15 meters. However, the Garmin units were calibrated with a high accuracy Trimble GPS unit which indicated a low average positional error of 1.18 meters.

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

I expect to find various spatial and temporal hot spots in the trail system surrounding String and Leigh Lake. Specifically, I predict that people will cluster around the eastern shoreline of String Lake, the area of shoreline that connects to the parking lots. I imagine this clustering will be influenced by a couple factors: 1.) this area is closest to the parking lots, allowing for easy access to and from vehicles; and 2.) this area provides beach access with sections of land denuded of vegetation providing spaces for picnicking, lounging, and watersport activity.

I expect that larger groups will take more breaks than smaller groups. Moreover, I predict a positive relationship between group size and stopping behavior, i.e. as group size increases, so will the stopping behavior. A process that may be influencing this pattern is that more people in a group increases the likelihood that at least one person will want, or need, to stop. Therefore, all people in the group will be more likely to stop.

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

As stated previously, my aim for this course is to learn analysis tools that will enable me to analyze my research data next Fall. There may be additional questions and tools I will discover throughout this course. As of now, to answer the above research questions, I intend to learn the following analyses:

Spatial Pattern Analysis Tools:

  1. Density analysis – where are there clusters in visitor use? I’d like to try doing a Kernel density analysis to achieve this.
  2. Hotspot analysis – are these clusters statistically significant compared to use in other areas of the trail system?
  3. Nearest Neighbor analysis – I am interested in learning how to use this tool but am unsure if it is appropriate given my data set. I need to investigate this analysis further.

Modeling Spatial Relationships Using Regression Analysis Tools:

  1. Ordinary Least Squares Regression – determine the relationship between group size (independent variable) and stopping behavior (dependent variable).
  2. Unknown. Perhaps there are more appropriate analyses available to answer this question. Ultimately, I would like to learn how to do bivariate and multivariate correlation analysis in this course as these approaches will be used in my own research.

Spatial- Temporal Analysis Tools:

  1. ArcGIS space-time cube – to determine the length of time people spend in certain areas. In general, I am interested in learning more about how to apply temporal analysis to these data.

5. Expected outcome: what do you want to produce — maps? statistical relationships? Other?

I’d like to create maps that represent visitor density and hot/cold spots. This will visually indicate where people are clustering both spatially and temporally.  I also want to produce a linear representation of the relationship between group size and stopping behavior. I’d also like to represent temporal results in a way that is digestible to outside audiences; perhaps through the space-time cube?

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

Parks and protected area land managers strive to provide a quality user experience while also protecting natural and cultural resources. Accurately understanding how people move and behave in a recreation system allow for more informed management decision making. For example, understanding where and when there are hot-spots in visitor use could indicate a need for additional infrastructure, signage, or educational initiatives depending on the management objectives for the area. Additionally, by exploring spatial relationships between variables (in this case, relationships between group size and stopping behavior), the results can have predictive power for managers.

In the scientific and academic communities, applying spatial methods to outdoor recreation science allows for a more accurate understanding of how people move, experience, and interact in outdoor spaces. By integrating GIScience with other common social science techniques in outdoor recreation — such as surveys, observations, and interviews — scientists glean richer results that can support and contribute to existing theory, generate deeper understandings about human behavior, and inspire additional studies.

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

I am new to all the tools necessary for answering these research questions. Therefore, I anticipate needing to spend additional time outside of class familiarizing myself to the software before diving into the analysis.

a.) Arc-Info — I took an introductory GIS course during Winter term 2018. While I did well in the course, I did not gain as much hands-on experience with ArcGIS as I would have liked.

b.) I used Modelbuilder once during a lab exercise. Other than that, I have little experience. I have no experience in Python.

c.) I have familiarity using R and became fast friends with YouTube and Google to learn how to use this software. I initially learned how to use R in the Statistics 511 course. I also used R to analyze and graphically represent summary statistics from numerous datasets for a large visitor use and visitor impact study for the National Park Service.

 

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1 Comment

  1.   jonesju — April 9, 2018 @ 7:58 am    

    hi Jenna,
    Thanks for your thorough blog post. At this point, your problem is very similar to Susie’s, so I recommend you sit next to her and that you both try to do the same thing. I suggest (as I suggested to Susie) that you select a subsample of your GPS tracks and create a GIS layer that shows each track as a set of segments with a particular time length associated with them (e.g., 30 minutes). Then you should try to filter the data spatially to ask, “How many tracks were within some distance XX of each other during the same 30-minute period?” This sets the groundwork for analyses of conflicts, since users presumably have to encounter one another in order to become in conflict.

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