Root Wiley Poster

SST-295-03 Introduction to GIS Professor Carter

Social Boundaries in the Physical World Spatio-Temporal Patterns at Grinnell College Mark Root-Wiley

ABSTRACT

Until the mid-to-late-20th century, social analyses ignored the inherent qualities of time and space (Urry 1985:20). The study of time-geography, however, points to the face that peoples’ movements are influenced by the activities they do and the order in which they do them. In short, movement is not random and a distribution of activity is based on many factors of both time and space. Hence, it should be theoretically possible to observe “aggregate phenomena” over time and within space. Using this lens of aggregate phenomena in the context of Grinnell College, one observes that student movement patterns are affected by what dorm they live in, what classes they take (and by extension what their major is), what activities they participate in, and who they associate with. Additionally, one may expect to find exaggerated patterns of movement at Grinnell compared to the “outside world” because everyone is primarily a student (Huisman and Forer 1998). This project sought to find some initial patterns of movement through time and space on the campus of Grinnell College. To quote Marx, this study searches for “the concrete concept [that] is a synthesis of many determinations, thus representing the unity of diverse aspects” (qtd. in Sayer 1985: 65).

INTRODUCTION

Grinnell College’s campus (Figure 1) is notable both for its loggias and its three distinct campuses on the Northwestern, Northeastern, and Southeastern sections of the campus. Over the past decade, the campus has changed enormously with the addition of East Campus, the new Athletic and Fitness Center, and the Joe Rosenfield Center. In addition to new buildings, some buildings have changed their primary use. With the opening of the Joe Rosenfield Center, the Forum (formerly the center of student life on campus) was converted to a computer lab, and Quad and Cowles changed from dining halls for South and North Campus to student housing and event space, respectively. All of these changes affected the patterns in which people moved about campus. No longer did the “jocks” eat at Cowles and the “hippies” at Quad. The Forum “beach,” formerly home to bocce and drum circles now lies dormant every spring. East Campus blurred the North Campus-South Campus dichotomy. Many believed that the introduction of the Joe Rosenfield Figure 1 Center would alleviate some of the perceived North Campus-South Campus divide, but the divide can only be limited so much due to the inherent distances between campuses. Furthermore, as people tend to live near their friends, many people find few reasons to go to campuses other than the one they live on. This research sought to find out where on-campus residents move about on campus and whether this is affected by which campus they live on, their major, and other characteristics. I hypothesize, that South, North, and East Campus residents all have different “boundaries” on campus within which they spend most of their time and will infrequently move between campuses. And while there is no data from previous eras at Grinnell College, one can learn where movement occurs and why it occurs, and begin to understand how changes to the campus may have affected the patterns of everyday student life.

METHODS

For this study, a stratified sample was used to select 25 students each from North and South and 24 from East campus. 74 students were sent three emails asking them to sign up to carry a Global Positioning System (GPS) unit for one full day. Of the 74 students, 48 responded (a response rate of 65%) and 34 students agreed to carry the units (an acceptance rate of 46% or 69% of those who responded). Of the participants, all but one provided usable GPS data (that person never turned their unit on). Both the response and acceptance rates were significantly higher than expected due to the day-long commitment of picking up, carrying, and dropping off the GPS unit. These rates alone suggest that the results may have at least some representativeness of the campus. Each participant was asked to come to the Grill (a Figure 2 centrally-located space on campus) the day before they were to carry the GPS unit. At the Grill, participants signed a consent form and were shown how to turn the GPS unit on. Upon waking up the next morning, participants turned the unit on and carried it with them in their bag or pocket until they returned the units to the Grill that evening. The GPS unit was set to its default tracking manner which attempts to balance point-taking instead of plotting a point based on a certain distance traveled or amount of time passed. Participants’ path data was then downloaded and mapped (Figure 2) and the GPS unit was distributed to another person that evening. Data was collected on Monday, November 11 through Thursday, November 20, and Monday, November 24. Hence, the results of this study are applicable for only weekdays and cold times. Once each participant turned in the unit, they were asked to fill out an 18-question survey about where and why they go places on campus. These answers were then joined to their GPS data and used to visualize the dataset in an attempt to find correlations between geographic presence in a certain place and a person’s traits and habits. Finally, maps made in ArcMap were used to visualize and analyze the data. The various methods of visualization are outlined in the “Results” section. Each participant was assigned a unique ID number for purposes of anonymizing the data. Therefore, this data can be shared with little risk to any of the participants. Furthermore, I attempted to only use mapping techniques that could not be used to identify individual participants in the study.

RESULTS

December 17, 2008

In order to see how the acadmic buildings, social locations, and dormitories effect students’ distribution on campus, there must be a baseline with which to compare the patterns found in the data. Figure 4 shows what the expected directional distribution and mean center of distribution of campus life would be were students to spend their time distributed evenly across all parts of campus with dormitories and academic buildings on them (see the Types of Analysis section below for a description of these and other methods used for this study). This is compared to the actual directional distribution and mean center of all the data points on or very near the college campus. One immediately observes a few important differences between the two ovals representing distribution of theoretical and actual students on campus . First, the baseline case is more compact and located directly above the Campus Center. However, the distribution based on the GPS data is more diffuse and is centered relatively closer to the Southern, academic side of campus. As more students (and participants in the study) live in the Northern half of campus than the Southern, this likely means that students spend a great deal of time going to classes (Grinnell is a college after all), studying, and doing extracurricular activities – behaviors conceived of as “activity programs” in the time-geography literature (Huisman and Forer 1998) – on the Southern half of campus. Because the GPS unit records locations at a relatively steady interval, there are more points Figure 3 where people spend large amounts of time. Hence, observed patterns are not purely spatial but also temporal. In this case, the effects of time and space cannot easily be separated, but as predicted by time-geography, the reason people come to Grinnell – education – clearly affects their spatio-temporal distribution. Having established that activities and other characteristics of students affect geographic trends, I analyzed the data with a focus on campus residence. Figure 4 shows all of the on-campus data points taken during the study. In order to get a general idea of what trends I might expect to find, I color-coded points based on which ones came from study participants on each campus. As seen in Figure 5, people from each campus, unsurprisingly, spend the most time on their own campus. However, while visualizing only points, it is hard to see what other patterns may exist. Additionally, one cannot tell where particularly heavy areas of overlap are for the different campuses. In order to understand the density of each individual group of campus residents, a series of four kernel density maps were created (Figures 6-9). Figure 6 shows the density of all recorded data points (those shown in Figure 4). This shows hot spots that are frequented by many people on campus. Figures 7-9 show the individual density maps for East, North, and South Campus, respectively. Like in the map of points, each campus has a high concentration near their respective dormitory. However, other patterns show up. For instance, it appears that South campus residents (Figure 9) frequent the Forum and Burling Library more than other campus’s residents. Additionally, one can see which high density places on the all-campus map in Figure 6 are caused by residents from a certain campus (such as the cluster near Burling) and which are caused by students from all campuses (ARH).

TYPES OF VISUAL ANALYSES

Points and Lines – Points and lines are the most basic visualization of GPS data used for this study. Points and lines can both be visualized with the same color or with a variety of Figure 4 colors based on a certain data field such as a person’s campus of residence (see Figure 5) or their major. Lines (as routes) can also be measured by length, although in the case of this study, the GPS “artifacts” (see Caveats section) were too great to make this a meaningful value. See Figures 2, 4, and 5. Kernel Density – The kernel density function is a complex way of determining the density of points at any one place on a map. Kernel density creates a smooth surface that can be used to approximate density on any part of the map. When mapping density, the more intense the hue, the denser the data points are. Kernel density has the ability to illuminate patterns not easily seen when visualized as points. See Figures 6-9.

CONCLUSIONS

As shown by the Results section, there are multiple traits that can be linked to spatio-temporal distributions of movement on Grinnell College’s campus. Place of residence is especially correlative. However, other macro trends can be found and are likely attributable to the overarching reason people come to Grinnell College: education. As a final way of visualizing the data focusing on campus residence, Figure 10 shows the directional distributions and mean centers for all three campuses as well as the campus as a whole. In this figure, the direction of movement is even more apparent than previously observed in the density or point visualizations of the data. The fairly narrow ovals for East and North campus greatly contrast to the nearly-circular distribution of South Campus. It also appears that East residents’ patterns of movement may be denser than North or South residents’. Just like the previous Figures, Figure 10 has both strengths and weaknesses for what it can show us about movement through time and space. Clearly, there is no one effective way to analyze the GPS data collected in this study (or any other for that matter). A multifaceted approach is warranted for any study of this type of data.

Figure 10

LIMITATIONS

Inherent in the technology and methods of this study are a few shortcomings that should be considered alongside the conclusions. First, the GPS units used in the study have an accuracy of +/- 3m when outside and up to +/- 20m inside. This can lead to stationary units “wandering” even when they are not moving. Additionally, units struggle to pick up a signal inside, so buildings on campus (especially Noyce) are underrepresented in the GPS units’ results. Second, data was collected in mid-November when the temperatures were dropping quickly relative to the previous weeks. A few respondents even mentioned that they would have been outside and visited more places had it been warmer. Hence, results of this study must be taken in the context of late fall/early winter. Third, while construction has been a constant presence on campus for all four years of the senior class’s time at Grinnell, the path between East and South campus (crossing 8th Avenue) was obstructed during most of the study. Therefore, many people may have been less likely to go between the two campuses, and the paths of those who did are different than from what one would expect on a campus free of construction. Fourth, like many studies, participants were likely to fall prey to a few biases. Although the response rate was high, it is still likely that those who chose to enter the study may represent a different demographic group on campus than those who did not. Also, while the researcher attempted to not tell the participants too much about the study’s focus before the units were carried, the participants may have altered their paths during the day because they knew their movements were being recorded. Specifically, the points of one path had to be altered to remove a message “written” with the GPS unit.

FUTURE DIRECTIONS

This study has the potential to spawn a great deal of future research. First, the data has yet to receive the level of in-depth analysis it deserves. Much of the data collected in the survey has yet to be visualized and statistically analyzed. It seems likely that there may be other patterns of movement or correlative variables waiting to be discovered. Second, due to the intensity of data collection, the data set is relatively small. However, the methods of this study are easily replicable and could be reused in order to add to this data set or create a comparable one for spring, summer, or fall. Third, as highlighted by Kwan and Ding (2008), time-geography often lacks a narrative approach despite the seemingly inherent narrative in any “activity program.” A more thorough ethnographic study of movement on this campus would doubtlessly add and built on to the conclusions of this study.

ACKNOWLEDGEMENTS

Mean Center – The mean center finds the central point of a distribution of points, lines or polygons. See Figures 3 and 10.

I am deeply appreciative of Professor Eric Carter for teaching me most of what I know about mapping and spatial analysis and advising me on my data collection and analysis. I am also indebted to the Grinnell College Anthropology Department for funding this study and providing me with the GPS units that made this all possible.

Directional Distribution – The directional distribution creates an oval that contains points within one standard deviation of the mean center. Additionally, it shows the angle at which a distribution lies on a map. While kernel density is effective at showing spots of high intensity, directional distribution is good at comparing how tightly clustered a distribution is (the larger the oval, the less clustered the data) and the general direction of movement. See Figures 3 and 10.

Figure 5

REFERENCES

Huisman, Otto, and Pip Forer. 1998. “Computational agents and urban life spaces: a preliminary realisation of the time-geography of student lifestyles”. In 3rd International Conference on GeoComputation. University of Bristol, United Kingdom http://www.geocomputation.org/1998/68/gc_68a.htm. Kwan, Mei-Po, and Guoxiang Ding. 2008. “Geo-Narrative: Extending Geographic Information Systems for Narrative Analysis in Qualitative and Mixed-Method Research.” The Professional Geographer. 60:443-465. Root-Wiley, Mark. 2008. "Grinnell College Student GPS Paths and Survey," unreleased data set. Sayer, Andrew. 1985. “The Difference that Space Makes.” Pp. 49-66 in Social Relations, Space, and Time. New York: St. Martin's Press. Urry, Gregory. 1985. “Social Relations, Space, and Time.” Pp. 20-48 in Social Relations, Space, and Time. New York: St. Martin's Press. Figure 6

Figure 7

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Figure 9

Yu, H. 2007. “Visualizing and Analyzing Activities in an Integrated Space-time Environment: Temporal GIS Design and Implementation.” In Transportation Research Board 86th Annual Meeting, Washington.