Data-Driven Park Planning: Comparative Study of Survey with Social Media Data
| dc.contributor.author | Sim, Jisoo | en |
| dc.contributor.committeechair | Bohannon, C. L. | en |
| dc.contributor.committeechair | Miller, Patrick A. | en |
| dc.contributor.committeemember | Swarup, Samarth | en |
| dc.contributor.committeemember | Schubert, Robert P. | en |
| dc.contributor.committeemember | Skuzinski, Thomas Stefan | en |
| dc.contributor.department | Architecture | en |
| dc.date.accessioned | 2020-05-06T08:00:56Z | en |
| dc.date.available | 2020-05-06T08:00:56Z | en |
| dc.date.issued | 2020-05-05 | en |
| dc.description.abstract | The purpose of this study was (1) to identify visitors’ behaviors in and perceptions of linear parks, (2) to identify social media users’ behaviors in and perceptions of linear parks, and (3) to compare small data with big data. This chapter discusses the main findings and their implications for practitioners such as landscape architects and urban planners. It has three sections. The first addresses the main findings in the order of the research questions at the center of the study. The second describes implications and recommendations for practitioners. The final section discusses the limitations of the study and suggests directions for future work. This study compares two methods of data collection, focused on activities and benefits. The survey asked respondents to check all the activities they did in the park. Social media users’ activities were detected by term frequency in social media data. Both results ordered the activities similarly. For example social interaction and art viewing were most popular on the High Line, then the 606, then the High Bridge according to both methods. Both methods also reported that High Line visitors engaged in viewing from overlooks the most. As for benefits, according to both methods vistors to the 606 were more satisfied than High Line visitors with the parks’ social and natural benefits. These results suggest social media analytics can replace surveys when the textual information is sufficient for analysis. Social media analytics also differ from surveys in accuracy of results. For example, social media revealed that 606 users were interested in events and worried about housing prices and crimes, but the pre-designed survey could not capture those facts. Social media analytics can also catch hidden and more general information: through cluster analysis, we found possible reasons for the High Line’s success in the arts and in the New York City itself. These results involve general information that would be hard to identify through a survey. On the other hand, surveys provide specific information and can describe visitors’ demographics, motivations, travel information, and specific benefits. For example, 606 users tend to be young, high-income, well educated, white, and female. These data cannot be collected through social media. | en |
| dc.description.abstractgeneral | Turning unused infrastructure into green infrastructure, such as linear parks, is not a new approach to managing brownfields. In the last few decades, changes in the industrial structure and the development of transportation have had a profound effect on urban spatial structure. As the need for infrastructure, which played an important role in the development of past industry, has decreased, many industrial sites, power plants, and military bases have become unused. This study identifies new ways of collecting information about a new type of park, linear parks, using a new method, social media analytics. The results are then compared with survey results to establish the credibility of social media analytics. Lastly, shortcomings of social media analytics are identified. This study is meaningful in helping us understand the users of new types of parks and suggesting design and planning strategies. Regarding methodology, this study also involves evaluating the use of social media analytics and its advantages, disadvantages, and reliability. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:23463 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/97978 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | linear park | en |
| dc.subject | survey | en |
| dc.subject | small data | en |
| dc.subject | social media | en |
| dc.subject | big data analytics | en |
| dc.title | Data-Driven Park Planning: Comparative Study of Survey with Social Media Data | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Architecture and Design Research | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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