| dc.contributor.author | Lecy, Evan | |
| dc.description.abstract | Every world class city has a landmark, in Vegas it is “The Strip." Yet, this important gateway to Las Vegas venues is little more than a 10 lane downtown street controlled by stop lights. Reminiscent of 1970’s streets in downtown Atlanta, LA or Dallas. Many public review platforms such as TripAdvisor, Expedia, we8there, Foursquare, Google places, etc. have collected an increasing amount of feedback in the form of unstructured user reviews. These reviews are self-posted, volunteer created text about user experiences with products and services which differs from surveys asking targeted questions. Studies show that the Hospitality industries adoption of user reviews has been successful (Xie et al, 2016). Regional planning teams have also begun to adopt these practices. Machine learning technologies such as decision trees and natural language processing were implemented in these studies. (Büschken, J., & Allenby, G. M., 2016). However, there have been no serious attempts to implement review data for public spaces. Public spaces share the same review platforms as otels/restaurants/museums. This paper aims to fill this intellectual gap and explore the utility of online reviews on public space research and design. A dataset of reviews charting the past 10 years has been created. This data set contains approximately 20,500 genuine online reviews, over 1.6 million words from patrons of the strip of Las Vegas in Nevada. Next, a natural language processing machine called Latent Dirichlet allocation will be applied to make sense of the topics that are expressed int he words used to describe visitor experiences. Results of this paper will specify significant environmental and programmatic elements in the reviews and their correlations with the ratings. We will also present a design guideline based on our analysis to be followed in the redesigning of the Las Vegas strip. We believe online reviews provide strong empirical evidence for user experience in built environment projects. We hope Landscape Architects, Urban Planners and Policy Makers might realize the potential of implementing our approach and change the future of design and research related to the built environment. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | Distribution license signed by student (2019) and included as background license document (see bitstream attachments). | |
| dc.title | Reflection: The Las Vegas Strip - Introducing Big Data into Project Planning for User Informed Design | en_US |
| dc.type | Thesis | en_US |
| dc.type | Plan or Blueprint | en_US |
| dc.type | Presentation | en_US |
| dc.type | Image | en_US |
| dc.date.accessioned | 2019-06-10T16:57:43Z | |
| dc.date.available | 2019-06-10T16:57:43Z | |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/29811 | |
| dc.subject.lcsh | Las Vegas Strip (Nev.) | en_US |
| dc.subject.lcsh | Public spaces. | en_US |
| dc.subject.lcsh | Streetscapes (Urban design) | en_US |
| dc.subject.lcsh | Public opinion -- Data processing. | en_US |
| dc.subject.lcsh | Data mining. | en_US |
| dc.subject.lcsh | Big data. | en_US |
| dc.subject.lcsh | Las Vegas (Nev.) | |
| dc.subject.lcsh | Nevada. | |
| ndsu.degree | Master of Landscape Architecture (MLArch) | |
| ndsu.college | Arts, Humanities and Social Sciences | |
| ndsu.department | Architecture and Landscape Architecture | |
| ndsu.program | Landscape Architecture | |
| ndsu.advisor | Kost, Jason | |
| ndsu.advisor | Song, Yang | |
| ndsu.award | Dennis C. Colliton Memorial Award for Landscape Architectural Design Winner | |
