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Qian, Xiao (2017) Structuring place experience using social media data. PhD thesis, University of Nottingham.

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Abstract

Under the influence of place marketing, the aim of this study is to provide a new perspective on ‘place experience’, and encourage urban designers to focus more on place experience creation. An innovative approach named ‘people-generated image study’ is adopted to examine the theoretical perspective.

Based on a combination of place marketing perspectives and urban design theory, this study considers place experience as a way of transferring specific place knowledge. As a product the place experience can be decomposed into four components containing different types of information, and as a process it can be divided into four steps where each step represents an information delivery. The four steps include defining the theme, setting the event, designing the stage and examining the emotions. Basically, the theme is presented as a story told in a variety of interrelated events on a particular stage. And following the emotion analysis, urban designers can identify the elements that should be preserved and enhanced, and the elements should be removed or replaced. In practice, place experience is obtained with different narratives in different kinds of urban space. Paths, including lanes, alleys, streets, and avenues, provide space for ‘rise’, which is comprised of a group of scenes showing an enlargement both in form and content. Portals, as the point of shift from path to place, are used to display the ‘preview’ of the places, exhibiting the most important characteristics of the ‘climax’. Places, including squares, gardens, and parks, service the ‘climax’, portraying the most dramatic moments of the whole narrative.

In order to examine the theoretical perspective with an empirical case study, this research adopts people-generated image and then develops an innovative approach combining content analysis with image analysis to analyse the data. Instagram was selected to be the primary digital source for data collection, providing over 3.5 million pieces of data to case studies. Three advanced analytical tools are selected and employed, which include ArcGIS, IBM SPSS Modeler, and Cultural Analytics tools. The data sources involve photographic, text and geo-data. ArcGIS is used to narrow down the acquired data based on the analysis of geographic aggregation. IBM SPSS Modeler provides platform and tools to perform content analysis on textual information. Cultural Analytics tools are used to conduct a descriptive analysis on massive image data.

The multiple case study is conducted in two Chinese cities, Beijing and Shanghai. Four precincts are selected, including Nanluoguxiang, Tianzifang, Sanlitun Village and Xintiandi, due to their high popularity amongst Instagram users. The comparative study of Nanluoguxiang and Tianzifang is designed to examine the theoretical framework for creating themed place experiences. And the analysis of Sanlitun Village and Xintiandi aims to evaluate the implementation of the framework in three kinds of urban space. A series of suggestions are ultimately proposed.

This study brings a new perspective from the discipline of marketing to urban design, persuading urban designers to refresh their understanding of place experience and their skills in creating a specific experience for people. Besides, this study also develops an innovative approach to place experience studies, which will have great potential for application in the near future. However, there are several technical limitations in this study, which concerns the quantitative analysis of people-generated data. This will be the main direction for future research.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Heath, Tim Zhu, Yan
Keywords:	place experience, place marketing, urban design, social media, people-generated image, content analysis, image analysis, Instagram, Beijing, Shanghai
Subjects:	N Fine Arts > NA Architecture
Faculties/Schools:	UK Campuses > Faculty of Engineering > Built Environment
Item ID:	44954
Depositing User:	Qian, Xiao
Date Deposited:	13 Dec 2017 04:40
Last Modified:	14 Dec 2017 08:24
URI:	https://eprints.nottingham.ac.uk/id/eprint/44954

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