Spatial Analysis and Modeling of Visual Interest : A New Methodology for Visual Resource Assessment Using Geospatial Information Tools

• Material Type: 博士論文
• Title: Spatial Analysis and Modeling of Visual Interest : A New Methodology for Visual Resource Assessment Using Geospatial Information Tools
• Author/Editor: Sugimoto, Koun; スギモト, コウウン; 杉本, 興運
• Author Heading: Sugimoto, Koun; スギモト, コウウン; 杉本, 興運
• Publication Date: 2014-03-25
• Publication Date (W3CDTF): 2014-03-25
• Alternative Title: 関心生起の空間分析とモデリング : 地理空間情報技術を応用した景観資源評価の新たな方法論
• Pages: 1-163
• Degree grantor/type: 首都大学東京
• Date Granted: 2014-03-25
• Dissertation Number: 甲第433号
• Degree Type: 博士(都市科学)
• Text Language Code: eng
• NDLC: UT51
• Target Audience: 一般
• Note (General): This thesis provides new methods for visual resource assessment and modeling, an important research theme in micro-scale level tourist/recreational site planning and management. By acquiring and manipulating a combination of digital camera images and geospatial information tools such as geographic information systems, the proposed assessment method draws results from the analysis and modeling of visitors' visual interest during their on-site experiences of geographic space. To acquire spatial data on visitors’ visual interest, two surveys are conducted. First, participants are requested to photograph positive scenes while walking using digital cameras embedded with GPS and electronic compass. Next, the participants complete questionnaires that help them to evaluate their visual experiences and categorize visual objects. Second, the height or width of the visual objects projected in the photographs taken by the participants is measured using a laser distance meter. Based on the data acquired from these surveys, spatial point and line data of visitors’ visual interest, named points of visual interest (PVI) and lines of visual interest (LVI), are extracted. Four types of applications for the analysis and modeling of visual interest are presented. Two applications attempt to analyze visual interest from location point data that visitors' interest generate. The first application conducts an exploratory analysis of spot characteristics; several spatial clusters are extracted based on similarities in preference levels. The characteristics of the representative spots are statistically described using multiple indicators including the above clusters. The second application involves modeling and visualizing the sightseeing potential of locations. Embedded into the algorithm of density computation are mechanisms for removing bias and weighting preference level scores. The subsequent two applications focus mainly on spatial line data of visitors’ interest visual lines. The third application creates a visualization of the spatial intensity of visual lines. Three map representation techniques are presented: density estimation for line data, grid-based aggregation, and flow data representation. In addition, the advantages and disadvantages of each technique are described. The final application described is the construction of a prediction model for visual interest flows. Spatial interaction models are used to predict the level of total flow between locations through explanatory variables related to origin and destination potentials, landscape elements, and distance between locations. These models allow one to assess the entire target area, rather than being limited strictly to the scenes that visitors perceive. They also overcome the notable limitations of typical photographic surveys, and they clearly show new and useful techniques in visual resource management and modeling for specific sites. The spatial intensity of PVI, LVI, and geovisualization provide location-specific potential and attractiveness of scores for scenes and spaces.; 首都大学東京, 2014-03-25, 博士(都市科学), 甲第433号
• Source: fulltext; https://tokyo-metro-u.repo.nii.ac.jp/?action=repository_action_common_download&item_id=3460&item_no=1&attribute_id=18&file_no=1
• Access Restrictions: インターネット公開
• Data Provider (Database): 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• Original Data Provider (Database): 東京都立大学 : みやこ鳥