Extracting and understanding urban areas of interest using geotagged photos. (November 2015)

• Record Type: Journal Article
• Authors: Hu, Yingjie; Gao, Song; Janowicz, Krzysztof; Yu, Bailang; Li, Wenwen; Prasad, Sathya
• Is Part Of: Computers, environment and urban systems. Volume 54(2015)
• Page Start: 240
• Page End: 254
• Publication Date: 2015-11
• Subjects: Areas of interest -- AOI -- Social media -- Flickr -- DBSCAN -- Chi-shape -- Tag extraction -- Photo analysis -- Data mining; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2015.09.001
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: Urban areas of interest (AOI) refer to the regions within an urban environment that attract people's attention. Such areas often have high exposure to the general public, and receive a large number of visits. As a result, urban AOI can reveal useful information for city planners, transportation analysts, and location-based service providers to plan new business, extend existing infrastructure, and so forth. Urban AOI exist in people's perception and are defined by behaviors. However, such perception was rarely captured until the Social Web information technology revolution. Social media data record the interactions between users and their surrounding environment, and thus have the potential to uncover interesting urban areas and their underlying spatiotemporal dynamics. This paper presents a coherent framework for extracting and understanding urban AOI based on geotagged photos. Six different cities from six different countries have been selected for this study, and Flickr photo data covering these cities in the past ten years (2004–2014) have been retrieved. We identify AOI using DBSCAN clustering algorithm, understand AOI by extracting distinctive textual tags and preferable photos, and discuss the spatiotemporal dynamics as well as some insights derived from the AOI. An interactive prototype has also been implemented as a proof-of-concept. While Flickr data have been used in this study, the presented framework can also be applied to other geotagged photos. Highlights: We propose a framework for extracting and understanding urban AOI from geotagged photos. We design an experiment to construct optimal polygons from point clusters. We mine knowledge from the extracted AOI, and investigate their spatiotemporal dynamics. An online system has been developed as a proof-of-concept to show the AOI in different cities.