Photographing a City: An Analysis of Place Concepts Based on Spatial Choices

Abstract

We ask whether the photographs published in web-based image collections do represent different conceptualizations of a city and present a method for gathering and analyzing a data set of more than 12,000 images from Amsterdam, Bamberg, Cardiff, and Dublin. We then propose a measure for the popularity of a location in a city. The analysis of the data set reveals that the popularity follows a power law with very few highly popular locations and a long tail of places in a city that are visited only occasionally. The most popular locations can be identified with the semantic core of the conceptualization of the city in terms of images. This raises the issue of individual differences. We propose another measure that permits to identify users with similar conceptualizations of a city.

Areal Delineation of Home Regions from Contribution and Editing Patterns in OpenStreetMap

“…Such information can be used for the analysis of people's spatio-temporal travel patterns and their perception of space. Examples include the extraction of people's movement trajectories [2,3], events [4], popular places [5], and vernacular regions [6] from the shared image websites Panoramio and Flickr. Furthermore, tweets have been used to extract knowledge about significant personal places in people's everyday lives [7], people's activity patterns [8,9], transit riders' sentiments about transit services [10], and people's happiness [11].…”

Road-based travel recommendation using geo-tagged images

“…As Schlieder and Matyas (2009) substantiated already, the landmark popularity contributed by user-generated image follows a power law distribution. Specifically, the first image of a user increases the popularity of the landmark, while subsequent images of that same user add less and less popularity.…”

Measure of Landmark Semantic Salience through Geosocial Data Streams

“…Thus, UGPDs may account fictitious places that de facto cannot serve as landmark candidates (e.g., [61]). Nonetheless, we would like to moderate the impact of this disadvantage since additional parameters can be taken into account (e.g., the number of distinct users who have published check-ins).…”

“…They extract what they call "cognitively significant geographic objects" (i.e., objects that may serve as a landmark) by decoding the spatial context of web documents. Regarding crowdsourced data, most of solutions focus on the analysis of geotagged photos (see [59][60][61]). Recently, researchers used geolocated data of a location-based game to identify structural landmarks [62].…”