Identifying Locally- and Globally-Distinctive Urban Place Descriptors from Heterogeneous User-Generated Content

Abstract

Place, which can be seen simply as space with meaning, has long been recognized as an important concept for understanding how individuals perceive, utilize and value their surroundings. There is increasing interest in mining information from geo-referenced user-generated content (UGC) and volunteered geographic information (VGI) to gain new insights into how people describe and delimit urban places such as neighbourhoods and vernacular landmarks and locales. In this paper, we aim to extend recent efforts to explore semantic similarity in these data by examining differences in place descriptors through georeferenced photo tags across multiple scales for selected cities in the USA. We compute measures of tag importance using both a naïve aspatial approach and a method based on spatial relations. We then compare the results of these methods for understanding tag semantics, and reveal to what degree certain characterizations as represented in tag-space are also spatially structured. Tag metrics are computed for multiple fixed resolutions that approximate typical urban place sizes (e.g. city, block, neighbourhood) and a simple extension of a well-known tag-frequency metric is proposed to capture differences in locally distinctive and globally distinctive tags. We present this analysis as an adaptation of traditional text analysis methods with ideas from spatial analysis in order to reveal hidden spatial structure within UGC.