Abstract
Place types are often used to query places or retrieve data in gazetteers. Existing gazetteers do not use the same place type classification schemes, and the various typing schemes can cause difficulties in data alignment and matching. Different place types may share some level of similarities. However, previous studies have paid little attention to the place type similarities. This study proposes an analytical approach to measuring similarities between place types in multiple typing schemes based on functional signatures extracted from web-harvested place descriptions. In this study, a functional signature consists of three component signature factors: place affordance, events, and key-descriptors. The proposed approach has been tested in a case study using Twitter data. The case study finds high similarity scores between some pairs of types and summarizes the situations when high similarities could occur. The research makes two innovative contributions: First, it proposes a new analytical approach to measuring place type similarities. Second, it demonstrates the potential and benefits of using location-based social media data to better understand places.
Highlights
In gazetteers, place type is a crucial element used to search for places
The similarity scores can be calculated for three factors: place affordances, events,proposes and key-descriptors
Three respective values are combined to obOur approach a similarityThe measurement among place types using place de
Summary
Place type is a crucial element used to search for places (e.g., schools in Clarke County, GA). The place type scheme used in a gazetteer refers to the classification system of places. Place classifications are used to describe places in different contexts such as nature or urban spaces (e.g., mountains, streams, and population-centric places such as cities). Place classifications are notably different between studies of nature and urban environments. This study proposes an approach to measuring the similarity between place classifications based on functional signatures extracted from web-harvested place descriptions. Functional signatures are related to people’s activities in terms of three factors: place affordances, events, and key-descriptors. The proposed approach was tested through a case study by matching place types used by local gazetteers using Twitter data
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