Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in N ew Y ork City (2023)

Abstract: Critical toponymy examines the dynamics of power, capital, and resistance through place names and the sites to which they refer. Studies here have traditionally focused on the semantic content of toponyms and the top-down institutional processes that produce them. However, they have generally ignored the ways in which toponyms are used by ordinary people in everyday discourse, as well as the other strategies of geospatial description that accompany and contextualize toponymic reference. Here, we develop computational methods to measure how cultural and economic capital shape the ways in which people refer to places, through a novel annotated dataset of 47,440 New York City Airbnb listings from the 2010s. Building on this dataset, we introduce a new named entity recognition (NER) model able to identify important discourse categories integral to the characterization of place. Our findings point toward new directions for critical toponymy and to a range of previously understudied linguistic signals relevant to research on neighborhood status, housing and tourism markets, and gentrification.

## introduction places are not only bounded regions and the material forms they contain, but are vested with and shaped by symbolic associations (e.g., @xcite @xcite . the names we use for places-toponyms-play a key role in the "production of space" @xcite , stabilizing the social reality of both place associations and place boundaries in ways that typically reflect power dynamics. the fact that new york city real estate developers have tried to rebrand parts of harlem as "soha" @xcite , for example, suggests that the area's previous names were seen as undesirable to the tenants they hope to attract. critical toponymy refers to the field of research that takes as its focus this relationship between places, their names, and the practices and systems of power that link the two @xcite . we apply the critical toponymic perspective to a large dataset of annotated airbnb listings in new york city, paying particular attention to the sociospatial circulation of neighborhood names. airbnb hosts need to communicate aspects of the location of their listings to potential renters. nearly all hosts rhetorically situate their units in a set of spatial identities and relations, but their ways of doing so are diverse. some use conventional neighborhood names (perhaps including nearby neighborhoods as well), others describe proximity to nearby landmarks, while still others simply describe accessibility to types of institutions and businesses such as hospitals, police stations, and restaurants. spatial variation in these linguistic strategies of emplacement is the main object of our analysis. in other words, we ask: what can we learn about urban dynamics from the ways in which residents of different neighborhoods describe their property locations to prospective renters? whereas conventional toponymic analysis is usually limited to the semantic content of place names, we expand our focus to include a wide range of linguistic features that reflect spatial relationships (e.g., expressions of proximity, embeddedness, and connectivity)-alongside formal toponyms for neighborhoods, streets, landmarks, businesses, and so on. to extract references to place and spatial relations from unstructured listing descriptions, we train a custom named entity recognition (ner) model on a novel, hand-annotated dataset to identify a range of toponyms and spatial relationships. we analyze these linguistic features alongside a range of sociodemographic variables measured at the neighborhood level, demonstrating multiple associations between toponymic practices and neighborhood status. in doing so, we offer a number of contributions. first, we expand the methodological and conceptual scope of critical toponymy. nearly all toponymic studies have focused on the centralized naming practices of elites such as mayors or large real estate developers, a "top-down" approach @xcite . by contrast, our dataset reflects the "bottom-up" toponymic practices of a larger and more diverse set of social actors at a scale impossible without modern computational techniques. in addition, our ner model allows us to expand the range of place-descriptive resources from the semantics of toponyms in isolation to more subtle, variegated, and relational linguistic strategies, while still retaining an orienting emphasis on neighborhood names, boundaries, and their relationship to neighborhood status and change. thus, we are able to investigate not only what urban areas are called, but also in which socio-spatial contexts they are invoked by name in the first place. secondly, we contribute to the growing literature on airbnb and housing dynamics. others have shown the ways in which airbnb and short-term rentals (strs) more broadly accelerate gentrification, widen the "rent gap" @xcite , and remove housing from long-term rental markets @xcite @xcite . nevertheless, little attention has been paid to the linguistic strategies that mediate economic transactions between hosts and renters. by uncovering linguistic signals related to gentrification and neighborhood status, we offer findings that can be extended to other residential contexts as well -in particular where new avenues for "technologically and culturally driven" gentrification @xcite are being opened up by the growing sector of "platform real estate" @xcite . finally, we offer two methodological contributions. first, we introduce a new schema for geospatial ner labeling and a corresponding human-annotated dataset with accompanying models. while this data is particular to new york city, our model is able to generalize beyond the annotated entities in the training data (see appendix b). second, we propose a set of new lightweight but accurate methods for toponymy resolution and geospatial dependency parsing. to anticipate our results, our models considerably outperform offthe-shelf ner models on our data and task (see ## discussion we suggest that critical toponymy is an underexplored theoretical framework with fruitful applications in applied natural language processing. mining text data to discover place names is an old task in data science (e.g., @xcite . recent innovations in embedding-based models have greatly improved our ability to infer toponyms in figure 4 : visualizing toponymic span. the left column compares the unfiltered spans, defined as the complete complex hull area of all neighborhood mentions, of two adjacent neighborhoods: williamsburg (a) and bed-stuy (c). williamsburg's considerably larger total span suggests its perceived relevance to much of the city, whereas mentions of bed-stuy are more locally constrained. the right column depicts the convex hulls @xcite after filtering mentions entailing claims of membership (e.g., located in) and after filtering with mahalanobis distance to remove outliers. again, williamsburg's (b) greater ratio of its filtered span area to its canonical neighborhood area suggests its greater status as a neighborhood compared to @xcite . here we offer preliminary steps in this direction, introducing novel data and a bespoke ner model to investigate the relationship between bottom-up toponymic practices and neighborhood status in the context of the airbnb market. we demonstrate multiple ways in which the toponymic language reflects a variety of urban geospatial and sociocultural dynamics. not all hosts locate their units in reference to their residential neighborhood. with some exceptions-an exploration of which would require a much more fine-grained discussion of the idiosyncrasies of new york city neighborhoods than is appropriate here-more peripheral neigh2) the proportion of airbnbs that mention their neighborhood by name is also positively correlated with gentrification, as is the neighborhoods toponymic span (3). 4) on the other hand, toponymic references to categories other than neighborhood is negatively correlatted with gentification, suggesting that hosts turn to other toponymic resources when their neighborhoods lack cultural capital in the eyes of airbnb consumers. unlike in the natural sciences, pearson correlation values in @xmath0.2-0.5 range are regularly reported as substantively meaningful associations (e.g., @xcite . furthermore, we identify a relationship between gentrification dynamics and what we call toponymy span: the ratio between the area within which people claim membership in a neighborhood, and the area of its canonical boundaries. given the demonstrated association between airbnbs and gentrification (suggested by our data as well; see figure 5 , leftmost panel), this points to the possibility of a circular process. as a neighborhood gentrifies and acquires desirability among a class of largely white, middle-and upper-middle class, young professionals, airbnb hosts and guests at the geographic fringes of those neighborhoods become more likely to locate themselves within it. identifying themselves with new desirable toponyms might proceed to attract guests with greater efficacy, increasing the "rent gap" introduced by airbnbs @xcite and perhaps accelerating the gentrification processes set in motion by strs. while such causal dynamics would be difficult to properly model, and we certainly do not do so here, our findings in combination with prior research on the effect of airbnbs suggest that toponymic practices might not merely reflect ongoing urban change, but play a more active role therein. ## future work due to space limitations, here we primarily focus on only two of the 21 categories our model is trained to identify: neighborhood toponyms and spatio-temporal entities. future work should investigate toponymic practices of different kinds. larger text corpora, spanning longer periods of time, could reveal how these dynamics-for example, the relationship between toponym span and gentrification-play out diachronically. many conventional indicators of gentrification process are measured with census data that may lag behind the on-the-ground experience and economic effects of gentrification. if such linguistic signals could be shown to capture gentrification dynamics before they fully manifest in conventional sociodemographic data, the importance of toponymic analysis in urban contexts would become all the more apparent. finally, researchers in human-computer interaction could expand the scope of this research program. qualitative analyses could add considerable depth to our understanding of how tourists, commuters, and prospective residents mobilize toponymic knowledge in the process of housing search practices, whether for short-or long-term rentals and home-buying. on the supply side, the same could be done for airbnb hosts. given the increasing professionalization of airbnb hosting @xcite , the ways in which such practices vary across highly professionalized (and sometimes corporatized) airbnb hosts compared with hosts who simply rent their spare rooms or apartments when they are away could merit attention as well.