In this paper we discuss three studies in which we performed toponym extraction from OpenStreetMap. The studies operated at three diferent levels of geographical resolution: city level (Macao), national level (Italy and its regions), and province level (Geneva and its district). We present a single algorithm that we used in each study to extract toponyms from the text database associated to the corresponding OSM map. For each study, we provide a summary of the results, some observations on the languagespecific methodological and theoretical aspects, and language-general/cross-linguistic considerations. We conclude by analyzing the reliability of OSM for toponym extraction and linguistic theory.
OpenStreetMap (henceforth: OSM; https://openstreetmap.com) is an on-line platform that ofers
“a free, editable map of the world”, since its inception in 2004 [
Studies analyzing OSM-based data in GIS cover an expansive domain of topics (e.g. [12]). Several works investigate the accuracy of OSM maps and data when compared to oficial gazetteers (e.g. Ordnance surveys) and other Authoritative Geographic Information sources (henceforth: AGI, [13, 14, 15, 16, 17]). Many AGI sources have become open access (e.g. oficial gazetteers, Google Maps) and thus freely accessible to the public. Hence, contributors can combine these AGI-based data with personal volunteered knowledge, thus contributing to large data imports [18]. Many works have also studied how OSM can provide real-time and long-term information regarding complex situations and risks afecting places (e.g. natural disasters: [ 19, 20, 21]; epidemic difusion: [ 22, 23]). OSM maps can therefore ofer highly detailed, synchronous geo-located information, often thanks to local contributors’ direct knowledge. However, the soundness of this information usually correlates with contributors’ formal education, motivation and commitment to rigorous, professional-like data insertion [24, 25, 26, 27, 28].
Studies using OSM for the retrieval and analysis of toponyms, i.e. names for places, seem to represent an emerging field of study (e.g. [ 29, 30, 31, 32]). In OSM toponyms act as labels for tags; they are linked to content-rich descriptions of objects, and thus to the places that these objects represent [33, 34]. The increasing integration of AGI and VGI has contributed to the growth of toponyms mapping in OSM. For instance, the Parisian toponym database has experienced a fast expansion due to contributors’ access of public gazetteers, combined with their grassroots knowledge [35]. The Jerusalem toponym database seems heavily biased towards Hebrew toponyms, but the insertion of Palestinian Arabic toponyms is currently gaining heavy momentum, too [36]. Similar patterns of increasing toponym coverage are attested across regions and cultures (e.g. China: [37]; Italy: [15]; Kenya: [38]). OSM and its users benefit from this dramatic increase of coverage. However, such a situation raises questions about this empirical growth and its theoretical consequences for geographic and linguistic disciplines.
The goal of this paper is to answer two questions that arise from the evolving status of OSM as a toponym database. The first question is how reliable can be toponym extraction via OSM, so that it can feed linguistic and toponomastic (i.e. interdisciplinary) analysis. We thus present three studies in which we implemented a single extraction algorithm for OSM data and AGI sources. We then discuss the quantitative and qualitative aspects of these results. The second question is how accurate can be OSM-based data when used for cross-linguistic analysis, i.e. analysis involving data from several languages. We show that by using OSM, one can operate at ifne-grained levels of resolution irrespective the scale of analysis, and provide detailed linguistic analyses of toponyms across languages. Section 2 presents the general methodology; Section 3 the specific studies and results; Section 4 ofers a discussion and conclusions.
We used one methodological approach across our three studies; we discuss language-specific adjustments in section 3. The first study features the city of Macao, one of the two China’s special administrative regions. The second study features Italy as a national territory, though it also involves the analysis of toponyms at a regional resolution. The third study features the district/province of Geneva, Switzerland. The respective data sets were used for the creation of language-specific studies focusing on various unaddressed linguistic problems (i.e. [ 39, 40, 41]). Here we report a meta-analysis of the studies with a cross-linguistic import. Given the linguistic focus of these studies, we did not discuss fine-grained methodological problems, as they were not OSM turbopass
Data cleaning Comparison with AGIs
Geographical distribution
Linguistic analysis
Data description crucial to the study-specific goals. Hence, the cross-linguistic comparison was still outstanding.
The methodology worked as follows (cf. also Fig. 1 for the flow-chart). We accessed to OSM data through the the platform we used the platform overpass-turbo (https://overpass-turbo.eu/), sizing our search in the relevant geographical areas. The output .csv file easily supports statistical data analysis, visual data representation and linguistic categorization. We aimed to achieve a form of triangulation, i.e. to verify that the same data set via two partially diferent methods [42, 43]. We thus compared the OSM data with data extracted from AGI sources specific to the regions under study. After obtaining the toponyms data, we turned to the linguistic analysis of these data together with the geographical distribution. We performed frequency and geographical distributions analyses so that toponyms could be organised according to the types and research questions specific to each study. We discuss the questions underpinning our study and the problems that emerged relative to the questions in the next Section.
In the first study [ 39], we investigated the grammatical and lexical properties of Macanese toponyms. Macao is a city and a special administrative region in the Pearl River Delta, SouthEast China [44]. Cantonese is the most commonly spoken language (90% of the population), and has oficial status along Portuguese, which is however slowly disappearing (2% of the population: [45]). Toponyms are reported in gazetteers and atlases in the Portuguese and Chinese written systems [46]. Macanese Portuguese is near-identical to European Portuguese; thus, no spelling diferences exist in Macanese toponyms. Cantonese toponyms are written in the Chinese simplified characters system, and are thus intelligible to speakers of Cantonese and other Sinitic languages (e.g. Mandarin, Hakka). Our goal was to analyse the internal order of toponym constituents in both Macanese languages (i.e. their grammatical properties), and analyse how toponyms could potentially classify places via their lexical content/meaning.
The details of our methodology for this study were as follows. In the first step, one researcher extracted Portuguese and Chinese OSM toponyms. In the second step, two researchers compared these data with an oficial gazetteer in CD-ROM form, as an AGI source [ 47]. The researchers obtained two lists of 1394 toponyms, comparing the tokens on a case-by-case basis. We performed an analysis of the Jaccard Index of similarity (from 0 to 1: the closer to 1, the more similar two populations, [48]) between the two lists, and obtained a 0.989 as a result. Qualitatively, the only diference was that Portuguese toponyms featured minor spelling variants in OSM (e.g. accent omission: Rua de Santo Antonio instead of Rua de Santo António ’Saint Anthony street’). Aside this minor divergence, we could confirm that the OSM data were as accurate as those found in the CD-ROM. We thus conjecture that OSM contributors have not inserted information about places not attested in oficial maps (e.g. shops, cafes, and other smaller places).
The analysis overall showed that toponym extraction via OSM proved as reliable as extraction via the CD-ROM gazetteer: our first question finds a positive answer. Furthermore, the OSM data lent themselves to a direct linguistic comparison of Chinese and Portuguese toponyms: the dual set of tokens confirmed that all places on the map(s) included two toponyms. For instance, the two toponyms 龍鬚街 lung4 sou1 gaai1 ‘Dragon Beard Street’ and Rua Central ‘Central Street’ reported the same geo-coordinates in their database entry. This fact showed that they were the respective Chinese and Portuguese names for this place. Our second question also finds a positive answer. Overall, our methodology successfully allowed us to extract the Portuguese and Chinese toponyms from OSM, obtain an exhaustive number of tokens for both languages. It also allowed us to perform a (cross-)linguistic analysis and comparison of these systems and their origins, therefore shedding light on the linguistic properties of these toponyms.
In the second study [40], we investigated the distribution of toponyms of dialectal origin in Italy. Italian toponyms often find their roots in the languages of the pre-Italic populations that once inhabited Italy, but also in the local dialects spoken across the country [49, 50]. Since dialectal toponyms are reported via standard Italian spelling in gazetteers and other AGI sources, we investigated the possibility that dialectal toponyms correlate in geographical distribution with their dialects. We then used the output .csv file to create a database of toponyms organized according to their distribution in each of the 20 Italian administrative regions. We could thus compare toponyms distribution with the dialect(s) spoken in each region.
We obtained 452,538 toponyms, with distribution among regions being quite uneven. Regions with higher numbers of cities and urban centers ofered the highest number of toponyms. For instance, Milan and its region Lombardy covered 11.36% of the total; the capital city of Rome and its region Lazio 8.11% of the total toponyms. Small regions such as Abruzzo, in central Italy, only ofered 2.91% of the total. Nevertheless, all regions and major dialects ofered evidence for toponyms of local/dialectal origins. This is the case because for toponyms displaying clear dialectal origins, we could observe that their distribution was often limited to the regions in which a toponym was found. For instance, toponyms for streets in Venice and the surrounding Veneto region may include the generic (i.e. classificatory) term calle, literally ‘narrow alley’. Outside this region, we only found sporadic cases of toponyms including calle; this is strong evidence that these toponyms have Venetian (i.e. dialectal) origins.
For the goals of our paper, the study ofers two answers. First, OSM critically ofers a higher number of toponyms than some AGI sources. We compared this result with a previous study in which we extracted toponyms from the YellowPages online directory (https://paginegialle.it; [51]). In this latter study, we extracted toponyms from province-based directories, with “province” being the immediate administrative unit below regions. We obtained 213,218 toponyms, i.e. less than half of the toponyms extracted via OSM. One possible explanation is that the YellowPages directory includes directories for minor urban centers, villages and hamlets. However, these directories tend to ofer lower- resolution maps than those for major urban centers (e.g. 1:5000 against 1:3000). Non-urban places are also absent, and one must use dedicated gazetteers for retrieving those toponyms (e.g. plates for mountainous ranges: [50]). We also computed a Jaccard index on these lists, and the result was clear (0.088): the YellowPages directory data represent a sub-set of the OSM data. OSM thus now provides a more reliable and a faster source for toponym extraction, at least for the regional and national scale level of Italy.
Second, OSM did not ofer cues on the dialectal origins of toponyms. We had to analyse the lexical properties of toponyms (e.g. their etymologies and senses), and their geo-linguistic distribution and correlation with dialects. The higher number of toponyms also entailed that we could access toponyms not reported in the YellowPages gazetteers. For instance, the Sicilian term ronco designates a type of narrow alley mostly found in the city of Syracuse and in other cities from this island. In OSM, we could find several instances of toponyms including this term. In the YellowPages directory these terms were missing, possibly due to these alleys being too small to appear in the gazetteers’ 1:3000 maps, compared to OSM’s average scale of 1:1000. Hence, OSM data may require further linguistic analysis when one attempts comparisons across languages, but these data may be quantitatively superior than in AGI sources.
In the third study [41], we investigated the lexicographic properties of toponyms in Geneva and its surrounding canton (i.e. district). Geneva is a Francophonic enclave in Switzerland and a global hub for diplomatic and economic communities (see [52, 53] for some sociological studies). Geneva’s district represents a departure point for the lush mountainous environments and natural attractions for which Switzerland attracts millions of tourists and acts as a target for "scientific tourism" [ 54]. We therefore studied the geo-linguistic distribution of toponyms referring to typical urban and rural places and their linguistic properties. For this study, we used two sources for database creation. One is the platform overpass-turbo; the other is the dedicated repository Noms géographiques du canton de Genève (https://noms-geographiques.app.ge.ch/), an oficial on-line gazetteer for the Geneva canton.
We found 3843 toponyms in the oficial repository, 3713 in OSM, thus detecting a small asymmetry. We analyzed the two lists to verify that the toponyms matched (Jaccard index: 0.680). The city of Geneva covered most of the attested toponyms (i.e. 918 tokens in OSM (24.72% of the total); 927 tokens (24.12%) in the dedicated repository). The Geneva district covered the remainder of toponyms, with the municipalities of Meyrin (5.47% OSM; 4.81% repository), and Lancy (4.23% OSM; 4.24% repository). Furthermore, a lexicographic analysis suggested that Geneva and nearby cities mostly featured toponyms for urban places (e.g. rue ’urban street’ and avenue ’broad road in a urban agglomeration’). Conversely, rural zones made up the rest of the district’s toponyms (e.g. route ’road’ and chemin ’path’). The sharp distinction between urban and rural territories was mostly mirrored in the toponyms lexicon for this Canton.
We obtained two key answers for our target questions. First, OSM and repository seem to minimally diverge in their coverage. Since the repository is an open access platform, we conjecture that OSM contributors may have uploaded the data semi-manually from the repository, though this process is still undergoing. Second, and likely a consequence of the first result, the distribution of toponyms correlated with urban centers. Outside urban centers, paths but also touristic attractions (i.e. mountainous paths or chemins in French) are the chief objects also including information about toponyms. Genevian OSM data therefore seem highly reliable though less so than the repository as its AGI counterpart, and amenable to linguistic analysis. They may also provide a relatively clear geo-linguistic picture on toponyms types’ distribution.
We propose two general results as language-general answers to our questions.
The first result is that toponym extraction from OSM seems now highly reliable, modulo
the use of dedicated algorithms and databases providing geographically-bound toponyms lists
(e.g. Macanese toponyms). Reliability also stems from the fact that OSM as a VGI source
can provide data that may be quantitatively and qualitatively superior to AGI sources (cf.
[
The second result is that OSM-based data now provide accurate information on which to develop a cross-linguistic analysis that builds on language-specific results. From the first study, we know that Macanese places have dual (i.e. Portuguese, Chinese) toponyms. From the second study, we know that Italian toponyms often have dialectal, geographically specific roots. From the third study, we know that Geneva and district include a high number of toponyms for urban places. Hence, each study provides a language-specific set of results. The three studies also show that OSM and other AGI sources include similar data-sets, as we summarise in Table 1: Study Macao Geneva Italy
OSM toponyms 1,394 3,713 452,538
AGI toponyms Jaccard Index 1,394 0.989 3,843 0.680 213,218 0.088
We conjecture that these results may be correlated with the size of the places and corresponding toponym lists that we targeted in our studies (cf. also [55] for discussion). The data from the first study (the city of Macao) suggest that AGI and VGI sources can be identically detailed on places providing smaller, well-documented toponym lists. The data from the second study (province of Geneva) suggest that AGI sources may still ofer more detailed pictures. However, the data from the third study (Italy and its regions) suggest that the opposite trend may become the norm in the future. Thus, OSM as a VGI source may slowly turn into a more accurate and thorough source for toponyms, since it can integrate data from several AGI’s into a single map.
From these results, certain cross-linguistic generalizations with a geo-linguistic import logically emerge. First, toponyms often appear as compound nouns, in which a classifier or “generic term” may either precede or follow a byname or “specific term”. The analysis of this category often found in Anglophonic toponomastics (e.g. [56, 57]) thus seems to have crosslinguistic import. Second, access to the type of high-resolution data that OSM can improve the quality of this interdisciplinary analysis. Overall, OSM may provide direct access to toponyms that may only be found in e.g. national/regional-, provincial/district- and city-based AGI gazetteers (e.g. [30]; [33]–[39]). Hence, it allows researchers to address cross- and geo-linguistic questions via one data extraction methodology, at least in our case.
This study has provided recent evidence that OSM can ofer highly reliable data regarding toponyms. However, this reliability hinges on the regions of interest and contributors’ access to AGI data and their nuanced knowledge of local toponyms. Hence, OSM data can now support single- and cross-linguistic generalizations: the meta-analysis of our three OSM-based studies supports this conclusion. This result entails that “platial” (i.e. place-based, [58]) research in linguistics can find a veritable methodological ally in OSM. Furthermore, the study has also shown that OSM can potentially provide a wealth of toponymic data: our algorithm extracted toponyms irrespective of the writing system in which toponyms were reported. We acknowledge that the choice of regions for such studies can heavily influence reliability. For regions on which OSM data may still not scale up to AGI sources, perhaps we may not obtain similar results. We leave this and other challenges for future studies. data on a property graph, in: Central European Conference on Information and Intelligent Systems, Faculty of Organization and Informatics Varazdin, 2021, pp. 193–199. [9] J. M. Almendros-Jiménez, A. Becerra-Terón, M. G. Merayo, M. Núñez, Metamorphic testing of OpenStreetMap, Information and Software Technology 138 (2021) 106631. doi:138.106631.10.1016/j.infsof.2021.1066319. [10] T. Cresswell, Place: an introduction, John Wiley & Sons, 2014. [11] J. Malpas, Place and experience: A philosophical topography, Routledge, 2018. [12] J. J. Arsanjani, A. Zipf, P. Mooney, M. Helbich, OpenStreetMap in GIScience, Lecture notes in geoinformation and cartography (2015) 324. [13] E. Bortolini, S. P. Camboim, Mapeamento colaborativo de favelas com a plataforma openstreetmap collaborative slum mapping with Openstreetmap, Mapeamento participativo: tecnologia e cidadania (2019). [14] J. Fize, L. Moncla, B. Martins, Deep learning for toponym resolution: Geocoding based on pairs of toponyms, ISPRS International Journal of Geo-Information 10 (2021) 818. doi:10.818.10.3390/ijgi10120818. [15] G. Salvucci, L. Salvati, Oficial statistics, building censuses, and OpenStreetMap completeness in Italy, ISPRS International Journal of Geo-Information 11 (2022) 29. doi:10.3390/ ijgi11010029. [16] S. Garba, H. Musa, S. Bala, N. Hafiz, M. İya, H. Náiya, M. Mustapha, G. Sule, Quality Analysis of OpenStreetMap and Digital Elevation Data Based North-Western, FIG Congress 2022 Volunteering for the future - Geospatial excellence for a better living Warsaw, Poland, 11–15 September 2022 (2022). [17] T. Holthaus, A. Thiemermann, Identifikation deutscher Straßenentwurfsklassen im Straßennetz von OpenStreetMap, Road Network (2022). doi:10.14627/537728010. [18] R. Witt, L. Loos, A. Zipf, Analysing the Impact of Large Data Imports in OpenStreetMap,
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