A quantitative analysis of global gazetteers: Patterns of coverage for common feature types

Acheson, Elise; Sabbata, Stefano De; Purves, Ross S.

doi:10.1016/j.compenvurbsys.2017.03.007

Cited by 69 publications

(34 citation statements)

References 20 publications

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“…The GeoNames gazetteer was chosen for GeoTxt because of its extensive coverage, quality, inclusion of metadata (such as alternate names and geographic hierarchical information), and frequent updates (Acheson, Sabbata, & Purves, ). The larger the gazetteer (in terms of coverage and detail), the harder the task of toponym resolution, since the gazetteer will contain a higher number of ambiguous names (Buscaldi, ).…”

Section: System Componentsmentioning

confidence: 99%

GeoTxt: A scalable geoparsing system for unstructured text geolocation

Karimzadeh

Pezanowski

MacEachren

et al. 2019

Transactions in GIS

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In this article we present GeoTxt, a scalable geoparsing system for the recognition and geolocation of place names in unstructured text. GeoTxt offers six named entity recognition (NER) algorithms for place name recognition, and utilizes an enterprise search engine for the indexing, ranking, and retrieval of toponyms, enabling scalable geoparsing for streaming text. GeoTxt offers a flexible application programming interface (API), allowing for customized attribute and/or spatial ranking of retrieved toponyms. We evaluate the system on a corpus of manually geo-annotated tweets.First, we benchmark the performance of the six NERs that GeoTxt provides access to. Second, we assess GeoTxt toponym resolution accuracy incrementally, demonstrating improvements in toponym resolution achieved (or not achieved) by adding specific heuristics and disambiguation methods. Compared to using the GeoNames web service, GeoTxt's toponym resolution demonstrates a 20% accuracy gain. Our results show that places mentioned in the same tweet do not tend to be geographically proximate.

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Section: System Componentsmentioning

confidence: 99%

GeoTxt: A scalable geoparsing system for unstructured text geolocation

Karimzadeh

Pezanowski

MacEachren

et al. 2019

Transactions in GIS

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“…34 Data based on URL https://trends.google.com/trends/explore?date=all&q=Wikimapia,OpenStreetMap in the "Region of interest" section (accessed on 17 June 2017). (Acheson et al, 2017): This would explain the higher volume of searches across the Global South. This data also suggests that, despite its better ethical standing and richer data model, OpenStreetMap seems to fail to capture location-related searches in medium-and low-income regions, which are instead served by Wikimapia.…”

Section: A Geography Of Wikimapia and Openstreetmap Searchesmentioning

confidence: 99%

Placing Wikimapia: an exploratory analysis

Ballatore

Arsanjani

2018

International Journal of Geographical Information Science

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Wikimapia is a major privately-owned volunteered geographic information (VGI) project to collect information about places. Over the past ten years, Wikimapia has attracted hundreds of thousands of contributors and collected millions of data points, including towns, restaurants, lakes, and tourist attractions (http://wikimapia.org). Unlike OpenStreetMap, Wikimapia adopts a "placial" perspective, favouring rich descriptions over detailed geometries and encouraging the collection of textual and visual content about places with approximate footprints. In this article, we first trace the origin and development of Wikimapia as a for-profit project, intimately linked with search engine advertising. Drawing on an in-depth interview with a former developer, we analyse project's data model and characteristics of its community. As Wikimapia discussions are rife with copyright issues, we discuss the project's intellectual property, as well as its strategies for quality management. Second, we focus on the popularity of the project, which is crucial to the longevity and sustainability of VGI projects. Using behavioural data from Google Trends, we trace a geography of interest in Wikimapia, comparing with that in OpenStreetMap, from a temporal and spatial perspective. While OpenStreetMap attracts more interest in high-income countries, Wikimapia emerges as relatively more popular in low-and middle-income countries, countering the received notion of VGI as a Global North phenomenon. Our study suggests that Wikimapia's popularity is steadily declining.

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“…None of the above tools support resolving place names to GeoNames, the richest gazetteer available at the time of this writing [7]. Further, geo-annotation with toponyms requires at least (1) an interactive map interface (linked with text annotation), (2) strategies for managing geo-annotation provided by multiple annotators, and (3) the ability of software to assign more than one toponym to a place name (necessary for cases when more than one correct toponym can be identified by humans).…”

Section: Review Of Annotation Tools In Other Domainsmentioning

confidence: 99%

“…For each document, the final product includes identified place names, their position in the text (character offset) and the gazetteer toponym(s) to which each name is resolved. GeoAnnotator uses GeoNames as its default gazetteer because of its frequent updates, ease of ability by users to correct information, extensive coverage and quality [7] and inclusion of metadata items necessary for geoparsing/geo-annotation, such as alternative names and spatial hierarchies. All GeoNames toponyms have a unique identifier, enabling creation of corpora useful for linked data applications.…”

Section: Introductionmentioning

confidence: 99%

GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora

Karimzadeh¹,

MacEachren

2019

IJGI

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Ground-truth datasets are essential for the training and evaluation of any automated algorithm. As such, gold-standard annotated corpora underlie most advances in natural language processing (NLP). However, only a few relatively small (geo-)annotated datasets are available for geoparsing, i.e., the automatic recognition and geolocation of place references in unstructured text. The creation of geoparsing corpora that include both the recognition of place names in text and matching of those names to toponyms in a geographic gazetteer (a process we call geo-annotation), is a laborious, time-consuming and expensive task. The field lacks efficient geo-annotation tools to support corpus building and lacks design guidelines for the development of such tools. Here, we present the iterative design of GeoAnnotator, a web-based, semi-automatic and collaborative visual analytics platform for geo-annotation. GeoAnnotator facilitates collaborative, multi-annotator creation of large corpora of geo-annotated text by generating computationally-generated pre-annotations that can be improved by human-annotator users. The resulting corpora can be used in improving and benchmarking geoparsing algorithms as well as various other spatial language-related methods. Further, the iterative design process and the resulting design decisions can be used in annotation platforms tailored for other application domains of NLP.

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A quantitative analysis of global gazetteers: Patterns of coverage for common feature types

Cited by 69 publications

References 20 publications

GeoTxt: A scalable geoparsing system for unstructured text geolocation

GeoTxt: A scalable geoparsing system for unstructured text geolocation

Placing Wikimapia: an exploratory analysis

GeoAnnotator: A Collaborative Semi-Automatic Platform for Constructing Geo-Annotated Text Corpora

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