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Documents authored by Jones, Christopher B.


Document
What Do You Mean You're in Trafalgar Square? Comparing Distance Thresholds for Geospatial Prepositions

Authors: Niloofar Aflaki, Kristin Stock, Christopher B. Jones, Hans Guesgen, Jeremy Morley, and Yukio Fukuzawa

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)


Abstract
Natural language location descriptions frequently describe object locations relative to other objects (the house near the river). Geospatial prepositions (e.g.near) are a key element of these descriptions, and the distances associated with proximity, adjacency and topological prepositions are thought to depend on the context of a specific scene. When referring to the context, we include consideration of properties of the relatum such as its feature type, size and associated image schema. In this paper, we extract spatial descriptions from the Google search engine for nine prepositions across three locations, compare their acceptance thresholds (the distances at which different prepositions are acceptable), and study variations in different contexts using cumulative graphs and scatter plots. Our results show that adjacency prepositions next to and adjacent to are used for a large range of distances, in contrast to beside; and that topological prepositions in, at and on can all be used to indicate proximity as well as containment and collocation. We also found that reference object image schema influences the selection of geospatial prepositions such as near and in.

Cite as

Niloofar Aflaki, Kristin Stock, Christopher B. Jones, Hans Guesgen, Jeremy Morley, and Yukio Fukuzawa. What Do You Mean You're in Trafalgar Square? Comparing Distance Thresholds for Geospatial Prepositions. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 1:1-1:14, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


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@InProceedings{aflaki_et_al:LIPIcs.COSIT.2022.1,
  author =	{Aflaki, Niloofar and Stock, Kristin and Jones, Christopher B. and Guesgen, Hans and Morley, Jeremy and Fukuzawa, Yukio},
  title =	{{What Do You Mean You're in Trafalgar Square? Comparing Distance Thresholds for Geospatial Prepositions}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{1:1--1:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.1},
  URN =		{urn:nbn:de:0030-drops-168865},
  doi =		{10.4230/LIPIcs.COSIT.2022.1},
  annote =	{Keywords: contextual factors, spatial descriptions, acceptance model, spatial template, applicability model, geospatial prepositions}
}
Document
Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections

Authors: Ruoxuan Liao, Pragyan P. Das, Christopher B. Jones, Niloofar Aflaki, and Kristin Stock

Published in: LIPIcs, Volume 240, 15th International Conference on Spatial Information Theory (COSIT 2022)


Abstract
A considerable proportion of records that describe biological specimens (flora, soil, invertebrates), and especially those that were collected decades ago, are not attached to corresponding geographical coordinates, but rather have their location described only through textual descriptions (e.g. North Canterbury, Selwyn River near bridge on Springston-Leeston Rd). Without geographical coordinates, millions of records stored in museum collections around the world cannot be mapped. We present a method for predicting the distance and direction associated with human language location descriptions which focuses on the interpretation of geospatial prepositions and the way in which they modify the location represented by an associated reference place name (e.g. near the Manawatu River). We study eight distance-oriented prepositions and eight direction-oriented prepositions and use machine learning regression to predict distance or direction, relative to the reference place name, from a collection of training data. The results show that, compared with a simple baseline, our model improved distance predictions by up to 60% and direction predictions by up to 31%.

Cite as

Ruoxuan Liao, Pragyan P. Das, Christopher B. Jones, Niloofar Aflaki, and Kristin Stock. Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections. In 15th International Conference on Spatial Information Theory (COSIT 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 240, pp. 4:1-4:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2022)


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@InProceedings{liao_et_al:LIPIcs.COSIT.2022.4,
  author =	{Liao, Ruoxuan and Das, Pragyan P. and Jones, Christopher B. and Aflaki, Niloofar and Stock, Kristin},
  title =	{{Predicting Distance and Direction from Text Locality Descriptions for Biological Specimen Collections}},
  booktitle =	{15th International Conference on Spatial Information Theory (COSIT 2022)},
  pages =	{4:1--4:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-257-0},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{240},
  editor =	{Ishikawa, Toru and Fabrikant, Sara Irina and Winter, Stephan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2022.4},
  URN =		{urn:nbn:de:0030-drops-168892},
  doi =		{10.4230/LIPIcs.COSIT.2022.4},
  annote =	{Keywords: geospatial prepositions, biological specimen collections, georeferencing, natural language processing, locative expressions, locality descriptions, geoparsing, geocoding, geographic information retrieval, regression, machine learning}
}
Document
Short Paper
Detecting the Geospatialness of Prepositions from Natural Language Text (Short Paper)

Authors: Mansi Radke, Prarthana Das, Kristin Stock, and Christopher B. Jones

Published in: LIPIcs, Volume 142, 14th International Conference on Spatial Information Theory (COSIT 2019)


Abstract
There is increasing interest in detecting the presence of geospatial locative expressions that include spatial relation terms such as near or within <some distance>. Being able to do so provides a foundation for interpreting relative descriptions of location and for building corpora that facilitate the development of methods for spatial relation extraction and interpretation. Here we evaluate the use of a spatial role labelling procedure to distinguish geospatial uses of prepositions from other spatial and non-spatial uses and experiment with the use of additional machine learning features to improve the quality of detection of geospatial prepositions. An annotated corpus of nearly 2000 instances of preposition usage was created for training and testing the classifiers.

Cite as

Mansi Radke, Prarthana Das, Kristin Stock, and Christopher B. Jones. Detecting the Geospatialness of Prepositions from Natural Language Text (Short Paper). In 14th International Conference on Spatial Information Theory (COSIT 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 142, pp. 11:1-11:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)


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@InProceedings{radke_et_al:LIPIcs.COSIT.2019.11,
  author =	{Radke, Mansi and Das, Prarthana and Stock, Kristin and Jones, Christopher B.},
  title =	{{Detecting the Geospatialness of Prepositions from Natural Language Text}},
  booktitle =	{14th International Conference on Spatial Information Theory (COSIT 2019)},
  pages =	{11:1--11:8},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-115-3},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{142},
  editor =	{Timpf, Sabine and Schlieder, Christoph and Kattenbeck, Markus and Ludwig, Bernd and Stewart, Kathleen},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2019.11},
  URN =		{urn:nbn:de:0030-drops-111033},
  doi =		{10.4230/LIPIcs.COSIT.2019.11},
  annote =	{Keywords: spatial language, natural language processing, geospatial language}
}
Document
Short Paper
Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper)

Authors: Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert

Published in: LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018)


Abstract
Social media has considerable potential as a source of passive citizen science observations of the natural environment, including wildlife monitoring. Here we compare and combine two main strategies for using social media postings to predict species distributions: (i) identifying postings that explicitly mention the target species name and (ii) using a text classifier that exploits all tags to construct a model of the locations where the species occurs. We find that the first strategy has high precision but suffers from low recall, with the second strategy achieving a better overall performance. We furthermore show that even better performance is achieved with a meta classifier that combines data on the presence or absence of species name tags with the predictions from the text classifier.

Cite as

Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 34:1-34:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2018)


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@InProceedings{jeawak_et_al:LIPIcs.GISCIENCE.2018.34,
  author =	{Jeawak, Shelan S. and Jones, Christopher B. and Schockaert, Steven},
  title =	{{Mapping Wildlife Species Distribution With Social Media: Augmenting Text Classification With Species Names}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{34:1--34:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-083-5},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{114},
  editor =	{Winter, Stephan and Griffin, Amy and Sester, Monika},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.34},
  URN =		{urn:nbn:de:0030-drops-93626},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.34},
  annote =	{Keywords: Social media, Text mining, Volunteered Geographic Information, Ecology}
}
Document
Using Flickr for Characterizing the Environment: An Exploratory Analysis

Authors: Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert

Published in: LIPIcs, Volume 86, 13th International Conference on Spatial Information Theory (COSIT 2017)


Abstract
The photo-sharing website Flickr has become a valuable informal information source in disciplines such as geography and ecology. Some ecologists, for instance, have been manually analysing Flickr to obtain information that is more up-to-date than what is found in traditional sources. While several previous works have shown the potential of Flickr tags for characterizing places, it remains unclear to what extent such tags can be used to derive scientifically useful information for ecologists in an automated way. To obtain a clearer picture about the kinds of environmental features that can be modelled using Flickr tags, we consider the problem of predicting scenicness, species distribution, land cover, and several climate related features. Our focus is on comparing the predictive power of Flickr tags with that of structured data from more traditional sources. We find that, broadly speaking, Flickr tags perform comparably to the considered structured data sources, being sometimes better and sometimes worse. Most importantly, we find that combining Flickr tags with structured data sources consistently, and sometimes substantially, improves the results. This suggests that Flickr indeed provides information that is complementary to traditional sources.

Cite as

Shelan S. Jeawak, Christopher B. Jones, and Steven Schockaert. Using Flickr for Characterizing the Environment: An Exploratory Analysis. In 13th International Conference on Spatial Information Theory (COSIT 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 86, pp. 21:1-21:13, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2017)


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@InProceedings{jeawak_et_al:LIPIcs.COSIT.2017.21,
  author =	{Jeawak, Shelan S. and Jones, Christopher B. and Schockaert, Steven},
  title =	{{Using Flickr for Characterizing the Environment: An Exploratory Analysis}},
  booktitle =	{13th International Conference on Spatial Information Theory (COSIT 2017)},
  pages =	{21:1--21:13},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-043-9},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{86},
  editor =	{Clementini, Eliseo and Donnelly, Maureen and Yuan, May and Kray, Christian and Fogliaroni, Paolo and Ballatore, Andrea},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2017.21},
  URN =		{urn:nbn:de:0030-drops-77523},
  doi =		{10.4230/LIPIcs.COSIT.2017.21},
  annote =	{Keywords: Social media, Volunteered Geographic Information, Ecology}
}
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