5 Search Results for "Hu, Yingjie"

Document
DOI: 10.4230/LIPIcs.COSIT.2024.9
Probing the Information Theoretical Roots of Spatial Dependence Measures

Authors: Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai, and Ivan Majic

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
Intuitively, there is a relation between measures of spatial dependence and information theoretical measures of entropy. For instance, we can provide an intuition of why spatial data is special by stating that, on average, spatial data samples contain less than expected information. Similarly, spatial data, e.g., remotely sensed imagery, that is easy to compress is also likely to show significant spatial autocorrelation. Formulating our (highly specific) core concepts of spatial information theory in the widely used language of information theory opens new perspectives on their differences and similarities and also fosters cross-disciplinary collaboration, e.g., with the broader AI/ML communities. Interestingly, however, this intuitive relation is challenging to formalize and generalize, leading prior work to rely mostly on experimental results, e.g., for describing landscape patterns. In this work, we will explore the information theoretical roots of spatial autocorrelation, more specifically Moran’s I, through the lens of self-information (also known as surprisal) and provide both formal proofs and experiments.
Cite as

Zhangyu Wang, Krzysztof Janowicz, Gengchen Mai, and Ivan Majic. Probing the Information Theoretical Roots of Spatial Dependence Measures. In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 9:1-9:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{wang_et_al:LIPIcs.COSIT.2024.9,
  author =	{Wang, Zhangyu and Janowicz, Krzysztof and Mai, Gengchen and Majic, Ivan},
  title =	{{Probing the Information Theoretical Roots of Spatial Dependence Measures}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{9:1--9:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.9},
  URN =		{urn:nbn:de:0030-drops-208247},
  doi =		{10.4230/LIPIcs.COSIT.2024.9},
  annote =	{Keywords: Spatial Autocorrelation, Moran’s I, Information Theory, Surprisal, Self-Information}
}
Document
DOI: 10.4230/LIPIcs.COSIT.2024.11
Semantic Perspectives on the Lake District Writing: Spatial Ontology Modeling and Relation Extraction for Deeper Insights

Authors: Erum Haris, Anthony G. Cohn, and John G. Stell

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
Extracting spatial details from historical texts can be difficult, hindering our understanding of past landscapes. The study addresses this challenge by analyzing the Corpus of the Lake District Writing, focusing on the English Lake District region. We systematically link the theoretical notions from the core concepts of spatial information to provide basis for the problem domain. The conceptual foundation is further complemented with a spatial ontology and a custom gazetteer, allowing a formal and insightful semantic exploration of the massive unstructured corpus. The other contrasting side of the framework is the usage of LLMs for spatial relation extraction. We formulate prompts leveraging understanding of the LLMs of the intended task, curate a list of spatial relations representing the most recurring proximity or vicinity relations terms and extract semantic triples for the top five place names appearing in the corpus. We compare the extraction capabilities of three benchmark LLMs for a scholarly significant historical archive, representing their potential in a challenging and interdisciplinary research problem. Finally, the network comprising the semantic triples is enhanced by incorporating a gazetteer-based classification of the objects involved thus improving their spatial profiling.
Cite as

Erum Haris, Anthony G. Cohn, and John G. Stell. Semantic Perspectives on the Lake District Writing: Spatial Ontology Modeling and Relation Extraction for Deeper Insights. In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 11:1-11:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{haris_et_al:LIPIcs.COSIT.2024.11,
  author =	{Haris, Erum and Cohn, Anthony G. and Stell, John G.},
  title =	{{Semantic Perspectives on the Lake District Writing: Spatial Ontology Modeling and Relation Extraction for Deeper Insights}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{11:1--11:20},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.11},
  URN =		{urn:nbn:de:0030-drops-208268},
  doi =		{10.4230/LIPIcs.COSIT.2024.11},
  annote =	{Keywords: spatial humanities, spatial narratives, ontology, large language models}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.31
Large Language Models: Testing Their Capabilities to Understand and Explain Spatial Concepts (Short Paper)

Authors: Majid Hojati and Rob Feick

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

Abstract
Interest in applying Large Language Models (LLMs), which use natural language processing (NLP) to provide human-like responses to text-based questions, to geospatial tasks has grown rapidly. Research shows that LLMs can help generate software code and answer some types of geographic questions to varying degrees even without fine-tuning. However, further research is required to explore the types of spatial questions they answer correctly, their abilities to apply spatial reasoning, and the variability between models. In this paper we examine the ability of four LLM models (GPT3.5 and 4, LLAma2.0, Falcon40B) to answer spatial questions that range from basic calculations to more advanced geographic concepts. The intent of this comparison is twofold. First, we demonstrate an extensible method for evaluating LLM’s limitations to supporting spatial data science through correct calculations and code generation. Relatedly, we also consider how these models can aid geospatial learning by providing text-based explanations of spatial concepts and operations. Our research shows common strengths in more basic types of questions, and mixed results for questions relating to more advanced spatial concepts. These results provide insights that may be used to inform strategies for testing and fine-tuning these models to increase their understanding of key spatial concepts.
Cite as

Majid Hojati and Rob Feick. Large Language Models: Testing Their Capabilities to Understand and Explain Spatial Concepts (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 31:1-31:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{hojati_et_al:LIPIcs.COSIT.2024.31,
  author =	{Hojati, Majid and Feick, Rob},
  title =	{{Large Language Models: Testing Their Capabilities to Understand and Explain Spatial Concepts}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{31:1--31:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.31},
  URN =		{urn:nbn:de:0030-drops-208460},
  doi =		{10.4230/LIPIcs.COSIT.2024.31},
  annote =	{Keywords: Geospatial concepts, Large Language Models, LLM, GPT, Llama, Falcon}
}
Document
DOI: 10.4230/LIPIcs.GIScience.2021.I.6
How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey

Authors: Yingjie Hu and Jimin Wang

Published in: LIPIcs, Volume 177, 11th International Conference on Geographic Information Science (GIScience 2021) - Part I (2020)

Abstract
Social media platforms, such as Twitter, have been increasingly used by people during natural disasters to share information and request for help. Hurricane Harvey was a category 4 hurricane that devastated Houston, Texas, USA in August 2017 and caused catastrophic flooding in the Houston metropolitan area. Hurricane Harvey also witnessed the widespread use of social media by the general public in response to this major disaster, and geographic locations are key information pieces described in many of the social media messages. A geoparsing system, or a geoparser, can be utilized to automatically extract and locate the described locations, which can help first responders reach the people in need. While a number of geoparsers have already been developed, it is unclear how effective they are in recognizing and geo-locating the locations described by people during natural disasters. To fill this gap, this work seeks to understand how people describe locations during a natural disaster by analyzing a sample of tweets posted during Hurricane Harvey. We then identify the limitations of existing geoparsers in processing these tweets, and discuss possible approaches to overcoming these limitations.
Cite as

Yingjie Hu and Jimin Wang. How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey. In 11th International Conference on Geographic Information Science (GIScience 2021) - Part I. Leibniz International Proceedings in Informatics (LIPIcs), Volume 177, pp. 6:1-6:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020)

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@InProceedings{hu_et_al:LIPIcs.GIScience.2021.I.6,
  author =	{Hu, Yingjie and Wang, Jimin},
  title =	{{How Do People Describe Locations During a Natural Disaster: An Analysis of Tweets from Hurricane Harvey}},
  booktitle =	{11th International Conference on Geographic Information Science (GIScience 2021) - Part I},
  pages =	{6:1--6:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-166-5},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{177},
  editor =	{Janowicz, Krzysztof and Verstegen, Judith A.},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2021.I.6},
  URN =		{urn:nbn:de:0030-drops-130410},
  doi =		{10.4230/LIPIcs.GIScience.2021.I.6},
  annote =	{Keywords: Geoparsing, geographic informational retrieval, social media, tweet analysis, disaster response}
}
Document
DOI: 10.4230/LIPIcs.GISCIENCE.2018.5
An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance

Authors: Yingjie Hu and Krzysztof Janowicz

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

Abstract
While Points Of Interest (POIs), such as restaurants, hotels, and barber shops, are part of urban areas irrespective of their specific locations, the names of these POIs often reveal valuable information related to local culture, landmarks, influential families, figures, events, and so on. Place names have long been studied by geographers, e.g., to understand their origins and relations to family names. However, there is a lack of large-scale empirical studies that examine the localness of place names and their changes with geographic distance. In addition to enhancing our understanding of the coherence of geographic regions, such empirical studies are also significant for geographic information retrieval where they can inform computational models and improve the accuracy of place name disambiguation. In this work, we conduct an empirical study based on 112,071 POIs in seven US metropolitan areas extracted from an open Yelp dataset. We propose to adopt term frequency and inverse document frequency in geographic contexts to identify local terms used in POI names and to analyze their usages across different POI types. Our results show an uneven usage of local terms across POI types, which is highly consistent among different geographic regions. We also examine the decaying effect of POI name similarity with the increase of distance among POIs. While our analysis focuses on urban POI names, the presented methods can be generalized to other place types as well, such as mountain peaks and streets.
Cite as

Yingjie Hu and Krzysztof Janowicz. An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance. In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 5:1-5:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{hu_et_al:LIPIcs.GISCIENCE.2018.5,
  author =	{Hu, Yingjie and Janowicz, Krzysztof},
  title =	{{An Empirical Study on the Names of Points of Interest and Their Changes with Geographic Distance}},
  booktitle =	{10th International Conference on Geographic Information Science (GIScience 2018)},
  pages =	{5:1--5:15},
  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.2018.5},
  URN =		{urn:nbn:de:0030-drops-93337},
  doi =		{10.4230/LIPIcs.GISCIENCE.2018.5},
  annote =	{Keywords: Place names, points of interest, geographic information retrieval, semantic similarity, geospatial semantics}
}
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