Exploring the law of text geographic information

• URL: http://arxiv.org/abs/2309.00180v1
• Date: Fri, 1 Sep 2023 00:14:51 GMT
• Title: Exploring the law of text geographic information
• Abstract summary: We contend that geographic information is influenced by human behavior, cognition, expression, and thought processes. Through rigorous experiments on a diverse range of 24 datasets encompassing different languages and types, we have substantiated this hypothesis. We estimate the upper bounds of human utilization of geographic information, pointing towards the existence of uncharted territories.
• Score: 3.333401582174629
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Textual geographic information is indispensable and heavily relied upon in practical applications. The absence of clear distribution poses challenges in effectively harnessing geographic information, thereby driving our quest for exploration. We contend that geographic information is influenced by human behavior, cognition, expression, and thought processes, and given our intuitive understanding of natural systems, we hypothesize its conformity to the Gamma distribution. Through rigorous experiments on a diverse range of 24 datasets encompassing different languages and types, we have substantiated this hypothesis, unearthing the underlying regularities governing the dimensions of quantity, length, and distance in geographic information. Furthermore, theoretical analyses and comparisons with Gaussian distributions and Zipf's law have refuted the contingency of these laws. Significantly, we have estimated the upper bounds of human utilization of geographic information, pointing towards the existence of uncharted territories. Also, we provide guidance in geographic information extraction. Hope we peer its true countenance uncovering the veil of geographic information.

Related papers

• GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics [91.17301794848025]
  This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions.<n>Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies.
  arXiv  Detail & Related papers  (2026-02-13T04:48:05Z)
• Geospatial Mechanistic Interpretability of Large Language Models [6.0272491755196045]
  Large Language Models (LLMs) have demonstrated unprecedented capabilities across various natural language processing tasks.<n>Our aim is to advance our understanding of the internal representations that these complex models generate while processing geographical information.
  arXiv  Detail & Related papers  (2025-05-06T09:40:06Z)
• Combining Observational Data and Language for Species Range Estimation [63.65684199946094]
  We propose a novel approach combining millions of citizen science species observations with textual descriptions from Wikipedia. Our framework maps locations, species, and text descriptions into a common space, enabling zero-shot range estimation from textual descriptions. Our approach also acts as a strong prior when combined with observational data, resulting in more accurate range estimation with less data.
  arXiv  Detail & Related papers  (2024-10-14T17:22:55Z)
• Causal Representation Learning in Temporal Data via Single-Parent Decoding [66.34294989334728]
  Scientific research often seeks to understand the causal structure underlying high-level variables in a system. Scientists typically collect low-level measurements, such as geographically distributed temperature readings. We propose a differentiable method, Causal Discovery with Single-parent Decoding, that simultaneously learns the underlying latents and a causal graph over them.
  arXiv  Detail & Related papers  (2024-10-09T15:57:50Z)
• Enhancing Worldwide Image Geolocation by Ensembling Satellite-Based Ground-Level Attribute Predictors [4.415977307120618]
  We examine the challenge of estimating the location of a single ground-level image in the absence of GPS or other location metadata. We introduce a novel metric, Recall vs Area, which measures the accuracy of estimated distributions of locations. We then examine an ensembling approach to global-scale image geolocation, which incorporates information from multiple sources.
  arXiv  Detail & Related papers  (2024-07-18T19:15:52Z)
• Probing the Information Theoretical Roots of Spatial Dependence Measures [3.661228054439679]
  There is a relation between measures of spatial dependence and information theoretical measures of entropy. We will explore the information theoretical roots of spatial autocorrelation through the lens of self-information.
  arXiv  Detail & Related papers  (2024-05-28T17:44:35Z)
• Learning Geospatial Region Embedding with Heterogeneous Graph [16.864563545518124]
  We present GeoHG, an effective heterogeneous graph structure for learning comprehensive region embeddings for various downstream tasks. Specifically, we tailor satellite image representation learning through geo-entity segmentation and point-of-interest (POI) integration for expressive intra-regional features. GeoHG unifies informative spatial interdependencies and socio-environmental attributes into a powerful heterogeneous graph to encourage explicit modeling of higher-order inter-regional relationships.
  arXiv  Detail & Related papers  (2024-05-23T03:19:02Z)
• Where Do We Go from Here? Multi-scale Allocentric Relational Inference from Natural Spatial Descriptions [18.736071151303726]
  This paper introduces the Rendezvous (RVS) task and dataset, which includes 10,404 examples of English geospatial instructions for reaching a target location using map-knowledge. Our analysis reveals that RVS exhibits a richer use of spatial allocentric relations, and requires resolving more spatial relations simultaneously compared to previous text-based navigation benchmarks.
  arXiv  Detail & Related papers  (2024-02-26T07:33:28Z)
• Spatial Implicit Neural Representations for Global-Scale Species Mapping [72.92028508757281]
  Given a set of locations where a species has been observed, the goal is to build a model to predict whether the species is present or absent at any location. Traditional methods struggle to take advantage of emerging large-scale crowdsourced datasets. We use Spatial Implicit Neural Representations (SINRs) to jointly estimate the geographical range of 47k species simultaneously.
  arXiv  Detail & Related papers  (2023-06-05T03:36:01Z)
• GeoGLUE: A GeoGraphic Language Understanding Evaluation Benchmark [56.08664336835741]
  We propose a GeoGraphic Language Understanding Evaluation benchmark, named GeoGLUE. We collect data from open-released geographic resources and introduce six natural language understanding tasks. We pro vide evaluation experiments and analysis of general baselines, indicating the effectiveness and significance of the GeoGLUE benchmark.
  arXiv  Detail & Related papers  (2023-05-11T03:21:56Z)
• GeoNet: Benchmarking Unsupervised Adaptation across Geographies [71.23141626803287]
  We study the problem of geographic robustness and make three main contributions. First, we introduce a large-scale dataset GeoNet for geographic adaptation. Second, we hypothesize that the major source of domain shifts arise from significant variations in scene context. Third, we conduct an extensive evaluation of several state-of-the-art unsupervised domain adaptation algorithms and architectures.
  arXiv  Detail & Related papers  (2023-03-27T17:59:34Z)
• Gaussianizing the Earth: Multidimensional Information Measures for Earth Data Analysis [9.464720193746395]
  Information theory is an excellent framework for analyzing Earth system data. It allows us to characterize uncertainty and redundancy, and is universally interpretable. We show how information theory measures can be applied in various Earth system data analysis problems.
  arXiv  Detail & Related papers  (2020-10-13T15:30:34Z)
• From Topic Networks to Distributed Cognitive Maps: Zipfian Topic Universes in the Area of Volunteered Geographic Information [59.0235296929395]
  We investigate how language encodes and networks geographic information on the aboutness level of texts. Our study shows a Zipfian organization of the thematic universe in which geographical places are located in online communication. Places, whether close to each other or not, are located in neighboring places that span similarworks in the topic universe.
  arXiv  Detail & Related papers  (2020-02-04T18:31:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.