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Related Experiment Video

Updated: Jul 19, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Spatial analysis of news sources.

Andrew Mehler¹, Yunfan Bao, Xin Li

¹Department of Computer Science, Stony Brook University, USA. mehler@cs.sunysb.edu

IEEE Transactions on Visualization and Computer Graphics

|November 4, 2006

Summary

Newspaper analysis reveals regional interests by mapping named entities. This system identifies entities with distinct geographical biases, offering insights into localized public attention.

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Area of Science:

Computational Social Science
Data Visualization
Geospatial Analysis

Background:

Public discourse and interests vary geographically.
Newspaper content reflects the prevailing topics and interests within a specific region.
Analyzing large-scale newspaper data can reveal patterns of localized interest.

Purpose of the Study:

To develop entity datamaps for spatial visualization of interest in named entities.
To identify named entities that exhibit significant regional biases in their coverage.
To understand how localized interests manifest in media content.

Main Methods:

Utilized a large-scale newspaper analysis system (Lydia) for data collection and processing.
Developed a spatial visualization technique to create entity datamaps.
Created a model to estimate entity reference frequency based on surrounding city data.
Implemented techniques to evaluate the spatial significance of entity distributions.

Main Results:

Successfully generated entity datamaps visualizing interest distribution across geographical areas.
Identified specific named entities demonstrating clear regional biases in newspaper coverage.
The developed model effectively estimated entity reference frequencies from localized data.

Conclusions:

Entity datamaps provide a powerful tool for understanding spatial variations in public interest.
Newspaper data analysis can effectively uncover and quantify regional biases in named entity discourse.
The methodology offers a novel approach to geospatial analysis of media content and public attention.