The Warn Notice Toolbox: Open-source geovisualization of large layoff events data

Xinyue Ye, Michael C. Carroll

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Developed using the Python object-oriented scripting language, Warn Notice Toolbox is an open-source package designed for the exploratory visualization of unemployment events recorded by the PDF files of warn-notices. The goal is the visual representation of large-scale collections of both non-numerical and numerical information in a GIS environment. This article presents an outline of the design and structure of Warn Notice Toolbox, its implementation, functionality, and future plans. A group of its visualization capabilities are also illustrated, which highlight the power and flexibility of the toolbox through a case study of downloaded WARN Notices from 1996 to 2010. These notices are provided by employers to the Ohio Department of Job and Family Services, Bureau of WIA, Rapid Response Section. This toolbox demonstrates its potentials by identifying the patterns and trends of economic downturn in Ohio.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 19th International Conference on Geoinformatics, Geoinformatics 2011
DOIs
StatePublished - 2011
Externally publishedYes
Event2011 19th International Conference on Geoinformatics, Geoinformatics 2011 - Shanghai, China
Duration: Jun 24 2011Jun 26 2011

Publication series

NameProceedings - 2011 19th International Conference on Geoinformatics, Geoinformatics 2011

Conference

Conference2011 19th International Conference on Geoinformatics, Geoinformatics 2011
Country/TerritoryChina
CityShanghai
Period6/24/116/26/11

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

Keywords

  • Geographic Information System (GIS)
  • WARN act
  • geovisualization
  • open source

Fingerprint

Dive into the research topics of 'The Warn Notice Toolbox: Open-source geovisualization of large layoff events data'. Together they form a unique fingerprint.

Cite this