Implementing Spatial Segregation Measures in R
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| Publicado en: | PLoS One vol. 9, no. 11 (Nov 2014), p. e113767 |
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| Autor principal: | |
| Otros Autores: | , |
| Publicado: |
Public Library of Science
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 001 | 1626822609 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1932-6203 | ||
| 024 | 7 | |a 10.1371/journal.pone.0113767 |2 doi | |
| 035 | |a 1626822609 | ||
| 045 | 2 | |b d20141101 |b d20141130 | |
| 084 | |a 174835 |2 nlm | ||
| 100 | 1 | |a Seong-Yun, Hong | |
| 245 | 1 | |a Implementing Spatial Segregation Measures in R | |
| 260 | |b Public Library of Science |c Nov 2014 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Reliable and accurate estimation of residential segregation between population groups is important for understanding the extent of social cohesion and integration in our society. Although there have been considerable methodological advances in the measurement of segregation over the last several decades, the recently developed measures have not been widely used in the literature, in part due to their complex calculation. To address this problem, we have implemented several newly proposed segregation indices in R, an open source software environment for statistical computing and graphics, as a package called seg. Although there are already a few standalone applications and add-on packages that provide access to similar methods, our implementation has a number of advantages over the existing tools. First, our implementation is flexible in the sense that it provides detailed control over the calculation process with a wide range of input parameters. Most of the parameters have carefully chosen defaults, which perform acceptably in many situations, so less experienced users can also use the implemented functions without too much difficulty. Second, there is no need to export results to other software programs for further analysis. We provide coercion methods that enable the transformation of our output classes into general R classes, so the user can use thousands of standard and modern statistical techniques, which are already available in R, for the post-processing of the results. Third, our implementation does not require commercial software to operate, so it is accessible to a wider group of people. | |
| 610 | 4 | |a University of Tokyo | |
| 651 | 4 | |a United States--US | |
| 651 | 4 | |a California | |
| 651 | 4 | |a Japan | |
| 653 | |a Software | ||
| 653 | |a Population | ||
| 653 | |a Geography | ||
| 653 | |a Computer programs | ||
| 653 | |a Censuses | ||
| 653 | |a Mathematical analysis | ||
| 653 | |a Science | ||
| 653 | |a Studies | ||
| 653 | |a Open source software | ||
| 653 | |a Sociology | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Spatial data | ||
| 653 | |a Social sciences | ||
| 653 | |a Population (statistical) | ||
| 653 | |a Social | ||
| 653 | |a Information science | ||
| 653 | |a Statistical methods | ||
| 653 | |a Post-production processing | ||
| 653 | |a Property | ||
| 653 | |a Social cohesion | ||
| 653 | |a Computation | ||
| 653 | |a Residential segregation | ||
| 653 | |a Implementation | ||
| 653 | |a Measures | ||
| 653 | |a Coercion | ||
| 653 | |a Measurement | ||
| 653 | |a Transformation | ||
| 653 | |a Copyright | ||
| 700 | 1 | |a O'Sullivan, David | |
| 700 | 1 | |a Sadahiro, Yukio | |
| 773 | 0 | |t PLoS One |g vol. 9, no. 11 (Nov 2014), p. e113767 | |
| 786 | 0 | |d ProQuest |t Health & Medical Collection | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/1626822609/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/1626822609/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/1626822609/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |