Beyond the Bell‐Shaped Curve: Poisson Models in Spatial Data Analysis

Griffith, Daniel A.

doi:10.1111/j.0016-7363.2006.03802001.x

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Regression and correlation have remained fundamental tools of analysis for human geographers, and over the years, GA has published many articles advancing the discipline's understanding of how to apply these models and methods properly to spatial data. This literature includes a number of special issues of the journal dealing with theory and method: “Methodological Developments in Spatial Econometrics and Statistics” (LeSage, Pace, and Tiefelsdorf ); “Poisson Models in Spatial Data Analysis” (Griffith ); and “Recent Advances in Software for Spatial Analysis in the Social Sciences” (Rey and Anselin ). This last special issue (vol.…”

Section: Spatial Statisticsmentioning

confidence: 99%

Geographical Analysis: Its First 40 Years

Griffith

Chun

O’Kelly

et al. 2012

Geographical Analysis

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Geographical Analysis (GA), in cooperation with the Wiley‐Blackwell publishing business, undertook a project to complete the digitizing of all back issues of the journal and make those for the period 1969–2001 free to the academic community; the volumes for 2002–2005 already were, and continue to be, free. This article commemorates the completion of this event late in 2010, drawing upon this new resource to highlight important contributions made through the journal to the geography and spatial sciences community. Besides summarizing publication trends for the journal, in the tradition of Fenneman's Venn diagram conceptualization of overlaps between geography and its cognate disciplines, this article highlights prominent articles published in the journal that contributed to the four themes of urban and economic geography, transportation geography, spatial statistics, and geographic information science. Geographical Analysis (GA) en cooperación con publicaciones Wiley‐Blackwell llevo a cabo el proyecto de digitación de todos los números pasados de la revista, los cuales están ahora a disposición de la comunidad académica sin costo alguno; los volúmenes de los años 2000–5 ya estaban disponibles y continúan siendo gratis. El presente artículo conmemora la culminación de dicho evento a fines de 2010, recogiendo los aportes de esta nueva fuente de información y resaltando varias contribuciones importantes hechas vía GA a la comunidad de geografía y ciencias espaciales. Además de resumir las tendencia de las publicaciones de la revista, en la tradición de la conceptualización de traslapes entre la geografía y disciplinas afines de los diagramas Venn Fanneman, el presente artículo resalta, artículos prominentes publicados en GA que contribuyeron a los cuatro temas principales: geografía urbana y económica, geografía del transporte, estadística especial y ciencias (sistemas) de información geográfica. 地理分析（GA）和Wiley‐Blackwell出版商合作完成了对本刊1969‐2001年所有刊册数字化的项目，并向学术界免费开放。事实上，从2002年第5卷就已经开始并将一直持续免费。本文记述了2010年末完成的这一事件，并依托这一新的数据资源，重新回顾了本刊对地理学和空间科学研究群体做出的重要贡献。本文总结了本刊论文发表趋势，从Fenneman的韦恩图概念传统来看是地理学及其相关学科的交叉。此外还回顾了本刊发表的对城市与经济地理、交通地理、空间统计以及地理信息科学方法做出卓越贡献的论文。

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Section: Spatial Statisticsmentioning

confidence: 99%

Geographical Analysis: Its First 40 Years

Griffith

Chun

O’Kelly

et al. 2012

Geographical Analysis

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, large n ijk and small p ijk make the Poisson distribution more sensible and appropriate in this case. Griffith (2006) and Griffith and Haining (2006) make additional arguments for Poisson modeling. Law et al (2006) use the Poisson model in a Bayesian framework.…”

Section: Hierarchical Model For Disease Ratesmentioning

confidence: 99%

A Bayesian Dynamic Spatio‐Temporal Interaction Model: An Application to Prostate Cancer Incidence

Kim

Oleson

2007

Geographical Analysis

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During the past three decades, prostate cancer incidence has changed substantially in the United States. A fully Bayesian hierarchical spatio-temporal interaction model is proposed to estimate prostate cancer incidence rates in the state of Iowa. We introduce random spatial effects to capture the local dependence among regions, random temporal effects to explain the nonlinearity of rates over time, and random spatio-temporal interactions. In addition, we introduce fixed age effects because most epidemiologic data are strongly related to age. We find that prostate cancer incidence in Iowa counties increased sharply over age while incidence rates increased initially, then decreased over time. We identify hot spots of high and low rates for age groups and time periods using disease mapping.

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Beyond the Bell‐Shaped Curve: Poisson Models in Spatial Data Analysis

Cited by 2 publications

References 5 publications

Geographical Analysis: Its First 40 Years

Geographical Analysis: Its First 40 Years

A Bayesian Dynamic Spatio‐Temporal Interaction Model: An Application to Prostate Cancer Incidence

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