<b>GeoXp</b>: An<i>R</i>Package for Exploratory Spatial Data Analysis

Laurent, Thibault; Ruiz‐Gazen, Anne; Thomas‐Agnan, Christine

doi:10.18637/jss.v047.i02

Cited by 22 publications

(13 citation statements)

References 27 publications

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“…Los programas que lo implementan combinan gráficos con mapas. Al seleccionar un conjunto de datos en un gráfico, se ven resaltados inmediatamente sobre el mapa y a la inversa (Laurent, Ruiz-Gazen, & Thomas-Agnan, 2009). Por ejemplo, al seleccionar en un box plot, un histograma o un diagrama de dispersión los valores atípicos, se resaltan en el mapa las localizaciones geográficas correspondientes.…”

Section: Vinculación Interactiva De Gráficos Y Mapasunclassified

“…En el artículo que presenta el paquete se presenta un ejemplo que compara el número de estudiantes y el índice de ruralidad en Francia. (Laurent et al, 2009). …”

Section: Vinculación Interactiva De Gráficos Y Mapasunclassified

“…También vale la pena mencionar el diagrama de dispersión de Moran, que permite observar de manera interactiva, en cuatro cuadrantes, las zonas donde la variable tiene valores altos rodeados de altos, bajos rodeados de bajos, altos rodeados de bajos y bajos rodeados de altos (Laurent et al, 2009). …”

Section: Dependencia Espacialunclassified

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Usos de información georreferenciada para prestación de servicios a la población: una revisión de literatura

Mayorga¹,

Molina²

2017

GeoFocus

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RESUMENEl creciente volumen de información estadística georreferenciada es una oportunidad para mejorar la prestación de servicios a la población desde una perspectiva territorial. Sin embargo, el aprovechamiento de los datos requiere la apropiación de metodologías adecuadas. Este documento sintetiza algunas de ellas, alrededor de los cinco conceptos fundamentales del análisis espacial enunciados por Buzai (2010): localización, distribución, asociación, interacción y evolución. Para cada una de estos se presentan varias técnicas que permiten su aplicación para mejorar la prestación de servicios a la población.Palabras clave: análisis espacial, SIG, asociación espacial, interacción espacial, análisis espaciotemporal. USING GEOREFERENCED INFORMATION TO SUPPORT THE SERVICES PROVISION FOR POPULATION: A LITERATURE REVIEW ABSTRACTThe growing volume of geo-referenced statistical data it is an opportunity to improve the provision of services to the population considering a territorial perspective. However, to leverage data is required the use of adequate methodologies. This paper summarizes some of them around of the five basic concepts of the spatial analysis stated by Buzai (2010): localization, distribution, association, interaction and evolution. For each one, are presented techniques that allow the improvement in the services provision for population.

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Section: Vinculación Interactiva De Gráficos Y Mapasunclassified

“…En el artículo que presenta el paquete se presenta un ejemplo que compara el número de estudiantes y el índice de ruralidad en Francia. (Laurent et al, 2009). …”

Section: Vinculación Interactiva De Gráficos Y Mapasunclassified

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Usos de información georreferenciada para prestación de servicios a la población: una revisión de literatura

Mayorga¹,

Molina²

2017

GeoFocus

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“…Interactive tables and micromaps are available for cancer statistics at the Pennsylvania Cancer Atlas ( http://www.geovista.psu.edu/grants/CDC/) and at the United States Atlas of Cancer prototype (http://www.geovista.psu.edu/grants/ CDC/national.html). Recently, the authors of the GeoXP package for interactive graphics for exploratory spatial data analysis mentioned that a new tool for that package might include a micromap display (Laurent, Ruiz-Gazen, and Thomas-Agnan 2012).…”

Section: Challenges and Future Developmentsmentioning

confidence: 99%

micromap: A Package for Linked Micromaps

Payton¹,

McManus²,

Weber³

et al. 2015

J. Stat. Soft.

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The R package micromap is used to create linked micromaps, which display statistical summaries associated with areal units, or polygons. Linked micromaps provide a means to simultaneously summarize and display both statistical and geographic distributions by linking statistical summaries to a series of small maps. The package contains functions dependent on the ggplot2 package to produce a row-oriented graph composed of different panels, or columns, of information. These panels at a minimum typically contain maps, a legend, and statistical summaries, with the color-coded legend linking the maps and statistical summaries. We first describe the layout of linked micromaps and then the structure required for both the spatial and statistical datasets. The function create_map_table in the micromap package converts the input of an sp SpatialPolygonsDataFrame into a data frame that can be linked with the statistical dataset. Highly detailed polygons are not appropriate for display in linked micromaps so we describe how polygon boundaries can be simplified, decreasing the time required to draw the graphs, while retaining adequate detail for detection of spatial patterns. Our worked examples of linked micromaps use public health data as well as environmental data collected from spatially balanced probabilistic surveys.

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“…Moreover, the R programming language (R Core Team 2017) has various packages devoted to geostatistics, such as the gstat package for geostatistical modeling, prediction and simulation (Pebesma and Wesseling 1998;Pebesma 2004;Pebesma and Bivand 2005), geoR (Ribeiro Jr. and Diggle 2001;Diggle and Ribeiro Jr. 2007), which contains functions for model-based geostatistics, as well as the geospt (Melo, Santacruz, and Melo 2015), RandomFields (Schlather, Malinowski, Menck, Oesting, and Strokorb 2015) and RGeostats (Renard, Desassis, Beucher, Ors, and Laporte 2014) packages, or ad-hoc routines for graphical interface in the ecological modeling software Bio7 (Austenfeld and Beyschlag 2012) or for exploratory spatial data analysis (Laurent, Ruiz-Gazen, and Thomas-Agnan 2012). Other contributions concern specialized routines and packages for spatio-temporal analysis (De Cesare, Myers, and Posa 2002;De Iaco, Myers, Palma, and Posa 2010;Pebesma 2012;Gabriel, Rowlingson, and Diggle 2013).…”

Section: Introductionmentioning

confidence: 99%

The cgeostat Software for Analyzing Complex-Valued Random Fields

Iaco¹

2017

J. Stat. Soft.

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Given a vectorial data set in two dimensions, a representation on a complex domain is often convenient. This representation is rarely considered in geostatistics, although interesting applications can be found in environmental sciences and meteorology (e.g., for wind fields). In such a case, some computational difficulties are related to the lack of software for estimating and modeling a complex covariance function, for predicting complex variables as well as for representing the output results. In this paper, the new Fortran software cgeostat for geostatistical analysis of complex-valued random fields is presented and an application is demonstrated.

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GeoXp: AnRPackage for Exploratory Spatial Data Analysis

Cited by 22 publications

References 27 publications

Usos de información georreferenciada para prestación de servicios a la población: una revisión de literatura

Usos de información georreferenciada para prestación de servicios a la población: una revisión de literatura

micromap: A Package for Linked Micromaps

The cgeostat Software for Analyzing Complex-Valued Random Fields

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