Sun, 27 Jul 2008 07:16:41 BST CiteULike: neteler's spatial http://www.citeulike.org/user/neteler/tag/spatial CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/neteler/article/2776563 <i>Trends in Ecology & Evolution, Vol. 20, No. 6. (June 2005), pp. 328-336.</i><br /><br />Spatial epidemiology is the study of spatial variation in disease risk or incidence. Several ecological processes can result in strong spatial patterns of such risk or incidence: for example, pathogen dispersal might be highly localized, vectors or reservoirs for pathogens might be spatially restricted, or susceptible hosts might be clumped. Here, we briefly describe approaches to spatial epidemiology that are spatially implicit, such as metapopulation models of disease transmission, and then focus on research in spatial epidemiology that is spatially explicit, such as the creation of risk maps for particular geographical areas. Although the spatial dynamics of infectious diseases are the subject of intensive study, the impacts of landscape structure on epidemiological processes have so far been neglected. The few studies that demonstrate how landscape composition (types of elements) and configuration (spatial positions of those elements) influence disease risk or incidence suggest that a true integration of landscape ecology with epidemiology will be fruitful. Spatial epidemiology: an emerging (or re-emerging) discipline Richard Ostfeld Gregory Glass Felicia Keesing doi:10.1016/j.tree.2005.03.009 Trends in Ecology & Evolution, Vol. 20, No. 6. (June 2005), pp. 328-336. 2008-05-09T20:21:42-00:00 2005 Trends in Ecology & Evolution 20 6 328 336 disease epidemiology gis habitat spatial statistics http://www.citeulike.org/user/neteler/article/2680254 <i>International Journal of Health Geographics, Vol. 7 (15 April 2008), 15.</i> Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens Michael Wimberly Adam Baer Michael Yabsley doi:10.1186/1476-072X-7-15 International Journal of Health Geographics, Vol. 7 (15 April 2008), 15. 2008-04-17T06:06:36-00:00 2008 International Journal of Health Geographics 1476-072X 7 15 disease ehrlichiosis gis prediction prediction-error spatial tick-borne ticks http://www.citeulike.org/user/neteler/article/1714360 <i>Journal of the American Statistical Association, Vol. 97, No. 458. (June 2002), pp. 632-648.</i><br /><br />Global positioning systems (GPSs) and geographical information systems (GISs) have been widely used to collect and synthesize spatial data from a variety of sources. New advances in satellite imagery and remote sensing now permit scientists to access spatial data at several different resolutions. The Internet facilitates fast and easy data acquisition. In any one study, several different types of data may be collected at differing scales and resolutions, at different spatial locations, and in different dimensions. Many statistical issues are associated with combining such data for modeling and inference. This article gives an overview of these issues and the approaches for integrating such disparate data, drawing on work from geography, ecology, agriculture, geology, and statistics. Emphasis is on state-of-the-art statistical solutions to this complex and important problem. Combining Incompatible Spatial Data CA Gotway LJ Young Journal of the American Statistical Association, Vol. 97, No. 458. (June 2002), pp. 632-648. 2007-10-01T08:17:28-00:00 2002 Journal of the American Statistical Association 97 458 632 648 error error-propagation gis gps remote-sensing scale spatial uncertainty http://www.citeulike.org/user/neteler/article/852137 <i>Computers & Geosciences, Vol. 30, No. 7. (August 2004), pp. 683-691.</i><br /><br />This paper discusses advantages and shortcomings of the S environment for multivariable geostatistics, in particular when extended with the gstat package, an extension package for the S environments (R, S-Plus). The gstat S package provides multivariable geostatistical modelling, prediction and simulation, as well as several visualisation functions. In particular, it makes the calculation, simultaneous fitting, and visualisation of a large number of direct and cross (residual) variograms very easy. Gstat was started 10 years ago and was released under the GPL in 1996; gstat.org was started in 1998. Gstat was not initially written for teaching purposes, but for research purposes, emphasising flexibility, scalability and portability. It can deal with a large number of practical issues in geostatistics, including change of support (block kriging), simple/ordinary/universal (co)kriging, fast local neighbourhood selection, flexible trend modelling, variables with different sampling configurations, and efficient simulation of large spatially correlated random fields, indicator kriging and simulation, and (directional) variogram and cross variogram modelling. The formula/models interface of the S language is used to define multivariable geostatistical models. This paper introduces the gstat S package, and discusses a number of design and implementation issues. It also draws attention to a number of papers on integration of spatial statistics software, GIS and the S environment that were presented on the spatial statistics workshop and sessions during the conference Distributed Statistical Computing 2003. Multivariable geostatistics in S: the gstat package Edzer Pebesma doi:10.1016/j.cageo.2004.03.012 Computers & Geosciences, Vol. 30, No. 7. (August 2004), pp. 683-691. 2006-09-20T22:35:21-00:00 2004 Computers & Geosciences 30 7 683 691 geostatistics grass open r_stats source spatial http://www.citeulike.org/user/neteler/article/345888 <i>Emerg Infect Dis, Vol. 8, No. 9. (September 2002), pp. 943-948.</i><br /><br />Geographic information systems combined with methods of spatial analysis provide powerful new tools for understanding the epidemiology of diseases and for improving disease prevention and control. In this study, the spatial distribution of a newly recognized tick-borne disease, human granulocytic ehrlichiosis (HGE), was investigated for nonrandom patterns and clusters in an area known to be endemic for tick-borne diseases. Analysis of confirmed cases of HGE identified in 1997-2000 in a 12-town area around Lyme, Connecticut, showed that HGE infections are not distributed randomly. Smoothed HGE incidence was higher around the mouth of the Connecticut River and lower to the north and west. Cluster analysis identified one area of increased HGE risk (relative risk=1.8, p=0.001). This study demonstrates the utility of geographic information systems and spatial analysis to clarify the epidemiology of HGE. Spatial analysis of human granulocytic ehrlichiosis near Lyme, Connecticut. EK Chaput JI Meek R Heimer Emerg Infect Dis, Vol. 8, No. 9. (September 2002), pp. 943-948. 2005-10-08T17:08:55-00:00 2002 Emerg Infect Dis 1080-6040 8 9 943 948 anaplasmosis disease ehrlichiosis lyme spatial http://www.citeulike.org/user/neteler/article/276958 <i>Environmental and Ecological Statistics, Vol. 8, No. 4. (December 2001), pp. 331-344.</i><br /><br />Objects in the terrestrial environment interact differentially with electromagnetic radiation according to their essential physical, chemical and biological properties. This differential interaction is manifest as variability in scattered radiation according to wavelength, location, time, geometries of illumination and observation and polarization. If the population of scattered radiation could be measured, then estimation of these essential properties would be straightforward. The only problem would be linking such estimates to environmental variables of interest. This review paper is divided into three parts. Part 1 is an overview of the attempts that have been made to sample the five domains of scattered radiation (spectral, spatial, temporal, geometrical, polarization) and then to use the results of this sampling to estimate environmental variables of interest. Part one highlights three issues: first, that relationships between remotely sensed data and environmental variables of interest are indirect; second, our ability to estimate these environmental variables is dependent upon our ability to capture a sound representation of variability in scattered radiation and third, a considerable portion of the useful information in remotely sensed images resides in the spatial domain (within the relations between the pixels in the image). This final point is developed in Part 2 that explores ways in which the spatial domain is utilized to describe spatial variation in remotely sensed and ground data; to design optimum sampling schemes for image data and ground data and to increase the accuracy with which remotely sensed data can be used to estimate both discontinuous and continuous variables. Part 3 outlines two specific uses of information in the spatial domain; first, to select an optimum spatial resolution and second, to inform an image classification. Remote sensing: Using the spatial domain Paul Curran Environmental and Ecological Statistics, Vol. 8, No. 4. (December 2001), pp. 331-344. 2005-08-08T19:45:41-00:00 2001 Environmental and Ecological Statistics 8 4 331 344 remote-sensing scale spatial http://www.citeulike.org/user/neteler/article/172907 <i>(2000)</i><br /><br />We report on work in progress on the integration of the GRASS GIS, the R data analysis programming language and environment, and the PostgreSQL database system. All of these components are released under Open Source licenses. This means that application programming interfaces are documented both in source code and in other materials, simplifying insight into the workings of the respective systems. Current versions of this note and accompanying code are to be found at the Hannover GRASS site,... Open source geocomputation: using the R data analysis language integrated with GRASS GIS and PostgreSQL data base systems R Bivand M Neteler (2000) 2005-04-27T19:36:54-00:00 2000 geostatistics grass open r_stats source spatial http://www.citeulike.org/user/neteler/article/172909 <i>Transactions in GIS, Vol. 8, No. 2. (April 2004), pp. 145-154.</i> GRASS as Open Source Free Software GIS: Accomplishments and Perspectives H Mitasova M Neteler doi:10.1111/j.1467-9671.2004.00172.x Transactions in GIS, Vol. 8, No. 2. (April 2004), pp. 145-154. 2005-04-27T19:38:05-00:00 2004 Transactions in GIS 1361-1682 8 2 145 154 data gis grass interoperability open source spatial