Seamless integration of spatial statistics and GIS: The S-PLUS for ArcView and the S+Grassland Links

Bao, Shuming; Anselin, Luc; Martin, Doug; Stralberg, Diana

doi:10.1007/pl00011459

Cited by 16 publications

(7 citation statements)

References 11 publications

(13 reference statements)

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“…Rather than reporting the result of testing a formal statistical hypothesis, as in GIS AND SPATIAL ANALYSIS 51 a classical scientific approach, they successively apply graphical or other visualization tools, data enhancement methods, and a mix of descriptive statistics and more formal data models. For this purpose, a number of prototype analysis systems have been proposed and constructed (2,4,9,31,74). Carr et al (15) developed linked micromap plots that show, in one connected view, relationships between attributes of areas and the spatial pattern of disease rates.…”

Section: Software Systems For Exploratory Spatial Data Analysismentioning

confidence: 99%

Public Health, GIS, and Spatial Analytic Tools

Rushton

2003

Annu. Rev. Public Health

245

157

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Monte Carlo simulations Abstract We review literature that uses spatial analytic tools in contexts where Geographic Information Systems (GIS) is the organizing system for health data or where the methods discussed will likely be incorporated in GIS-based analyses in the future. We conclude the review with the point of view that this literature is moving toward the development and use of systems of analysis that integrate the information geo-coding and data base functions of GISystems with the geo-information processing functions of GIScience. The rapidity of this projected development will depend on the perceived needs of the public health community for spatial analysis methods to provide decision support. Recent advances in the analysis of disease maps have been influenced by and benefited from the adoption of new practices for georeferencing health data and new ways of linking such data geographically to potential sources of environmental exposures, the locations of health resources and the geodemographic characteristics of populations. This review focuses on these advances. INTRODUCTIONThe analytic capabilities of geographic information systems, available in a few cases as fully integrated systems, but more commonly as loosely coupled software systems, have developed rapidly in recent years. Public health is now presented with the opportunity to examine key relationships between the health characteristics of populations and both human and physical environmental characteristics. Some authors suggest that a new discipline of spatial epidemiology now exists, recently described (22, p. v) as being "concerned with describing, quantifying, and explaining geographical variations in disease, especially with respect to variations in environmental exposures at the small-area scale." Although few implemented examples of such systems currently exist outside of the area of national disease and mortality mapping systems, this is clearly the direction in which spatial analytic tools will be used in the future. Some descriptions of such systems in various stages of implementation can be found in the public health literature (3,62,74). This period, too, has seen the beginning of a more critical literature that points out 0163 MAPPING DISEASE RATESWith the increased availability of spatial coverages of individual disease incidences in GIS, statements that were commonly made a decade ago to the effect that disease data are available for areas but rarely as point patterns, now appear quite dated. Several authors have pointed out that choropleth maps (where areas are shaded according to a defined category in a map legend) can be seen as smoothed maps of disease data with the areas chosen acting as filters-albeit of different spatial size. Viewed in this light, such maps are seen by many as inferior representations of the basic data. The traditional public health databases in which observations of diseases are columns and areas are rows reflect the practical requirements for databases prior to GIS. GIS bring the possibility not ...

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Section: Software Systems For Exploratory Spatial Data Analysismentioning

confidence: 99%

Public Health, GIS, and Spatial Analytic Tools

Rushton

2003

Annu. Rev. Public Health

245

157

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“…We performed variogram analysis and kriging interpolation (Lee et al, 2006;Webster and Oliver, 2001;Mowrer and Congalton, 2000;Bailey and Gatrell, 1995) in order to identify the spatial autocorrelation and variability; modern objected-oriented language and the SPLUS system, with the optional S+ +SpatialStats module (Bao et al, 2000;MathSoft, 1997), were employed. In this study, the variogram is used to measure the spatial variation in imagery by automatically fitting spherical variogram parameters (nugget, sill, and range).…”

Section: 2 Spatial Autocorrelation Analysis Of Uhdmentioning

confidence: 99%

Determining the Effect of Green Spaces on Urban Heat Distribution Using Satellite Imagery

Choi¹,

Lee²,

Byun³

2012

Asian J. Atmos. Environ

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Urbanization has led to a reduction in green spaces and thus transformed the spatial pattern of urban land use. An increase in air temperature directly affects forest vegetation, phenology, and biodiversity in urban areas. In this paper, we analyze the changing land use patterns and urban heat distribution (UHD) in Seoul on the basis of a spatial assessment. It is necessary to monitor and assess the functions of green spaces in order to understand the changes in the green space. In addition, we estimated the influence of green space on urban temperature using Landsat 7 Enhanced Thematic Mapper Plus (ETM + + ) imagery and climatic data. Results of the assessment showed that UHD differences cause differences in temperature variation and the spatial extent of temperature reducing effects due to urban green space. The ratio of urban heat area to green space cooling area increases rapidly with increasing distance from a green space boundary. This shows that urban green space plays an important role for mitigating urban heating in central areas. This study demonstrated the importance of green space by characterizing the spatiotemporal variations in temperature associated with urban green spaces.

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“…In contrast to the other packages, Info-Map included its own visualization. The latter was absent from SpaceStat, whereas S+SpatialStats relied on a link with ESRI’s ArcView or the GRASS GIS for mapping of results (Bao and Martin 1997; Bao et al 2000). With the exception of S-Plus, which also ran in the unix operating system, these applications were implemented on the Microsoft Windows platform.…”

Section: Introductionmentioning

confidence: 99%

From SpaceStat to CyberGIS

Anselin

2012

International Regional Science Review

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This essay assesses the evolution of the way in which spatial data analytical methods have been incorporated into software tools over the past two decades. It is part retrospective and prospective, going beyond a historical review to outline some ideas about important factors that drove the software development, such as methodological advances, the open source movement and the advent of the Internet and cyberinfrastructure. The review highlights activities carried out by the author and his collaborators and uses SpaceStat, GeoDa, PySAL, and recent spatial analytical web services developed at the ASU GeoDa Center as illustrative examples. It outlines a vision for a spatial econometrics workbench as an example of the incorporation of spatial analytical functionality in a cyberGIS.

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Seamless integration of spatial statistics and GIS: The S-PLUS for ArcView and the S+Grassland Links

Cited by 16 publications

References 11 publications

Public Health, GIS, and Spatial Analytic Tools

Public Health, GIS, and Spatial Analytic Tools

Determining the Effect of Green Spaces on Urban Heat Distribution Using Satellite Imagery

From SpaceStat to CyberGIS

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