Algorithm 761: Scattered-data surface fitting that has the accuracy of a cubic polynomial

Akima, Hiroshi

doi:10.1145/232826.232856

Cited by 91 publications

(68 citation statements)

References 4 publications

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“…Here, Quartimin results were almost completely similar to Varimax results. Finally, loading maps [or empirical orthogonal functions (EOFs)] were generated by interpolating and plotting the Varimax results over the map of Italy; the contour plots were carried out using a linear interpolation algorithm from Akima R-package (Akima, 1978(Akima, , 1996.…”

Section: Discussionmentioning

confidence: 99%

Annual and seasonal precipitation over Italy from 1961 to 2006

Toreti

Fioravanti

Perconti

et al. 2009

Intl Journal of Climatology

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Annual and seasonal precipitation series were derived from a set of 59 synoptic meteorological stations homogeneously distributed over Italy, in order to evaluate possible changes in precipitation behaviour and identifying areas of coherent variability. The time series were homogenized and standardized anomaly series were calculated for three areas: north, centre and south of Italy. Rotated principal component analysis (PCA) was applied to monthly data and the related loading maps were generated. The annual series do not show significant trends, while among the seasonal series only those of winter in northern and central Italy are non-stationary; they are characterized, respectively, by a decreasing trend for the entire period and by a positive trend since 1989. Seven common patterns were identified from clustered rotated principal components and linked with synoptical weather regimes.

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Section: Discussionmentioning

confidence: 99%

Annual and seasonal precipitation over Italy from 1961 to 2006

Toreti

Fioravanti

Perconti

et al. 2009

Intl Journal of Climatology

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“…We calculated also a Kernel density estimate as described in Akima (1978Akima ( , 1996 based on bilinear (left panel) or bi-cubic spline (middle panel) smoothing as shown in Figure 6. Here, the bilinear smoothing was computed using a Delaunay triangulation algorithm related to the spatial location as displayed in the right panel of Figure 6.…”

Section: Spatio-temporal Processesmentioning

confidence: 99%

Spatio-temporal analysis of fox rabies cases in Germany 2005-2006

et al. 2015

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Aiming to achieve new insights into rabies dynamics, this paper is the first to investigate fox rabies in Germany from a space-time pattern perspective. Based on a locally restricted dataset covering a fourteen month period, our findings indicate a strongly aggregated spatiotemporal point pattern resulting from an inhomogeneous stochastic process. In contrast to spatial or temporal approaches or cellular automata, our analysis focuses on the disease dynamics in time and space in a continuous time domain. Our findings confirm existing theories regarding fox rabies control highlighting the potential risk of urban areas and the need for effective rabies vaccination.

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“…In the analyses, we have selected an interpolation function package, named akima 6 . It is a package for a linear or cubic spline interpolation for irregularly gridded data (Akima, 1996). A p p l y i n g v a r i o u s a d d -o n p a c k a g e s o f R , w e h a v e e a s i l y d e v e l o p e d t h e p r o g r a m t h a t calculates appropriate 2-dimensional distributions of temperature.…”

Section: Indoor Temperature Monitoring Systemmentioning

confidence: 99%