Showing 15 of total 15 results (show query)
spatstat
spatstat:Spatial Point Pattern Analysis, Model-Fitting, Simulation, Tests
Comprehensive open-source toolbox for analysing Spatial Point Patterns. Focused mainly on two-dimensional point patterns, including multitype/marked points, in any spatial region. Also supports three-dimensional point patterns, space-time point patterns in any number of dimensions, point patterns on a linear network, and patterns of other geometrical objects. Supports spatial covariate data such as pixel images. Contains over 3000 functions for plotting spatial data, exploratory data analysis, model-fitting, simulation, spatial sampling, model diagnostics, and formal inference. Data types include point patterns, line segment patterns, spatial windows, pixel images, tessellations, and linear networks. Exploratory methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the functions ppm(), kppm(), slrm(), dppm() similar to glm(). Types of models include Poisson, Gibbs and Cox point processes, Neyman-Scott cluster processes, and determinantal point processes. Models may involve dependence on covariates, inter-point interaction, cluster formation and dependence on marks. Models are fitted by maximum likelihood, logistic regression, minimum contrast, and composite likelihood methods. A model can be fitted to a list of point patterns (replicated point pattern data) using the function mppm(). The model can include random effects and fixed effects depending on the experimental design, in addition to all the features listed above. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots.
Maintained by Adrian Baddeley. Last updated 7 days ago.
cluster-processcox-point-processgibbs-processkernel-densitynetwork-analysispoint-processpoisson-processspatial-analysisspatial-dataspatial-data-analysisspatial-statisticsspatstatstatistical-methodsstatistical-modelsstatistical-testsstatistics
200 stars 16.25 score 5.5k scripts 40 dependentsspatstat
spatstat.geom:Geometrical Functionality of the 'spatstat' Family
Defines spatial data types and supports geometrical operations on them. Data types include point patterns, windows (domains), pixel images, line segment patterns, tessellations and hyperframes. Capabilities include creation and manipulation of data (using command line or graphical interaction), plotting, geometrical operations (rotation, shift, rescale, affine transformation), convex hull, discretisation and pixellation, Dirichlet tessellation, Delaunay triangulation, pairwise distances, nearest-neighbour distances, distance transform, morphological operations (erosion, dilation, closing, opening), quadrat counting, geometrical measurement, geometrical covariance, colour maps, calculus on spatial domains, Gaussian blur, level sets of images, transects of images, intersections between objects, minimum distance matching. (Excludes spatial data on a network, which are supported by the package 'spatstat.linnet'.)
Maintained by Adrian Baddeley. Last updated 4 days ago.
classes-and-objectsdistance-calculationgeometrygeometry-processingimagesmensurationplottingpoint-patternsspatial-dataspatial-data-analysis
7 stars 12.14 score 241 scripts 229 dependentsspatstat
spatstat.data:Datasets for 'spatstat' Family
Contains all the datasets for the 'spatstat' family of packages.
Maintained by Adrian Baddeley. Last updated 12 days ago.
kernel-densitypoint-processspatial-analysisspatial-dataspatial-data-analysisspatstatstatistical-analysisstatistical-methodsstatistical-testsstatistics
6 stars 11.07 score 186 scripts 228 dependentsspatstat
spatstat.explore:Exploratory Data Analysis for the 'spatstat' Family
Functionality for exploratory data analysis and nonparametric analysis of spatial data, mainly spatial point patterns, in the 'spatstat' family of packages. (Excludes analysis of spatial data on a linear network, which is covered by the separate package 'spatstat.linnet'.) Methods include quadrat counts, K-functions and their simulation envelopes, nearest neighbour distance and empty space statistics, Fry plots, pair correlation function, kernel smoothed intensity, relative risk estimation with cross-validated bandwidth selection, mark correlation functions, segregation indices, mark dependence diagnostics, and kernel estimates of covariate effects. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported.
Maintained by Adrian Baddeley. Last updated 9 days ago.
cluster-detectionconfidence-intervalshypothesis-testingk-functionroc-curvesscan-statisticssignificance-testingsimulation-envelopesspatial-analysisspatial-data-analysisspatial-sharpeningspatial-smoothingspatial-statistics
1 stars 10.18 score 67 scripts 150 dependentsspatstat
spatstat.linnet:Linear Networks Functionality of the 'spatstat' Family
Defines types of spatial data on a linear network and provides functionality for geometrical operations, data analysis and modelling of data on a linear network, in the 'spatstat' family of packages. Contains definitions and support for linear networks, including creation of networks, geometrical measurements, topological connectivity, geometrical operations such as inserting and deleting vertices, intersecting a network with another object, and interactive editing of networks. Data types defined on a network include point patterns, pixel images, functions, and tessellations. Exploratory methods include kernel estimation of intensity on a network, K-functions and pair correlation functions on a network, simulation envelopes, nearest neighbour distance and empty space distance, relative risk estimation with cross-validated bandwidth selection. Formal hypothesis tests of random pattern (chi-squared, Kolmogorov-Smirnov, Monte Carlo, Diggle-Cressie-Loosmore-Ford, Dao-Genton, two-stage Monte Carlo) and tests for covariate effects (Cox-Berman-Waller-Lawson, Kolmogorov-Smirnov, ANOVA) are also supported. Parametric models can be fitted to point pattern data using the function lppm() similar to glm(). Only Poisson models are implemented so far. Models may involve dependence on covariates and dependence on marks. Models are fitted by maximum likelihood. Fitted point process models can be simulated, automatically. Formal hypothesis tests of a fitted model are supported (likelihood ratio test, analysis of deviance, Monte Carlo tests) along with basic tools for model selection (stepwise(), AIC()) and variable selection (sdr). Tools for validating the fitted model include simulation envelopes, residuals, residual plots and Q-Q plots, leverage and influence diagnostics, partial residuals, and added variable plots. Random point patterns on a network can be generated using a variety of models.
Maintained by Adrian Baddeley. Last updated 2 months ago.
density-estimationheat-equationkernel-density-estimationnetwork-analysispoint-processesspatial-data-analysisstatistical-analysisstatistical-inferencestatistical-models
6 stars 9.58 score 35 scripts 42 dependents
jeremygelb
spNetwork:Spatial Analysis on Network
Perform spatial analysis on network. Implement several methods for spatial analysis on network: Network Kernel Density estimation, building of spatial matrices based on network distance ('listw' objects from 'spdep' package), K functions estimation for point pattern analysis on network, k nearest neighbours on network, reachable area calculation, and graph generation References: Okabe et al (2019) <doi:10.1080/13658810802475491>; Okabe et al (2012, ISBN:978-0470770818);Baddeley et al (2015, ISBN:9781482210200).
Maintained by Jeremy Gelb. Last updated 14 days ago.
kernelkernel-density-estimationnetworknetwork-analysisspatialspatial-analysisspatial-data-analysiscpp
38 stars 7.69 score 52 scripts
stscl
sdsfun:Spatial Data Science Complementary Features
Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.
Maintained by Wenbo Lv. Last updated 27 days ago.
geoinformaticsspatial-data-analysisspatial-data-sciencespatial-statisticsopenblascppopenmp
16 stars 6.58 score 6 scripts 8 dependents
rubenfcasal
npsp:Nonparametric Spatial Statistics
Multidimensional nonparametric spatial (spatio-temporal) geostatistics. S3 classes and methods for multidimensional: linear binning, local polynomial kernel regression (spatial trend estimation), density and variogram estimation. Nonparametric methods for simultaneous inference on both spatial trend and variogram functions (for spatial processes). Nonparametric residual kriging (spatial prediction). For details on these methods see, for example, Fernandez-Casal and Francisco-Fernandez (2014) <doi:10.1007/s00477-013-0817-8> or Castillo-Paez et al. (2019) <doi:10.1016/j.csda.2019.01.017>.
Maintained by Ruben Fernandez-Casal. Last updated 5 months ago.
geostatisticsspatial-data-analysisstatisticsfortranopenblas
4 stars 5.71 score 64 scripts
egpivo
SpatPCA:Regularized Principal Component Analysis for Spatial Data
Provide regularized principal component analysis incorporating smoothness, sparseness and orthogonality of eigen-functions by using the alternating direction method of multipliers algorithm (Wang and Huang, 2017, <DOI:10.1080/10618600.2016.1157483>). The method can be applied to either regularly or irregularly spaced data, including 1D, 2D, and 3D.
Maintained by Wen-Ting Wang. Last updated 7 months ago.
admmcovariance-estimationeigenfunctionslassomatrix-factorizationpcarcpparmadillorcppparallelregularizationspatialspatial-data-analysissplinesopenblascppopenmp
20 stars 5.53 score 17 scripts
cpanse
recmap:Compute the Rectangular Statistical Cartogram
Implements the RecMap MP2 construction heuristic <doi:10.1109/INFVIS.2004.57>. This algorithm draws maps according to a given statistical value, e.g., election results, population, or epidemiological data. The basic idea of the RecMap algorithm is that each map region, e.g., different countries, is represented by a rectangle. The area of each rectangle represents the statistical value given as input (maintain zero cartographic error). C++ is used to implement the computationally intensive tasks. The vignette included in this package provides documentation about the usage of the recmap algorithm.
Maintained by Christian Panse. Last updated 2 months ago.
cartogramdemographicsgenetic-algorithmgeovisualizationgraph-drawingspatialspatial-data-analysisvalue-by-area-mapscpp
21 stars 5.51 score 31 scripts
jefworks-lab
scatterbar:Scattered Stacked Bar Chart Plots
Provides a powerful and flexible tool for visualizing proportional data across spatially resolved contexts. By combining the concepts of scatter plots and stacked bar charts, `scatterbar` allows users to create scattered bar chart plots, which effectively display the proportions of different categories at each (x, y) location. This visualization is particularly useful for applications where understanding the distribution of categories across spatial coordinates is essential. This package features automatic determination of optimal scaling factors based on data, customizable scaling and padding options for both x and y axes, flexibility to specify custom colors for each category, options to customize the legend title, and integration with `ggplot2` for robust and high-quality visualizations. For more details, see Velazquez et al. (2024) <doi:10.1101/2024.08.14.606810>.
Maintained by Dee Velazquez. Last updated 26 days ago.
data-visualizationspatial-analysisspatial-data-analysisspatial-transcriptomics
6 stars 4.65 score 15 scripts
alexwhitworth
synthACS:Synthetic Microdata and Spatial MicroSimulation Modeling for ACS Data
Provides access to curated American Community Survey (ACS) base tables via a wrapper to library(acs). Builds synthetic micro-datasets at any user-specified geographic level with ten default attributes; and, conducts spatial microsimulation modeling (SMSM) via simulated annealing. SMSM is conducted in parallel by default. Lastly, we provide functionality for data-extensibility of micro-datasets <doi:10.18637/jss.v104.i07>.
Maintained by Alex Whitworth. Last updated 2 years ago.
acsacs-datamicrosimulationspatial-data-analysiscpp
5 stars 4.29 score 78 scripts
santoroma
CircSpaceTime:Spatial and Spatio-Temporal Bayesian Model for Circular Data
Implementation of Bayesian models for spatial and spatio-temporal interpolation of circular data using Gaussian Wrapped and Gaussian Projected distributions. We developed the methods described in Jona Lasinio G. et al. (2012) <doi: 10.1214/12-aoas576>, Wang F. et al. (2014) <doi: 10.1080/01621459.2014.934454> and Mastrantonio G. et al. (2016) <doi: 10.1007/s11749-015-0458-y>.
Maintained by Mario Santoro. Last updated 6 years ago.
bayesian-statisticscircular-statisticsprojected-gaussianprojected-normalspatial-data-analysisspatio-temporalwrapped-gaussianwrapped-normalopenblascppopenmp
7 stars 3.98 score 27 scripts
f8l5h9
spqdep:Testing for Spatial Independence of Cross-Sectional Qualitative Data
Testing for Spatial Dependence of Qualitative Data in Cross Section. The list of functions includes join-count tests, Q test, spatial scan test, similarity test and spatial runs test. The methodology of these models can be found in <doi:10.1007/s10109-009-0100-1> and <doi:10.1080/13658816.2011.586327>.
Maintained by Fernando Lopez. Last updated 3 months ago.
categorical-dataqualitative-analysisspatial-data-analysisspatial-econometricsstatistical-tests
3.00 score 3 scripts
sciviews
aurelhy:Hydrometeorological Interpolation
Hydrometeorological interpolation using the AURELHY method.
Maintained by Philippe Grosjean. Last updated 1 years ago.
interpolationspatial-data-analysis
2 stars 2.00 score 5 scripts