Search results
Results from the WOW.Com Content Network
The simplest forms of spatial data are gridded data, in which a scalar quantity is measured for each point in a regular grid of points, and point sets, in which a set of coordinates (e.g. of points in the plane) is observed. An example of gridded data would be a satellite image of forest density
Spatial data science & location intelligence Spatial SQL, spatial data science, location analytics, site selection, data visualization, mapping, geocoding and app development. Access to a catalog of 1,000s of spatial datasets. Proprietary (with free trial available). Fract No Linux, Windows, Unix, iOS, Android, Windows Phone, Cloud: Fract, Inc.
This implementation can use various index structures for sub-quadratic runtime and supports arbitrary distance functions and arbitrary data types, but it may be outperformed by low-level optimized (and specialized) implementations on small data sets. MATLAB includes an implementation of DBSCAN in its "Statistics and Machine Learning Toolbox ...
In statistics, the Matérn covariance, also called the Matérn kernel, [1] is a covariance function used in spatial statistics, geostatistics, machine learning, image analysis, and other applications of multivariate statistical analysis on metric spaces. It is named after the Swedish forestry statistician Bertil Matérn. [2]
Integrated nested Laplace approximations (INLA) is a method for approximate Bayesian inference based on Laplace's method. [1] It is designed for a class of models called latent Gaussian models (LGMs), for which it can be a fast and accurate alternative for Markov chain Monte Carlo methods to compute posterior marginal distributions.
The method is entirely local, as it is based on a minimal subset of data locations that excludes locations that, while close, are more distant than another location in a similar direction. The method is spatially adaptive, automatically adapting to local variation in data density or spatial arrangement.
Spatial statistics is a field of applied statistics dealing with spatial data. It involves stochastic processes ( random fields , point processes ), sampling , smoothing and interpolation , regional ( areal unit ) and lattice ( gridded ) data, point patterns , as well as image analysis and stereology .
Spatial analysis includes a variety of techniques using different analytic approaches, especially spatial statistics. It may be applied in fields as diverse as astronomy , with its studies of the placement of galaxies in the cosmos , or to chip fabrication engineering, with its use of "place and route" algorithms to build complex wiring structures.