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ArcGIS Pro is desktop GIS software developed by Esri, which replaces their ArcMap software generation. [1] The product was announced as part of Esri's ArcGIS 10.3 release, [ 2 ] ArcGIS Pro is notable in having a 64 bit architecture, combined 2-D, 3-D support, ArcGIS Online integration and Python 3 support.
ArcGIS 9 was released in May 2004, which included ArcGIS Server and ArcGIS Engine for developers. [39] The ArcGIS 9 release includes a geoprocessing environment that allows execution of traditional GIS processing tools (such as clipping, overlay, and spatial analysis) interactively or from any scripting language that supports COM standards.
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
Spatial analysis confronts many fundamental issues in the definition of its objects of study, in the construction of the analytic operations to be used, in the use of computers for analysis, in the limitations and particularities of the analyses which are known, and in the presentation of analytic results.
ArcGIS includes Internet capabilities in all Esri software products. The services, provided through ArcGIS Online at www.arcgis.com, include web APIs, hosted map and geoprocessing services, and a user sharing program. A variety of basemaps is a signature feature of ArcGIS Online.
Shoelace scheme for determining the area of a polygon with point coordinates (,),..., (,). The shoelace formula, also known as Gauss's area formula and the surveyor's formula, [1] is a mathematical algorithm to determine the area of a simple polygon whose vertices are described by their Cartesian coordinates in the plane. [2]
A class's prior may be calculated by assuming equiprobable classes, i.e., () =, or by calculating an estimate for the class probability from the training set: = To estimate the parameters for a feature's distribution, one must assume a distribution or generate nonparametric models for the features from the training set.
The Pearson correlation can be accurately calculated for any distribution that has a finite covariance matrix, which includes most distributions encountered in practice. However, the Pearson correlation coefficient (taken together with the sample mean and variance) is only a sufficient statistic if the data is drawn from a multivariate normal ...