Namespace for the TerraLib SA module. More...

Classes
class	BayesGlobalDialog

class	BayesGlobalOperation
	Class used to execute the bayes global operations. More...

class	BayesInputParams
	Class that represents the Bayes input parameters. More...

class	BayesLocalDialog

class	BayesLocalOperation

class	BayesOutputParams
	Class that represents the Bayes output parameters. More...

struct	EdgeRemovalInfo
	A struct that represents the skater partition values for each edge. More...

class	GeneralizedProximityMatrix
	This class defines a Generalized Proximity Matrix. More...

struct	GeostatisticalDataItem

class	GeostatisticalMethod
	Geostatistics is used for modelling spatial data. It provides accurate and reliable estimations of phenomena at locations where no measurements are available. More...

class	GeostatisticalMethodsDialog

class	GeostatisticalMethodSemivariogram
	Semivariogram is a function describing the degree of spatial dependence of a spatial random field. It is defined as the variance of the difference between field values at two locations (x and y) across realizations of the field. More...

class	GeostatisticalModel
	The empirical variogram cannot be computed at every lag distance h and due to variation in the estimation it is not ensured that it is a valid variogram, as defined above. However some Geostatistical methods such as kriging need valid semivariograms. In applied geostatistics the empirical variograms are thus often approximated by model function ensuring validity (Chiles&Delfiner 1999). More...

class	GeostatisticalModelExponential
	Class that represents the geostatistical exponential model. More...

class	GeostatisticalModelGaussian
	Class that represents the geostatistical gaussian model. More...

class	GeostatisticalModelSpherical
	Class that represents the geostatistical spherical model. More...

class	GPMBuilder
	This class defines the GPM Builder class. More...

class	GPMConstructorAbstractStrategy
	This class defines a an Abstract class for a GPM constructor. More...

class	GPMConstructorAdjacencyStrategy
	This class defines a an adjacency strategy class for a GPM constructor. More...

class	GPMConstructorDistanceStrategy
	This class defines a an distance strategy class for a GPM constructor. More...

class	GPMConstructorNearestNeighborStrategy
	This class defines a nearest neighbor class for a GPM constructor. More...

class	GPMWeightsAbstractStrategy
	This class defines a an Abstract class to calculates a weight for a GPM. More...

class	GPMWeightsInverseDistanceStrategy
	This class defines a class to calculates a weight for a GPM using Inverse Distance strategy. More...

class	GPMWeightsNoWeightsStrategy
	This class defines a class to calculates a weight for a GPM using No Weights strategy. More...

class	GPMWeightsSquaredInverseDistanceStrategy
	This class defines a class to calculates a weight for a GPM using Inverse Distance strategy. More...

class	KernelInputParams
	Class that represents the kernel input parameters. More...

class	KernelMapDialog

class	KernelMapOperation
	Class used to calculate the kernel map of a dataset. More...

class	KernelOperation
	Virtual class used to execute the kernel operations. More...

class	KernelOutputParams
	Class that represents the kernel output parameters. More...

class	KernelRatioDialog

class	KernelRatioOperation
	Class used to calculate the kernel ratio of a datasets. More...

class	MinimumSpanningTree

class	ProximityMatrixCreatorDialog

class	SamplePointsGeneratorAbstract
	Virtual class to generate samples points. More...

class	SamplePointsGeneratorDialog

class	SamplePointsGeneratorRandom
	Class to generate samples points using random strategy. More...

class	SamplePointsGeneratorStratified
	Class to generate samples points using stratified strategy. More...

class	SkaterDialog

class	SkaterInputParams
	Class that represents the skater input parameters. More...

class	SkaterOperation
	Class used to execute the skater operations. More...

class	SkaterOutputParams
	Class that represents the skater output parameters. More...

class	SkaterPartition
	Class that represents the skater partition operation. More...

class	SpatialStatisticsDialog

class	SpatialWeightsExchanger
	This class defines functions used to load and save gpm's using GAL and GWT formats, both formats use a ' ' as separator. More...

Typedefs
typedef std::vector< te::sa::GeostatisticalDataItem * >	GeostatisticalData

typedef std::map< int, std::pair< te::gm::Geometry *, double > >	KernelMap

typedef te::sam::rtree::Index< int >	KernelTree
	< Tree used to store the dataset geometries MBR and its id value More...

Enumerations
enum	GeostatisticalMethodType { Semivariogram }
	Geostatistical methods for measure of Spatial Variability. More...

enum	GeostatisticalModelType { Spherical, Exponential, Gaussian }
	Geostatistical models for measure of Spatial Variability. More...

enum	GPMConstructorStrategyType { AdjacencyStrategy, DistanceStrategy, NearestNeighbor }
	Strategies to construc a GPM. More...

enum	GPMWeightsStrategyType { NoWeightStrategy, InverseDistanceStrategy, SquaredInverseDistanceStrategy }
	Strategies to calculate the weight for a GPM. More...

enum	KernelCombinationType { Ratio, Log_Ratio, Abs_Difference, Relative_Difference, Abs_Sum, Relative_Sum }
	Types of kernel combination. More...

enum	KernelEstimationType { Density, Spatial_Moving_Average, Probability }

enum	KernelFunctionType { Quartic, Normal, Triangular, Uniform, Negative_Exp }

enum	KernelOutputType { Grid, Attribute }
	Defines the kernel result storage mode. More...

enum	SamplePointsGeneratorType { Random, Stratified }
	Generator types used to create sample of points. More...

enum	SkaterAggregationType { Clusters, Population, Both }
	Skater aggregation types used to partition the spanning tree. More...

Functions
TESAEXPORT int	AddGraphEdgeAttribute (te::graph::AbstractGraph *graph, std::string attrName, int dataType)
	Function used to create the edge attribute metadata in the graph of the gpm. More...

TESAEXPORT int	AddGraphVertexAttribute (te::graph::AbstractGraph *graph, std::string attrName, int dataType, int srid=TE_UNKNOWN_SRS, int subType=te::gm::UnknownGeometryType)
	Function used to create the vertex attribute metadata in the graph of the gpm. More...

TESAEXPORT int	AssociateGPMVertexAttribute (te::sa::GeneralizedProximityMatrix gpm, te::da::DataSource ds, std::string dataSetName, std::string attrLink, std::string attr, int dataType, int srid=TE_UNKNOWN_SRS, int subType=te::gm::UnknownGeometryType)
	Function used to set a an attribute valeu from a dataset to the vertex objects from a gpm. More...

TESAEXPORT int	AssociateGPMVertexAttribute (te::sa::GeneralizedProximityMatrix gpm, te::da::DataSet ds, std::string attrLink, std::string attr, int dataType, int srid=TE_UNKNOWN_SRS, int subType=te::gm::UnknownGeometryType)
	Function used to set a an attribute valeu from a dataset to the vertex objects from a gpm. More...

TESAEXPORT void	BoxMap (te::sa::GeneralizedProximityMatrix *gpm, double mean)
	Function used to calculate the box map info for a gpm, classifies the objects in quadrants based in the scatterplot of moran index. More...

TESAEXPORT double	CalculateDistance (te::gm::Geometry *geom, te::gm::Coord2D &coord)
	Function used to calculate the distance from a coord to the center of a geometry. More...

TESAEXPORT void	CalculateMoments (const boost::numeric::ublas::matrix< double > &matrix, double &mean, double &variance)
	Function used to calculate mean and variance from a matrix. More...

TESAEXPORT void	CreateBayesGrouping (te::map::AbstractLayerPtr layer)

TESAEXPORT te::da::DataSourcePtr	CreateGDALDataSource (std::string path, std::string dataSetName)

TESAEXPORT te::sa::GeostatisticalData	CreateGeostatisticalData (te::da::DataSet *dataSet, int attrIdx, int geomIdx)

TESAEXPORT void	CreateKernelColorMaping (te::map::AbstractLayerPtr layer)

TESAEXPORT void	CreateKernelGrouping (te::map::AbstractLayerPtr layer, std::string kernelAttr)

TESAEXPORT te::map::AbstractLayerPtr	CreateLayer (te::da::DataSourcePtr ds, std::string dataSetName)

TESAEXPORT boost::numeric::ublas::matrix< double >	CreateMatrixFromDataSet (te::da::DataSet *dataSet, int attrIdx, int geomIdx)
	Function used to create a matrix with values, distance and angle for each element from dataset. More...

TESAEXPORT te::da::DataSourcePtr	CreateOGRDataSource (std::string repository)

TESAEXPORT te::da::DataSourcePtr	CreateOGRDataSource (std::string path, std::string dataSetName)

TESAEXPORT void	CreateSampleGeneratorStratifiedGrouping (te::map::AbstractLayerPtr layer, std::vector< std::string > strVec)

TESAEXPORT void	CreateSkaterGrouping (te::map::AbstractLayerPtr layer, int nClasses)

TESAEXPORT void	DataSetAdaptRadiusKernel (te::sa::KernelInputParams params, te::sa::KernelTree &kTree, te::sa::KernelMap &kMap, te::mem::DataSet ds, int kernelIdx, int geomIdx)
	Evaluates kernel value using a dataset as output data and a adaptative value for radius. More...

TESAEXPORT void	DataSetKernelNormalize (te::sa::KernelInputParams params, te::sa::KernelMap &kMap, te::mem::DataSet ds, int kernelIdx, int geomIdx, double totKernel)
	Normalizes kernel values based on type of estimation. More...

TESAEXPORT void	DataSetRatioKernel (te::sa::KernelOutputParams params, te::mem::DataSet dsA, te::mem::DataSet dsB, te::mem::DataSet dsOut, int kernelIdx, int geomIdx)
	Evaluates kernel ratio value using a dataset as output data. More...

TESAEXPORT void	DataSetStatRadiusKernel (te::sa::KernelInputParams params, te::sa::KernelTree &kTree, te::sa::KernelMap &kMap, te::mem::DataSet ds, int kernelIdx, int geomIdx, double radius)
	Evaluates kernel value using a dataset as output data and a fixed value for radius. More...

TESAEXPORT double	FirstMoment (te::sa::GeneralizedProximityMatrix *gpm, int attrIdx)
	Function used to calculate mean (first moment) of a specific attribute from a gpm. More...

TESAEXPORT double	FirstMoment (std::vector< double > vec)
	Function used to calculate mean (first moment) from a vector with double values. More...

TESAEXPORT double	GetArea (te::gm::Geometry *geom)
	Function used to get area of a geometry. More...

TESAEXPORT te::color::ColorBar *	GetColorBar (std::string catalog, std::string group, std::string schema)

TESAEXPORT double	GetDataValue (te::dt::AbstractData *ad)
	Function used to get the numeric value from a gpm property. More...

TESAEXPORT bool	GetGraphEdgeAttrIndex (te::graph::AbstractGraph *graph, std::string attrName, int &index)
	Function used to get the edge attribute index in the graph of the gpm. More...

TESAEXPORT bool	GetGraphVertexAttrIndex (te::graph::AbstractGraph *graph, std::string attrName, int &index)
	Function used to get the vertex attribute index in the graph of the gpm. More...

TESAEXPORT double	GlobalMoranSignificance (te::sa::GeneralizedProximityMatrix *gpm, int attrIdx, int permutationsNumber, double moranIndex)
	Function used to calculate the global moran significance. More...

TESAEXPORT void	GridAdaptRadiusKernel (te::sa::KernelInputParams params, te::sa::KernelTree &kTree, te::sa::KernelMap &kMap, te::rst::Raster raster)
	Evaluates kernel value using a raster as output data and a adaptative value for radius. More...

TESAEXPORT void	GridKernelNormalize (te::sa::KernelInputParams params, te::sa::KernelMap &kMap, te::rst::Raster raster, double totKernel)
	Normalizes kernel values based on type of estimation. More...

TESAEXPORT void	GridRatioKernel (te::sa::KernelOutputParams params, te::rst::Raster rasterA, te::rst::Raster rasterB, te::rst::Raster rasterOut)
	Evaluates kernel ratio value using a raster as output data. More...

TESAEXPORT void	GridStatRadiusKernel (te::sa::KernelInputParams params, te::sa::KernelTree &kTree, te::sa::KernelMap &kMap, te::rst::Raster raster, double radius)
	Evaluates kernel value using a raster as output data and a fixed value for radius. More...

TESAEXPORT void	GStatistics (te::sa::GeneralizedProximityMatrix *gpm, int attrIdx)
	The local spatial statistic G is calculated for each zone based on the spatial weights object used. The value returned is a Z-value, and may be used as a diagnostic tool. High positive values indicate the posibility of a local cluster of high values of the variable being analysed, very low relative values a similar cluster of low values. More...

TESAEXPORT double	KernelGeometricMean (te::sa::KernelMap &kMap)
	Calculates the geometric mean from kernel map (intensity value) using log. More...

TESAEXPORT double	KernelNegExponential (double tau, double distance, double intensity)
	Kernel functions for Negative Exponential type. More...

TESAEXPORT double	KernelNormal (double tau, double distance, double intensity)
	Kernel functions for Normal type. More...

TESAEXPORT double	KernelQuartic (double tau, double distance, double intensity)
	Kernel functions for Quartic type. More...

TESAEXPORT double	KernelRatioValue (te::sa::KernelOutputParams *params, double area, double kernelA, double kernelB)
	Evaluates kernel ratio value. More...

TESAEXPORT double	KernelTriangular (double tau, double distance, double intensity)
	Kernel functions for Triangular type. More...

TESAEXPORT double	KernelUniform (double tau, double distance, double intensity)
	Kernel functions for Uniform type. More...

TESAEXPORT double	KernelValue (te::sa::KernelInputParams *params, te::sa::KernelMap &kMap, double radius, te::gm::Coord2D &coord, std::vector< int > idxVec)
	Evaluates kernel value of events with intensity. More...

TESAEXPORT void	LISAMap (te::sa::GeneralizedProximityMatrix *gpm, int permutationsNumber)
	Function used to calculate the lisa map info for a gpm, classifies the objects based in the statistical significance of the moran local indexes (LISA). More...

TESAEXPORT void	LisaStatisticalSignificance (te::sa::GeneralizedProximityMatrix *gpm, int permutationsNumber)
	Function used to calculate LISA Statical Significance for each gpm element. More...

TESAEXPORT void	LocalMean (te::sa::GeneralizedProximityMatrix *gpm, int attrIdx)
	Function used to calculate the local mean of each vertex from gpm graph. More...

TESAEXPORT double	MoranIndex (te::sa::GeneralizedProximityMatrix *gpm)
	Function used to calculate the moran index, also calculates the local moran value. More...

TESAEXPORT double	MoranIndex (te::sa::GeneralizedProximityMatrix *gpm, double mean, double variance, int attrIdx)
	Function used to calculate the moran index to calculate the significance of the global moran index. More...

TESAEXPORT void	MoranMap (te::sa::GeneralizedProximityMatrix *gpm)
	Function used to calculate the moran map info for a gpm, classifies the objects based in the scatterplot of Moran index and its statistical significance. More...

TESAEXPORT double	SecondMoment (te::sa::GeneralizedProximityMatrix *gpm, int attrIdx, double mean)
	Function used to calculate variance (second moment) of a specific attribute from a gpm. More...

TESAEXPORT void	SetMainDiagonal (boost::numeric::ublas::matrix< double > &matrix, te::da::DataSet *dataSet, int attrIdx)
	Function used to set new values in the matrix main diagonal. More...

TESAEXPORT double	Sum (te::sa::GeneralizedProximityMatrix *gpm, int attrIdx)
	Function used to calculate sum of a specific attribute from a gpm. More...

TESAEXPORT double	Sum (te::sa::KernelMap &kMap)
	Function used to calculate sum of the intensity value from a kernel map. More...

TESAEXPORT void	ZAndWZ (te::sa::GeneralizedProximityMatrix *gpm, int attrIdx)
	Function used to calculate the standard deviation Z and local mean of the desviation Z (WZ). of each vertex from gpm graph. More...

Detailed Description

Namespace for the TerraLib SA module.

Typedef Documentation

typedef std::vector<te::sa::GeostatisticalDataItem*> te::sa::GeostatisticalData

Definition at line 66 of file GeostatisticalFunctions.h.

typedef std::map<int, std::pair<te::gm::Geometry*, double> > te::sa::KernelMap

Definition at line 52 of file KernelFunctions.h.

typedef te::sam::rtree::Index<int> te::sa::KernelTree

< Tree used to store the dataset geometries MBR and its id value

Map with id, associated with geometry and its attribute (intensity value)

Definition at line 49 of file KernelFunctions.h.

Enumeration Type Documentation

enum te::sa::GeostatisticalMethodType

Geostatistical methods for measure of Spatial Variability.

Enumerator
Semivariogram

Definition at line 126 of file Enums.h.

enum te::sa::GeostatisticalModelType

Geostatistical models for measure of Spatial Variability.

Enumerator
Spherical
Exponential
Gaussian

Definition at line 136 of file Enums.h.

enum te::sa::GPMConstructorStrategyType

Strategies to construc a GPM.

Enumerator
AdjacencyStrategy
DistanceStrategy
NearestNeighbor

Definition at line 39 of file Enums.h.

enum te::sa::GPMWeightsStrategyType

Strategies to calculate the weight for a GPM.

Enumerator
NoWeightStrategy
InverseDistanceStrategy
SquaredInverseDistanceStrategy

Definition at line 51 of file Enums.h.

enum te::sa::KernelCombinationType

Types of kernel combination.

Enumerator
Ratio
Log_Ratio
Abs_Difference
Relative_Difference
Abs_Sum
Relative_Sum

Definition at line 89 of file Enums.h.

enum te::sa::KernelEstimationType

Enumerator
Density
Spatial_Moving_Average
Probability

Definition at line 77 of file Enums.h.

enum te::sa::KernelFunctionType

Enumerator
Quartic
Normal
Triangular
Uniform
Negative_Exp

Definition at line 63 of file Enums.h.

enum te::sa::KernelOutputType

Defines the kernel result storage mode.

Enumerator
Grid
Attribute

Definition at line 104 of file Enums.h.

enum te::sa::SamplePointsGeneratorType

Generator types used to create sample of points.

Enumerator
Random
Stratified

Definition at line 115 of file Enums.h.

enum te::sa::SkaterAggregationType

Skater aggregation types used to partition the spanning tree.

Enumerator
Clusters
Population
Both

Definition at line 148 of file Enums.h.

Function Documentation

TESAEXPORT int te::sa::AddGraphEdgeAttribute	(	te::graph::AbstractGraph *	graph,
		std::string	attrName,
		int	dataType
	)

Function used to create the edge attribute metadata in the graph of the gpm.

Parameters

graph	Pointer to the graph associated to the gpm.
attrName	Attribute name that will be created.
dataType	The data type of the new attribute.

Returns: Return the edge attribute index.

TESAEXPORT int te::sa::AddGraphVertexAttribute	(	te::graph::AbstractGraph *	graph,
		std::string	attrName,
		int	dataType,
		int	srid = `TE_UNKNOWN_SRS`,
		int	subType = `te::gm::UnknownGeometryType`
	)

Function used to create the vertex attribute metadata in the graph of the gpm.

Parameters

graph	Pointer to the graph associated to the gpm.
attrName	Attribute name that will be created.
dataType	The data type of the new attribute.
srid	If the new attribute was a geometry type

Returns: Return the vertex attribute index.

TESAEXPORT int te::sa::AssociateGPMVertexAttribute	(	te::sa::GeneralizedProximityMatrix *	gpm,
		te::da::DataSource *	ds,
		std::string	dataSetName,
		std::string	attrLink,
		std::string	attr,
		int	dataType,
		int	srid = `TE_UNKNOWN_SRS`,
		int	subType = `te::gm::UnknownGeometryType`
	)

Function used to set a an attribute valeu from a dataset to the vertex objects from a gpm.

Parameters

gpm	Pointer to gpm that has the graph information.
ds	DataSource pointer that has the dataset information.
dataSetName	Dataset name to get the attribute information.
attrLink	Attribute name used to link the vertex id to dataset.
attr	Attribute name that will be associated to the graph.
dataType	The type of the attribute that will be associated.
srid	If the new attribute was a geometry type

Returns: Return the vertex attribute index.

TESAEXPORT int te::sa::AssociateGPMVertexAttribute	(	te::sa::GeneralizedProximityMatrix *	gpm,
		te::da::DataSet *	ds,
		std::string	attrLink,
		std::string	attr,
		int	dataType,
		int	srid = `TE_UNKNOWN_SRS`,
		int	subType = `te::gm::UnknownGeometryType`
	)

Function used to set a an attribute valeu from a dataset to the vertex objects from a gpm.

Parameters

gpm	Pointer to gpm that has the graph information.
ds	DataSet pointer
attrLink	Attribute name used to link the vertex id to dataset.
attr	Attribute name that will be associated to the graph.
dataType	The type of the attribute that will be associated.
srid	If the new attribute was a geometry type

Returns: Return the vertex attribute index.

TESAEXPORT void te::sa::BoxMap	(	te::sa::GeneralizedProximityMatrix *	gpm,
		double	mean
	)

Function used to calculate the box map info for a gpm, classifies the objects in quadrants based in the scatterplot of moran index.

Parameters

gpm	Pointer to the gpm.
mean	Mean value

Note: This functions only works if the gpm has the Z and WZ attributes calculated.

TESAEXPORT double te::sa::CalculateDistance	(	te::gm::Geometry *	geom,
		te::gm::Coord2D &	coord
	)

Function used to calculate the distance from a coord to the center of a geometry.

Parameters

geom	Pointer to a geometry
coord	Reference to a coord

Returns: Return a double value with distance information

Note: It's only possible if the geom has a centroid.

TESAEXPORT void te::sa::CalculateMoments	(	const boost::numeric::ublas::matrix< double > &	matrix,
		double &	mean,
		double &	variance
	)

Function used to calculate mean and variance from a matrix.

Parameters

matrix	Reference to a valid matrix.
mean	Reference to set the mean value.
variance	Reference to set the variance value.

TESAEXPORT void te::sa::CreateBayesGrouping ( te::map::AbstractLayerPtr layer )

TESAEXPORT te::da::DataSourcePtr te::sa::CreateGDALDataSource	(	std::string	path,
		std::string	dataSetName
	)

TESAEXPORT te::sa::GeostatisticalData te::sa::CreateGeostatisticalData	(	te::da::DataSet *	dataSet,
		int	attrIdx,
		int	geomIdx
	)

TESAEXPORT void te::sa::CreateKernelColorMaping ( te::map::AbstractLayerPtr layer )

TESAEXPORT void te::sa::CreateKernelGrouping	(	te::map::AbstractLayerPtr	layer,
		std::string	kernelAttr
	)

TESAEXPORT te::map::AbstractLayerPtr te::sa::CreateLayer	(	te::da::DataSourcePtr	ds,
		std::string	dataSetName
	)

TESAEXPORT boost::numeric::ublas::matrix<double> te::sa::CreateMatrixFromDataSet	(	te::da::DataSet *	dataSet,
		int	attrIdx,
		int	geomIdx
	)

Function used to create a matrix with values, distance and angle for each element from dataset.

Parameters

dataSet	Pointer to input data.
attrIdx	Attribute index used to create the matrix.
geomIdx	Attribute index used to identify the geometric attribute.

Returns: A matrix with values from dataset (main diagonal), distance (top of the matrix), angle (bottom of the matrix).

TESAEXPORT te::da::DataSourcePtr te::sa::CreateOGRDataSource ( std::string repository )

TESAEXPORT te::da::DataSourcePtr te::sa::CreateOGRDataSource	(	std::string	path,
		std::string	dataSetName
	)

TESAEXPORT void te::sa::CreateSampleGeneratorStratifiedGrouping	(	te::map::AbstractLayerPtr	layer,
		std::vector< std::string >	strVec
	)

TESAEXPORT void te::sa::CreateSkaterGrouping	(	te::map::AbstractLayerPtr	layer,
		int	nClasses
	)

TESAEXPORT void te::sa::DataSetAdaptRadiusKernel	(	te::sa::KernelInputParams *	params,
		te::sa::KernelTree &	kTree,
		te::sa::KernelMap &	kMap,
		te::mem::DataSet *	ds,
		int	kernelIdx,
		int	geomIdx
	)

Evaluates kernel value using a dataset as output data and a adaptative value for radius.

Parameters

params	Pointer to a Kernel Params
kTree	Reference to tree with all geometries from input data
kMap	Reference to the kernel Map
ds	Pointer to dataset to set the kernel values
kernelIdx	Attribute index to save the kernel information
geomIdx	Attribute index with geometry information

TESAEXPORT void te::sa::DataSetKernelNormalize	(	te::sa::KernelInputParams *	params,
		te::sa::KernelMap &	kMap,
		te::mem::DataSet *	ds,
		int	kernelIdx,
		int	geomIdx,
		double	totKernel
	)

Normalizes kernel values based on type of estimation.

Parameters

params	Pointer to a Kernel Params (used to get the estimation type)
kMap	Reference to the kernel Map
ds	Pointer to dataset to set the kernel values
kernelIdx	Attribute index to save the kernel information
geomIdx	Attribute index with geometry information
totKernel	Sum of all kernel values from each raster pixel

TESAEXPORT void te::sa::DataSetRatioKernel	(	te::sa::KernelOutputParams *	params,
		te::mem::DataSet *	dsA,
		te::mem::DataSet *	dsB,
		te::mem::DataSet *	dsOut,
		int	kernelIdx,
		int	geomIdx
	)

Evaluates kernel ratio value using a dataset as output data.

Parameters

params	Pointer to a Kernel Params
dsA	Pointer to a dataset A with kernel information
dsB	Pointer to a dataset B with kernel information
dsOut	Pointer to dataset to set the kernel ratio values
kernelIdx	Attribute index to save the kernel information
geomIdx	Attribute index with geometry information

TESAEXPORT void te::sa::DataSetStatRadiusKernel	(	te::sa::KernelInputParams *	params,
		te::sa::KernelTree &	kTree,
		te::sa::KernelMap &	kMap,
		te::mem::DataSet *	ds,
		int	kernelIdx,
		int	geomIdx,
		double	radius
	)

Evaluates kernel value using a dataset as output data and a fixed value for radius.

Parameters

params	Pointer to a Kernel Params
kTree	Reference to tree with all geometries from input data
kMap	Reference to the kernel Map
ds	Pointer to dataset to set the kernel values
kernelIdx	Attribute index to save the kernel information
geomIdx	Attribute index with geometry information
radius	Radius used to calculate the kernel

TESAEXPORT double te::sa::FirstMoment	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	attrIdx
	)

Function used to calculate mean (first moment) of a specific attribute from a gpm.

Parameters

gpm	Pointer to the gpm
attrIdx	Attribute index used to calculate the mean.

Returns: Return the mean value.

TESAEXPORT double te::sa::FirstMoment ( std::vector< double > vec )

Function used to calculate mean (first moment) from a vector with double values.

Parameters

vec	Vector with double values

Returns: Return the mean value.

TESAEXPORT double te::sa::GetArea ( te::gm::Geometry * geom )

Function used to get area of a geometry.

Parameters

geom	Pointer to a geometry

Returns: Return a double value with area information

Note: It's only possible if the geom has a area

TESAEXPORT te::color::ColorBar* te::sa::GetColorBar	(	std::string	catalog,
		std::string	group,
		std::string	schema
	)

TESAEXPORT double te::sa::GetDataValue ( te::dt::AbstractData * ad )

Function used to get the numeric value from a gpm property.

Parameters

ad	Pointer to a abstract data that represents a property value from a gpm.

Returns: Return a double value if its possible.

TESAEXPORT bool te::sa::GetGraphEdgeAttrIndex	(	te::graph::AbstractGraph *	graph,
		std::string	attrName,
		int &	index
	)

Function used to get the edge attribute index in the graph of the gpm.

Parameters

graph	Pointer to the graph associated to the gpm.
attrName	Attribute name to be searched.
index	The index of the attribute searched.

Returns: Return true if the attribute was found and false in other case.

TESAEXPORT bool te::sa::GetGraphVertexAttrIndex	(	te::graph::AbstractGraph *	graph,
		std::string	attrName,
		int &	index
	)

Function used to get the vertex attribute index in the graph of the gpm.

Parameters

graph	Pointer to the graph associated to the gpm.
attrName	Attribute name to be searched.
index	The index of the attribute searched.

Returns: Return true if the attribute was found and false in other case.

TESAEXPORT double te::sa::GlobalMoranSignificance	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	attrIdx,
		int	permutationsNumber,
		double	moranIndex
	)

Function used to calculate the global moran significance.

Parameters

gpm	Pointer to the gpm.
attrIdx	Attribute index used to calculate the global moran significance.
permutationsNumber	Value of pertumations.
moranIndex	The global moran index value.

Returns: Double value that represents the global moran significance.

TESAEXPORT void te::sa::GridAdaptRadiusKernel	(	te::sa::KernelInputParams *	params,
		te::sa::KernelTree &	kTree,
		te::sa::KernelMap &	kMap,
		te::rst::Raster *	raster
	)

Evaluates kernel value using a raster as output data and a adaptative value for radius.

Parameters

params	Pointer to a Kernel Params
kTree	Reference to tree with all geometries from input data
kMap	Reference to the kernel Map
raster	Pointer to raster to set the kernel values

TESAEXPORT void te::sa::GridKernelNormalize	(	te::sa::KernelInputParams *	params,
		te::sa::KernelMap &	kMap,
		te::rst::Raster *	raster,
		double	totKernel
	)

Normalizes kernel values based on type of estimation.

Parameters

params	Pointer to a Kernel Params (used to get the estimation type)
kMap	Reference to the kernel Map
raster	Pointer to raster with the kernel values
totKernel	Sum of all kernel values from each raster pixel

TESAEXPORT void te::sa::GridRatioKernel	(	te::sa::KernelOutputParams *	params,
		te::rst::Raster *	rasterA,
		te::rst::Raster *	rasterB,
		te::rst::Raster *	rasterOut
	)

Evaluates kernel ratio value using a raster as output data.

Parameters

params	Pointer to a Kernel Params
rasterA	Pointer to a raster A with kernel information
rasterB	Pointer to a raster B with kernel information
raster	Pointer to raster to set the kernel ratio values

TESAEXPORT void te::sa::GridStatRadiusKernel	(	te::sa::KernelInputParams *	params,
		te::sa::KernelTree &	kTree,
		te::sa::KernelMap &	kMap,
		te::rst::Raster *	raster,
		double	radius
	)

Evaluates kernel value using a raster as output data and a fixed value for radius.

Parameters

params	Pointer to a Kernel Params
kTree	Reference to tree with all geometries from input data
kMap	Reference to the kernel Map
raster	Pointer to raster to set the kernel values
radius	Radius used to calculate the kernel

TESAEXPORT void te::sa::GStatistics	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	attrIdx
	)

The local spatial statistic G is calculated for each zone based on the spatial weights object used. The value returned is a Z-value, and may be used as a diagnostic tool. High positive values indicate the posibility of a local cluster of high values of the variable being analysed, very low relative values a similar cluster of low values.

Parameters

gpm	Pointer to the gpm
attrIdx	Attribute index used to calculate the GStatistics.

TESAEXPORT double te::sa::KernelGeometricMean ( te::sa::KernelMap & kMap )

Calculates the geometric mean from kernel map (intensity value) using log.

Parameters

kMap	Reference to the kernel Map

Returns: Double value with the geometric mean

TESAEXPORT double te::sa::KernelNegExponential	(	double	tau,
		double	distance,
		double	intensity
	)

Kernel functions for Negative Exponential type.

Parameters

tau	spatial threshold to define neighboorhood
distance	distance between event and region centroid
intensity	attribute value for event

Returns: Kernel value

TESAEXPORT double te::sa::KernelNormal	(	double	tau,
		double	distance,
		double	intensity
	)

Kernel functions for Normal type.

Parameters

tau	spatial threshold to define neighboorhood
distance	distance between event and region centroid
intensity	attribute value for event

Returns: Kernel value

TESAEXPORT double te::sa::KernelQuartic	(	double	tau,
		double	distance,
		double	intensity
	)

Kernel functions for Quartic type.

Parameters

tau	spatial threshold to define neighboorhood
distance	distance between event and region centroid
intensity	attribute value for event

Returns: Kernel value

TESAEXPORT double te::sa::KernelRatioValue	(	te::sa::KernelOutputParams *	params,
		double	area,
		double	kernelA,
		double	kernelB
	)

Evaluates kernel ratio value.

Parameters

params	Pointer to a Kernel Params
area	Value to represent the area of the element
kernelA	Double value with kernel value from A element
kernelB	Double value with kernel value from B element

Returns: Kernel value for one element.

TESAEXPORT double te::sa::KernelTriangular	(	double	tau,
		double	distance,
		double	intensity
	)

Kernel functions for Triangular type.

Parameters

tau	spatial threshold to define neighboorhood
distance	distance between event and region centroid
intensity	attribute value for event

Returns: Kernel value

TESAEXPORT double te::sa::KernelUniform	(	double	tau,
		double	distance,
		double	intensity
	)

Kernel functions for Uniform type.

Parameters

tau	spatial threshold to define neighboorhood
distance	distance between event and region centroid
intensity	attribute value for event

Returns: Kernel value

TESAEXPORT double te::sa::KernelValue	(	te::sa::KernelInputParams *	params,
		te::sa::KernelMap &	kMap,
		double	radius,
		te::gm::Coord2D &	coord,
		std::vector< int >	idxVec
	)

Evaluates kernel value of events with intensity.

Parameters

params	Pointer to a Kernel Params
kMap	Reference to the kernel Map
radius	Double value with radius information
coord	Coord of the element to calculate the kernel value
idxVec	Index of all elements that interects the box created by coord with radius value

Returns: Kernel value for one element.

TESAEXPORT void te::sa::LISAMap	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	permutationsNumber
	)

Function used to calculate the lisa map info for a gpm, classifies the objects based in the statistical significance of the moran local indexes (LISA).

Parameters

gpm	Pointer to the gpm.
int	The number of permutations.

Note: This functions only works if the gpm has the LISASig (LisaStatisticalSignificance) attribute calculated.

TESAEXPORT void te::sa::LisaStatisticalSignificance	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	permutationsNumber
	)

Function used to calculate LISA Statical Significance for each gpm element.

Parameters

gpm	Pointer to the gpm.
int	The number of permutations.

Note: This functions only works if the gpm has the Z, Local Moran and Number of Neighbours attributes calculated.

TESAEXPORT void te::sa::LocalMean	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	attrIdx
	)

Function used to calculate the local mean of each vertex from gpm graph.

Parameters

gpm	Pointer to the gpm.
attrIdx	Attribute index used to calculate the local mean.

TESAEXPORT double te::sa::MoranIndex ( te::sa::GeneralizedProximityMatrix * gpm )

Function used to calculate the moran index, also calculates the local moran value.

Parameters

gpm	Pointer to the gpm.

Returns: Double value that represents the moran index.

Note: This functions only works if the gpm has the Z and WZ attributes calculated.

TESAEXPORT double te::sa::MoranIndex	(	te::sa::GeneralizedProximityMatrix *	gpm,
		double	mean,
		double	variance,
		int	attrIdx
	)

Function used to calculate the moran index to calculate the significance of the global moran index.

Parameters

gpm	Pointer to the gpm.
mean	The mean of the original gpm of the attrIdx.
variance	The variance of the original gpm of the attrIdx.
attrIdx	Attribute selected to calculate the moran index.

Returns: Double value that represents the moran index.

Note: This is a internal function used in GlobalMoranSignificance method.

TESAEXPORT void te::sa::MoranMap ( te::sa::GeneralizedProximityMatrix * gpm )

Function used to calculate the moran map info for a gpm, classifies the objects based in the scatterplot of Moran index and its statistical significance.

Parameters

gpm	Pointer to the gpm.

Note: This functions only works if the gpm has the LISAMap and BoxMAP attributes calculated.

TESAEXPORT double te::sa::SecondMoment	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	attrIdx,
		double	mean
	)

Function used to calculate variance (second moment) of a specific attribute from a gpm.

Parameters

gpm	Pointer to the gpm
attrIdx	Attribute index used to calculate the variance.

Returns: Return the variance value.

TESAEXPORT void te::sa::SetMainDiagonal	(	boost::numeric::ublas::matrix< double > &	matrix,
		te::da::DataSet *	dataSet,
		int	attrIdx
	)

Function used to set new values in the matrix main diagonal.

Parameters

matrix	Reference to a valid matrix.
dataSet	Pointer to input data.
attrIdx	Attribute index used to reset the diagonal values.

Returns: A matrix with values from dataset (main diagonal), distance (top of the matrix), angle (bottom of the matrix).

TESAEXPORT double te::sa::Sum	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	attrIdx
	)

Function used to calculate sum of a specific attribute from a gpm.

Parameters

gpm	Pointer to the gpm
attrIdx	Attribute index used to calculate the sum.

Returns: Return the sum value.

TESAEXPORT double te::sa::Sum ( te::sa::KernelMap & kMap )

Function used to calculate sum of the intensity value from a kernel map.

Parameters

kMap	Referente to a kernel map.

Returns: Return the sum value.

TESAEXPORT void te::sa::ZAndWZ	(	te::sa::GeneralizedProximityMatrix *	gpm,
		int	attrIdx
	)

Function used to calculate the standard deviation Z and local mean of the desviation Z (WZ). of each vertex from gpm graph.

Parameters

gpm	Pointer to the gpm.
attrIdx	Attribute index used to calculate the Z and WZ.

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