JSDoc: Global

Methods

azpGreedy(weights, k, values, inits, initRegion, minBoundValues, minBounds, maxBoundValues, maxBounds, scaleMethod, distanceMethod, seed) → {Object}

A greedy algorithm to solve the AZP problem

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`k`	Number	The number of spatially constrained clusters
`values`	Array	The list of numeric vectors of selected variable.
`inits`	Number	The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
`initRegion`	Array	The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
`minBoundValues`	Array	The list of numeric array of selected minimum bounding variables.
`minBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
`maxBoundValues`	Array	The list of numeric array of selected maximum bounding variables.
`maxBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be less than.
`scaleMethod`	String	The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
`distanceMethod`	String	The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
`seed`	Number	The seed for random number generator.

Source:

geoda-proxy.js, line 1697

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

azpSA(weights, k, values, coolingRate, saMaxIt, inits, initRegion, minBoundValues, minBounds, maxBoundValues, maxBounds, scaleMethod, distanceMethod, seed) → {Object}

A simulated annealing algorithm to solve the AZP problem

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`k`	Number	The number of spatially constrained clusters
`values`	Array	The list of numeric vectors of selected variable.
`coolingRate`	Number	The number of iterations of simulated annealing. Defaults to 1
`saMaxIt`	Number	The number of iterations of simulated annealing. Defaults to 1
`inits`	Number	The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
`initRegion`	Array	The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
`minBoundValues`	Array	The list of numeric array of selected minimum bounding variables.
`minBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
`maxBoundValues`	Array	The list of numeric array of selected maximum bounding variables.
`maxBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be less than.
`scaleMethod`	String	The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
`distanceMethod`	String	The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
`seed`	Number	The seed for random number generator.

Source:

geoda-proxy.js, line 1756

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

azpTabu(weights, k, values, tabuLength, convTabu, inits, initRegion, minBoundValues, minBounds, maxBoundValues, maxBounds, scaleMethod, distanceMethod, seed) → {Object}

A tabu-search algorithm to solve the AZP problem.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`k`	Number	The number of spatially constrained clusters
`values`	Array	The list of numeric vectors of selected variable.
`tabuLength`	Number	The length of a tabu search heuristic of tabu algorithm. Defaults to 10.
`convTabu`	Number	The number of non-improving moves. Defaults to 10.
`inits`	Number	The number of construction re-runs, which is for ARiSeL "automatic regionalization with initial seed location"
`initRegion`	Array	The initial regions that the local search starts with. Default is empty. means the local search starts with a random process to "grow" clusters
`minBoundValues`	Array	The list of numeric array of selected minimum bounding variables.
`minBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
`maxBoundValues`	Array	The list of numeric array of selected maximum bounding variables.
`maxBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be less than.
`scaleMethod`	String	The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
`distanceMethod`	String	The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
`seed`	Number	The seed for random number generator.

Source:

geoda-proxy.js, line 1817

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

callLisa(weights, values, permutations, seed) → {Object}

Helper function: apply LISA statistics

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`values`	Array	The values that local moran statistics will be applied on.
`permutations`	Number	The number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`seed`	Number	The seed for random number generator used in LISA statistics. Default: 123456789.

Source:

geoda-proxy.js, line 1165

Returns:

An instance of LisaResult

Type: Object

cartogram(mapUid, values) → {Array}

Create cartogram using the values in the map.

Create cartogram using the values in the map. In cartograms, the size of a variable's value corresponds to the size of a shape. The location of the circles is aligned as closely as possible to the location of the associated area through a nonlinear optimization routine

Parameters:

Name	Type	Description
`mapUid`	String	A unique map Id
`values`	Array	The values that the classify algorithm will be applied on.

Source:

geoda-proxy.js, line 1003

Returns:

Returns an array of circles, which is defined as: { "properties": { "id" : 1}, "position": [0.01, 0.01], "radius": 0.1 }

Type: Array

checkDistanceMethod(distanceMethod) → {Boolean}

Helper function: check if distance method is valid.

Parameters:

Name	Type	Description
`distanceMethod`	String

Source:

geoda-proxy.js, line 1549

Returns:

Type: Boolean

checkInputKernel(kernel) → {Boolean}

check if input kernel is valid

Parameters:

Name	Type	Description
`kernel`	*

Source:

geoda-proxy.js, line 469

Returns:

Type: Boolean

checkMapUid(mapUid) → {Boolean}

Check if map uid is valid

Parameters:

Name	Type	Description
`mapUid`	String

Source:

geoda-proxy.js, line 152

Returns:

Type: Boolean

checkScaleMethod(scaleMethod) → {Boolean}

Helper function: check if scale method is valid.

Parameters:

Name	Type	Description
`scaleMethod`	String

Source:

geoda-proxy.js, line 1534

Returns:

Type: Boolean

customBreaks(breakName, values, k) → {Object}

Custom breaks that wraps {'natural_breaks', 'quantile_breaks', 'stdDevBreaks', 'hinge15Breaks', 'hinge30Breaks'}

Parameters:

Name	Type	Description
`breakName`	String	The break name: {'natural_breaks', 'quantile_breaks', 'stdDevBreaks', 'hinge15Breaks', 'hinge30Breaks'}
`values`	*	The values of selected variable.
`k`	*	The number of breaks.

Source:

geoda-proxy.js, line 846

Returns:

{'k','bins','breaks','id_array'}

Type: Object

distanceMethods() → {Object}

Help function: Get distance methods.

Source:

geoda-proxy.js, line 1231

Returns:

Type: Object

empiricalBayesRisk(eventValues, baseValues) → {Array}

Empirical Bayes (EB) Smoothed Rate

Parameters:

Name	Type	Description
`eventValues`	Array	The values of an event variable.
`baseValues`	Array	The values of an base variable.

Source:

geoda-proxy.js, line 922

Returns:

Type: Array

excessRisk(eventValues, baseValues) → {Array}

Excess Risk

Parameters:

Name	Type	Description
`eventValues`	Array	The values of an event variable.
`baseValues`	Array	The values of an base variable.

Source:

geoda-proxy.js, line 909

Returns:

Type: Array

free()

Free the memory used by wasm

Source:

geoda-proxy.js, line 143

generateUid() → {String}

Help function: create a unique id for a Geojson map

Source:

geoda-proxy.js, line 34

Returns:

Type: String

getBounds(mapUid) → {Array}

Get map bounds

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id that has been read into GeoDaWasm.

Source:

geoda-proxy.js, line 166

Returns:

Type: Array

getCentroids(mapUid) → {Array}

Get the centroids of geojson map.

Get the centroids of geojson map. Same as GEOS.algorithm.Centroid: the centroid were computed as a weighted sum of the centroids of a decomposition of the area into (possibly overlapping) triangles. The algorithm has been extended to handle holes and multi-polygons

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id

Source:

geoda-proxy.js, line 237

Returns:

Returns an array of [x,y] coordinates (not projected) of the centroids.

Type: Array

Example

// In node.js
const fs = require('fs');
const jsgeoda = require('jsgeoda');
const geoda = await jsgeoda.New();

let ab = fs.readFileSync("NAT.geojson").buffer;
let nat = geoda.read_geojson("NAT", ab);
let cent = geoda.get_centroids(nat);

getClusteringResult(r) → {Object}

Helper function: get clustering results

Parameters:

Name	Type	Description
`r`	Object

Source:

geoda-proxy.js, line 1564

Returns:

{'clusters', 'total_ss', 'between_ss', 'within_ss', 'ratio'}

Type: Object

getClusters() → {Array}

cluster indicators

Source:

geoda-lisa.js, line 40

Returns:

Type: Array

getColors() → {Array}

Get colors

Source:

geoda-lisa.js, line 72

Returns:

Type: Array

getColumn(mapUid, colName) → {Array}

Get the values (numeric|string) of a column or field.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`colName`	String	A string of column or field name.

Source:

geoda-proxy.js, line 291

Returns:

Returns the values of a column of field.

Type: Array

getColumnNames(mapUid) → {Array}

Get the column names of the geojson map

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.

Source:

geoda-proxy.js, line 275

Returns:

Returns the column names

Type: Array

getConnectivity(weights) → {Object}

Get connectivity graph from a weights object

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`

Source:

geoda-proxy.js, line 539

Returns:

{arcs, targets, sources}

Type: Object

getDistanceWeights(mapUid, distThreshold, power, isInverse, isArc, isMile) → {Object}

Create a Distance-based weights.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`distThreshold`	Number	A positive numeric value of distance threshold used to find neighbors.
`power`	Number	The power (or exponent) indicates how many times to use the number in a multiplication.
`isInverse`	Boolean	A bool flag indicates whether or not to apply inverse on distance value.
`isArc`	Boolean	A bool flag indicates if compute arc distance (true) or Euclidean distance (false).
`isMile`	Boolean	A bool flag indicates if the distance unit is mile (true) or km (false).

Source:

geoda-proxy.js, line 413

Returns:

An instance of GeoDaWeights

Type: Object

getKernelKnnWeights(mapUid, k, kernel, adaptiveBandwidth, useKernelDiagonals, power, isInverse, isArc, isMile) → {Object}

Create a (adaptive) KNN kernel weights.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`k`	Number	A positive integer number for k-nearest neighbors
`kernel`	String	The name of the kernel function, which could be one of the following: {triangular, uniform, quadratic, epanechnikov, quartic, gaussian}
`adaptiveBandwidth`	Boolean	A bool flag indicates whether to use adaptive bandwidth or the max distance of all observation to their k-nearest neighbors.
`useKernelDiagonals`	Boolean	A bool flag indicates whether or not the lower order neighbors should be included in the weights structure.
`power`	Number	The power (or exponent) indicates how many times to use the number in a multiplication.
`isInverse`	Boolean	A bool flag indicates whether or not to apply inverse on distance value.
`isArc`	Boolean	A bool flag indicates if compute arc distance (true) or Euclidean distance (false).
`isMile`	Boolean	A bool flag indicates if the distance unit is mile (true) or km (false)

Source:

geoda-proxy.js, line 446

Returns:

An instance of GeoDaWeights

Type: Object

getKernelWeights(mapUid, bandwidth, kernel, adaptive_bandwidth, useKernelDiagonals, power, isInverse, isArc, isMile) → {Object}

Create a kernel weights with fixed bandwidth.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`bandwidth`	Number	The bandwidth (distance threshold).
`kernel`	String	The name of the kernel function, which could be one of the following: {triangular, uniform, quadratic, epanechnikov, quartic, gaussian}
`adaptive_bandwidth`	Boolean	A bool flag indicates whether to use adaptive bandwidth or the max distance of all observation to their k-nearest neighbors.
`useKernelDiagonals`	Boolean	A bool flag indicates whether or not the lower order neighbors should be included in the weights structure.
`power`	Number	The power (or exponent) indicates how many times to use the number in a multiplication.
`isInverse`	Boolean	A bool flag indicates whether or not to apply inverse on distance value.
`isArc`	Boolean	A bool flag indicates if compute arc distance (true) or Euclidean distance (false).
`isMile`	Boolean	A bool flag indicates if the distance unit is mile (true) or km (false).

Source:

geoda-proxy.js, line 500

Returns:

An instance of GeoDaWeights

Type: Object

getKnnWeights(mapUid, k, power, isInverse, isArc, isMile) → {Object}

Create a K-Nearest Neighbors weights.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`k`	Number	A positive integer number for k-nearest neighbors
`power`	Number	The power (or exponent) indicates how many times to use the number in a multiplication.
`isInverse`	Boolean	A bool flag indicates whether or not to apply inverse on distance value.
`isArc`	Boolean	A bool flag indicates if compute arc distance (true) or Euclidean distance (false).
`isMile`	Boolean	A bool flag indicates if the distance unit is mile (true) or km (false).

Source:

geoda-proxy.js, line 386

Returns:

An instance of GeoDaWeights

Type: Object

getLabels() → {Array}

Get labels

Source:

geoda-lisa.js, line 64

Returns:

Type: Array

getLisaValues() → {Array}

lisa values

Source:

geoda-lisa.js, line 48

Returns:

Type: Array

getMapType(mapUid) → {Number}

Get map type

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id

Source:

geoda-proxy.js, line 101

Returns:

return map type

Type: Number

getMinDistanceThreshold(mapUid, isArc, isMile) → {Number}

Get a distance that guarantees that every observation has at least 1 neighbor.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`isArc`	Boolean	A bool flag indicates if compute arc distance (true) or Euclidean distance (false).
`isMile`	Boolean	A bool flag indicates if the distance unit is mile (true) or km (false).

Source:

geoda-proxy.js, line 362

Returns:

Type: Number

getNeighbors(weights, idx) → {Array}

Get neighbors (indices) of an observation.

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`idx`	Number	An integer number represents the index of which observation to get its neighbors.

Source:

geoda-proxy.js, line 525

Returns:

The indices of neighbors.

Type: Array

getNeighbors() → {Array}

nearest neighbors

Source:

geoda-lisa.js, line 56

Returns:

Type: Array

getNumberObservations(mapUid) → {Number}

Get the number of observations or rows in the geojson map.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id

Source:

geoda-proxy.js, line 254

Returns:

Returns the number of observations or rows in the geojson map.

Type: Number

getPValues() → {Array}

psudo-p values

Source:

geoda-lisa.js, line 32

Returns:

Type: Array

getQueenWeights(mapUid, order, includeLowerOrder, precision_threshold) → {Object}

Create a contiguity weights.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`order`	Number	An integet number for order of contiguity
`includeLowerOrder`	Boolean	Indicate if include lower order when creating weights
`precision_threshold`	Number	Used when the precision of the underlying shape file is insufficient to allow for an exact match of coordinates to determine which polygons are neighbors.

Source:

geoda-proxy.js, line 342

Returns:

An instance of GeoDaWeights

Type: Object

getRookWeights(mapUid, order, includeLowerOrder, precisionThreshold) → {Object}

Create a Rook contiguity weights.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id.
`order`	Number	An integet number for order of contiguity
`includeLowerOrder`	Boolean	Indicate if include lower order when creating weights
`precisionThreshold`	Number	Used when the precision of the underlying shape file is insufficient to allow for an exact match of coordinates to determine which polygons are neighbors.

Source:

geoda-proxy.js, line 321

Returns:

An instance of GeoDaWeights

Type: Object

getViewport(mapUid, mapHeight, mapWidth) → {Object}

Get viewport for e.g.

Get viewport for e.g. Deck.gl or GoogleMaps

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id
`mapHeight`	Number	The height of map (screen pixel)
`mapWidth`	Number	The width of map (screen pixel)

Source:

geoda-proxy.js, line 179

Returns:

Type: Object

has(mapUid) → {Boolean}

Check if a geojson map has been read into GeoDaWasm.

Parameters:

Name	Type	Description
`mapUid`	String	A unique map id that has been read into GeoDaWasm.

Source:

geoda-proxy.js, line 136

Returns:

Returns True if the geojson map has been read. Otherwise, returns False.

Type: Boolean

hinge15Breaks(values) → {Array}

Boxplot (hinge=1.5) breaks, including the top, bottom, median, and two quartiles of the data

Parameters:

Name	Type	Description
`values`	Array	The values of selected variable.

Source:

geoda-proxy.js, line 806

Returns:

Returns an array of break point values.

Type: Array

hinge30Breaks(values) → {Array}

Boxplot (hinge=3.0) breaks, including the top, bottom, median, and two quartiles of the data

Parameters:

Name	Type	Description
`values`	Array	The values of selected variable.

Source:

geoda-proxy.js, line 824

Returns:

Returns an array of break point values.

Type: Array

isInt(n) → {Boolean}

Help function: check if number is an integer.

Parameters:

Name	Type	Description
`n`	Number

Source:

geoda-proxy.js, line 582

Returns:

Type: Boolean

localBiJoinCount(weights, values1, values2, permutations, significanceCutoff, seed) → {Object}

Bivariate or no-colocation local join count works when two events cannot happen in the same location.

Bivariate or no-colocation local join count works when two events cannot happen in the same location. It can be used to identify negative spatial autocorrelation.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`values1`	Array	The first numeric column that contains the binary values (e.g. 0 and 1) for LISA statistics
`values2`	Array	The second numeric column that contains the binary values (e.g. 0 and 1) for LISA statistics
`permutations`	Number	The number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`significanceCutoff`	Number	The cutoff value for significance p-values to filter not-significant clusters. Default: 0.05
`seed`	Number	The seed for random number generator

Source:

geoda-proxy.js, line 1347

Returns:

LISA object GeoDaLisa

Type: Object

localG(weights, values, permutations, seed) → {Object}

Apply local G statistics

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`values`	Array	The values that local moran statistics will be applied on.
`permutations`	Number	the number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`seed`	Number	The seed for random number generator used in LISA statistics. Default: 123456789.

Source:

geoda-proxy.js, line 1065

Returns:

An instance of LisaResult

Type: Object

localGeary(weights, values, permutations, permutationMethod, seed) → {Object}

Apply local Geary statistics

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`values`	Array	The values that local moran statistics will be applied on.
`permutations`	Number	the number of permutations for the LISA computation. Default: 999.
`permutationMethod`	String	The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`seed`	Number	The seed for random number generator used in LISA statistics. Default: 123456789.

Source:

geoda-proxy.js, line 1097

Returns:

An instance of LisaResult

Type: Object

localGStar(weights, values, permutations, seed) → {Object}

Apply local G* statistics

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`values`	Array	The values that local moran statistics will be applied on.
`permutations`	Number	the number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`seed`	Number	The seed for random number generator used in LISA statistics. Default: 123456789.

Source:

geoda-proxy.js, line 1081

Returns:

An instance of LisaResult

Type: Object

localJoinCount(weights, values, permutations, seed) → {Object}

Apply local Join Count statistics

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`values`	Array	The values that local moran statistics will be applied on.
`permutations`	Number	the number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`seed`	Number	The seed for random number generator used in LISA statistics. Default: 123456789.

Source:

geoda-proxy.js, line 1113

Returns:

An instance of LisaResult

Type: Object

localMoran(weights, values, permutations, seed) → {Object}

Apply local Moran statistics

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`values`	Array	The values that local moran statistics will be applied on.
`permutations`	Number	The number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`seed`	Number	The seed for random number generator used in LISA statistics. Default: 123456789.

Source:

geoda-proxy.js, line 1049

Returns:

An instance of LisaResult

Type: Object

localMultiGeary(weights, values1, permutations, significanceCutoff, seed) → {Object}

Multivariate local geary is a multivariate extension of local geary which measures the extent to which neighbors in multiattribute space are also neighbors in geographical space.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`values1`	Array	The array of the numeric columns that contains the values for LISA statistics
`permutations`	Number	The number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`significanceCutoff`	Number	The cutoff value for significance p-values to filter not-significant clusters. Default: 0.05
`seed`	Number	The seed for random number generator

Source:

geoda-proxy.js, line 1309

Returns:

LISA object GeoDaLisa

Type: Object

localMultiJoinCount(weights, values, permutations, significanceCutoff, seed) → {Object}

Multivariate or colocation local join count (2019) works when two or more events happen in the same location.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`values`	Array	The array of numeric columns that contains the binary values (e.g. 0 and 1) for LISA statistics
`permutations`	Number	The number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`significanceCutoff`	Number	The cutoff value for significance p-values to filter not-significant clusters. Default: 0.05
`seed`	Number	The seed for random number generator

Source:

geoda-proxy.js, line 1399

Returns:

LISA object GeoDaLisa

Type: Object

maxpGreedy(weights, values, iterations, minBoundValues, minBounds, maxBoundValues, maxBounds, scaleMethod, distanceMethod, seed) → {Object}

A greedy algorithm to solve the max-p-region problem.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`values`	Array	The list of numeric vectors of selected variable.
`iterations`	Number	The number of iterations of greedy algorithm. Defaults to 1.
`minBoundValues`	Array	The list of numeric array of selected minimum bounding variables.
`minBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
`maxBoundValues`	Array	The list of numeric array of selected maximum bounding variables.
`maxBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be less than.
`scaleMethod`	String	The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
`distanceMethod`	String	The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
`seed`	Number	The seed for random number generator

Source:

geoda-proxy.js, line 1871

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

maxpSA(weights, values, coolingRate, saMaxIt, iterations, minBoundValues, minBounds, maxBoundValues, maxBounds, scaleMethod, distanceMethod, seed) → {Object}

A simulated annealing algorithm to solve the max-p-region problem.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`values`	Array	The list of numeric vectors of selected variable.
`coolingRate`	Number	The cooling rate of a simulated annealing algorithm. Defaults to 0.85
`saMaxIt`	Number	The number of iterations of simulated annealing. Defaults to 1
`iterations`	Number	The number of iterations of greedy algorithm. Defaults to 1.
`minBoundValues`	Array	The list of numeric array of selected minimum bounding variables.
`minBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
`maxBoundValues`	Array	The list of numeric array of selected maximum bounding variables.
`maxBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be less than.
`scaleMethod`	String	The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
`distanceMethod`	String	The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
`seed`	Number	The seed for random number generator.

Source:

geoda-proxy.js, line 1926

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

maxpTabu(weights, values, tabuLength, convTabu, iterations, minBoundValues, minBounds, maxBoundValues, maxBounds, scaleMethod, distanceMethod, seed) → {Object}

A tabu-search algorithm to solve the max-p-region problem

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`values`	Array	The list of numeric vectors of selected variable.
`tabuLength`	Number	The length of a tabu search heuristic of tabu algorithm. Defaults to 10.
`convTabu`	Number	The number of non-improving moves. Defaults to 10.
`iterations`	Number	The number of iterations of greedy algorithm. Defaults to 1.
`minBoundValues`	Array	The list of numeric array of selected minimum bounding variables.
`minBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be greater than.
`maxBoundValues`	Array	The list of numeric array of selected maximum bounding variables.
`maxBounds`	Array	The list of minimum value that the sum value of bounding variables in each cluster should be less than.
`scaleMethod`	String	The scaling methods {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Defaults to 'standardize'.
`distanceMethod`	String	The distance methods {"euclidean", "manhattan"}. Defaults to 'euclidean'.
`seed`	Number	The seed for random number generator.

Source:

geoda-proxy.js, line 1983

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

multiQuantileLisa(weights, ks, quantiles, values, permutations, significanceCutoff, seed) → {Object}

Multivariate Quantile LISA (2019) is a type of local spatial autocorrelation that applies multivariate local join count statistics to quantiles of multiple continuous variables.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`ks`	Array	The array of integer numbers that specify quantiles for each variable
`quantiles`	Array	The array of integer numbers that specify which quantile is used for each variable
`values`	Array	The array of numeric columns that contains the binary values (e.g. 0 and 1) for LISA statistics
`permutations`	Number	The number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`significanceCutoff`	Number	The cutoff value for significance p-values to filter not-significant clusters. Default: 0.05
`seed`	Number	The seed for random number generator

Source:

geoda-proxy.js, line 1463

Returns:

LISA object GeoDaLisa

Type: Object

naturalBreaks(k, values) → {Array}

Natural breaks

Parameters:

Name	Type	Description
`k`	Number	Number of breaks
`values`	Array	The values that the classify algorithm will be applied on.

Source:

geoda-proxy.js, line 739

Returns:

Returns an array of break point values.

Type: Array

neighborMatchTest(mapUid, knn, data, scaleMethod, distanceMethod, power, isInverse, isArc, isMile) → {Array}

The local neighbor match test is a method to identify significant locations by assessing the extent of overlap between k-nearest neighbors in geographical space and k-nearest neighbors in multi-attribute space.

Parameters:

Name	Type	Description
`mapUid`	String	A unique string represents the geojson map that has been read into GeoDaWasm.
`knn`	Number	k nearest neighbor for both attribute and geographical space
`data`	Array	The array of numeric columns that contains the values for neighbor match test
`scaleMethod`	String	The scaling method: {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}. Default: 'standardize'
`distanceMethod`	String	The distance method: {'euclidean', 'manhattan'}. Default: 'euclidean'.
`power`	Number	The power/exponent corresponds to the number of times the base (dist_band) is used as a factor. Default: 1.
`isInverse`	Boolean	The bool value indicates if apply inverse on distance value. Default: False.
`isArc`	Boolean	The bool value indicates if compute arc distance between two observations. Default: FALSE.
`isMile`	Boolean	The bool value indicates if convert distance unit from mile to kilometer(KM). Default: TRUE.

Source:

geoda-proxy.js, line 1262

Returns:

{'cardinality', 'probability'}

Type: Array

New() → {Object}

Create a GeoDaWasm instance built using WASM.

Source:

index.js, line 19

Returns:

geoda - a GeoDaWasm instance built from WASM

Type: Object

Example

const jsgeoda = require('jsgeoda')
let geoda = await jsgeoda.New()

parseVecDouble(input) → {Object}

Help function: convert GeoDa std::vector to javascript Array e.g.

Help function: convert GeoDa std::vector to javascript Array e.g. []

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 640

Returns:

Type: Object

parseVecInt(input) → {Object}

Help function: convert GeoDa std::vector to javascript Array e.g.

Help function: convert GeoDa std::vector to javascript Array e.g. []

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 591

Returns:

Type: Object

parseVecString(input) → {Object}

Help function: convert GeoDa std::vector to javascript Array e.g.

Help function: convert GeoDa std::vector to javascript Array e.g. []

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 653

Returns:

Type: Object

parseVecVecDouble(input) → {Object}

Help function: convert GeoDa 2d std::vector to javascript 2d Array e.g.

Help function: convert GeoDa 2d std::vector to javascript 2d Array e.g. [[]]

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 622

Returns:

Type: Object

parseVecVecInt(input) → {Object}

Help function: convert GeoDa 2d std::vector to javascript 2d Array e.g.

Help function: convert GeoDa 2d std::vector to javascript 2d Array e.g. [[]]

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 604

Returns:

Type: Object

percentileBreaks(values) → {Array}

Percentile breaks

Parameters:

Name	Type	Description
`values`	Array	The values of selected variable.

Source:

geoda-proxy.js, line 776

Returns:

Returns an array of break point values.

Type: Array

quantileBreaks(k, values) → {Array}

Quantile breaks

Parameters:

Name	Type	Description
`k`	Number	The number of breaks
`values`	Array	The values of selected variable.

Source:

geoda-proxy.js, line 758

Returns:

Returns an array of break point values.

Type: Array

quantileLisa(weights, values, permutations, seed) → {Object}

Apply Quantile LISA statistics

Parameters:

Name	Type	Description
`weights`	String	The weights object `WeightsResult`
`values`	Array	The values that local moran statistics will be applied on.
`permutations`	Number	the number of permutations for the LISA computation. Default: 999. @param {String} permutationMethod The permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'.
`seed`	Number	The seed for random number generator used in LISA statistics. Default: 123456789.

Source:

geoda-proxy.js, line 1129

Returns:

An instance of LisaResult

Type: Object

readGeoJSON(ab) → {String}

Read a geojson map from a file object in the format of ArrayBuffer.

Read a geojson map from a file object in the format of ArrayBuffer. You can use readFileSync in fs to read the geojson file and return a ArrayBuffer; Or use FileReader.readAsArrayBuffer to read the content of a specified Blob of File.

Parameters:

Name	Type	Description
`ab`	ArrayBuffer	The content of the geojson file in format of ArrayBuffer.

Source:

geoda-proxy.js, line 64

Returns:

A unique id of the geoda object.

Type: String

Example

// In node.js
const fs = require('fs');
const jsgeoda = require('jsgeoda');
const geoda = await jsgeoda.New();

let ab = fs.readFileSync("NAT.geojson").buffer;
let nat = geoda.read_geojson(ab);
let numObs = geoda.get_numobs(nat);

E.g. the geojson file name.

redcap(weights, k, values, method, minBound, boundVals, scaleMethod, distanceMethod) → {Object}

Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP)

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`k`	Number	The number of clusters
`values`	Array	The list of numeric vectors of selected variable
`method`	String	The REDCAP method: {'single-linkage', 'average-linkage', 'complete-linkage', 'Ward-linkage'}.
`minBound`	Number	The minimum value that the sum value of bounding variable in each cluster should be greater than
`boundVals`	Array	The numeric vector of selected bounding variable
`scaleMethod`	String	The scaling method: {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}
`distanceMethod`	String	The distance method: {"euclidean", "manhattan"}

Source:

geoda-proxy.js, line 1594

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

redcapMethods() → {Array}

Helper function: Get REDCAP methods.

Source:

geoda-proxy.js, line 1498

Returns:

Type: Array

scaleMethods() → {Object}

Help function: Get scale methods.

Source:

geoda-proxy.js, line 1215

Returns:

Type: Object

schc(weights, k, values, method, minBound, boundVals, scaleMethod, distanceMethod) → {Object}

Spatially Constrained Hierarchical Clucstering (SCHC)

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`k`	Number	The number of clusters
`values`	Array	The list of numeric vectors of selected variable
`method`	String	The method of agglomerative hierarchical clustering: {"single", "complete", "average","ward"}.
`minBound`	Number	The minimum value that the sum value of bounding variable in each cluster should be greater than
`boundVals`	Array	The numeric vector of selected bounding variable
`scaleMethod`	String	The scaling method: {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}
`distanceMethod`	String	The distance method: {"euclidean", "manhattan"}

Source:

geoda-proxy.js, line 1649

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'}

Type: Object

schcMethods() → {Array}

Get the SCHC methods.

Source:

geoda-proxy.js, line 1623

Returns:

Type: Array

skater(weights, k, values, minBound, boundVals, scaleMethod, distanceMethod) → {Object}

Spatial C(K)luster Analysis by Tree Edge Removal

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`k`	Number	The number of clusters
`values`	Array	The list of numeric vectors of selected variable
`minBound`	Number	The minimum value that the sum value of bounding variable int each cluster should be greater than
`boundVals`	Array	The numeric vector of selected bounding variable
`scaleMethod`	String	The scaling method: {'raw', 'standardize', 'demean', 'mad', 'range_standardize', 'range_adjust'}
`distanceMethod`	String	The distance method: {"euclidean", "manhattan"}

Source:

geoda-proxy.js, line 1523

Returns:

Return a ClusteringResult object: {'total_ss', 'within_ss', 'between_ss', 'ratio', 'clusters'

Type: Object

spatialEmpiricalBayes(weights, eventValues, baseValues) → {Array}

Spatial Empirical Bayes (EB) Smoothing

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`eventValues`	Array	The values of an event variable.
`baseValues`	Array	The values of an base variable.

Source:

geoda-proxy.js, line 980

Returns:

Type: Array

spatialLag(weights, values, isBinary, rowStandardize, includeDiagonal) → {Array}

Compute spatially lagged variable.

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`values`	Array	The values of a selected variable.
`isBinary`	Boolean	The bool value indicates if the spatial weights is used as binary weights. Default: TRUE.
`rowStandardize`	Boolean	The bool value indicates if use row-standardized weights. Default: TRUE
`includeDiagonal`	Bollean	The bool value indicates if include diagonal of spatial weights. Default: FALSE

Source:

geoda-proxy.js, line 941

Returns:

Type: Array

spatialRate(weights, eventValues, baseValues) → {Array}

Spatial rate smoothing

Parameters:

Name	Type	Description
`weights`	WeightsResult	The weights object `WeightsResult`
`eventValues`	Array	The values of an event variable.
`baseValues`	Array	The values of an base variable.

Source:

geoda-proxy.js, line 963

Returns:

Type: Array

standardDeviationBreaks(values) → {Array}

Standard deviation breaks

Parameters:

Name	Type	Description
`values`	Array	The values of selected variable.

Source:

geoda-proxy.js, line 789

Returns:

Returns an array of break point values.

Type: Array

toVecDouble(input) → {Object}

Help function: convert javascript Array e.g.

Help function: convert javascript Array e.g. [] to GeoDa std::vector

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 692

Returns:

Type: Object

toVecInt(input) → {Object}

Help function: convert javascript Array e.g.

Help function: convert javascript Array e.g. [] to GeoDa std::vector

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 679

Returns:

Type: Object

toVecString(input) → {Object}

Help function: convert javascript Array e.g.

Help function: convert javascript Array e.g. [] to GeoDa std::vector

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 666

Returns:

Type: Object

toVecVecDouble(input) → {Object}

Help function: convert javascript 2d Array e.g.

Help function: convert javascript 2d Array e.g. [[]] to GeoDa 2d std::vector

Parameters:

Name	Type	Description
`input`	Array

Source:

geoda-proxy.js, line 709

Returns:

Type: Object