jsgeoda

Methods

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

A greedy algorithm to solve the AZP problem.

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 1688

Returns:

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

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.

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 1747

Returns:

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

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.

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 1808

Returns:

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

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

Helper function: apply LISA statistics.

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 1156

Returns:

An instance of LisaResultTypeObject

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 994

Returns:

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

checkDistanceMethod(distanceMethod) → {Boolean}

Helper function: check if distance method is valid.

Helper function: check if distance method is valid.

Parameters:

Name	Type	Description
distanceMethod	String

Source:

• geoda-proxy.js, line 1540

Returns:

TypeBoolean

checkInputKernel(kernel) → {Boolean}

check if input kernel is valid.

check if input kernel is valid

Parameters:

Name	Type	Description
kernel	*

Source:

• geoda-proxy.js, line 460

Returns:

TypeBoolean

checkMapUid(mapUid) → {Boolean}

Check if map uid is valid.

Check if map uid is valid

Parameters:

Name	Type	Description
mapUid	String

Source:

• geoda-proxy.js, line 152

Returns:

TypeBoolean

checkScaleMethod(scaleMethod) → {Boolean}

Helper function: check if scale method is valid.

Helper function: check if scale method is valid.

Parameters:

Name	Type	Description
scaleMethod	String

Source:

• geoda-proxy.js, line 1525

Returns:

TypeBoolean

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

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

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 837

Returns:

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

distanceMethods() → {Object}

Help function: Get distance methods.

Help function: Get distance methods.Source:

• geoda-proxy.js, line 1222

Returns:

TypeObject

ebRisk(eventValues, baseValues) → {Array}

Empirical Bayes (EB) Smoothed Rate.

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 913

Returns:

TypeArray

excessRisk(eventValues, baseValues) → {Array}

Excess Risk.

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 900

Returns:

TypeArray

free()

Free the memory used by wasm.

Free the memory used by wasmSource:

• geoda-proxy.js, line 143

generateUid() → {String}

Help function: create a unique id for a Geojson map.

Help function: create a unique id for a Geojson mapSource:

• geoda-proxy.js, line 34

Returns:

TypeString

getBounds(mapUid) → {Array}

Get map bounds.

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:

TypeArray

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.TypeArray

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.readGeoJSON("NAT", ab);
let cent = geoda.getCentroids(nat);

getClusteringResult(r) → {Object}

Helper function: get clustering results.

Helper function: get clustering results

Parameters:

Name	Type	Description
r	Object

Source:

• geoda-proxy.js, line 1555

Returns:

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

getClusters() → {Array}

cluster indicators.

cluster indicatorsSource:

• geoda-lisa.js, line 40

Returns:

TypeArray

getColumn(mapUid, colName) → {Array}

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

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 286

Returns:

Returns the values of a column of field.TypeArray

getColumnNames(mapUid) → {Array}

Get the column names of the geojson map.

Get the column names of the geojson map

Parameters:

Name	Type	Description
mapUid	String	A unique map id.

Source:

• geoda-proxy.js, line 270

Returns:

Returns the column namesTypeArray

getColors() → {Array}

Get colors.

Get colorsSource:

• geoda-lisa.js, line 72

Returns:

TypeArray

getConnectivity(weights) → {Object}

Get connectivity graph from a weights object.

Get connectivity graph from a weights object

Parameters:

Name	Type	Description
weights	String	The weights object WeightsResult

Source:

• geoda-proxy.js, line 530

Returns:

{arcs, targets, sources}TypeObject

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

Create a Distance-based weights.

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 404

Returns:

An instance of GeoDaWeightsTypeObject

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

Create a (adaptive) KNN kernel weights.

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 437

Returns:

An instance of GeoDaWeightsTypeObject

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

Create a kernel weights with fixed bandwidth.

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 491

Returns:

An instance of GeoDaWeightsTypeObject

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

Create a K-Nearest Neighbors weights.

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 377

Returns:

An instance of GeoDaWeightsTypeObject

getLabels() → {Array}

Get labels.

Get labelsSource:

• geoda-lisa.js, line 64

Returns:

TypeArray

getLisaValues() → {Array}

lisa values.

lisa valuesSource:

• geoda-lisa.js, line 48

Returns:

TypeArray

getMapType(mapUid) → {Number}

Get map type.

Get map type

Parameters:

Name	Type	Description
mapUid	String	A unique map id

Source:

• geoda-proxy.js, line 101

Returns:

return map typeTypeNumber

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

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

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 353

Returns:

TypeNumber

getNeighbors(weights, idx) → {Array}

Get neighbors (indices) of an observation.

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 516

Returns:

The indices of neighbors.TypeArray

getNeighbors() → {Array}

nearest neighbors.

nearest neighborsSource:

• geoda-lisa.js, line 56

Returns:

TypeArray

getNumObs(mapUid) → {Number}

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

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.TypeNumber

getPValues() → {Array}

psudo-p values.

psudo-p valuesSource:

• geoda-lisa.js, line 32

Returns:

TypeArray

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

Create a contiguity weights.

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 333

Returns:

An instance of GeoDaWeightsTypeObject

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

Create a Rook contiguity weights.

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 312

Returns:

An instance of GeoDaWeightsTypeObject

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:

TypeObject

has(mapUid) → {Boolean}

Check if a geojson map has been read into GeoDaWasm.

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.TypeBoolean

hinge15Breaks(values) → {Array}

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

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 797

Returns:

Returns an array of break point values.TypeArray

hinge30Breaks(values) → {Array}

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

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 815

Returns:

Returns an array of break point values.TypeArray

isInt(n) → {Boolean}

Help function: check if number is an integer.

Help function: check if number is an integer.

Parameters:

Name	Type	Description
n	Number

Source:

• geoda-proxy.js, line 573

Returns:

TypeBoolean

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 1338

Returns:

LISA object GeoDaLisaTypeObject

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

Apply local G statistics.

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 1056

Returns:

An instance of LisaResultTypeObject

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

Apply local Geary statistics.

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 1088

Returns:

An instance of LisaResultTypeObject

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

Apply local G* statistics.

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 1072

Returns:

An instance of LisaResultTypeObject

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

Apply local Join Count statistics.

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 1104

Returns:

An instance of LisaResultTypeObject

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

Apply local Moran statistics.

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 1040

Returns:

An instance of LisaResultTypeObject

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.

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 1300

Returns:

LISA object GeoDaLisaTypeObject

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.

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 1390

Returns:

LISA object GeoDaLisaTypeObject

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

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

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 1862

Returns:

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

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.

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 1917

Returns:

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

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.

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 1974

Returns:

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

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.

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 1454

Returns:

LISA object GeoDaLisaTypeObject

naturalBreaks(k, values) → {Array}

Natural breaks.

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 730

Returns:

Returns an array of break point values.TypeArray

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.

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 1253

Returns:

{'cardinality', 'probability'}TypeArray

New() → {Object}

Create a GeoDaWasm instance built using WASM.

Create a GeoDaWasm instance built using WASM.Source:

• index.js, line 19

Returns:

geoda - a GeoDaWasm instance built from WASMTypeObject

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 631

Returns:

TypeObject

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 582

Returns:

TypeObject

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 644

Returns:

TypeObject

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 613

Returns:

TypeObject

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 595

Returns:

TypeObject

percentileBreaks(values) → {Array}

Percentile breaks.

Percentile breaks

Parameters:

Name	Type	Description
values	Array	The values of selected variable.

Source:

• geoda-proxy.js, line 767

Returns:

Returns an array of break point values.TypeArray

quantileBreaks(k, values) → {Array}

Quantile breaks.

Quantile breaks

Parameters:

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

Source:

• geoda-proxy.js, line 749

Returns:

Returns an array of break point values.TypeArray

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

Apply Quantile LISA statistics.

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 1120

Returns:

An instance of LisaResultTypeObject

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.TypeString

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.readGeojson(ab);
let numObs = geoda.getNumObs(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).

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 1585

Returns:

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

redcapMethods() → {Array}

Helper function: Get REDCAP methods.

Helper function: Get REDCAP methods.Source:

• geoda-proxy.js, line 1489

Returns:

TypeArray

scaleMethods() → {Object}

Help function: Get scale methods.

Help function: Get scale methods.Source:

• geoda-proxy.js, line 1206

Returns:

TypeObject

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

Spatially Constrained Hierarchical Clucstering (SCHC).

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 1640

Returns:

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

schcMethods() → {Array}

Get the SCHC methods.

Get the SCHC methods.Source:

• geoda-proxy.js, line 1614

Returns:

TypeArray

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

Spatial C(K)luster Analysis by Tree Edge Removal.

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 1514

Returns:

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

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

Spatial Empirical Bayes (EB) Smoothing.

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 971

Returns:

TypeArray

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

Compute spatially lagged variable.

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 932

Returns:

TypeArray

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

Spatial rate smoothing.

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 954

Returns:

TypeArray

standardDeviationBreaks(values) → {Array}

Standard deviation breaks.

Standard deviation breaks

Parameters:

Name	Type	Description
values	Array	The values of selected variable.

Source:

• geoda-proxy.js, line 780

Returns:

Returns an array of break point values.TypeArray

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 683

Returns:

TypeObject

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 670

Returns:

TypeObject

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 657

Returns:

TypeObject

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 700

Returns:

TypeObject