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:
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:
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:
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:
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:
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:
Returns:
TypeBoolean
checkInputKernel(kernel) → {Boolean}
check if input kernel is valid.
check if input kernel is valid
Parameters:
Name
Type
Description
kernel
*
Source:
Returns:
TypeBoolean
checkMapUid(mapUid) → {Boolean}
Check if map uid is valid.
Check if map uid is valid
Parameters:
Name
Type
Description
mapUid
String
Source:
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:
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:
Returns:
{'k','bins','breaks','id_array'}TypeObject
distanceMethods() → {Object}
Help function: Get distance methods.
Help function: Get distance methods.Source:
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:
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:
Returns:
TypeArray
free()
Free the memory used by wasm.
Free the memory used by wasmSource:
generateUid() → {String}
Help function: create a unique id for a Geojson map.
Help function: create a unique id for a Geojson mapSource:
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:
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:
Returns:
Returns an array of [x,y] coordinates (not projected) of the centroids.TypeArray
Example
getClusteringResult(r) → {Object}
Helper function: get clustering results.
Helper function: get clustering results
Parameters:
Name
Type
Description
r
Object
Source:
Returns:
{'clusters', 'total_ss', 'between_ss', 'within_ss', 'ratio'}TypeObject
getClusters() → {Array}
cluster indicators.
cluster indicatorsSource:
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:
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:
Returns:
Returns the column namesTypeArray
getColors() → {Array}
Get colors.
Get colorsSource:
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:
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:
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:
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:
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:
Returns:
An instance of GeoDaWeightsTypeObject
getLabels() → {Array}
Get labels.
Get labelsSource:
Returns:
TypeArray
getLisaValues() → {Array}
lisa values.
lisa valuesSource:
Returns:
TypeArray
getMapType(mapUid) → {Number}
Get map type.
Get map type
Parameters:
Name
Type
Description
mapUid
String
A unique map id
Source:
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:
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:
Returns:
The indices of neighbors.TypeArray
getNeighbors() → {Array}
nearest neighbors.
nearest neighborsSource:
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:
Returns:
Returns the number of observations or rows in the geojson map.TypeNumber
getPValues() → {Array}
psudo-p values.
psudo-p valuesSource:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
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:
Returns:
{'cardinality', 'probability'}TypeArray
New() → {Object}
Create a GeoDaWasm instance built using WASM.
Create a GeoDaWasm instance built using WASM.Source:
Returns:
geoda - a GeoDaWasm instance built from WASMTypeObject
Example
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:
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:
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:
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:
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:
Returns:
TypeObject
percentileBreaks(values) → {Array}
Percentile breaks.
Percentile breaks
Parameters:
Name
Type
Description
values
Array
The values of selected variable.
Source:
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:
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:
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:
Returns:
A unique id of the geoda object.TypeString
Example
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:
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:
Returns:
TypeArray
scaleMethods() → {Object}
Help function: Get scale methods.
Help function: Get scale methods.Source:
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:
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:
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:
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:
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:
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:
Returns:
TypeArray
standardDeviationBreaks(values) → {Array}
Standard deviation breaks.
Standard deviation breaks
Parameters:
Name
Type
Description
values
Array
The values of selected variable.
Source:
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:
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:
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:
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:
Returns:
TypeObject
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