# LISA

In addition to various binning strategies, you can specify a variable that identifies statistically significant local clusters, also known as Local Indicators of Spatial Autocorrelation (or LISAs; Anselin, 1995). The LISA statistic identifies a hotspot where a core location and its neighbors are above average for the data and they have significant correlation, relative to a random spatial distribution (known as the reference distribution). For coldspots, the same LISA statistic finds areas below average. Spatial outliers are locations with above-average core locations and below-average neighbors or vice versa. Spatial clusters and outliers are displayed at a certain pseudo significance level (such as 0.05 or 0.01), and by default, only cluster core locations are displayed.

// ...variables

{

variable: "Population Density Hotspot",

numerator: "acs_data",

nProperty: "Total Population",

denominator: "acs_data",

dProperty: "Area Land",

lisa: true

}

By default, WebGeoDa uses Local Moran's I algorithm (Anselin, 1995) to calculate local spatial autocorrelation. You can specify an alternate LISA function with the

`lisaFunction`

parameter in your LISA variable spec.`lisaFunction`

should be the name of a jsGeoDa local spatial autocorrelation function. For more background on LISA statistics, visit GeoDa's intro to spatial data analysis docs.Additionally, you can provide additional LISA parameters for your jsGeoDa LISA function in

`lisaParams`

. The map ID and spatial weights ID will be automatically provided to jsGeoDa, but additional parameters can be provided as an array: // ...variables

{

// ...variable spec

lisa: true,

lisaFunction: 'localGeary',

lisaParams: [

999, // Number permutations,

'lookup',// String permutation_method,

0.05, // NUmber significance_cutoff,

123456789 // Number seed

]

}

By default, WebGeoDa uses Queen's Weights, but you can specify a preferred way to calculate spatial weights (such as distance weights or kernel weights). You specify this by calling the

`weightsFunction`

parameter in your LISA variable spec. `weightsFunction`

should be the name of a jsGeoDa weights function.Additionally, you can provide additional weights parameters for your jsGeoDa weights function in

`weightsParams`

. The map ID will be automatically provided to the jsGeoDa weights function, but additional parameters can be provided as an array: // ...variables

{

// ...variable spec

lisa: true,

weightsFunction: 'getDistanceWeights',

weightsParams: [

500, // Number distBand,

1, // Number power,

false, // Boolean isInverse,

false, // Boolean isArc,

true// Boolean isMile

]

}

Last modified 1yr ago