# Local Moran

Local indicators of spatial association or LISA is a class of local statistics that measure spatial association in sub-regions of the study region. They are often used to identify spatial outliers and local clusters. 

The Local Moran statistic was suggested in Anselin ([1995](http://geodacenter.github.io/workbook/6a_local_auto/lab6a.html#ref-Anselin:95)) as a way to identify local clusters and local spatial outliers. For more details, please read <http://geodacenter.github.io/workbook/6a_local_auto/lab6a.html>

## localMoran()

```sql
function localMoran(
    WeightResult w,
    Array val,
    Number permutations, 
    String permutation_method,
    NUmber significance_cutoff, 
    Number seed)
```

### Arguments

| Name                 | Type           | Description                                                                                                |
| -------------------- | -------------- | ---------------------------------------------------------------------------------------------------------- |
| w                    | *WeightResult* | the WeightResult object created from weights function                                                      |
| val                  | Array          | the values of a selected variable                                                                          |
| permutations         | Number         | the number of permutations for the LISA computation. Default: 999.                                         |
| permutation\_method  | String         | the permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'. |
| significance\_cutoff | Number         | the cutoff value for significance p-values to filter not-significant clusters. Default: 0.05.              |
| seed                 | Number         | the seed for random number generator used in LISA statistics. Default: 123456789.                          |

### Return

| Type       | Description                                                                                                                  |
| ---------- | ---------------------------------------------------------------------------------------------------------------------------- |
| LisaResult | The LisaResult object contains the results of LISA computation: *pvalues, clusters, lisa\_values, neighbors, labels, colors* |

**Examples**

{% tabs %}
{% tab title="Node.js" %}

```javascript
const jsgeoda = require('jsgeoda');
const fs = require('fs');

// load data
const data = fs.readFileSync('./data/natregimes.geojson').buffer;

// create jsgeoda instance
const geoda = await jsgeoda.New();

// load geojson in jsgeoda
const nat = geoda.readGeoJSON(data);

// create a queen contiguity weights
const w = geoda.getQueenWeights(nat);

// get HR60 values
const hr60 = geoda.getColumn(nat, "HR60");

// local moran statistics 
const lm = geoda.localMoran(w, hr60);


```

{% endtab %}

{% tab title="React" %}

```javascript
import React, { Component } from "react";
import ReactDOM from "react-dom";
import DeckGL from "@deck.gl/react";
import { GeoJsonLayer } from "@deck.gl/layers";
import { StaticMap } from "react-map-gl";
import jsgeoda from "jsgeoda";

// Set your mapbox access token here
const MAPBOX_TOKEN =
  "pk.eyJ1IjoibGl4dW45MTAiLCJhIjoiY2locXMxcWFqMDAwenQ0bTFhaTZmbnRwaiJ9.VRNeNnyb96Eo-CorkJmIqg";

// The geojson data
const DATA_URL = `https://webgeoda.github.io/data/natregimes.geojson`;

class App extends Component {
  constructor() {
    super();
    this.state = {
      mapId: "",
      layer: null,
      viewPort: {
        longitude: -100.4,
        latitude: 38.74,
        zoom: 2.5,
        maxZoom: 20
      }
    };
  }

  // load spatial data when mount this component
  loadSpatialData(geoda) {
    fetch(DATA_URL)
      .then((res) => res.arrayBuffer())
      .then((data) => {
        // load geojson in jsgeoda, an unique id (string) will be returned for further usage
        const nat = geoda.readGeoJSON(data);
        const w = geoda.getQueenWeights(nat);
        const hr60 = geoda.getColumn(nat, "HR60");
        const lm = geoda.localMoran(w, hr60, 999, "lookup");

        //const ue60 = geoda.get_col(nat, "UE60");
        //const nmt = geoda.neighbor_match_test(nat, 5, [hr60, ue60]);

        const lm_colors = lm.colors.map((c) =>
          c
            .toLowerCase()
            .match(/[0-9a-f]{2}/g)
            .map((x) => parseInt(x, 16))
        );
        // Viewport settings
        const view_port = geoda.get_viewport(
          nat,
          window.innerHeight,
          window.innerWidth
        );

        // Create GeoJsonLayer
        const layer = new GeoJsonLayer({
          id: "GeoJsonLayer",
          data: DATA_URL,
          filled: true,
          getFillColor: (f) => this.getFillColor(f, lm.clusters, lm_colors),
          stroked: true,
          pickable: true
        });

        // Trigger to draw map
        this.setState({
          mapId: nat,
          layer: layer,
          viewPort: view_port
        });
      });
  }

  componentDidMount() {
    // jsgeoda.New() function will create an instance from WASM
    jsgeoda.New().then((geoda) => {
      this.loadSpatialData(geoda);
    });
  }

  // Determine which color for which geometry
  getFillColor(f, clusters, colors) {
    const i = f.properties.POLY_ID - 1;
    const c = clusters[i];
    return colors[c];
  }

  render() {
    return (
      <div>
        <DeckGL
          initialViewState={this.state.viewPort}
          layers={[this.state.layer]}
          controller={true}
          getTooltip={({ object }) =>
            object && `${object.properties.NAME}: ${object.properties.HR60}`
          }
        >
          <StaticMap mapboxApiAccessToken={MAPBOX_TOKEN} />
        </DeckGL>
      </div>
    );
  }
}

ReactDOM.render(<App />, document.getElementById("root"));

```

{% endtab %}
{% endtabs %}

**Try it yourself in the playground (jsgeoda + deck.gl):**

{% embed url="<https://codesandbox.io/s/5lisaunivariate-zhhop>" %}


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