Cluster and Outlier Analysis

The Cluster and Outlier analysis module allows you to calculate statistically significant hot-spots, cold-spots, and spatial outliers, then quickly visualize the results.

Using Cluster and Outlier Analysis, answer questions such as:

  • Which areas of California are disproportionately affected by pollution?
  • Are there any significant spatial patterns in the listing price of Berlin's Airbnbs?
  • Which restaurants in New York have significantly more/fewer visits than nearby competitors?

Background

To conduct the Cluster and Outlier analysis, Studio applies Anselin's Local Indicators of Spatial Association (LISA), specifically the local Moran statistic, to identify geographical clusters of values or find geographical outliers.

This method has been widely used in spatial applications including environmental and natural resource analysis, real estate analysis, criminology studies, public health research, political geography and demographics studies, and much more.

Find examples and more information in the Cluster and Outlier Analysis use case article.

Perform Cluster and Outlier Analysis

Follow these steps to perform a cluster and outlier analysis in Studio.

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Requirements:

Cluster and Outlier Analysis requires your map to contain a point or geojson layer with point or polygon geometries.

1. Open the Cluster and Outlier Analysis module

Navigate to the Analysis tab in Studio, then click Cluster and Outlier Analysis.

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The Analysis tab in Studio, which contains spatial analysis modules.

2. Select an Input Layer from your map

The input layer must be a point or geojson layer with point or polygon geometries. This is the layer on which Studio will conduct the analysis of local Moran statistics.

3. Select an Attribute Field from the dataset

Select a field to use as values for the analysis of local Moran statistics. The attribute field must be from a dataset associated with your input layer.

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Suggestion

For local Moran statistics, we recommend you select an attribute field containing quantitative variables.

4. Configure the Spatial Weights Creation

Studio provides two types of spatial weights:

Use # of Nearest Neighbors Weighting

Input the number of nearest neighbors to ensure all spatial objects have the same number of neighbors. Defaults to 4.

Use Distance Threshold Weighting

Input a distance unit (KM or Miles), creating a distance threshold to determine neighbors.

By default, Studio will suggest a distance that ensures each spatial object has at least one neighbor.

5. Configure the Local Moran Parameters

In local Moran statistics, permutation-based inference generates a pseudo p-value used to evaluate the significance of each cluster.

Studio allows you to modify the following local Moran parameters:

Control Distribution With Permutations

Permutations are used to determine the probability of finding the actual distribution of the values under analysis. This is accomplished by comparing many random datasets to the local Moran's I of your original data.

Input the number of permutations to compute the pseudo p-value. Defaults to 999.

Hide Less Significant Clusters With Thresholds

Input a number serving as a P-value threshold, allowing you to only display significant clusters on the map. Defaults to 0.05.

Note: In permutation-based inference, the smallest pseudo p-value is computed as 1/(permutations + 1).
For example, given a p-value of 999, the smallest pseudo p-value is 0.001.

6. Generate the results of the analysis

Click Run to generate the results of your cluster and outlier analysis.

The results of the analysis are shown in a preview table. If you are not satisfied with the results, tweak the parameters and click Rerun.

The results are stored in a data table containing the following columns:

Column NameDescription
Attribute FieldThe value of the selected Attribute Field
latitude (optional)The latitude value, only when Input Layer is a Point layer
longitude (optional)The longitude value, only when Input Layer is a Point layer
lisaThe local Moran's I value
spatial_lagThe average (standardized) value of the neighbors
clusterThe type of spatial association - 0 for not significant, 1 for High-High, 2 for Low-Low, 3 for High-Low, 4 for Low-High, 5 for isolated (no neighbors)
pvalueThe pseudo p-value is the significance value computed from the random permutations
neighborsThe array of row indices of the neighbors

When you are satisfied with the results of your analysis, click Confirm to proceed to the visualization.

Analyze Results

Upon completing the cluster and outlier analysis, a new layer and dataset are generated.


Visualizing a layer containing California Environment scores and its cluster-outlier analysis visualization.

Point Layer Results

If the input layer was a point layer, a connectivity graph will appear to visualize the neighboring/connectivity relationship among spatial objects. Mouse over a point to highlight neighboring points (defined by the spatial weights configuration).

Cluster Types

Cluster types are visualized by color-coding geometries to represent the cluster type. A chart will generate, serving as a legend for the cluster types.

The local Moran statistic takes the data values and the associated geographical locations as input, then returns statistically significant clusters in four types:

ClusterDescription
High-HighHot spot clusters with high values surrounded by other high values.
Low-LowCold spot clusters with low values surrounded by other low values.
High-LowSpatial outlier with high values surrounded by low values.
Low-HighSpatial outlier with low values surrounded by high values.

This visualization can be customized via the Layer configuration.

Interactive Example

CalEnviroScreen is a screening methodology that can be used to help identify California communities that are disproportionately burdened by multiple sources of pollution. Use the slider to view statewide data on the left, and a cluster/outlier analysis on the right.

Data source: https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-40

Remarks

Use Cases

Find examples and other information in the Cluster and Outlier Analysis use case article.

Ongoing Development

The Cluster-outlier Analysis module is undergoing continued development. Visit our community Slack channel, or contact us directly via email for any inquiries regarding this module.


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