Global power plants clustered and categorized by fuel type. Clustering summarizes the layer's renderer so you can see the spatial density of features at a glance.
What is clustering?Clustering is a method of reducing features in a layer by grouping them into clusters based on their spatial proximity to one another. Typically, clusters are proportionally sized based on the number of features within each cluster.
This is an effective way to show areas where many points stack on top of one another.
Clustering allows you to effectively visualize where points stack on top of another or are in very close proximity to each other. Use the swipe widget above to compare an unclustered layer of power plants with a clustered version.
NoteClustering can only be configured in a MapView.
Why is clustering useful?Large layers can be deceptive. What appears to be just a few features can in reality be several thousand. Clustering allows you to visually represent large numbers of features in relatively small areas.
For example, the following map shows the locations of thousands of power plants. In the image below, regions A and B both have a high density of points, making them impossible to compare.
Region A and region B both have a high density of points. It is impossible to tell how many points overlap in each area.However, when clustering is enabled, the user can now clearly see that region B has nearly twice as many points as region A.
Clustering allows the user to easily compare the density of overlapping features at a glance. WarningClustering should only be used as a visualization technique to reduce visual clutter because of many overlapping features or to provide a quick preview of feature density. It should not be used as a means of performing statistical analysis of data.
How clustering worksClustering is configured on the featureReduction property of the layer. You can enable clustering with minimal code by setting the featureReduction type to cluster.
Use dark colors for code blocks Copy
1
2
3
layer.featureReduction = {
type: "cluster"
};The featureReduction property gives you control over many other cluster properties. The clusterRadius defines each cluster's area of influence for including features. You may also define popupTemplates and labels for clusters to summarize the features included in the cluster.
Use discretion when clustering polygon and polyline features, as the underlying data could be misrepresented.
Clustering works best when features have a regular size smaller than the cluster radius. Clustering large features with a small cluster radius will result in displaying misleading patterns that may confuse the audience. For example, some areas covered by large features may show no clusters at all even though the area is completely covered by large features. As a guideline, small features like parcels, buildings, culverts, or connectors are well-suited for clustering. Large, irregularly shaped features like counties, states, or countries do not need to be aggregated.
TipThe following are suggestions for improving the readability of clustered layers:
10k instead of 10000). See the Suggested cluster defaults section below for an example of this.Clustering is typically used to visualize large point layers, but may be used with any geometry type (since version 4.31). In the case of clustering polyline or polygon features, the centroid of the line or polygon is used to determine the cluster in which it is placed. This may lead to some features being placed in unexpected clusters. Because of this, special considerations should be taken when clustering lines and polygons.
TipUse the following guidelines when clustering lines or polygons:
maxScale in the feature reduction configuration to disable clustering at larger scales (when zoomed in). This allows you to view features in full fidelity.See the Clustered polygons sample for a good example of a clustered polygon layer.
Examples Basic clusteringThe following example demonstrates how to enable clustering and configure labels and a popup for displaying the cluster count.
The aggregate fields used by clusters are generated once clustering is enabled on the layer. By default, all clustered layers have a cluster_count aggregate field. This can be used in the labels and the popup for each cluster. Other fields used in the layer's renderer may be accessible for display in the popup. You can learn more about how to use these in the FeatureReductionCluster.popupTemplate documentation.
See the Related samples and resources below for more examples of how to summarize data within a cluster's popup.
Global power plants clustered by count.
ArcGIS JS API
Expand
Use dark colors for code blocks Copy48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84 84
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
clusteredLayer.featureReduction = {
type: "cluster",
clusterMinSize: 16.5,
// defines the label within each cluster
labelingInfo: [
{
deconflictionStrategy: "none",
labelExpressionInfo: {
expression: "Text($feature.cluster_count, '#,###')",
},
symbol: {
type: "text",
color: "white",
font: {
family: "Noto Sans",
size: "12px",
},
},
labelPlacement: "center-center",
},
],
// information to display when the user clicks a cluster
popupTemplate: {
title: "Cluster Summary",
content: "This cluster represents <b>{cluster_count}</b> features.",
fieldInfos: [
{
fieldName: "cluster_count",
format: {
places: 0,
digitSeparator: true,
},
},
],
},
};
By default, the cluster symbol always summarizes the features in the cluster. When a layer has a UniqueValueRenderer, the symbol of each cluster represents the predominant value of features in the cluster. When a layer has any visual variables applied to it, the average of each variable in the cluster is applied to the cluster symbol. The fields describing the predominant type and average of numeric fields can be referenced in the cluster popup and label.
This example uses smart mapping methods to demonstrate how to generate the suggested cluster configuration specific to the layer's renderer.
Global power plants clustered and categorized by fuel type. Clustering summarizes the layer renderer so you can see a summary of the features contained by the cluster at a glance.
ArcGIS JS API
Expand
Use dark colors for code blocks Copy102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 122 122 122 122 122 122 122 122 122
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
// generates default popupTemplate
const popupTemplate = await clusterPopupCreator
.getTemplates({ layer })
.then((popupTemplateResponse) => popupTemplateResponse.primaryTemplate.value);
// generates default labelingInfo
const { labelingInfo, clusterMinSize } = await clusterLabelCreator
.getLabelSchemes({
layer,
view,
})
.then((labelSchemes) => labelSchemes.primaryScheme);
// Set this object on layer.featureReduction
return {
type: "cluster",
popupTemplate,
labelingInfo,
clusterMinSize,
};
By default, when a layer has a UniqueValueRenderer or ClassBreaksRenderer, the symbol of each cluster represents the predominant category within the cluster.
You may prefer to visualize clusters of type-based renderers as pie charts rather than the predominant category. To do this, you can call the pieChart.createRendererForClustering method, which will create a pie chart renderer based on the categories defined in a UniqueValueRenderer or a ClassBreaksRenderer.
The fields and renderer returned from this method should be set directly on the FeatureReductionCluster instance on the layer.
Use dark colors for code blocks Copy
1
2
3
4
5
6
7
8
9
10
const { renderer, fields } = await pieChartRendererCreator.createRendererForClustering({
layer,
shape: "donut"
});
layer.featureReduction = {
type: "cluster",
fields,
renderer
}311 incident reports categorized by fuel type. Clustering summarizes the layer renderer as a pie chart for layers with a UniqueValueRenderer or ClassBreaksRenderer.
API supportThe following table describes the geometry and view types that are suited well for each visualization technique.
Full support Partial support No supportRetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4