diff --git a/ci/36.yaml b/ci/36.yaml
index 91424574..80a7553f 100644
--- a/ci/36.yaml
+++ b/ci/36.yaml
@@ -35,3 +35,4 @@ dependencies:
   - pywget
   - proplot
   - contextily
+  - scikit-plot
diff --git a/ci/37.yaml b/ci/37.yaml
index 5381ba10..affa540b 100644
--- a/ci/37.yaml
+++ b/ci/37.yaml
@@ -35,3 +35,4 @@ dependencies:
   - pywget
   - proplot
   - contextily
+  - scikit-plot
diff --git a/ci/38.yaml b/ci/38.yaml
index db98c31f..84396022 100644
--- a/ci/38.yaml
+++ b/ci/38.yaml
@@ -35,3 +35,4 @@ dependencies:
   - pywget
   - proplot
   - contextily
+  - scikit-plot
diff --git a/environment.yml b/environment.yml
index 12c0b5be..6d261a81 100644
--- a/environment.yml
+++ b/environment.yml
@@ -25,4 +25,4 @@ dependencies:
   - tobler >=0.2.1
   - proplot
   - contextily
-  
+  - scikit-plot
diff --git a/geosnap/_community.py b/geosnap/_community.py
index 105efece..0857536e 100644
--- a/geosnap/_community.py
+++ b/geosnap/_community.py
@@ -3,6 +3,7 @@
 
 import geopandas as gpd
 import pandas as pd
+import scikitplot as skplt
 
 from ._data import _Map, datasets
 from .analyze import cluster as _cluster
@@ -321,6 +322,30 @@ def cluster_spatial(
         comm.models[model_name] = model
         return comm
 
+    def silplot(self, model_name=None, year=None, **kwargs):
+        """ Returns a silhouette plot of the model that is passed to it.
+
+        Parameters
+        ----------
+        model_name : str , required
+                     model to be silhouette plotted
+        year       : int, optional
+                     year to be plotted if model created with pooling=='unique'
+        kwargs     : **kwargs, optional
+                     pass through to plot_silhouette()
+        Returns
+        -------
+        silhouette plot of given model.
+
+        """
+        if not year:
+            plot = skplt.metrics.plot_silhouette(self.models[model_name].X, self.models[model_name].labels,
+                                                 **kwargs)
+        else:
+            plot = skplt.metrics.plot_silhouette(self.models[model_name][year].X, self.models[model_name][year].labels,
+                                                 **kwargs)
+        return plot
+
     def transition(
         self, cluster_col, time_var="year", id_var="geoid", w_type=None, permutations=0
     ):
diff --git a/geosnap/analyze/analytics.py b/geosnap/analyze/analytics.py
index c7163eab..48810ca8 100644
--- a/geosnap/analyze/analytics.py
+++ b/geosnap/analyze/analytics.py
@@ -1,7 +1,5 @@
 """Tools for the spatial analysis of neighborhood change."""
 
-from collections import namedtuple
-
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
@@ -25,9 +23,47 @@
     ward_spatial,
 )
 
-ModelResults = namedtuple(
-    "model", ["X", "columns", "labels", "instance", "W"], rename=False
-)
+
+class ModelResults:
+    """Stores data about cluster and cluster_spatial models.
+
+    Attributes
+    ----------
+    X: array-like
+        data used to compute model
+    columns: list-like
+        columns used in model
+    W: libpysal.weights.W 
+        libpysal spatial weights matrix used in model
+    labels: array-like
+        labels of each column
+    instance: instance of model class used to generate neighborhood labels.
+        fitted model instance, e.g sklearn.cluster.AgglomerativeClustering object 
+        or other model class used to estimate class labels
+
+    """
+    def __init__(self, X, columns, labels,instance,W,):
+        """Initialize a new ModelResults instance.
+
+        Parameters
+        ----------
+        X: array-like
+            data of the cluster
+        columns: list-like
+            columns used to compute model
+        W: libpysal.weights.W
+            libpysal spatial weights matrix used in model
+        labels: array-like
+            labels of each column
+        instance: AgglomerativeCluserting object, or other model specific object type
+            how many clusters model was computed with
+
+        """
+        self.columns = columns
+        self.X = X
+        self.W = W
+        self.instance = instance
+        self.labels = labels
 
 
 def cluster(