From 33267f642dc603ed0bce37fab9ed30cad3afd4f4 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Wed, 20 Jun 2018 17:52:03 -0700
Subject: [PATCH 01/17] add `.plot()`, `.scatterplot()` and `.LISA_map()`
 methods to `esda.Moran_Local` object

therefore call `splot` functonality
---
 esda/moran.py | 137 ++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 137 insertions(+)

diff --git a/esda/moran.py b/esda/moran.py
index 8b558524..a607ce29 100644
--- a/esda/moran.py
+++ b/esda/moran.py
@@ -1006,6 +1006,143 @@ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **s
         return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
                                    outvals=outvals, stat=cls,
                                    swapname=cls.__name__.lower(), **stat_kws)
+    
+    def plot(self, gdf, attribute, p=0.05,
+             region_column=None, mask=None,
+             mask_color='#636363', quadrant=None,
+             legend=True, scheme='Quantiles',
+             cmap='YlGnBu', figsize=(15,4)):
+        """Produce three-plot visualization of Moran Scatteprlot,
+        LISA cluster and Choropleth, with Local Moran
+        region and quadrant masking
+
+        Parameters
+        ----------
+        gdf : geopandas dataframe
+            The Dataframe containing information to plot the two maps.
+        attribute : str
+            Column name of attribute which should be depicted in Choropleth map.
+        p : float, optional
+            The p-value threshold for significance. Points and polygons will
+            be colored by significance. Default = 0.05.
+        region_column: string, optional
+            Column name containing mask region of interest. Default = None
+        mask: str, optional
+            Identifier or name of the region to highlight. Default = None
+        mask_color: str, optional
+            Color of mask. Default = '#636363'
+        quadrant : int, optional
+            Quadrant 1-4 in scatterplot masking values in LISA cluster and
+            Choropleth maps. Default = None
+        figsize: tuple, optional
+            W, h of figure. Default = (15,4)
+        legend: boolean, optional
+            If True, legend for maps will be depicted. Default = True
+        scheme: str, optional
+            Name of PySAL classifier to be used. Default = 'Quantiles'
+        cmap: str, optional
+            Name of matplotlib colormap used for plotting the Choropleth.
+            Default = 'YlGnBU'
+
+        Returns
+        -------
+        fig : matplotlip Figure instance
+            Figure of LISA cluster map
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+            
+        Examples
+        --------
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method will be available as soon as splot is released in September 2018',
+                          UserWarning)
+            raise e
+
+        fig, axs = splot.esda.plot_local_autocorrelation(self, gdf=gdf,
+                                                        attribute=attribute,
+                                                        p=p,
+                                                        region_column=region_column,
+                                                        mask=mask,
+                                                        mask_color=mask_color,
+                                                        quadrant=quadrant,
+                                                        legend=legend,
+                                                        scheme=scheme,
+                                                        cmap=cmap,
+                                                        figsize=figsize)
+        return fig, axs
+    
+    def scatterplot(self, p=0.05, **kwargs):
+        """Plot Local Moran Scatterplot
+
+        Parameters
+        ----------
+        p : float, optional
+            The p-value threshold for significance. Points will
+            be colored by LISA and significance. Default=0.05
+        **kwargs : keyword arguments, optional
+            Keywords used for creating and designing the plot.
+
+        Returns
+        -------
+        fig : matplotlip Figure instance
+            Figure of LISA cluster map
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+            
+        Examples
+        --------
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method will be available as soon as splot is released in September 2018',
+                          UserWarning)
+            raise e
+
+        fig, ax = splot.esda.moran_loc_scatterplot(self, p=p, **kwargs)
+        return fig, ax
+
+
+    def LISA_map(self, gdf, p=0.05,
+                 legend=True, **kwargs):
+        """Plot LISA cluster map
+
+        Parameters
+        ----------
+        gdf : geopandas dataframe instance
+            The Dataframe containing information to plot. Note that `gdf` will be
+            modified, so calling functions should use a copy of the user
+            provided `gdf`. (either using gdf.assign() or gdf.copy())
+        p : float, optional
+            The p-value threshold for significance. Points will
+            be colored by significance. Default =0.05
+        legend : boolean, optional
+            If True, legend for maps will be depicted. Default = True
+        **kwargs : keyword arguments, optional
+            Keywords used for creating and designing the plot.
+
+        Returns
+        -------
+        fig : matplotlip Figure instance
+            Figure of LISA cluster map
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+            
+        Examples
+        --------
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method will be available as soon as splot is released in September 2018',
+                          UserWarning)
+            raise e
+
+        fig, ax = splot.esda.lisa_cluster(self, p=p, **kwargs)
+        return fig, ax
 
 
 class Moran_Local_BV(object):

From 0b02b04e706f97549ba755415c03f917c1d78c17 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Wed, 20 Jun 2018 18:00:11 -0700
Subject: [PATCH 02/17] add test for methods `.plt()`, `.scatterplot()` and
 `.LISA_map()`

fix parameters in `splot.esda.lisa_cluster()` called in `moran.py`
---
 esda/moran.py            |  2 +-
 esda/tests/test_moran.py | 56 +++++++++++++++++++++++++++++++++++++++-
 2 files changed, 56 insertions(+), 2 deletions(-)

diff --git a/esda/moran.py b/esda/moran.py
index a607ce29..06b046be 100644
--- a/esda/moran.py
+++ b/esda/moran.py
@@ -1141,7 +1141,7 @@ def LISA_map(self, gdf, p=0.05,
                           UserWarning)
             raise e
 
-        fig, ax = splot.esda.lisa_cluster(self, p=p, **kwargs)
+        fig, ax = splot.esda.lisa_cluster(self, gdf, p=p, **kwargs)
         return fig, ax
 
 
diff --git a/esda/tests/test_moran.py b/esda/tests/test_moran.py
index 055c9106..4413a9ce 100644
--- a/esda/tests/test_moran.py
+++ b/esda/tests/test_moran.py
@@ -113,6 +113,60 @@ def test_by_col(self):
         self.assertAlmostEqual(lm.z_z_sim[0], -0.68493799168603808)
         self.assertAlmostEqual(lm.z_p_z_sim[0],  0.24669152541631179)
 
+    def test_plot(self):
+        import matplotlib.pyplot as plt
+        import libpysal.api as lp
+        from libpysal import examples
+        import geopandas as gpd
+        link = examples.get_path('columbus.shp')
+        gdf = gpd.read_file(link)
+        self.y = gdf['HOVAL'].values
+        self.w = lp.Queen.from_dataframe(gdf)
+        self.w.transform = 'r'
+        self = moran.Moran_Local(self.y, self.w)
+        fig, _ = moran.Moran_Local.plot(self, gdf, 'HOVAL')
+        plt.close(fig)
+        # also test with quadrant and mask
+        fig, _ = moran.Moran_Local.plot(self, gdf, 'HOVAL', p=0.05,
+                                        region_column='POLYID',
+                                        mask=['1', '2', '3'], quadrant=1)
+        plt.close(fig)
+
+
+    def test_scatterplot(self):
+        import matplotlib.pyplot as plt
+        import libpysal.api as lp
+        from libpysal import examples
+        import geopandas as gpd
+        link = examples.get_path('columbus.shp')
+        gdf = gpd.read_file(link)
+        self.y = gdf['HOVAL'].values
+        self.w = lp.Queen.from_dataframe(gdf)
+        self.w.transform = 'r'
+        self = moran.Moran_Local(self.y, self.w)
+        fig, _ = moran.Moran_Local.scatterplot(self)
+        plt.close(fig)
+        # also test with quadrant and mask
+        fig, _ = moran.Moran_Local.scatterplot(self, figsize=(10,20))
+        plt.close(fig)
+        
+
+    def test_LISA_map(self):
+        import matplotlib.pyplot as plt
+        import libpysal.api as lp
+        from libpysal import examples
+        import geopandas as gpd
+        link = examples.get_path('columbus.shp')
+        gdf = gpd.read_file(link)
+        self.y = gdf['HOVAL'].values
+        self.w = lp.Queen.from_dataframe(gdf)
+        self.w.transform = 'r'
+        self = moran.Moran_Local(self.y, self.w)
+        fig, _ = moran.Moran_Local.LISA_map(self, gdf)
+        plt.close(fig)
+        # also test with quadrant and mask
+        fig, _ = moran.Moran_Local.LISA_map(self, gdf, figsize=(10,20))
+        plt.close(fig)
 
 class Moran_Local_BV_Tester(unittest.TestCase):
     def setUp(self):
@@ -144,7 +198,7 @@ def test_by_col(self):
         self.assertAlmostEqual(bvz[0],  1.657427, 5)
         self.assertAlmostEqual(bvzp[0], 0.048717, 5)
 
-
+        
 class Moran_Local_Rate_Tester(unittest.TestCase):
     def setUp(self):
         np.random.seed(10)

From 475628e3244255ceab8f2bf23499cb5dbf4ff069 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Wed, 20 Jun 2018 18:23:01 -0700
Subject: [PATCH 03/17] add `matplotlib` and `splot` install to `.travis.yml`

---
 .travis.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 59293df0..2e5151fc 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -26,6 +26,8 @@ install:
   - conda install --yes --file requirements.txt;
   - pip install libpysal
   - if [[ PYSAL_PLUS ]]; then conda install --yes numba; fi
+  - pip install https://github.com/pysal/splot/archive/master.zip
+  - pip install matplotlib
 
 script:
   - pwd

From f220208064cd64f918037469a257193dfe134eff Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Wed, 20 Jun 2018 18:33:51 -0700
Subject: [PATCH 04/17] add  `pip install
 https://github.com/geopandas/geopandas/archive/master.zip` to `.travis.yml`
 for testing

---
 .travis.yml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 2e5151fc..80e5dccf 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -26,6 +26,9 @@ install:
   - conda install --yes --file requirements.txt;
   - pip install libpysal
   - if [[ PYSAL_PLUS ]]; then conda install --yes numba; fi
+  # We need geopandas master, the legend_kwds is not in 0.3.0
+  - pip install https://github.com/geopandas/geopandas/archive/master.zip
+  # We need splot master, since splot is not released yet
   - pip install https://github.com/pysal/splot/archive/master.zip
   - pip install matplotlib
 

From be4e6a0b3090fb3710a861a88104357e50147d17 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Wed, 20 Jun 2018 19:01:30 -0700
Subject: [PATCH 05/17] add `import warnings` to `moran.py`

---
 esda/moran.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/esda/moran.py b/esda/moran.py
index 06b046be..5f6e8159 100644
--- a/esda/moran.py
+++ b/esda/moran.py
@@ -9,6 +9,7 @@
 from .tabular import _univariate_handler, _bivariate_handler
 import scipy.stats as stats
 import numpy as np
+import warnings
 
 __all__ = ["Moran", "Moran_Local", "Moran_BV", "Moran_BV_matrix",
            "Moran_Local_BV", "Moran_Rate", "Moran_Local_Rate"]

From 60c458c2ee3fe1037a00c36fa2e05cd51f15f536 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Wed, 20 Jun 2018 23:02:48 -0700
Subject: [PATCH 06/17] debug `test_moran.py`

change `self = moran.Moran_Local(self.y, self.w)` to `mloc = moran.Moran_Local(self.y, self.w)`
---
 esda/tests/test_moran.py | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/esda/tests/test_moran.py b/esda/tests/test_moran.py
index 4413a9ce..bb98d418 100644
--- a/esda/tests/test_moran.py
+++ b/esda/tests/test_moran.py
@@ -123,11 +123,11 @@ def test_plot(self):
         self.y = gdf['HOVAL'].values
         self.w = lp.Queen.from_dataframe(gdf)
         self.w.transform = 'r'
-        self = moran.Moran_Local(self.y, self.w)
-        fig, _ = moran.Moran_Local.plot(self, gdf, 'HOVAL')
+        mloc = moran.Moran_Local(self.y, self.w)
+        fig, _ = mloc.plot(self, gdf, 'HOVAL')
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = moran.Moran_Local.plot(self, gdf, 'HOVAL', p=0.05,
+        fig, _ = mloc.plot(self, gdf, 'HOVAL', p=0.05,
                                         region_column='POLYID',
                                         mask=['1', '2', '3'], quadrant=1)
         plt.close(fig)
@@ -143,11 +143,11 @@ def test_scatterplot(self):
         self.y = gdf['HOVAL'].values
         self.w = lp.Queen.from_dataframe(gdf)
         self.w.transform = 'r'
-        self = moran.Moran_Local(self.y, self.w)
-        fig, _ = moran.Moran_Local.scatterplot(self)
+        mloc = moran.Moran_Local(self.y, self.w)
+        fig, _ = mloc.scatterplot(self)
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = moran.Moran_Local.scatterplot(self, figsize=(10,20))
+        fig, _ = mloc.scatterplot(self, figsize=(10,20))
         plt.close(fig)
         
 
@@ -161,11 +161,11 @@ def test_LISA_map(self):
         self.y = gdf['HOVAL'].values
         self.w = lp.Queen.from_dataframe(gdf)
         self.w.transform = 'r'
-        self = moran.Moran_Local(self.y, self.w)
-        fig, _ = moran.Moran_Local.LISA_map(self, gdf)
+        moran_loc = moran.Moran_Local(self.y, self.w)
+        fig, _ = moran_loc.LISA_map(self, gdf)
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = moran.Moran_Local.LISA_map(self, gdf, figsize=(10,20))
+        fig, _ = moran_loc.LISA_map(self, gdf, figsize=(10,20))
         plt.close(fig)
 
 class Moran_Local_BV_Tester(unittest.TestCase):

From 1a2308e54c2a0111ffbc6de32b451ae097f44e84 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Thu, 21 Jun 2018 14:22:04 -0700
Subject: [PATCH 07/17] debug `test_moran.py`

remove `self` inside test funciton for plots
Please enter the commit message for your changes. Lines starting
---
 esda/tests/test_moran.py | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/esda/tests/test_moran.py b/esda/tests/test_moran.py
index bb98d418..d2e67296 100644
--- a/esda/tests/test_moran.py
+++ b/esda/tests/test_moran.py
@@ -120,16 +120,16 @@ def test_plot(self):
         import geopandas as gpd
         link = examples.get_path('columbus.shp')
         gdf = gpd.read_file(link)
-        self.y = gdf['HOVAL'].values
-        self.w = lp.Queen.from_dataframe(gdf)
-        self.w.transform = 'r'
-        mloc = moran.Moran_Local(self.y, self.w)
-        fig, _ = mloc.plot(self, gdf, 'HOVAL')
+        y = gdf['HOVAL'].values
+        w = lp.Queen.from_dataframe(gdf)
+        w.transform = 'r'
+        mloc = moran.Moran_Local(y, w)
+        fig, _ = mloc.plot(gdf, 'HOVAL')
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = mloc.plot(self, gdf, 'HOVAL', p=0.05,
-                                        region_column='POLYID',
-                                        mask=['1', '2', '3'], quadrant=1)
+        fig, _ = mloc.plot(gdf, 'HOVAL', p=0.05,
+                           region_column='POLYID',
+                           mask=['1', '2', '3'], quadrant=1)
         plt.close(fig)
 
 
@@ -140,14 +140,14 @@ def test_scatterplot(self):
         import geopandas as gpd
         link = examples.get_path('columbus.shp')
         gdf = gpd.read_file(link)
-        self.y = gdf['HOVAL'].values
-        self.w = lp.Queen.from_dataframe(gdf)
-        self.w.transform = 'r'
-        mloc = moran.Moran_Local(self.y, self.w)
-        fig, _ = mloc.scatterplot(self)
+        y = gdf['HOVAL'].values
+        w = lp.Queen.from_dataframe(gdf)
+        w.transform = 'r'
+        mloc = moran.Moran_Local(y, w)
+        fig, _ = mloc.scatterplot()
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = mloc.scatterplot(self, figsize=(10,20))
+        fig, _ = mloc.scatterplot(figsize=(10,20))
         plt.close(fig)
         
 
@@ -158,14 +158,14 @@ def test_LISA_map(self):
         import geopandas as gpd
         link = examples.get_path('columbus.shp')
         gdf = gpd.read_file(link)
-        self.y = gdf['HOVAL'].values
-        self.w = lp.Queen.from_dataframe(gdf)
-        self.w.transform = 'r'
-        moran_loc = moran.Moran_Local(self.y, self.w)
-        fig, _ = moran_loc.LISA_map(self, gdf)
+        y = gdf['HOVAL'].values
+        w = lp.Queen.from_dataframe(gdf)
+        w.transform = 'r'
+        moran_loc = moran.Moran_Local(y, w)
+        fig, _ = moran_loc.LISA_map(gdf)
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = moran_loc.LISA_map(self, gdf, figsize=(10,20))
+        fig, _ = moran_loc.LISA_map(gdf, figsize=(10,20))
         plt.close(fig)
 
 class Moran_Local_BV_Tester(unittest.TestCase):

From 2345dd9689fe081570a8b698bfa0a9c3fc1e3e37 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Tue, 26 Jun 2018 10:55:38 -0700
Subject: [PATCH 08/17] update testing in line 69 to
 `np.testing.assert_allclose(pval, 0.009, rtol=1e-7, atol=0)`

---
 esda/tests/test_moran.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/esda/tests/test_moran.py b/esda/tests/test_moran.py
index d2e67296..8dae84c1 100644
--- a/esda/tests/test_moran.py
+++ b/esda/tests/test_moran.py
@@ -73,7 +73,7 @@ def test_by_col(self):
         sidr = np.unique(mi["SID79-BIR79_moran_rate"].values)
         pval = np.unique(mi["SID79-BIR79_p_sim"].values)
         np.testing.assert_allclose(sidr, 0.16622343552567395, rtol=RTOL, atol=ATOL)
-        self.assertAlmostEqual(pval, 0.009)
+        np.testing.assert_allclose(pval, 0.009, rtol=1e-7, atol=0)
 
 
 

From 054d33186d67f6d03bbb612c8296f553c0aadbab Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Tue, 26 Jun 2018 18:03:19 -0700
Subject: [PATCH 09/17] set random seed and change reference `pval` to `0.008`
 in `test_by_col`

---
 esda/tests/test_moran.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/esda/tests/test_moran.py b/esda/tests/test_moran.py
index 8dae84c1..eeb99d5e 100644
--- a/esda/tests/test_moran.py
+++ b/esda/tests/test_moran.py
@@ -55,6 +55,7 @@ def test_by_col(self):
 
 class Moran_Rate_Tester(unittest.TestCase):
     def setUp(self):
+        np.random.seed(15432)
         self.w = pysal.open(pysal.examples.get_path("sids2.gal")).read()
         f = pysal.open(pysal.examples.get_path("sids2.dbf"))
         self.e = np.array(f.by_col['SID79'])
@@ -73,8 +74,7 @@ def test_by_col(self):
         sidr = np.unique(mi["SID79-BIR79_moran_rate"].values)
         pval = np.unique(mi["SID79-BIR79_p_sim"].values)
         np.testing.assert_allclose(sidr, 0.16622343552567395, rtol=RTOL, atol=ATOL)
-        np.testing.assert_allclose(pval, 0.009, rtol=1e-7, atol=0)
-
+        np.testing.assert_allclose(pval, 0.008, rtol=RTOL, atol=ATOL)
 
 
 class Moran_BV_matrix_Tester(unittest.TestCase):

From c571a2133a32ca244763d8664b034da8b01b0f3e Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Tue, 26 Jun 2018 18:25:09 -0700
Subject: [PATCH 10/17] try to debug TravisCI package install.

also change version number to fix a warning in the TravisCI log
instal matplotlib with conda
---
 .travis.yml | 4 ++--
 setup.py    | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 80e5dccf..fa911093 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -26,11 +26,11 @@ install:
   - conda install --yes --file requirements.txt;
   - pip install libpysal
   - if [[ PYSAL_PLUS ]]; then conda install --yes numba; fi
+  - conda install matplotlib
   # We need geopandas master, the legend_kwds is not in 0.3.0
   - pip install https://github.com/geopandas/geopandas/archive/master.zip
   # We need splot master, since splot is not released yet
-  - pip install https://github.com/pysal/splot/archive/master.zip
-  - pip install matplotlib
+  - pip install -v https://github.com/pysal/splot/archive/master.zip
 
 script:
   - pwd
diff --git a/setup.py b/setup.py
index 3c698d00..62793ddb 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 from distutils.command.build_py import build_py
 
 setup(name='esda',  # name of package
-      version='1.0.1dev',
+      version='1.0.1.dev0',
       description='Package with statistics for exploratory spatial data analysis',
       url='https://github.com/pysal/esda',
       maintainer='Sergio Rey',

From fb19fb3b8e8fe12c8ff34edd3da616ba032f5d70 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Mon, 9 Jul 2018 13:38:28 -0700
Subject: [PATCH 11/17] adapt `moran.py` and `test_moran.py` to new `splot`
 functionality

add examples and documentation
---
 esda/moran.py            | 88 +++++++++++++++++++++++++++++++++-------
 esda/tests/test_moran.py | 13 +++---
 2 files changed, 80 insertions(+), 21 deletions(-)

diff --git a/esda/moran.py b/esda/moran.py
index 5f6e8159..17848ca5 100644
--- a/esda/moran.py
+++ b/esda/moran.py
@@ -1054,6 +1054,24 @@ def plot(self, gdf, attribute, p=0.05,
             
         Examples
         --------
+        >>> import matplotlib.pyplot as plt
+        >>> import geopandas as gpd
+        >>> import libpysal.api as lp
+        >>> from libpysal import examples
+        >>> from esda.moran import Moran_Local
+        Load data and calculate Moran Local statistics
+        >>> link = examples.get_path('columbus.shp')
+        >>> gdf = gpd.read_file(link)
+        >>> y = gdf['HOVAL'].values
+        >>> w = lp.Queen.from_dataframe(gdf)
+        >>> w.transform = 'r'
+        >>> moran_loc = Moran_Local(y, w)
+        plot
+        >>> moran_loc.scatterplot()
+        >>> plt.show()
+        customize plot
+        >>> moran_loc.scatterplot(fitline_kwds=dict(color='#4393c3'))
+        >>> plt.show()
         """
         try:
             import splot.esda
@@ -1074,17 +1092,28 @@ def plot(self, gdf, attribute, p=0.05,
                                                         cmap=cmap,
                                                         figsize=figsize)
         return fig, axs
-    
-    def scatterplot(self, p=0.05, **kwargs):
-        """Plot Local Moran Scatterplot
+
+
+    def scatterplot(self, zstandard=True, p=0.05,
+                    ax=None, scatter_kwds=None, fitline_kwds=None):
+        """
+        Moran Scatterplot with option of coloring of Local Moran Statistics
 
         Parameters
         ----------
         p : float, optional
-            The p-value threshold for significance. Points will
-            be colored by LISA and significance. Default=0.05
-        **kwargs : keyword arguments, optional
-            Keywords used for creating and designing the plot.
+            If given, the p-value threshold for significance. Points will
+            be colored by significance. By default it will not be colored.
+            Default =None.
+        ax : Matplotlib Axes instance, optional
+            If given, the Moran plot will be created inside this axis.
+            Default =None.
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline.
+            Default =None.
 
         Returns
         -------
@@ -1095,6 +1124,24 @@ def scatterplot(self, p=0.05, **kwargs):
             
         Examples
         --------
+        >>> import matplotlib.pyplot as plt
+        >>> import geopandas as gpd
+        >>> import libpysal.api as lp
+        >>> from libpysal import examples
+        >>> from esda.moran import Moran_Local
+        Load data and calculate Moran Local statistics
+        >>> link = examples.get_path('columbus.shp')
+        >>> gdf = gpd.read_file(link)
+        >>> y = gdf['HOVAL'].values
+        >>> w = lp.Queen.from_dataframe(gdf)
+        >>> w.transform = 'r'
+        >>> moran_loc = Moran_Local(y, w)
+        plot
+        >>> moran_loc.lisa_map(gdf)
+        >>> plt.show()
+        customize plot
+        >>> moran_loc.lisa_map(gdf, legend=False)
+        >>> plt.show()
         """
         try:
             import splot.esda
@@ -1103,13 +1150,16 @@ def scatterplot(self, p=0.05, **kwargs):
                           UserWarning)
             raise e
 
-        fig, ax = splot.esda.moran_loc_scatterplot(self, p=p, **kwargs)
+        fig, ax = splot.esda.moran_loc_scatterplot(self, zstandard=zstandard, p=p,
+                                                   ax=ax, scatter_kwds=scatter_kwds,
+                                                   fitline_kwds=fitline_kwds)
         return fig, ax
 
 
-    def LISA_map(self, gdf, p=0.05,
-                 legend=True, **kwargs):
-        """Plot LISA cluster map
+    def lisa_map(self, gdf, p=0.05, ax=None,
+                 legend=True, legend_kwds=None, **kwargs):
+        """
+        Plot LISA cluster map
 
         Parameters
         ----------
@@ -1119,12 +1169,19 @@ def LISA_map(self, gdf, p=0.05,
             provided `gdf`. (either using gdf.assign() or gdf.copy())
         p : float, optional
             The p-value threshold for significance. Points will
-            be colored by significance. Default =0.05
+            be colored by significance.
+        ax : matplotlib Axes instance, optional
+            Axes in which to plot the figure in multiple Axes layout.
+            Default = None
         legend : boolean, optional
             If True, legend for maps will be depicted. Default = True
+        legend_kwds : dict, optional
+            Dictionary to control legend formatting options. Example:
+            ``legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)}``
+            Default = None
         **kwargs : keyword arguments, optional
-            Keywords used for creating and designing the plot.
-
+            Keywords designing and passed to geopandas.GeoDataFrame.plot().
+        
         Returns
         -------
         fig : matplotlip Figure instance
@@ -1142,7 +1199,8 @@ def LISA_map(self, gdf, p=0.05,
                           UserWarning)
             raise e
 
-        fig, ax = splot.esda.lisa_cluster(self, gdf, p=p, **kwargs)
+        fig, ax = splot.esda.lisa_cluster(self, gdf, p=p, ax=ax,
+                                          legend=legend, legend_kwds=legend_kwds, **kwargs)
         return fig, ax
 
 
diff --git a/esda/tests/test_moran.py b/esda/tests/test_moran.py
index eeb99d5e..13f2d4ca 100644
--- a/esda/tests/test_moran.py
+++ b/esda/tests/test_moran.py
@@ -123,11 +123,11 @@ def test_plot(self):
         y = gdf['HOVAL'].values
         w = lp.Queen.from_dataframe(gdf)
         w.transform = 'r'
-        mloc = moran.Moran_Local(y, w)
-        fig, _ = mloc.plot(gdf, 'HOVAL')
+        moran_loc = moran.Moran_Local(y, w)
+        fig, _ = moran_loc.plot(gdf, 'HOVAL')
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = mloc.plot(gdf, 'HOVAL', p=0.05,
+        fig, _ = moran_loc.plot(gdf, 'HOVAL', p=0.05,
                            region_column='POLYID',
                            mask=['1', '2', '3'], quadrant=1)
         plt.close(fig)
@@ -147,7 +147,7 @@ def test_scatterplot(self):
         fig, _ = mloc.scatterplot()
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = mloc.scatterplot(figsize=(10,20))
+        fig, _ = mloc.scatterplot(fitline_kwds=dict(color='#4393c3'))
         plt.close(fig)
         
 
@@ -162,12 +162,13 @@ def test_LISA_map(self):
         w = lp.Queen.from_dataframe(gdf)
         w.transform = 'r'
         moran_loc = moran.Moran_Local(y, w)
-        fig, _ = moran_loc.LISA_map(gdf)
+        fig, _ = moran_loc.lisa_map(gdf)
         plt.close(fig)
         # also test with quadrant and mask
-        fig, _ = moran_loc.LISA_map(gdf, figsize=(10,20))
+        fig, _ = moran_loc.lisa_map(gdf, legend=False)
         plt.close(fig)
 
+
 class Moran_Local_BV_Tester(unittest.TestCase):
     def setUp(self):
         np.random.seed(10)

From 734ac4a52672a27eeecd803509479dd59d23cd9d Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Mon, 9 Jul 2018 17:14:08 -0700
Subject: [PATCH 12/17] add `--yes` to `matplotlib` install in `.travis.yml`

---
 .travis.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.travis.yml b/.travis.yml
index fa911093..950940a3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -26,7 +26,7 @@ install:
   - conda install --yes --file requirements.txt;
   - pip install libpysal
   - if [[ PYSAL_PLUS ]]; then conda install --yes numba; fi
-  - conda install matplotlib
+  - conda install matplotlib --yes
   # We need geopandas master, the legend_kwds is not in 0.3.0
   - pip install https://github.com/geopandas/geopandas/archive/master.zip
   # We need splot master, since splot is not released yet

From a0abd72911548ad0e978ff0d7f27a8244a98ed9a Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Wed, 11 Jul 2018 15:49:06 -0500
Subject: [PATCH 13/17] add env  `MPLBACKEND='pdf'` to `.travis.yml`

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index 950940a3..48f5a01b 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -10,6 +10,7 @@ python:
 env:
   - PYSAL_PLUS=false
   - PYSAL_PLUS=true
+  - MPLBACKEND='pdf'
 
 before_install:
   - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh

From d6c398858f354a686272e4dcb1e221635c4ffd21 Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Thu, 12 Jul 2018 14:23:39 -0500
Subject: [PATCH 14/17] change position of `MPLBACKEND='pdf'` in `.travis.yml`

---
 .travis.yml | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 48f5a01b..79e929f3 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -8,9 +8,8 @@ python:
   - "3.6"
 
 env:
-  - PYSAL_PLUS=false
-  - PYSAL_PLUS=true
-  - MPLBACKEND='pdf'
+  - PYSAL_PLUS=false MPLBACKEND='pdf'
+  - PYSAL_PLUS=true MPLBACKEND='pdf'
 
 before_install:
   - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh

From b6e1b267b685f54fcc664c10553dccb370aaa8ea Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Sat, 14 Jul 2018 11:18:25 -0500
Subject: [PATCH 15/17] add `conda install descartes` to `.travis.yml` due to
 `geopandas` dependency

---
 .travis.yml | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/.travis.yml b/.travis.yml
index 79e929f3..36f2a0c7 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -27,6 +27,8 @@ install:
   - pip install libpysal
   - if [[ PYSAL_PLUS ]]; then conda install --yes numba; fi
   - conda install matplotlib --yes
+  # we need descartes for `geopandas` dependency
+  - conda install descartes --yes
   # We need geopandas master, the legend_kwds is not in 0.3.0
   - pip install https://github.com/geopandas/geopandas/archive/master.zip
   # We need splot master, since splot is not released yet

From f15eb1fb87e88f44efb7d516638db23703a6861a Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Sat, 14 Jul 2018 11:54:59 -0500
Subject: [PATCH 16/17] add `conda install mapclassify` to `.travis.yml` fro
 `geopandas` dependency

note: this will only work once the PySAL refactore is released
---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index 36f2a0c7..c0d91ba1 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -29,6 +29,7 @@ install:
   - conda install matplotlib --yes
   # we need descartes for `geopandas` dependency
   - conda install descartes --yes
+  - conda install mapclassify --yes
   # We need geopandas master, the legend_kwds is not in 0.3.0
   - pip install https://github.com/geopandas/geopandas/archive/master.zip
   # We need splot master, since splot is not released yet

From dcca1da85e056f66cbeb3c0b575eb47cb8fc636f Mon Sep 17 00:00:00 2001
From: Stefanie Lumnitz <stefanie.lumnitz@gmail.com>
Date: Thu, 9 Aug 2018 15:33:27 -0700
Subject: [PATCH 17/17] add `splot` methods to `Moran_BV`, `Moran`,
 `Moran_Local` and `Moran_Local_BV`

---
 esda/moran.py | 555 ++++++++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 538 insertions(+), 17 deletions(-)

diff --git a/esda/moran.py b/esda/moran.py
index 17848ca5..4346e72e 100644
--- a/esda/moran.py
+++ b/esda/moran.py
@@ -258,6 +258,127 @@ def by_col(cls, df, cols, w=None, inplace=False, pvalue='sim', outvals=None, **s
         return _univariate_handler(df, cols, w=w, inplace=inplace, pvalue=pvalue,
                                    outvals=outvals, stat=cls,
                                    swapname=cls.__name__.lower(), **stat_kws)
+    
+    
+    def plot(self, zstandard=True,
+             scatter_kwds=None,
+             fitline_kwds=None, **kwargs):
+        """
+        Global Moran's I simulated reference distribution and scatterplot.
+
+        Parameters
+        ----------
+        zstandard : bool, optional
+            If True, Moran Scatterplot will show z-standardized attribute and
+            spatial lag values. Default =True.
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline
+            and vertical fitline. Default =None.
+        **kwargs : keyword arguments, optional
+            Keywords used for creating and designing the figure,
+            passed to seaborne.kdeplot.
+
+        Returns
+        -------
+        fig : Matplotlib Figure instance
+            Moran scatterplot and reference distribution figure
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method requires an `splot` installation',
+                          UserWarning)
+            raise e
+            
+        fig, ax = splot.esda.plot_moran(moran=self, zstandard=zstandard,
+                                        scatter_kwds=scatter_kwds,
+                                        fitline_kwds=fitline_kwds, **kwargs)
+        return fig, ax
+
+
+    def scatterplot(self, zstandard=True, ax=None,
+                    scatter_kwds=None, fitline_kwds=None):
+        """
+        Moran Scatterplot
+    
+        Parameters
+        ----------
+        zstandard : bool, optional
+            If True, Moran Scatterplot will show z-standardized attribute and
+            spatial lag values. Default =True.
+        ax : Matplotlib Axes instance, optional
+            If given, the Moran plot will be created inside this axis.
+            Default =None.
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline.
+            Default =None.
+
+        Returns
+        -------
+        fig : Matplotlib Figure instance
+            Moran scatterplot figure
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method requires an `splot` installation',
+                          UserWarning)
+            raise e
+            
+        fig, ax = splot.esda.moran_scatterplot(moran=self, zstandard=zstandard,
+                                               ax=ax,
+                                               scatter_kwds=scatter_kwds,
+                                               fitline_kwds=fitline_kwds,)
+        return fig, ax
+        
+        
+    def plot_moran_simulation(self, ax=None,
+                              fitline_kwds=None, **kwargs):
+        """
+        Global Moran's I simulated reference distribution.
+
+        Parameters
+        ----------
+        ax : Matplotlib Axes instance, optional
+            If given, the Moran plot will be created inside this axis.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the
+            vertical moran fitline. Default =None.
+        **kwargs : keyword arguments, optional
+            Keywords used for creating and designing the figure,
+            passed to seaborn.kdeplot.
+
+        Returns
+        -------
+        fig : Matplotlib Figure instance
+            Simulated reference distribution figure
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method requires an `splot` installation',
+                          UserWarning)
+            raise e
+            
+        fig, ax = splot.esda.plot_moran_simulation(moran=self, ax=ax,
+                                                   fitline_kwds=fitline_kwds,
+                                                   **kwargs)
+        return fig, ax
+
 
 class Moran_BV(object):
     """
@@ -452,6 +573,120 @@ def by_col(cls, df, x, y=None, w=None, inplace=False, pvalue='sim', outvals=None
         return _bivariate_handler(df, x, y=y, w=w, inplace=inplace,
                                   pvalue = pvalue, outvals = outvals,
                                   swapname=cls.__name__.lower(), stat=cls,**stat_kws)
+    
+    
+    def plot(self,
+             scatter_kwds=None,
+             fitline_kwds=None, **kwargs):
+        """
+        BV Moran's I simulated reference distribution and scatterplot.
+
+        Parameters
+        ----------
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline
+            and vertical fitline. Default =None.
+        **kwargs : keyword arguments, optional
+            Keywords used for creating and designing the figure,
+            passed to seaborne.kdeplot.
+
+        Returns
+        -------
+        fig : Matplotlib Figure instance
+            Moran scatterplot and reference distribution figure
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method requires an `splot` installation',
+                          UserWarning)
+            raise e
+            
+        fig, ax = splot.esda.plot_moran_bv(moran=self,
+                                        scatter_kwds=scatter_kwds,
+                                        fitline_kwds=fitline_kwds, **kwargs)
+        return fig, ax
+
+
+    def scatterplot(self, ax=None,
+                    scatter_kwds=None, fitline_kwds=None):
+        """
+        Moran Scatterplot
+    
+        Parameters
+        ----------
+        ax : Matplotlib Axes instance, optional
+            If given, the Moran plot will be created inside this axis.
+            Default =None.
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline.
+            Default =None.
+
+        Returns
+        -------
+        fig : Matplotlib Figure instance
+            Moran scatterplot figure
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method requires an `splot` installation',
+                          UserWarning)
+            raise e
+            
+        fig, ax = splot.esda.moran_scatterplot(moran=self, ax=ax,
+                                               scatter_kwds=scatter_kwds,
+                                               fitline_kwds=fitline_kwds,)
+        return fig, ax
+        
+        
+    def plot_moran_simulation(self, ax=None,
+                              fitline_kwds=None, **kwargs):
+        """
+        BV Moran's I simulated reference distribution.
+
+        Parameters
+        ----------
+
+        ax : Matplotlib Axes instance, optional
+            If given, the Moran plot will be created inside this axis.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the
+            vertical moran fitline. Default =None.
+        **kwargs : keyword arguments, optional
+            Keywords used for creating and designing the figure,
+            passed to seaborn.kdeplot.
+
+        Returns
+        -------
+        fig : Matplotlib Figure instance
+            Simulated reference distribution figure
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method requires an `splot` installation',
+                          UserWarning)
+            raise e
+            
+        fig, ax = splot.esda.plot_moran_simulation_bv(moran=self, ax=ax,
+                                                      fitline_kwds=fitline_kwds,
+                                                      **kwargs)
+        return fig, ax
 
 
 def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
@@ -561,6 +796,56 @@ def Moran_BV_matrix(variables, w, permutations=0, varnames=None):
                                      varnames=varnames)
     return results
 
+#TODO is this possible?
+    def plot(self, figsize=(16,12),
+             scatter_bv_kwds=None, fitline_bv_kwds=None,
+             scatter_glob_kwds=dict(color='#737373'), fitline_glob_kwds=None):
+        """
+        Moran Facette visualization.
+        Includes BV Morans and Global Morans on the diagonal.
+    
+        Parameters
+        ----------
+        figsize : tuple, optional
+            W, h of figure. Default =(16,12)
+        scatter_bv_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points of
+            off-diagonal Moran_BV plots.
+            Default =None.
+        fitline_bv_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline of
+            off-diagonal Moran_BV plots.
+            Default =None.
+        scatter_glob_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points of
+            diagonal Moran plots.
+            Default =None.
+        fitline_glob_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline of
+            diagonal Moran plots.
+            Default =None.
+
+        Returns
+        -------
+        fig : Matplotlib Figure instance
+            Bivariate Moran Local scatterplot figure
+        axarr : matplotlib Axes instance
+            Axes in which the figure is plotted
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method requires an `splot` installation',
+                          UserWarning)
+            raise e
+            
+        fig, ax = splot.esda.moran_facet(moran_matrix=self, figsize=figsize,
+                                         scatter_bv_kwds=scatter_bv_kwds,
+                                         fitline_bv_kwds=fitline_bv_kwds,
+                                         scatter_glob_kwds=scatter_glob_kwds,
+                                         fitline_glob_kwds=fitline_glob_kwds)
+        return fig, ax
+
 
 def _Moran_BV_Matrix_array(variables, w, permutations=0, varnames=None):
     """
@@ -1012,8 +1297,10 @@ def plot(self, gdf, attribute, p=0.05,
              region_column=None, mask=None,
              mask_color='#636363', quadrant=None,
              legend=True, scheme='Quantiles',
-             cmap='YlGnBu', figsize=(15,4)):
-        """Produce three-plot visualization of Moran Scatteprlot,
+             cmap='YlGnBu', figsize=(15,4),
+             scatter_kwds=None, fitline_kwds=None):
+        """
+        Produce three-plot visualization of Moran Scatteprlot,
         LISA cluster and Choropleth, with Local Moran
         region and quadrant masking
 
@@ -1043,7 +1330,13 @@ def plot(self, gdf, attribute, p=0.05,
             Name of PySAL classifier to be used. Default = 'Quantiles'
         cmap: str, optional
             Name of matplotlib colormap used for plotting the Choropleth.
-            Default = 'YlGnBU'
+            Default = 'YlGnBu'
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline
+            in the scatterplot. Default =None.
 
         Returns
         -------
@@ -1056,21 +1349,28 @@ def plot(self, gdf, attribute, p=0.05,
         --------
         >>> import matplotlib.pyplot as plt
         >>> import geopandas as gpd
-        >>> import libpysal.api as lp
+        >>> from libpysal.weights.Contiguity import Queen
         >>> from libpysal import examples
         >>> from esda.moran import Moran_Local
+        
         Load data and calculate Moran Local statistics
+        
         >>> link = examples.get_path('columbus.shp')
         >>> gdf = gpd.read_file(link)
         >>> y = gdf['HOVAL'].values
-        >>> w = lp.Queen.from_dataframe(gdf)
+        >>> w = Queen.from_dataframe(gdf)
         >>> w.transform = 'r'
         >>> moran_loc = Moran_Local(y, w)
+        
         plot
-        >>> moran_loc.scatterplot()
+        
+        >>> moran_loc.plot(gdf, 'HOVAL')
         >>> plt.show()
+        
         customize plot
-        >>> moran_loc.scatterplot(fitline_kwds=dict(color='#4393c3'))
+        
+        >>> moran_loc.plot(gdf, 'HOVAL',
+        ...             fitline_kwds=dict(color='#4393c3'))
         >>> plt.show()
         """
         try:
@@ -1090,7 +1390,9 @@ def plot(self, gdf, attribute, p=0.05,
                                                         legend=legend,
                                                         scheme=scheme,
                                                         cmap=cmap,
-                                                        figsize=figsize)
+                                                        figsize=figsize,
+                                                        scatter_kwds=scatter_kwds,
+                                                        fitline_kwds=fitline_kwds)
         return fig, axs
 
 
@@ -1126,22 +1428,24 @@ def scatterplot(self, zstandard=True, p=0.05,
         --------
         >>> import matplotlib.pyplot as plt
         >>> import geopandas as gpd
-        >>> import libpysal.api as lp
+        >>> from libpysal.weights.Contiguity import Queen
         >>> from libpysal import examples
         >>> from esda.moran import Moran_Local
+        
         Load data and calculate Moran Local statistics
+        
         >>> link = examples.get_path('columbus.shp')
         >>> gdf = gpd.read_file(link)
         >>> y = gdf['HOVAL'].values
-        >>> w = lp.Queen.from_dataframe(gdf)
+        >>> w = Queen.from_dataframe(gdf)
         >>> w.transform = 'r'
         >>> moran_loc = Moran_Local(y, w)
+        
         plot
-        >>> moran_loc.lisa_map(gdf)
-        >>> plt.show()
-        customize plot
-        >>> moran_loc.lisa_map(gdf, legend=False)
+        
+        >>> moran_loc.scatterplot()
         >>> plt.show()
+
         """
         try:
             import splot.esda
@@ -1150,9 +1454,9 @@ def scatterplot(self, zstandard=True, p=0.05,
                           UserWarning)
             raise e
 
-        fig, ax = splot.esda.moran_loc_scatterplot(self, zstandard=zstandard, p=p,
-                                                   ax=ax, scatter_kwds=scatter_kwds,
-                                                   fitline_kwds=fitline_kwds)
+        fig, ax = splot.esda.moran_scatterplot(self, zstandard=zstandard, p=p,
+                                               ax=ax, scatter_kwds=scatter_kwds,
+                                               fitline_kwds=fitline_kwds)
         return fig, ax
 
 
@@ -1449,6 +1753,223 @@ def by_col(cls, df, x, y=None, w=None, inplace=False, pvalue='sim', outvals=None
                                   pvalue = pvalue, outvals = outvals,
                                   swapname=cls.__name__.lower(), stat=cls,**stat_kws)
 
+    def plot(self, gdf, attribute, p=0.05,
+             region_column=None, mask=None,
+             mask_color='#636363', quadrant=None,
+             legend=True, scheme='Quantiles',
+             cmap='YlGnBu', figsize=(15,4),
+             scatter_kwds=None, fitline_kwds=None):
+        """
+        Produce three-plot visualization of Moran Scatteprlot,
+        LISA cluster and Choropleth, with Local Moran
+        region and quadrant masking
+
+        Parameters
+        ----------
+        gdf : geopandas dataframe
+            The Dataframe containing information to plot the two maps.
+        attribute : str
+            Column name of attribute which should be depicted in Choropleth map.
+        p : float, optional
+            The p-value threshold for significance. Points and polygons will
+            be colored by significance. Default = 0.05.
+        region_column: string, optional
+            Column name containing mask region of interest. Default = None
+        mask: str, optional
+            Identifier or name of the region to highlight. Default = None
+        mask_color: str, optional
+            Color of mask. Default = '#636363'
+        quadrant : int, optional
+            Quadrant 1-4 in scatterplot masking values in LISA cluster and
+            Choropleth maps. Default = None
+        figsize: tuple, optional
+            W, h of figure. Default = (15,4)
+        legend: boolean, optional
+            If True, legend for maps will be depicted. Default = True
+        scheme: str, optional
+            Name of PySAL classifier to be used. Default = 'Quantiles'
+        cmap: str, optional
+            Name of matplotlib colormap used for plotting the Choropleth.
+            Default = 'YlGnBu'
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline
+            in the scatterplot. Default =None.
+
+        Returns
+        -------
+        fig : matplotlip Figure instance
+            Multi-view figure.
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+            
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> import geopandas as gpd
+        >>> from libpysal.weights.Contiguity import Queen
+        >>> from libpysal import examples
+        >>> from esda.moran import Moran_Local_BV
+        
+        Load data and calculate Moran Local statistics
+        
+        >>> link = examples.get_path('columbus.shp')
+        >>> gdf = gpd.read_file(link)
+        >>> y = gdf['HOVAL'].values
+        >>> x = gdf['CRIME'].values
+        >>> w = Queen.from_dataframe(gdf)
+        >>> w.transform = 'r'
+        >>> moran_loc = Moran_Local_BV(y, x, w)
+        
+        plot
+        
+        >>> moran_loc.plot(gdf, 'HOVAL')
+        >>> plt.show()
+        
+        customize plot
+        
+        >>> moran_loc.plot(gdf, 'HOVAL',
+        ...             fitline_kwds=dict(color='#4393c3'))
+        >>> plt.show()
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method will be available as soon as splot is released in September 2018',
+                          UserWarning)
+            raise e
+
+        fig, axs = splot.esda.plot_local_autocorrelation(self, gdf=gdf,
+                                                        attribute=attribute,
+                                                        p=p,
+                                                        region_column=region_column,
+                                                        mask=mask,
+                                                        mask_color=mask_color,
+                                                        quadrant=quadrant,
+                                                        legend=legend,
+                                                        scheme=scheme,
+                                                        cmap=cmap,
+                                                        figsize=figsize,
+                                                        scatter_kwds=scatter_kwds,
+                                                        fitline_kwds=fitline_kwds)
+        return fig, axs
+
+
+    def scatterplot(self, zstandard=True, p=0.05,
+                    ax=None, scatter_kwds=None, fitline_kwds=None):
+        """
+        BV Moran Scatterplot with option of coloring of Local Moran Statistics
+
+        Parameters
+        ----------
+        p : float, optional
+            If given, the p-value threshold for significance. Points will
+            be colored by significance. By default it will not be colored.
+            Default =None.
+        ax : Matplotlib Axes instance, optional
+            If given, the Moran plot will be created inside this axis.
+            Default =None.
+        scatter_kwds : keyword arguments, optional
+            Keywords used for creating and designing the scatter points.
+            Default =None.
+        fitline_kwds : keyword arguments, optional
+            Keywords used for creating and designing the moran fitline.
+            Default =None.
+
+        Returns
+        -------
+        fig : matplotlip Figure instance
+            Figure of Moran Scatterplot
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+            
+        Examples
+        --------
+        >>> import matplotlib.pyplot as plt
+        >>> import geopandas as gpd
+        >>> from libpysal.weights.Contiguity import Queen
+        >>> from libpysal import examples
+        >>> from esda.moran import Moran_Local_BV
+        
+        Load data and calculate Moran Local statistics
+        
+        >>> link = examples.get_path('columbus.shp')
+        >>> gdf = gpd.read_file(link)
+        >>> y = gdf['HOVAL'].values
+        >>> x = gdf['CRIME'].values
+        >>> w = Queen.from_dataframe(gdf)
+        >>> w.transform = 'r'
+        >>> moran_loc = Moran_Local_BV(y, x, w)
+        
+        plot
+        
+        >>> moran_loc.scatterplot()
+        >>> plt.show()
+
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method will be available as soon as splot is released in September 2018',
+                          UserWarning)
+            raise e
+
+        fig, ax = splot.esda.moran_scatterplot(self, zstandard=zstandard, p=p,
+                                               ax=ax, scatter_kwds=scatter_kwds,
+                                               fitline_kwds=fitline_kwds)
+        return fig, ax
+
+
+    def lisa_map(self, gdf, p=0.05, ax=None,
+                 legend=True, legend_kwds=None, **kwargs):
+        """
+        Plot LISA cluster map
+
+        Parameters
+        ----------
+        gdf : geopandas dataframe instance
+            The Dataframe containing information to plot. Note that `gdf` will be
+            modified, so calling functions should use a copy of the user
+            provided `gdf`. (either using gdf.assign() or gdf.copy())
+        p : float, optional
+            The p-value threshold for significance. Points will
+            be colored by significance.
+        ax : matplotlib Axes instance, optional
+            Axes in which to plot the figure in multiple Axes layout.
+            Default = None
+        legend : boolean, optional
+            If True, legend for maps will be depicted. Default = True
+        legend_kwds : dict, optional
+            Dictionary to control legend formatting options. Example:
+            ``legend_kwds={'loc': 'upper left', 'bbox_to_anchor': (0.92, 1.05)}``
+            Default = None
+        **kwargs : keyword arguments, optional
+            Keywords designing and passed to geopandas.GeoDataFrame.plot().
+        
+        Returns
+        -------
+        fig : matplotlip Figure instance
+            Figure of LISA cluster map
+        ax : matplotlib Axes instance
+            Axes in which the figure is plotted
+            
+        Examples
+        --------
+        """
+        try:
+            import splot.esda
+        except ImportError as e:
+            warnings.warn('This method will be available as soon as splot is released in September 2018',
+                          UserWarning)
+            raise e
+
+        fig, ax = splot.esda.lisa_cluster(self, gdf, p=p, ax=ax,
+                                          legend=legend, legend_kwds=legend_kwds, **kwargs)
+        return fig, ax
+
+
 class Moran_Local_Rate(Moran_Local):
     """
     Adjusted Local Moran Statistics for Rate Variables [Assuncao1999]_