If any part of this notebook is used in your research, please cite with the reference found in README.md.
Author: James D. Gaboardi jgaboardi@gmail.com
This notebook is an advanced walk-through for:
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%load_ext watermark
%watermark
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import libpysal
import matplotlib
import matplotlib_scalebar
from matplotlib_scalebar.scalebar import ScaleBar
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy
import spaghetti
%matplotlib inline
%watermark -w
%watermark -iv
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try:
from IPython.display import set_matplotlib_formats
set_matplotlib_formats("retina")
except ImportError:
pass
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ntw = spaghetti.Network(in_data=libpysal.examples.get_path("streets.shp"))
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pp_name = "crimes"
pp_shp = libpysal.examples.get_path("%s.shp" % pp_name)
ntw.snapobservations(pp_shp, pp_name, attribute=True)
ntw.pointpatterns
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n200 = ntw.split_arcs(200.0)
n200
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print(dir(n200))
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n200.pointpatterns
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# 'full' unsegmented network
vtx_df, arc_df = spaghetti.element_as_gdf(ntw, vertices=True, arcs=True)
# network segmented at 200-meter increments
vtx200_df, arc200_df = spaghetti.element_as_gdf(n200, vertices=True, arcs=True)
# crimes point pattern
pp_df = spaghetti.element_as_gdf(ntw, pp_name=pp_name)
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def plotter():
"""Generate a spatial plot."""
def _patch(_kws, labinfo):
"""Generate a legend patch."""
label = "%s — %s" % tuple(labinfo)
_kws.update({"lw":0, "label":label, "alpha":.5})
return matplotlib.lines.Line2D([], [], **_kws)
def _legend(handles, anchor=(1., .75)):
"""Generate a legend."""
lkws = {"fancybox":True,"framealpha":0.85, "fontsize":"xx-large"}
lkws.update({"bbox_to_anchor": anchor, "labelspacing": 2.})
lkws.update({"borderpad": 1., "handletextpad":1.})
lkws.update({"title": "Characteristics of a\nsegmented network", "title_fontsize":20})
matplotlib.pyplot.legend(handles=handles, **lkws)
base = arc_df.plot(color="k", figsize=(9, 9), alpha=.25, zorder=0)
patches = []
gdfs, alphas = [vtx_df, vtx200_df, pp_df], [.25, .5, .5]
colors, zo = ["k", "g", "r"], [1 ,2 ,3]
markers, markersizes = ["o", "o", "x"], [100, 15, 30]
labels = [["Original vertices"], ["200-meter vertices"], ["Crimes"]]
iterinfo = list(zip(gdfs, colors, zo, markers, markersizes, labels, alphas))
for gdf, c, z, m, ms, lab, a in iterinfo:
gdf.plot(ax=base, c=c, marker=m, markersize=ms, zorder=z, alpha=a)
ms = ms/5. if z != 1 else ms/9.
patch_args = {"marker":m, "markersize":ms,"c":c}, lab+[gdf.shape[0]]
patches.append(_patch(*patch_args))
_legend(patches)
carto_elements(base)
def carto_elements(b):
"""Add/adjust cartographic elements."""
scalebar = ScaleBar(1, units="m", location="lower left")
b.add_artist(scalebar)
b.set(xticklabels=[], xticks=[], yticklabels=[], yticks=[]);
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plotter()
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def fetch_cpl(net, pp, mean=True):
"""Create a counts per link object and find mean."""
cpl = net.count_per_link(net.pointpatterns[pp].obs_to_arc, graph=False)
if mean:
mean_cpl = sum(list(cpl.values())) / float(len(cpl.keys()))
return cpl, mean_cpl
return cpl
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ntw_counts, ntw_ctmean = fetch_cpl(ntw, pp_name)
ntw_ctmean
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n200_counts, n200_ctmean = fetch_cpl(n200, pp_name)
n200_ctmean
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def counts_col(cts, df, join, col, transform="log10"):
"""Create a counts per link dataframe column and transform."""
df[col] = df[join].map(cts).fillna(0.)
if transform:
t = getattr(numpy, "log10")
df["%s_%s"%(col, transform)] = df[col].apply(lambda x:
0.0 if x == 0. else (.2 if x == 1. else t(x))
)
return df
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arc_df = counts_col(ntw_counts, arc_df, "id", "n_crimes")
arc_df.head()
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arc200_df = counts_col(n200_counts, arc200_df, "id", "n_crimes")
arc200_df.head()
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def truncated_cmap(cm, vmin, vmax, steps):
"""Truncate a matplotlib colormap object"""
lspace = numpy.linspace(vmin, vmax, steps)
args = "trunc(%s,%.2f,%.2f)" % (cm.name, vmin, vmax), cm(lspace)
tcmap = matplotlib.colors.LinearSegmentedColormap.from_list(*args)
return tcmap
in_cmap = matplotlib.pyplot.get_cmap("inferno")
out_cmap = truncated_cmap(in_cmap, 0.2, 0.8, 100)
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def sidexside_plot(df1, df2, col, cmap, supt, subt1, subt2, figsize=(12, 12)):
"""Create a side-by-side plot."""
# set figure & subplot args
sub_args = {"gridspec_kw":{"width_ratios": [1, .86]}, "figsize":figsize}
fig, arr = matplotlib.pyplot.subplots(1, 2, **sub_args)
# set plotting args and plot
arc_args = {"column":col, "cmap":cmap, "lw":6, "alpha":.9, "legend":True}
for ar, df, t in zip([0,1], (df1, df2), (subt1, subt2)):
if ar == 1:
arc_args["legend"], cax = False, None
else:
divider = make_axes_locatable(arr[ar])
cax = divider.append_axes("right", size="10%", pad=0.3)
df.plot(ax=arr[ar], cax=cax, **arc_args)
arr[ar].set_title(t, fontsize=20)
carto_elements(arr[ar])
fig.suptitle(supt, y=0.8, fontsize=25)
fig.tight_layout()
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suptitle = "Crimes associated with network arcs — raw count"
subtitle1 = "Original ($n=%s$)" % arc_df.shape[0]
subtitle2 = "200-meter split ($n=%s$)" % arc200_df.shape[0]
title_args = (suptitle, subtitle1, subtitle2)
sidexside_plot(arc_df, arc200_df, "n_crimes", out_cmap, *title_args)
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suptitle = "Crimes associated with network arcs — log10 transformed"
subtitle1 = "Original ($n=%s$)" % arc_df.shape[0]
subtitle2 = "200-meter split ($n=%s$)" % arc200_df.shape[0]
title_args = (suptitle, subtitle1, subtitle2)
sidexside_plot(arc_df, arc200_df, "n_crimes_log10", out_cmap, *title_args)