# 需要导入模块: from matplotlib import pylab [as 别名]

# 或者: from matplotlib.pylab import rcParams [as 别名]

def quantiles_MoranI_Plot(data_df,zipPolygon):

caseWeekly_unstack=data_df['CasesWeekly'].unstack(level=0)

zip_codes= gpd.read_file(zipPolygon)

data_df_zipGPD=zip_codes.merge(caseWeekly_unstack,left_on='zip', right_on=caseWeekly_unstack.index)

# print(data_df_zipGPD.head())

# print(data_df_zipGPD.describe())

print(data_df_zipGPD.columns)

weeks=idx_weekNumber=data_df.index.get_level_values('Week Number')

weeks=np.unique(weeks)

nrows=2

ncols=math.ceil(len(weeks)/nrows)

fig, axes = plt.subplots(nrows=nrows, ncols=ncols,figsize = (30,15))

for i in range(nrows):

for j in range(ncols):

# print("_"*50)

# print(str(weeks[i*ncols+j]))

try:

plt.rcParams.update({'font.size': 5})

ax = axes[i,j]

data_df_zipGPD.plot(ax=ax, column=weeks[i*ncols+j], cmap='OrRd', scheme='quantiles', legend=True,)

ax.set_title('daily cases %s Quintiles'%weeks[i*ncols+j])

ax.axis('off')

leg = ax.get_legend()

leg.set_bbox_to_anchor((0.8, 0.15, 0.16, 0.2))

data_df_zipGPD.apply(lambda x: ax.annotate(s=x.zip, xy=x.geometry.centroid.coords[0], ha='center'),axis=1)

leg = ax.get_legend()

leg.set_bbox_to_anchor((0., 0., 0.2, 0.2))

except:

pass

plt.tight_layout()

W=ps.lib.weights.Queen(data_df_zipGPD.geometry)

W.transform = 'R'

valArray=data_df_zipGPD[weeks].to_numpy()

valArray_fillNan=bfill(valArray).T

valArray_fillNan[np.isnan(valArray_fillNan)]=0

# print(valArray_fillNan,valArray_fillNan.shape)

mits=[Moran(cs,W) for cs in valArray_fillNan]

res = np.array([(mi.I, mi.EI, mi.seI_norm, mi.sim[974]) for mi in mits])

# print(res)

fig, ax = plt.subplots(nrows=1, ncols=1,figsize = (10,5) )

ax.plot(weeks, res[:,0], label='Moran\'s I')

#plot(years, res[:,1], label='E[I]')

ax.plot(weeks, res[:,1]+1.96*res[:,2], label='Upper bound',linestyle='dashed')

ax.plot(weeks, res[:,1]-1.96*res[:,2], label='Lower bound',linestyle='dashed')

ax.set_title("Global spatial autocorrelation for Covid-19-cases",fontdict={'fontsize':15})

# ax.set_xlim(weeks)

# plt.axhline(y=0, color='gray', linestyle='--',)

ax.legend()

#Moran's I >0表示空间正相关性，其值越大，空间相关性越明显，Moran's I <0表示空间负相关性，其值越小，空间差异越大，否则，Moran's I = 0，空间呈随机性。

#spatial markov about the explanation ref:蒲英霞,马荣华,葛莹,黄杏元.基于空间马尔可夫链的江苏区域趋同时空演变[J]. 地理学报.2005-09-23

#ref:https://pysal.org/pysal/generated/pysal.explore.giddy.markov.Spatial_Markov.html