def test_lisa_cluster():
df = _test_data_columbus()
moran_loc = _test_calc_moran_loc(df)
fig, _ = lisa_cluster(moran_loc, df)
plt.close(fig)
# test LineStrings
df_line = _test_LineString()
moran_loc = _test_calc_moran_loc(df_line, var="Length")
fig, _ = lisa_cluster(moran_loc, df_line)
plt.close(fig)
def moran_inverse(file):
# Read in shapefile
df = file
# df = gpd.read_file("C:\zoovision\data\Region1.shp")
# print(df.dtypes)
y = df['ind_100t']
# Calculate weight
# First calculate minimum threshold distance to nearest neightbor
thresh = ps.min_threshold_dist_from_shapefile(
"C:\zoovision\data\Region1.shp")
print(thresh)
# thresh = 1.1
# weight based on fixed distance,(0 if not in threshold)
w = ps.weights.DistanceBand.from_shapefile("C:\zoovision\data\Region1.shp",
threshold=thresh,
binary=False)
# weight based on non fixed distance,(0 if not in threshold)
# w = ps.weights.DistanceBand.from_shapefile("C:\zoovision\data\Region1.shp", binary=False)
# transform r= standardize
moran_loc = ps.Moran_Local(y, w, transformation="r", permutations=999)
print(moran_loc.p_sim)
fig, ax = lisa_cluster(moran_loc, df, p=0.05, figsize=(15, 10))
ax.set_title(
"Local Indicators of Spatial Association ",
fontsize=35) # plot_moran(moran_loc, zstandard=True, figsize=(10, 4))
def test_lisa_cluster():
link = examples.get_path('columbus.shp')
df = gpd.read_file(link)
y = df['HOVAL'].values
w = Queen.from_dataframe(df)
w.transform = 'r'
moran_loc = Moran_Local(y, w)
fig, _ = lisa_cluster(moran_loc, df)
plt.close(fig)
# Global spatial autocorrelation
y = sa_df[week]
moran = Moran(y, w)
# Local spatial autocorrelation
m_local = Moran_Local(y, w)
lisa = m_local.Is
# set CRS
sa_df = sa_df.to_crs("EPSG:3857")
#Plot map
fig, ax = plt.subplots(figsize=(9,9))
lisa_cluster(m_local, sa_df, p=0.05, figsize = (9,9),ax=ax)
description = 'Weekly Covid-19 Spatial Autocorrelation'
info_text = 'Hot- and coldspots indicates clusters of high and low infection rates. \nDonuts are regions with low infection-rates sorrounded by areas with high infection-rates. \nDiamonds are regions with high infection-rates sorrounded by regions with low infection-rates'
ax.set_title(str(week_name), fontdict={'fontsize': 22}, loc='left')
ax.annotate(description, xy=(0.325, 0.140), size=14, xycoords='figure fraction')
ax.annotate(info_text, xy=(0.325, 0.090), size=8, xycoords='figure fraction')
#Legend items
item1 = mlines.Line2D(range(1), range(1), color="white", marker='o', markerfacecolor="red", markersize=12)
item2 = mlines.Line2D(range(1), range(1), color="white", marker='o', markerfacecolor="orange", markersize=12)
item3 = mlines.Line2D(range(1), range(1), color="white", marker='o', markerfacecolor="lightblue", markersize=12)
item4 = mlines.Line2D(range(1), range(1), color="white", marker='o', markerfacecolor="blue", markersize=12)
plt.legend((item1, item2, item3, item4), ('Hotspot', 'Diamonds','Donuts','Coldspot')).set_bbox_to_anchor((0.240, 0.086)) #(x,y)
plt.savefig(filepath, dpi=150)
def moran_gen(file):
# Read in shapefile
df = file
# df = gpd.read_file(file)
# print(df.dtypes)
y = df['ind_100t']
# Calculate weight
# First calculate minimum threshold distance to nearest neightbor
thresh = ps.min_threshold_dist_from_shapefile(
"C:\zoovision\data\Region1.shp")
# thresh = 1
# print(thresh)
# weight based on fixed distance, for binary(0 or 1 if within threshold)
# arcgis_swm = ps.open('C:\zoovision\data\weightfiles\week1test.swm', 'r')
# w = arcgis_swm.read()
# arcgis_swm.close()
# e = open('C:\zoovision\data\Region1_count.txt')
# x = e.readlines()
# print(x.head())
# gwt = ps.open('C:\zoovision\weights.gwt', 'r')
# w = gwt.read()
# gwt.close()
# w = ps.open('C:\zoovision\data\Region1_count.txt', 'r', 'Region1_count').read()
testfile = ps.open('C:\zoovision\data\Region1_count.txt', 'r',
'arcgis_text')
testfile = ps.open('C:\zoovision\data\Region1_count.txt', 'r',
'arcgis_text')
w = testfile.read()
testfile.close()
# testfile = ps.open('C:\zoovision\data\weightfiles\Region1_genweights.swm', 'r')
# w = testfile.read()
testfile.close()
w.n
# f = tempfile.NamedTemporaryFile(suffix='.txt')
# fname = f.name
# f.close()
# o = ps.open(fname, 'w', 'Region1_count')
# o.write(w)
# o.close()
# wnew = ps.open(fname, 'r', 'Region1_count').read()
# wnew.pct_nonzero == w.pct_nonzero
# os.remove(fname)
# arcgis_txt.close()
# w = ps.queen_from_shapefile("C:\zoovision\data\Region1.shp")
# w = ps.weights.DistanceBand.from_shapefile("C:\zoovision\data\Region1.shp", threshold=thresh, binary=False)
# print(tuple(w1))
# f = ps.open(ps.examples.get_path("stl_hom.txt"))
# y = np.array(f.by_col['HR8893'])
# w = ps.open(ps.examples.get_path("stl.gal")).read()
# np.random.seed(12345)
# moran_loc = ps.Moran_Local(y, w)
# print(tuple(w))
# w2 = ps.lat2W(6, 4)
# w = ps.w_union(w1, w2)
# w = w1.multiply(w2)
moran_loc = Moran_Local(y, w, transformation='r', permutations=999)
# moran_loc = ps.Moran_Local(y, w, permutations=999)
fig, ax = plt.subplots(figsize=(15, 10))
fig, ax = lisa_cluster(moran_loc, df, p=0.05, figsize=(15, 10))
ax.set_title(
"Local Indicators of Spatial Association ",
fontsize=35) # plot_moran(moran_loc, zstandard=True, figsize=(10, 4))
def moran_map(moran, dataset):
chart = lisa_cluster(moran, dataset, p=0.05, figsize=(9,9))
fig = chart[0]
st.pyplot(fig)
