def test_knnW(self):
kd = pysal.cg.kdtree.KDTree(np.array(self.points), distance_metric='euclidean')
wnn2 = pysal.knnW(kd, 2)
self.assertEqual(wnn2.neighbors[0], [1,3])
pts = [i.centroid for i in pysal.open(self.polyShp)]
kd = pysal.cg.kdtree.KDTree(pts)
wnn4 = pysal.knnW(kd, 4)
self.assertEqual(wnn4.neighbors[0], [2,1,3,7])
self.assertEqual(wnn4.neighbors[7], [3,6,12,11])
def test_knnW(self):
kd = pysal.cg.kdtree.KDTree(np.array(self.points),
distance_metric='euclidean')
wnn2 = pysal.knnW(kd, 2)
self.assertEqual(wnn2.neighbors[0], [1, 3])
pts = [i.centroid for i in pysal.open(self.polyShp)]
kd = pysal.cg.kdtree.KDTree(pts)
wnn4 = pysal.knnW(kd, 4)
self.assertEqual(wnn4.neighbors[0], [2, 1, 3, 7])
self.assertEqual(wnn4.neighbors[7], [3, 6, 12, 11])
def missing_zones(dset,rents,k):
#Zones dataset
z=dset.zones
z['zone_id']=z.index
rents=pd.merge(z, rents, left_on='zone_id', right_index=True, how='outer')
rents['ind']=np.arange(len(rents.index))
# Construct nearest neighbors
geo=pd.DataFrame(z['zonecentroid_x'])
geo['zonecentroid_y']=z['zonecentroid_y']
geo=np.array(geo)
n=len(geo)
w=pysal.knnW(geo, k=k)
wneigh=np.zeros((n,k))
for i in range(0,n):
wneigh[i,:]=w.neighbors[i]
ind=np.array(rents[np.isnan(rents['zonal_rent'])].ind)
rents.loc[np.isnan(rents['zonal_rent']),'zonal_rent']=rents.ind.apply(filling, args=(rents, 'zonal_rent', wneigh))
return pd.DataFrame(rents['zonal_rent'], index=rents.index)
def missing_zones(dset, rents, k):
#Zones dataset
z = dset.zones
z['zone_id'] = z.index
rents = pd.merge(z,
rents,
left_on='zone_id',
right_index=True,
how='outer')
rents['ind'] = np.arange(len(rents.index))
# Construct nearest neighbors
geo = pd.DataFrame(z['zonecentroid_x'])
geo['zonecentroid_y'] = z['zonecentroid_y']
geo = np.array(geo)
n = len(geo)
w = pysal.knnW(geo, k=k)
wneigh = np.zeros((n, k))
for i in range(0, n):
wneigh[i, :] = w.neighbors[i]
ind = np.array(rents[np.isnan(rents['zonal_rent'])].ind)
rents.loc[np.isnan(rents['zonal_rent']),
'zonal_rent'] = rents.ind.apply(filling,
args=(rents, 'zonal_rent',
wneigh))
return pd.DataFrame(rents['zonal_rent'], index=rents.index)
def setUp(self):
sids = pysal.open(pysal.examples.get_path('sids2.shp'), 'r')
self.sids = sids
self.d = np.array([i.centroid for i in sids])
self.w = knnW(self.d, k=5)
if not self.w.id_order_set:
self.w.id_order = self.w.id_order
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
self.b, self.e = np.array(sids_db[:, 8]), np.array(sids_db[:, 9])
def test_knnW_arc(self):
pts = [x.centroid for x in pysal.open(self.arcShp)]
dist = pysal.cg.sphere.arcdist # default radius is Earth KM
full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
len(pts))] for i in xrange(len(pts))])
kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
radius=pysal.cg.sphere.RADIUS_EARTH_KM)
w = pysal.knnW(kd, 4)
self.assertTrue((full.argsort()[:, 1:5] == np.array(
[w.neighbors[x] for x in range(len(pts))])).all())
def test_knnW(self):
x = np.indices((5, 5))
x, y = np.indices((5, 5))
x.shape = (25, 1)
y.shape = (25, 1)
data = np.hstack([x, y])
wnn2 = pysal.knnW(data, k=2)
wnn4 = pysal.knnW(data, k=4)
wnn4.neighbors[0]
self.assertEqual(wnn4.neighbors[0], [1, 5, 6, 2])
self.assertEqual(wnn2.neighbors[5], [0, 6])
self.assertEqual(wnn2.pct_nonzero, 0.080000000000000002)
wnn3e = pysal.knnW(data, p=2, k=3)
self.assertEqual(wnn3e.neighbors[0], [1, 5, 6])
wc = pysal.knnW_from_shapefile(self.polyShp)
self.assertEqual(wc.pct_nonzero, 0.040816326530612242)
self.assertEqual(wc.neighbors[0], [2, 1])
wc3 = pysal.knnW_from_shapefile(self.polyShp, k=3, idVariable="POLYID")
self.assertEqual(wc3.weights[1], [1, 1, 1])
self.assertEqual(wc3.neighbors[1], [3, 2, 4])
def test_knnW_arc(self):
pts = [x.centroid for x in pysal.open(self.arcShp)]
dist = pysal.cg.sphere.arcdist # default radius is Earth KM
full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
len(pts))] for i in xrange(len(pts))])
kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
radius=pysal.cg.sphere.RADIUS_EARTH_KM)
w = pysal.knnW(kd, 4)
self.assertEqual(set(w.neighbors[4]), set([1,3,9,12]))
self.assertEqual(set(w.neighbors[40]), set([31,38,45,49]))
def test_knnW(self):
x = np.indices((5, 5))
x, y = np.indices((5, 5))
x.shape = (25, 1)
y.shape = (25, 1)
data = np.hstack([x, y])
wnn2 = pysal.knnW(data, k=2)
wnn4 = pysal.knnW(data, k=4)
wnn4.neighbors[0]
self.assertEqual(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
self.assertEqual(set(wnn2.neighbors[5]), set([0, 6]))
self.assertEqual(wnn2.pct_nonzero, 8.0)
wnn3e = pysal.knnW(data, p=2, k=3)
self.assertEqual(set(wnn3e.neighbors[0]), set([1, 5, 6]))
wc = pysal.knnW_from_shapefile(self.polyShp)
self.assertEqual(wc.pct_nonzero, 4.081632653061225)
self.assertEqual(set(wc.neighbors[0]), set([2, 1]))
wc3 = pysal.knnW_from_shapefile(self.polyShp, k=3)
self.assertEqual(wc3.weights[1], [1, 1, 1])
self.assertEqual(set(wc3.neighbors[1]), set([0, 3, 7]))
def test_knnW_from_array(self):
import numpy as np
x, y = np.indices((5, 5))
x.shape = (25, 1)
y.shape = (25, 1)
data = np.hstack([x, y])
wnn2 = pysal.knnW_from_array(data, k=2)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
self.assertEquals(set(wnn4.neighbors[5]), set([0, 6, 10, 1]))
self.assertEquals(set(wnn2.neighbors[0]), set([1, 5]))
self.assertEquals(set(wnn2.neighbors[5]), set([0, 6]))
self.assertAlmostEquals(wnn2.pct_nonzero, 0.080000000000000002)
self.assertAlmostEquals(wnn4.pct_nonzero, 0.16)
wnn4 = pysal.knnW_from_array(data, k=4)
self.assertEquals(set(wnn4.neighbors[0]), set([1, 5, 6, 2]))
wnn3e = pysal.knnW(data, p=2, k=3)
self.assertEquals(set(wnn3e.neighbors[0]),set([1, 5, 6]))
wnn3m = pysal.knnW(data, p=1, k=3)
self.assertEquals(set(wnn3m.neighbors[0]), set([1, 5, 2]))
def test_knnW(self):
x = np.indices((5, 5))
x, y = np.indices((5, 5))
x.shape = (25, 1)
y.shape = (25, 1)
data = np.hstack([x, y])
wnn2 = pysal.knnW(data, k=2)
wnn4 = pysal.knnW(data, k=4)
wnn4.neighbors[0]
self.assertEqual(wnn4.neighbors[0], [1, 5, 6, 2])
self.assertEqual(wnn2.neighbors[5], [0, 6])
self.assertEqual(wnn2.pct_nonzero, 0.080000000000000002)
wnn3e = pysal.knnW(data, p=2, k=3)
self.assertEqual(wnn3e.neighbors[0], [1, 5, 6])
wc = pysal.knnW_from_shapefile(self.polyShp)
self.assertEqual(wc.pct_nonzero, 0.040816326530612242)
self.assertEqual(wc.neighbors[0], [2, 1])
wc3 = pysal.knnW_from_shapefile(self.polyShp, k=3, idVariable="POLYID")
self.assertEqual(wc3.weights[1], [1, 1, 1])
self.assertEqual(wc3.neighbors[1], [3, 2, 4])
def test_knnW_arc(self):
pts = [x.centroid for x in pysal.open(self.arcShp)]
dist = pysal.cg.sphere.arcdist # default radius is Earth KM
full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(
len(pts))] for i in xrange(len(pts))])
kd = pysal.cg.kdtree.KDTree(pts, distance_metric='Arc',
radius=pysal.cg.sphere.RADIUS_EARTH_KM)
w = pysal.knnW(kd, 4)
self.assertEqual(set(w.neighbors[4]), set([1,3,9,12]))
self.assertEqual(set(w.neighbors[40]), set([31,38,45,49]))
def test_knnW_arc(self):
pts = [x.centroid for x in pysal.open(self.arcShp)]
dist = pysal.cg.sphere.arcdist # default radius is Earth KM
full = np.matrix([[dist(pts[i], pts[j]) for j in xrange(len(pts))]
for i in xrange(len(pts))])
kd = pysal.cg.kdtree.KDTree(pts,
distance_metric='Arc',
radius=pysal.cg.sphere.RADIUS_EARTH_KM)
w = pysal.knnW(kd, 4)
self.assertTrue((full.argsort()[:, 1:5] == np.array(
[w.neighbors[x] for x in range(len(pts))])).all())
def data_zone_census( zones):
data_census=pd.read_csv(os.path.join(misc.data_dir(),'census_zone.csv'))
#del data_census['median_value']
data=pd.merge(zones, data_census, on='zone_id', how='inner')
#Income using census block group dat
data['median_income']=data['median_income'].astype(float)
data['ln_inc']=np.log(data['median_income'])
# Asked price (census)
#data['median_value']=data['median_value'].apply(float)
data['ln_price']=np.log(data['median_value'])
# Race composition
data['all races']=data['White alone'].apply(float)+ data['Black or African American alone'].apply(float)\
+ data['American Indian and Alaska Native alone'].apply(float)+ data['Asian alone'].apply(float)\
+data['Native Hawaiian and Other Pacific Islander alone'].apply(float)+ data['Some other race alone'].apply(float)\
+data['two races or more'].apply(float)
data['percent_white']=np.log(data['White alone']/data['all races'])
data['percent_black']=data['Black or African American alone']/data['all races']
data['percent_black2']=data['Black or African American alone']/data['all races']**2
data['ln_residential_sqft_mean2']=data['ln_residential_sqft_mean']**2
# Creating max and min income of neighbors ( can important have implications in terms of gentrification)
geo=pd.DataFrame(data['zonecentroid_x'])
geo['zonecentroid_y']=data['zonecentroid_y']
geo=np.array(geo)
w=pysal.knnW(geo, k=10)
n=len(geo)
neigh_income_max=np.zeros(n)
neigh_income_min=np.zeros(n)
for i in range(0, n-1):
arr=w.neighbors[i]
zone=np.zeros(n)
for j in arr:
zone[j]=1
data['neigh']=zone
neigh_income_max[i]=data[data['neigh']==1].median_income.max()
neigh_income_min[i]=data[data['neigh']==1].median_income.min()
data['ln_neigh_income_max']=np.log(neigh_income_max/data['median_income'])
data['ln_neigh_income_min']=np.log(neigh_income_min/data['median_income'])
data=data.set_index(data['zone_id'])
return data
def gpd_knnW(gpdf, k=2, p=2, ids=None):
"""
Constructs distance-based spatial weights from a geopandas object
:param gpd: geopandas dataframe
:param k: int number of nearest neighbors
:param p: float
:return weight
https://github.com/pysal/pysal/blob/master/pysal/weights/Distance.py
"""
points = gpd_points(gpdf)
kd = pysal.cg.kdtree.KDTree(points)
w = pysal.knnW(kd, k, p, ids)
return w
def main():
work_dir = r"D:\hcho_change\ROI\COI"
os.chdir(work_dir)
os.chdir("aod")
files = os.listdir(os.getcwd())
os.chdir("..")
# name = files[-1]
for name in files:
os.chdir("aod")
print name
f = gdal.Open(name)
cols = f.RasterXSize
rows = f.RasterYSize
gt = f.GetGeoTransform()
proj = f.GetProjection()
array = f.ReadAsArray(0, 0, cols, rows)
#预处理
array[np.where(~np.isfinite(array) == True)] = -1
array[np.where(array < 0)] = np.float("nan")
#------
y, x = np.where(np.isfinite(array))
lats = np.array([gt[3] + gt[5] * i for i in y])
lons = np.array([gt[0] + gt[1] * i for i in x])
data = array[y, x]
coor = np.vstack((lons, lats)).T
w = pysal.knnW(coor, k=3)
mr = pysal.Moran_Local(data, w).p_sim
array[np.where(np.isfinite(array))] = mr
array[np.where(~np.isfinite(array))] = 0.
dr = gdal.GetDriverByName("GTiff")
os.chdir("..")
os.chdir("out")
name = name[0:-4] + "moran.tif"
ds = dr.Create(name, cols, rows, 1, gdal.GDT_Float32)
ds.SetGeoTransform(gt)
ds.SetProjection(proj)
ds.GetRasterBand(1).WriteArray(array)
os.chdir("..")
print name + " has done."
def test_Headbanging_Median_Rate(self):
sids_d = np.array([i.centroid for i in self.sids])
sids_w = pysal.knnW(sids_d, k=5)
if not sids_w.id_order_set:
sids_w.id_order = sids_w.id_order
s_ht = sm.Headbanging_Triples(sids_d, sids_w, k=5)
sids_db = pysal.open(pysal.examples.get_path('sids2.dbf'), 'r')
s_e, s_b = np.array(sids_db[:, 9]), np.array(sids_db[:, 8])
sids_hb_r = sm.Headbanging_Median_Rate(s_e, s_b, s_ht)
sids_hb_rr5 = np.array([0.00075586, 0.,
0.0008285, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_rr5, sids_hb_r.r[:5])
sids_hb_r2 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, iteration=5)
sids_hb_r2r5 = np.array([0.0008285, 0.00084331,
0.00086896, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r2r5, sids_hb_r2.r[:5])
sids_hb_r3 = sm.Headbanging_Median_Rate(s_e, s_b, s_ht, aw=s_b)
sids_hb_r3r5 = np.array([0.00091659, 0.,
0.00156838, 0.0018315, 0.00498891])
np.testing.assert_array_almost_equal(sids_hb_r3r5, sids_hb_r3.r[:5])