def get_shapes(self, max_area, max_distance_meters):
"return the shapes [census tracts] that touch the center of max_area + max_distance"
query = {
'geometry': {
'$near':
SON([('$geometry',
SON([('type', 'Point'),
('coordinates', [longitude, latitude])])),
('$maxDistance', max_distance_meters)])
}
}
for shp in self.db.GENZ2010_140.find(query):
# add population to properties
p = shp['properties']
# for later use
p['label'] = "{} {} [{}]".format(
state_name(p['STATE']), county_name(p['STATE'], p['COUNTY']),
p['TRACT'])
pop = {}
# lookup population and area
raw_pop = int(
self.census.sf1.state_county_tract("P0010001", p['STATE'],
p['COUNTY'],
p['TRACT'])[0]["P0010001"])
pop['raw'] = raw_pop
#print("raw population: {}".format(raw_pop))
# compute area in our radius
county_shape = shape(shp['geometry'])
isect = county_shape.intersection(max_area)
# save the intersection for later use.
shp['intersection'] = isect
pop['area'] = county_shape.area
frac_contained = float(isect.area) / float(county_shape.area)
pop['frac_contained'] = frac_contained
#print("contained: {}".format(frac_contained))
# compute effective population
#print("contained population: {}".format(frac_contained * raw_pop))
pop['effective'] = frac_contained * raw_pop
p['population'] = pop
# filter out areas with very low population
# the mongodb geo query may be not exact
if frac_contained * raw_pop < 0.1:
print("WARN: clipping: {}".format(p['label']))
pprint(pop)
continue
yield shp
def plot_shapes(ax,
shapes,
filled=False,
show_states=False,
fc=None,
alpha=0.16):
# have to buffer the results to see how many colors to generate
if fc == None:
if filled:
color_list = plt.cm.Dark2(np.linspace(0, 1, len(shapes)))
fc = lambda x: color_list[x]
else:
fc = lambda x: 'none'
states = set()
geoid = {}
# get the shapes covered by the coverage area.
for i, shp in enumerate(shapes):
p = shp['properties']
#track states we hit
if p['STATE'] not in states:
states.add(p['STATE'])
# also store the goeid so we dont plot again later
geoid[p['GEO_ID']] = shp
if p['LSAD'] == 'Tract':
label = "{} {} [{}]".format(state_name(p['STATE']),
county_name(p['STATE'], p['COUNTY']),
p['TRACT'])
else:
label = p['NAME']
#print ("-"*10 + label + "-"*10)
#pprint(p['population'])
ec = 'black'
lw = 1
if 'population' in p:
if p['population']['effective'] < 0.01:
ec = 'blue'
lw = 2
#was fc = color_list[i],
patches = make_patch(shp['geometry'],
fc=fc(i),
lw=lw,
ec=ec,
label=label,
alpha=alpha)
for p in patches:
ax.add_patch(p)
def _add_pop_to_shape_intersct(self, shp, max_area):
"add the population inside the intersection of shp and max_area, shp is assumed to be a census tract."
# add population to properties
p = shp['properties']
#print(p)
# for later use
p['label'] = "{} {} [{}]".format(state_name(p['STATE']),
county_name(p['STATE'], p['COUNTY']),
p['TRACT'])
pop = {}
# lookup population and area try mongo first.
searchdoc = {
'state': p["STATE"],
'county': p['COUNTY'],
'tract': p['TRACT'],
'variable.P0010001': {
'$exists': True
}
}
#print("searchdoc: ",end="")
#pprint(searchdoc)
cdoc = self.census_col.find_one(searchdoc)
if cdoc != None:
#print("Hit.")
raw_pop = cdoc['variable']['P0010001']
else:
raw_pop = int(
self.census.sf1.state_county_tract("P0010001", p['STATE'],
p['COUNTY'],
p['TRACT'])[0]["P0010001"])
insdoc = {
'state': p["STATE"],
'county': p['COUNTY'],
'tract': p['TRACT'],
'variable': {
'P0010001': raw_pop
}
}
#print("inserting: ", end="")
#pprint(insdoc)
self.census_col.insert_one(insdoc)
pop['raw'] = raw_pop
#print("raw population: {}".format(raw_pop))
county_shape = shape(shp['geometry'])
if max_area != None:
# compute area in our radius
isect = county_shape.intersection(max_area)
union = county_shape.union(max_area)
# save the intersection for later use.
shp['intersection'] = mapping(isect)
shp['union'] = mapping(union)
pop['area'] = county_shape.area
frac_contained = float(isect.area) / float(county_shape.area)
pop['frac_contained'] = frac_contained
#print("contained: {}".format(frac_contained))
else:
shp['intersection'] = shp['geometry']
shp['union'] = shp['geometry']
pop['area'] = county_shape.area
frac_contained = 1.0
pop['frac_contained'] = 1.0
# compute effective population
#print("contained population: {}".format(frac_contained * raw_pop))
pop['effective'] = frac_contained * raw_pop
p['population'] = pop
shp['properties'] = p
# add area
compute_land_area(shp)
# have to buffer the results to see how many colors to generate
results = list(pbps.get_shapes(area, dist))
color_list = plt.cm.Dark2(np.linspace(0, 1, len(results)))
# get the shapes covered by the coverage area.
for i, shp in enumerate(results):
p = shp['properties']
#track states we hit
if p['STATE'] not in states:
states.add(p['STATE'])
# also store the goeid so we dont plot again later
geoid[p['GEO_ID']] = shp
label = "{} {} [{}]".format(state_name(p['STATE']),
county_name(p['STATE'], p['COUNTY']),
p['TRACT'])
#print ("-"*10 + label + "-"*10)
#pprint(p['population'])
ec = 'black'
lw = 2
if p['population']['effective'] < 0.01:
ec = 'blue'
lw = 4
patches = make_patch(shp['geometry'],
fc=color_list[i],
lw=lw,
ec=ec,
label=label)