def test_point_distance(self):
from geojson_utils import point_distance
fairyland_str = '{"type": "Point", "coordinates": [-122.260000705719, 37.80919060818706]}'
navalbase_str = '{"type": "Point", "coordinates": [-122.32083320617676, 37.78774223089045]}'
fairyland = json.loads(fairyland_str)
navalbase = json.loads(navalbase_str)
self.assertEqual(math.floor(
point_distance(fairyland, navalbase)), 5852)
def test_point_distance(self):
from geojson_utils import point_distance
fairyland_str = '{"type": "Point", "coordinates": [-122.260000705719, 37.80919060818706]}'
navalbase_str = '{"type": "Point", "coordinates": [-122.32083320617676, 37.78774223089045]}'
fairyland = json.loads(fairyland_str)
navalbase = json.loads(navalbase_str)
self.assertEqual(math.floor(
point_distance(fairyland, navalbase)), 5852)
def Workplace_Dist(FB, BS, BC):
FormalB = FB
Industry = BS
row = []
dist = []
listt = []
for y1 in range(0, len(FormalB)):
for y2 in range(0, len(Industry)):
dist.append(
math.floor(
point_distance(FormalB[y1]['geometry'],
Industry[y2]['geometry'])))
FormalB[y1]['properties']['distance'] = sorted(dist)[0]
FormalB[y1]['properties']['workplace'] = np.argsort(dist)[0]
listt.append(np.argsort(dist)[0])
if listt.count(np.argsort(dist)[0]) >= BC:
Industry[np.argsort(dist)[0]]['geometry']['coordinates'][0] = 0
Industry[np.argsort(dist)[0]]['geometry']['coordinates'][1] = 0
dist = []
# Normalize the distance values
for k in range(0, len(listt)):
if listt[k] <= 0:
FormalB[k]['properties']['distance'] = 0
else:
row.append(FormalB[k]['properties']['distance'])
maxx = max(row)
for k in range(0, len(listt)):
if listt[k] <= 0:
FormalB[k]['properties']['distance'] = 0
else:
FormalB[k]['properties']['distance'] = (
FormalB[k]['properties']['distance'] / maxx)
return FormalB
def compute_work_zone_distances(self):
for zone in self.buildings['features']:
zone_bid = str(zone['properties']['OBJECTID'])
if zone_bid not in self.home_building_ids: continue
self.work_zone_distances[zone_bid] = {
'work_zone_bid': [],
'distance': []
}
for work_zone in self.buildings['features']:
work_zone_bid = str(work_zone['properties']['OBJECTID'])
if work_zone_bid not in self.work_building_ids: continue
distance = point_distance(self.zone_centroid[work_zone_bid],
self.zone_centroid[zone_bid])
self.work_zone_distances[zone_bid]['work_zone_bid'].append(
work_zone_bid)
self.work_zone_distances[zone_bid]['distance'].append(distance)
self.work_zone_roulette[zone_bid] = build_roulette(
self.work_zone_distances[zone_bid]['distance'],
sigma=self.sigma,
kappa=self.kappa,
reverse=True)
def compute_school_distances(self):
for zone in self.buildings['features']:
zone_bid = str(zone['properties']['OBJECTID'])
if zone_bid not in self.home_building_ids: continue
self.school_distances[zone_bid] = {
'school_bid': [],
'distance': []
}
for school in self.schools['features']:
school_centroid = school['geometry']
school_bid = str(school['properties']['OBJECTID'])
distance = point_distance(school_centroid,
self.zone_centroid[zone_bid])
self.school_distances[zone_bid]['school_bid'].append(
school_bid)
self.school_distances[zone_bid]['distance'].append(distance)
self.school_roulette[zone_bid] = build_roulette(
self.school_distances[zone_bid]['distance'],
sigma=self.sigma,
kappa=self.kappa,
reverse=True)
def compute_restaurant_distances(self):
for zone in self.buildings['features']:
zone_bid = str(zone['properties']['OBJECTID'])
if zone_bid not in self.work_building_ids: continue
self.restaurant_distances[zone_bid] = {
'restaurant_bid': [],
'distance': []
}
for restaurant in self.restaurants['features']:
restaurant_centroid = restaurant['geometry']
restaurant_bid = str(restaurant['properties']['OBJECTID'])
distance = point_distance(restaurant_centroid,
self.zone_centroid[zone_bid])
self.restaurant_distances[zone_bid]['restaurant_bid'].append(
restaurant_bid)
self.restaurant_distances[zone_bid]['distance'].append(
distance)
self.restaurant_roulette[zone_bid] = build_roulette(
self.restaurant_distances[zone_bid]['distance'],
sigma=self.sigma,
kappa=self.kappa,
reverse=True)
def simplify_other(major, minor, dist):
"""
Simplify the point featurecollection of poi with another point features accoording by distance.
Attention: point featurecollection only
Keyword arguments:
major -- major geojson
minor -- minor geojson
dist -- distance
return a geojson featurecollection with two parts of featurecollection
"""
result = deepcopy(major)
if major['type'] == 'FeatureCollection' and minor[
'type'] == 'FeatureCollection':
arc = dist / 6371000 * 180 / math.pi * 2
for minorfeature in minor['features']:
minorgeom = minorfeature['geometry']
minorlng = minorgeom['coordinates'][0]
minorlat = minorgeom['coordinates'][1]
is_accept = True
for mainfeature in major['features']:
maingeom = mainfeature['geometry']
mainlng = maingeom['coordinates'][0]
mainlat = maingeom['coordinates'][1]
if abs(minorlat - mainlat) <= arc and abs(minorlng -
mainlng) <= arc:
distance = point_distance(maingeom, minorgeom)
if distance < dist:
is_accept = False
break
if is_accept:
result["features"].append(minorfeature)
return result