def get_subway_data(city):
"""
Function seacrches for subway data for cities on either local files or queries data from OSM.
city: city name (Ideally in (city, country) format)
Returns:
Geodataframe with subway stations as point features or None if OSM query contains no data
"""
string = city.split(",")[0]
str_ = string.replace(' ','')
sub_path = os.getcwd()+"\Subway and growth\subway_census_v1\station_points"
sub_data = gpd.read_file(sub_path+"\\subway_stations2.shp")
if str_ in sub_data.CITY1.unique():
city_data = sub_data[sub_data.CITY1 == str_]
return pois
else:
bbox = geocoder.arcgis(city).geojson['features'][0]['bbox']
amenities = ['subway', 'light_rail', 'metro', 'underground', 'monorail', 'tram']
osm_tags = '"railway"~"{}"'.format('|'.join(amenities))
try:
pois = osm.node_query(bbox[1], bbox[0], bbox[3], bbox[2],tags=osm_tags) ##lat_min, lng_min, lat_max, lng_max
return pois
except RuntimeError as e:
if e.args[0] == "OSM query results contain no data.":
return None
def get_nodes(b, tags):
nodes = osm.node_query(b[1], b[0], b[3], b[2], tags=tags)
nodes_geom = [Point(x) for x in zip(nodes['lon'], nodes['lat'])]
nodes_df = gpd.GeoDataFrame(nodes[['amenity', 'lat', 'lon']],
geometry=nodes_geom,
crs={'init': 'epgs:4326'})
return (nodes_df)
def test_node_query(bbox2):
tags = '"amenity"="restaurant"'
cafes = osm.node_query(*bbox2, tags=tags)
assert len(cafes) == 4
assert 'lat' in cafes.columns
assert 'lon' in cafes.columns
assert cafes['name'][2965338413] == 'Koja Kitchen'
def test_node_query(bbox2):
tags = '"amenity"="restaurant"'
cafes = osm.node_query(*bbox2, tags=tags)
assert len(cafes) == 2
assert 'lat' in cafes.columns
assert 'lon' in cafes.columns
assert cafes['name'][1419597327] == 'Cream'
def test_node_query(bbox2):
tags = '"amenity"="restaurant"'
cafes = osm.node_query(*bbox2, tags=tags)
assert len(cafes) == 4
assert 'lat' in cafes.columns
assert 'lon' in cafes.columns
assert cafes['name'][2965338413] == 'Koja Kitchen'
def add_feature(outputfile, bbox, network, tags, colname, mode='a'):
all_nodes = []
for tag in tags:
nodes = osm.node_query(*bbox, tags=tag)
all_nodes = all_nodes + [nodes.loc[:,['lat', 'lon']]]
all_nodes = pd.concat(all_nodes)
all_nodes_unique = all_nodes.drop_duplicates()
node_ids = network.get_node_ids(all_nodes_unique['lon'], all_nodes_unique['lat'])
all_nodes_unique["node_id"] = node_ids.values
with pd.HDFStore(outputfile, mode=mode) as store:
store[colname] = all_nodes_unique
def get_nodes(b, tags):
''' Extract nodes from OSM based on tag query using pandana loaders
INPUTS
b [list of numbers] - boundary list from shapely.bounds
tags [string] - filter to be send to pandana.osm.node_query
RETURNS
[geopandas dataframe]
'''
nodes = osm.node_query(b[1], b[0], b[3], b[2], tags=tags)
nodes_geom = [Point(x) for x in zip(nodes['lon'], nodes['lat'])]
nodes_df = gpd.GeoDataFrame(nodes[['amenity', 'lat', 'lon']],
geometry=nodes_geom,
crs={'init': 'epgs:4326'})
return (nodes_df)
def get_distance_hwy(gdf, city):
print("Populating distance to highways!!")
if not gdf.crs:
gdf.crs = {'init':'epsg:4326'}
gdf_copy = gdf.copy()
gdf_copy.to_crs(epsg=out_proj, inplace=True)
bbox = geocoder.arcgis("{}".format(city)).geojson['features'][0]['properties']['raw']['extent']
highway = ['motorway', 'trunk', 'primary', 'secondary', 'tertiary', 'residential']
osm_tags = '"highway"~"{}"'.format('|'.join(highway))
try:
highway_pois = osm.node_query(bbox['ymin'],bbox['xmin'],bbox['ymax'],bbox['xmax'],tags=osm_tags) ##lat_min, lng_min, lat_max, lng_max
highway_pois = highway_pois[['lat', 'lon', 'highway', 'name']]
## Adding geometry to the dataset
highway_pois['geometry'] = (list(zip(highway_pois.lon,highway_pois.lat)))
highway_pois['geometry'] = highway_pois.geometry.apply(lambda x: Point(x))
highway_pois = gpd.GeoDataFrame(highway_pois, geometry='geometry')
highway_pois.crs = {'init':'epsg:4326'}
highway_pois.to_crs(epsg=out_proj, inplace=True)
hwy_dist = []
dest_hwy = MultiPoint([i for i in highway_pois.geometry])
for i in gdf_copy.index:
if i % 1000 == 0:
print("{0} of {1} rows processed" .format(i, len(gdf_copy)))
temp_cent = gdf_copy.geometry.iloc[i].centroid
nearest_geoms = nearest_points(temp_cent, dest_hwy)
hwy_dist.append(nearest_geoms[0].distance(nearest_geoms[1]))
except RuntimeError as e:
if e.args[0] == "OSM query results contain no data.":
hwy_dist = [0 for i in range(len(gdf))]
else:
print(e)
gdf['dis_to_hwy'] = hwy_dist
return gdf
cbar_kwargs = {}
# keyword arguments to pass to basemap
bmap_kwargs = {}
# color to make the background of the axis
bgcolor = 'k'
start_time = time.time()
if os.path.isfile(poi_filename):
# if a points-of-interest file already exists, just load the dataset from that
pois = pd.read_csv(poi_filename)
method = 'loaded from CSV'
else:
# otherwise, query the OSM API for the specified amenities within the bounding box
osm_tags = '"amenity"~"{}"'.format('|'.join(amenities))
pois = osm.node_query(bbox[0], bbox[1], bbox[2], bbox[3], tags=osm_tags)
# using the '"amenity"~"school"' returns preschools etc, so drop any that aren't just 'school' then save to CSV
pois = pois[pois['amenity'].isin(amenities)]
pois.to_csv(poi_filename, index=False, encoding='utf-8')
method = 'downloaded from OSM'
print('{:,} POIs {} in {:,.2f} seconds'.format(len(pois), method, time.time() - start_time))
start_time = time.time()
if os.path.isfile(net_filename):
# if a street network file already exists, just load the dataset from that
network = pandana.network.Network.from_hdf5(net_filename)
method = 'loaded from HDF5'
else:
# otherwise, query the OSM API for the street network within the specified bounding box
def test_node_query_raises():
with pytest.raises(RuntimeError):
osm.node_query(37.8, -122.282, 37.8, -122.252)
# finding boundary of New York City
NYC = ox.gdf_from_place('New York City, New York, USA')
boundary = NYC.bounds
boundary
# In[2]:
# importing amenities
import pandana as pnda
from pandana.loaders import osm
# DINING
NYC_restaurants = osm.node_query(40.477399,
-74.25909,
40.916179,
-73.700181,
tags='"amenity"="restaurant"')
NYC_restaurants = NYC_restaurants[['lat', 'lon', 'amenity', 'name']]
NYC_cafes = osm.node_query(40.477399,
-74.25909,
40.916179,
-73.700181,
tags='"amenity"="cafe"')
NYC_cafes = NYC_cafes[['lat', 'lon', 'amenity', 'name']]
NYC_dining = NYC_restaurants.append(NYC_cafes)
NYC_dining
# In[134]:
def test_node_query_raises():
with pytest.raises(RuntimeError):
osm.node_query(37.8, -122.282, 37.8, -122.252)
""" reference
https://github.com/gboeing/urban-data-science/blob/master/20-Accessibility-Walkability/pandana-accessibility-demo-simple.ipynb"""
import pandana
import pandas
import matplotlib.pyplot as plt
from pandana.loaders import osm
bbox = [30.08, -98.02, 30.52, -97.56]
#lat-long bounding box for austin
amenity = 'supermarket' #accessibility to this type of amenity
distance = 10000 #max distance in meters
# first download the points of interest corresponding to the specified amenity type
pois = osm.node_query(bbox[0],
bbox[1],
bbox[2],
bbox[3],
tags='"shop"="{}"'.format(amenity))
pois[['shop', 'name', 'lat', 'lon']].tail()
# query the OSM API for the street network within the specified bounding box
network = osm.network_from_bbox(bbox[0], bbox[1], bbox[2], bbox[3])
# identify nodes that are connected to fewer than some threshold of other nodes within a given distance
# do nothing with this for now, but see full example in other notebook for more
lcn = network.low_connectivity_nodes(impedance=1000,
count=10,
imp_name='distance')
# precomputes the range queries (the reachable nodes within this maximum distance)
# so, as long as you use a smaller distance, cached results will be used
network.precompute(distance + 1)
