In [117]:
import fiona
from shapely.geometry import shape
import nhrc2
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from collections import defaultdict
import numpy as np
from matplotlib.patches import Polygon
from shapely.geometry import Point
%matplotlib inline
In [80]:
#the project root directory:
nhrc2dir = ('/').join(nhrc2.__file__.split('/')[:-1])+'/'
In [ ]:
c = fiona.open(nhrc2dir+'data/nh_neighborhoods/nh_neighborhoods.shp')
pol = c.next()
geom = shape(pol['geometry'])
In [50]:
c.crs
Out[50]:
{'init': u'epsg:4326'}
In [33]:
for i in c.items():
print(i[1])
{'geometry': {'type': 'Polygon', 'coordinates': [[(-72.91843899999999, 41.313173), (-72.921372, 41.314352), (-72.920858, 41.315444), (-72.919754, 41.317823), (-72.919323, 41.31873999999999), (-72.918906, 41.319638), (-72.918478, 41.320482), (-72.918431, 41.320571), (-72.917988, 41.321487), (-72.917782, 41.321893), (-72.91723299999998, 41.323046), (-72.916879, 41.323826), (-72.916488, 41.32467799999999), (-72.916348, 41.324973), (-72.915789, 41.326158), (-72.914894, 41.328111), (-72.91424399999998, 41.329431), (-72.914136, 41.329671), (-72.913966, 41.330012), (-72.913704, 41.330631), (-72.913621, 41.330819), (-72.91328999999999, 41.331576), (-72.913257, 41.331644), (-72.913086, 41.331999), (-72.912925, 41.332327), (-72.912529, 41.333192), (-72.911301, 41.332493), (-72.909651, 41.331517), (-72.909242, 41.33128), (-72.908947, 41.331103), (-72.908451, 41.330805), (-72.902536, 41.327202), (-72.89882199999998, 41.327902), (-72.897752, 41.32809799999999), (-72.8965, 41.328295), (-72.894741, 41.328669), (-72.89331, 41.328897), (-72.892895, 41.32801599999999), (-72.891692, 41.328471), (-72.889998, 41.326574), (-72.889471, 41.324999), (-72.890364, 41.324372999999994), (-72.891236, 41.323526), (-72.891629, 41.322419), (-72.891374, 41.321845), (-72.890299, 41.320984), (-72.891118, 41.320667), (-72.893132, 41.320167), (-72.89773299999999, 41.318845), (-72.904584, 41.317505), (-72.905431, 41.317035), (-72.905836, 41.316786), (-72.906164, 41.316613), (-72.906469, 41.316646), (-72.907458, 41.316697), (-72.907848, 41.31667), (-72.908268, 41.31657599999999), (-72.909581, 41.315749999999994), (-72.914247, 41.312767), (-72.91597599999999, 41.314022), (-72.917333, 41.314686), (-72.91843899999999, 41.313173)]]}, 'type': 'Feature', 'id': '0', 'properties': OrderedDict([(u'name', u'East Rock'), (u'created_at', '2013-08-22'), (u'updated_at', '2013-08-22'), (u'cartodb_id', 48)])}
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In [41]:
len(c)
Out[41]:
20
In [42]:
for i in c:
pol = i
geom = shape(pol['geometry'])
geom
In [43]:
geom
Out[43]:
In [118]:
#Based on code from Kelly Jordhal:
#http://nbviewer.ipython.org/github/mqlaql/geospatial-data/blob/master/Geospatial-Data-with-Python.ipynb
def plot_polygon(ax, poly):
a = np.asarray(poly.exterior)
ax.add_patch(Polygon(a, facecolor='#46959E', alpha=0.3))
ax.plot(a[:, 0], a[:, 1], color='black')
def plot_multipolygon(ax, geom):
""" Can safely call with either Polygon or Multipolygon geometry
"""
if geom.type == 'Polygon':
plot_polygon(ax, geom)
elif geom.type == 'MultiPolygon':
for poly in geom.geoms:
plot_polygon(ax, poly)
In [119]:
nhv_geom = defaultdict()
#colors = ['red', 'green', 'orange', 'brown', 'purple']
In [121]:
fig, ax = plt.subplots(figsize=(12,12))
for rec in c:
#print(rec['geometry']['type'])
hood = rec['properties']['name']
nhv_geom[hood] = shape(rec['geometry'])
plot_multipolygon(ax, nhv_geom[hood])
labels = ax.get_xticklabels()
for label in labels:
label.set_rotation(90)
ax.set_xlabel('Longitude')
ax.set_ylabel('Latitude')
ax.plot(scf_df.loc[0, 'lng'], scf_df.loc[0, 'lat'], 'o')
Out[121]:
[<matplotlib.lines.Line2D at 0x11b65e290>]
In this section I will read determine the region each issue occurs in. First to read in the issues:
In [82]:
import nhrc2.backend.read_seeclickfix_api_to_csv as rscf
In [83]:
scf_cats = rscf.read_categories(readfile=True)
In [85]:
scf_df = rscf.read_issues(scf_cats, readfile=True)
SNOW RELATED 8516
Bins for Trash & Recycling 5743
Graffiti 122
Hangers 6215
Health Complaints 5185
Illegal Dumping 1250
Other 374
Other - city responsibility 3018
Parking Meter 372
Parking Violation/Abandoned Auto 121
Parks Request 126
Policing Issue 2626
Potholes 116
Public Space, Streets and Drains 1249
Private Property Issue 1251
Sidewalks and Curb damage 117
Signs / Bus Shelters / Pavement Markings 373
Street Lamp 124
Traffic/Road Safety 2625
Traffic Signal / Pedestrian Signal 51
Trash & Recycling 1966
Tree Trimming 1853
Request for volunteers 5998
General Bus Request/Incident 4947
Post to Neighbors other
In [173]:
len(scf_cats)
Out[173]:
25
In [88]:
scf_df.head(3)
Out[88]:
acknowledged_at
address
category
closed_at
created_at
id
issue_id
lat
lng
reporter_id
reporter_name
reporter_role
shortened_url
status
summary
updated_at
int_issue_id
0
2015-03-30T16:03:33-04:00
25 Elmer Street New Haven, Connecticut
SNOW RELATED
None
2015-03-29T17:18:53-04:00
1559551
8516
41.310373
-72.924160
0
CAROL SANTACROCE
Registered User
http://bit.ly/1HVR8D7
Acknowledged
SNOW RELATED
2015-03-30T16:03:33-04:00
8516
1
None
Lenox Street And Clifton Street New Haven, Co...
SNOW RELATED
None
2015-03-17T12:04:30-04:00
1536264
8516
41.309046
-72.884204
6774
Department of Public Works
Verified Official
http://bit.ly/19w8mbW
Open
Unshoveled Sidewalks
2015-03-17T12:04:30-04:00
8516
2
None
591 Whitney Avenue New Haven, Connecticut
SNOW RELATED
None
2015-03-16T17:17:58-04:00
1534432
8516
41.326617
-72.915233
0
An anonymous SeeClickFix user
Registered User
http://bit.ly/1GeXnOd
Open
SNOW RELATED
2015-03-16T17:17:58-04:00
8516
In [89]:
len(scf_df)
Out[89]:
9080
Remove issues that do not have correct coordinates:
In [97]:
scf_df = scf_df[((scf_df['lat'] < 41.36) & (scf_df['lat'] > 41.24) & (scf_df['lng']>=-73.00) & (scf_df['lng'] <= -72.86))]
print(len(scf_df))
8076
In [98]:
scf_df.loc[0, 'lat']
Out[98]:
41.310372589942702
In [122]:
grid_point = Point(scf_df.loc[0, 'lng'], scf_df.loc[0, 'lat'])
In [159]:
for idx in range(5):
grid_point = Point(scf_df.loc[idx, 'lng'], scf_df.loc[idx, 'lat'])
print('Point {} at {}'.format(idx, scf_df.loc[idx, 'address']))
print('Downtown: {}'.format(grid_point.within(nhv_geom['Downtown'])))
print('East Rock: {}'.format(grid_point.within(nhv_geom['East Rock'])))
print('Fair Haven Heights: {}'.format(grid_point.within(nhv_geom['Fair Haven Heights'])))
Point 0 at 25 Elmer Street New Haven, Connecticut
Downtown: True
East Rock: False
Fair Haven Heights: False
Point 1 at Lenox Street And Clifton Street New Haven, Connecticut
Downtown: False
East Rock: False
Fair Haven Heights: True
Point 2 at 591 Whitney Avenue New Haven, Connecticut
Downtown: False
East Rock: True
Fair Haven Heights: False
Point 3 at 900 Block Of State St New Haven, Connecticut
Downtown: False
East Rock: True
Fair Haven Heights: False
Point 4 at 112-116 Court Street New Haven, Connecticut
Downtown: True
East Rock: False
Fair Haven Heights: False
In [172]:
print('Number of neighborhoods: {}'.format(len(nhv_geom.keys())))
for hood in nhv_geom.keys():
print(hood)
Number of neighborhoods: 20
Edgewood
Fair Haven Heights
Amity
Prospect Hill
West Rock
Beaver Hills
Quinnipiac
Wooster Square/Mill River
East Rock
Newhallville
Long Wharf
Downtown
West River
Westville
Dixwell
Annex
Fair Haven
Dwight
East Shore
Hill
In [168]:
def get_neighborhoods(scf_df, neighborhoods):
hoods = []
for idx in scf_df.index:
grid_point = Point(scf_df.loc[idx, 'lng'], scf_df.loc[idx, 'lat'])
for hoodnum, hood in enumerate(nhv_geom.keys()):
if grid_point.within(nhv_geom[hood]):
hoods.append(hood)
break
if hoodnum == 19:
#There are 20 neighborhoods. If you are the 20th (element 19 in
#zero-based indexing) and have not continued out of the iteration
#set the neighborhood name to "Other":
hoods.append('Other')
return hoods
In [169]:
%time nbrhoods = get_neighborhoods(scf_df, nhv_geom)
CPU times: user 5 s, sys: 12.5 ms, total: 5.02 s
Wall time: 5.02 s
In [170]:
print(len(scf_df))
print(len(nbrhoods))
8076
8076
In [ ]:
Content source: newhavenrc/nhrc2
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