78 lines
2.8 KiB
Python
78 lines
2.8 KiB
Python
import geopandas as gpd
|
|
from geopy import distance
|
|
import pandas as pd
|
|
from shapely.geometry import Point
|
|
import numpy as np
|
|
from shapely.ops import nearest_points
|
|
from sklearn.neighbors import KDTree
|
|
from tqdm import tqdm
|
|
from matplotlib import pyplot as plt
|
|
import sys
|
|
|
|
from util import constants as C
|
|
|
|
CITIES = [('NYC', 'New York'), ('SF', 'San Francisco'), ('Seattle', 'Seattle'), ('Boston', 'Boston'), ('Chicago', 'Chicago'), ('Philadelphia', 'Philadelphia'), ('DC', 'Washington'),
|
|
('LA', 'Los Angeles'), ('Baltimore', 'Baltimore'), ('Milwaukee', 'Milwaukee')]
|
|
|
|
class Zoning:
|
|
def __init__(self, path):
|
|
self.path = path
|
|
self.gdf = gpd.read_file(self.path)
|
|
self.zone_type = self.gdf.zone_type.tolist()
|
|
self._get_centroids()
|
|
|
|
def _get_centroids(self):
|
|
centroids = self.gdf.centroid
|
|
coords = []
|
|
for i, c in enumerate(centroids):
|
|
if c is None or self.zone_type[i] == 'roads':
|
|
coords.append([10000, 10000])
|
|
else:
|
|
coords.append([c.y, c.x])
|
|
self.coords = KDTree(np.array(coords), leaf_size=30)
|
|
|
|
def get_zone(self, lat, lon, n=-1, return_polygon=False):
|
|
if n == -1:
|
|
ind = range(len(self.gdf))
|
|
else:
|
|
ind = self.coords.query(np.array([lat, lon])[np.newaxis,:], k=n, return_distance=False).flatten()
|
|
dist = 10000
|
|
zone_type = None
|
|
zone = None
|
|
for i in list(ind):
|
|
_zone = self.gdf.geometry.iloc[i]
|
|
#for p in nearest_points(_zone, Point(lon, lat)):
|
|
p = nearest_points(_zone, Point(lon, lat))[0]
|
|
_lat, _lon = p.y, p.x
|
|
_dist = distance.distance((lat, lon), (_lat, _lon)).m
|
|
if _dist < dist:
|
|
zone_type = self.zone_type[i]
|
|
dist = _dist
|
|
zone = _zone
|
|
if return_polygon:
|
|
return zone_type, dist, zone
|
|
else:
|
|
return zone_type, dist
|
|
|
|
def calculate_zone(meta_path="/share/data/camera/deployment/verified_0425.csv"):
|
|
|
|
df = pd.read_csv(meta_path)
|
|
dfs = []
|
|
for city, city_tag in CITIES:
|
|
print(f"Loading zoning shapefile for [{city_tag}]..")
|
|
try:
|
|
zone = Zoning(f"/share/data/camera/zoning/{city_tag}_zoning_clean.shp")
|
|
except Exception as e:
|
|
print(str(e))
|
|
continue
|
|
|
|
final = df.query(f"city == '{city}'")
|
|
rows = []
|
|
for rid, row in tqdm(final.iterrows(), total=len(final)):
|
|
z, d = zone.get_zone(row['lat'], row['lon'], n=5)
|
|
row['zone_type'] = z
|
|
row['zone_distance'] = d
|
|
rows.append(row)
|
|
zone_final = pd.DataFrame(rows)
|
|
dfs.append(zone_final)
|
|
pd.concat(dfs).to_csv("/share/data/camera/deployment/verified_0425_zone.csv", index=False)
|