from matplotlib.pyplot import show, ylim, xlim from urbandata.parser import get_licensing_data, get_anti_social_data from urbandata.plot import scatter_plot_map #shape_filename = os.sep.join(os.getcwd().split(os.sep)[:-1]) + os.sep + os.sep.join(["data", "map", "london_wards.shp"]) shape_filename="/home/heiko/daten/Dropbox/urbandatahack/ipython-notebooks/map_inspiration/london/london_wards.shp" print shape_filename points = get_licensing_data()[["Long", "Lat"]] points = get_anti_social_data()[["Long", "Lat"]] fig=scatter_plot_map(points, shape_filename) show()
import os
import sys
import csv
import matplotlib.pyplot as plt
import numpy as np
sys.path.append('/home/vincent/Documents/Urbanhack2014/urbandatahack2014')
import urbandata.parser as parser
import urbandata.kde as kde
from scipy import stats
import pandas as pd
filename = './../data/WCC_CleansingAntiSocialBehaviour.csv'
# Loading the data
data_anti_social = parser.get_anti_social_data()
data_pub = parser.get_licensing_data()
#data_vomit = data_anti_social[data_anti_social['Vomit']]
#data_pub = data_licensing[data_licensing['Premisesuse'] == 'Type - Night clubs and discos']
len(data_pub[data_pub['Premisesuse'] == 'Type - Night clubs and discos'])
len(data_pub[data_pub['Premisesuse'] == 'Type - Wine'])
#data_pub
# density estimation on pubs
x_p = np.asarray(data_pub['Long'])
y_p = np.asarray(data_pub['Lat'])
I = np.where(x_p != 0)[0]
x_p =x_p[I]
y_p =y_p[I]
