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]