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fourStepModel.py
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import requests
import pandas
import geopandas
import json
import math
from haversine import haversine
from ipfn import ipfn
import networkx
from matplotlib import pyplot
from matplotlib import patheffects
url = 'https://tigerweb.geo.census.gov/arcgis/rest/services/TIGERweb/State_County/MapServer/37/query?where=state%3D06&f=geojson'
r = requests.get(url)
zones = geopandas.GeoDataFrame.from_features(r.json()['features'])
centroidFunction = lambda row: (row['geometry'].centroid.y, row['geometry'].centroid.x)
zones['centroid'] = zones.apply(centroidFunction, axis=1)
url = 'http://api.census.gov/data/2015/acs5/profile?get=NAME,DP03_0018E&for=county&in=state:06'
r = requests.get(url)
Production = pandas.DataFrame(r.json()[1:], columns = r.json()[0], dtype='int')
nameSplit = lambda x: x.split(',')[0]
Production['NAME'] = Production['NAME'].apply(nameSplit)
zones = pandas.merge(zones, Production)
zones['Production'] = zones['DP03_0018E']
def getEmployment(state, county):
prefix = 'EN'
seasonal_adjustment = 'U'
area = format(state, "02d") + format(county, "03d")
data_type = '1'
size = '0'
ownership = '0'
industry = '10'
seriesid = prefix + seasonal_adjustment + area + data_type + size + ownership + industry
headers = {'Content-type': 'application/json'}
data = json.dumps({"seriesid": [seriesid],"startyear":"2015", "endyear":"2015", "registrationKey": ""})
p = requests.post('https://api.bls.gov/publicAPI/v2/timeseries/data/', data=data, headers=headers)
employment = p.json()['Results']['series'][0]['data'][0]['value']
return(employment)
employment = lambda row: int(getEmployment(row['state'], row['county']))
zones['Attraction'] = zones.transpose().apply(employment)
zones['Production'] = zones['Production'] * zones.sum()['Attraction'] / zones.sum()['Production']
zones.index = zones.NAME
zones.sort_index(inplace=True)
def costFunction(zones, zone1, zone2, beta):
cost = math.exp(-beta * haversine(zones[zone1]['centroid'], zones[zone2]['centroid']))
return(cost)
def costMatrixGenerator(zones, costFunction, beta):
originList = []
for originZone in zones:
destinationList = []
for destinationZone in zones:
destinationList.append(costFunction(zones, originZone, destinationZone, beta))
originList.append(destinationList)
return(pandas.DataFrame(originList, index=zones.columns, columns=zones.columns))
def tripDistribution(tripGeneration, costMatrix):
costMatrix['ozone'] = costMatrix.columns
costMatrix = costMatrix.melt(id_vars=['ozone'])
costMatrix.columns = ['ozone', 'dzone', 'total']
production = tripGeneration['Production']
production.index.name = 'ozone'
attraction = tripGeneration['Attraction']
attraction.index.name = 'dzone'
aggregates = [production, attraction]
dimensions = [['ozone'], ['dzone']]
IPF = ipfn.ipfn(costMatrix, aggregates, dimensions)
trips = IPF.iteration()
return(trips.pivot(index='ozone', columns='dzone', values='total'))
beta = 0.01
costMatrix = costMatrixGenerator(zones.transpose(), costFunction, beta)
trips = tripDistribution(zones, costMatrix)
def modeChoiceFunction(zones, zone1, zone2, modes):
distance = haversine(zones[zone1]['centroid'], zones[zone2]['centroid'])
probability = {}
total = 0.0
for mode in modes:
total = total + math.exp(modes[mode] * distance)
for mode in modes:
probability[mode] = math.exp(modes[mode] * distance) / total
return(probability)
def probabilityMatrixGenerator (zones, modeChoiceFunction, modes):
probabilityMatrix = {}
for mode in modes:
originList = []
for originZone in zones:
destinationList = []
for destinationZone in zones:
destinationList.append(modeChoiceFunction(zones, originZone, destinationZone, modes)[mode])
originList.append(destinationList)
probabilityMatrix[mode] = pandas.DataFrame(originList, index=zones.columns, columns=zones.columns)
return(probabilityMatrix)
modes = {'walking': .05, 'cycling': .05, 'driving': .05}
probabilityMatrix = probabilityMatrixGenerator(zones.transpose(), modeChoiceFunction, modes)
drivingTrips = trips * probabilityMatrix['driving']
def routeAssignment(zones, trips):
G = networkx.Graph()
G.add_nodes_from(zones.columns)
for zone1 in zones:
for zone2 in zones:
if zones[zone1]['geometry'].touches(zones[zone2]['geometry']):
G.add_edge(zone1, zone2, distance = haversine(zones[zone1]['centroid'], zones[zone2]['centroid']), volume=0.0)
for origin in trips:
for destination in trips:
path = networkx.shortest_path(G, origin, destination)
for i in range(len(path) - 1):
G[path[i]][path[i + 1]]['volume'] = G[path[i]][path[i + 1]]['volume'] + trips[zone1][zone2]
return(G)
def visualize(G, zones):
fig = pyplot.figure(1, figsize=(10, 10), dpi=90)
ax = fig.add_subplot(111)
zonesT = zones.transpose()
zonesT.plot(ax = ax)
for i, row in zones.transpose().iterrows():
text = pyplot.annotate(s=row['NAME'], xy=((row['centroid'][1], row['centroid'][0])), horizontalalignment='center', fontsize=6)
text.set_path_effects([patheffects.Stroke(linewidth=3, foreground='white'), patheffects.Normal()])
for zone1 in G.edge:
for zone2 in G.edge[zone1]:
volume = G.edge[zone1][zone2]['volume']
x = [zones[zone1]['centroid'][1], zones[zone2]['centroid'][1]]
y = [zones[zone1]['centroid'][0], zones[zone2]['centroid'][0]]
ax.plot(x, y, color='#444444', linewidth=volume/10000, solid_capstyle='round', zorder=1)
pyplot.show(block=False)
G = routeAssignment(zones.transpose(), drivingTrips)
visualize(G, zones.transpose())