class SchellingModel(Model):
"""Model class for the Schelling segregation model."""
def __init__(self, density, minority_pc):
self.density = density
self.minority_pc = minority_pc
self.schedule = RandomActivation(self)
self.grid = GeoSpace(crs='epsg:4326')
self.happy = 0
self.datacollector = DataCollector(
{"happy": lambda m: m.happy}) # Model-level count of happy agents
self.running = True
# Set up the grid with patches for every NUTS region
regions = geojson.load(open('nuts_rg_60M_2013_lvl_2.geojson'))
self.grid.create_agents_from_GeoJSON(regions,
SchellingAgent,
model=self,
unique_id='NUTS_ID')
# Set up agents
for agent in self.grid.agents:
if random.random() < self.density:
if random.random() < self.minority_pc:
agent.atype = 1
else:
agent.atype = 0
self.schedule.add(agent)
# Update the bounding box of the grid and create a new rtree
self.grid.update_bbox()
self.grid.create_rtree()
def step(self):
"""Run one step of the model.
If All agents are happy, halt the model.
"""
self.happy = 0 # Reset counter of happy agents
self.schedule.step()
self.datacollector.collect(self)
if self.happy == self.schedule.get_agent_count():
self.running = False
self.grid.create_rtree()
class RecoveryModel(Model):
def __init__(self,
locations,
N1,
scheduleClass,
initialization=None,
seed=None):
if initialization == None:
raise Exception("Initialization cannot be none")
N = len(locations['features']
) * N1 #N1 =density of housholds in one building
#print(N)
self.landuse = locations['features'][0]['landuse']
#print(self.landuse[0])
self.running = True
self.num_agents = N
self.schedule = scheduleClass(self)
self.G = nx.complete_graph(self.num_agents)
self.nw = NetworkGrid(self.G)
self.grid = GeoSpace(crs='epsg:4326')
agent_kwargs = dict(model=self, unique_id='id')
self.grid.create_agents_from_GeoJSON(locations,
agent=LocationAgent,
**agent_kwargs)
self.locations = list(self.grid.agents)
self.initialize(initialization)
self.datacollector = DataCollector(
agent_reporters={
"NewShape": agent_loc,
"Satisfaction": agent_satisfaction,
"LandUse": agent_LU,
"H**o": agent_homo,
"WorkPlace": agent_workplace
})
self.grid.update_bbox()
self.grid.create_rtree()
def initialize(self, initialization):
list_of_random_nodes = random.sample(self.G.nodes(), self.num_agents)
for i in range(self.num_agents):
agent, self.landuse1 = initialization.next('Household ' + str(i),
self)
self.nw.place_agent(agent, list_of_random_nodes[i])
self.schedule.add(agent)
self.grid.add_agent(agent)
def calculate_homophily(self):
for i in range(self.G.number_of_nodes()):
HH_homphily = 0
total_homophily = 0
c = 1
for agent in self.G.node[i]['agent']:
attr_self = [agent.workplace, agent.income, agent.education]
agent_location = agent.shape
for HH in self.locations:
if HH.shape.contains(agent_location):
HH_polygon = HH.shape
neighbor_nodes = self.nw.get_neighbors(i, include_center=False)
for node in neighbor_nodes:
for nbr in self.G.node[node]['agent']:
attr_neighbor = [nbr.workplace, nbr.income, nbr.education]
self.G[i][node]['weight'] = jaccard_similarity(
attr_self, attr_neighbor)
total_homophily = total_homophily + jaccard_similarity(
attr_self, attr_neighbor)
neighbor_point = nbr.shape
if HH_polygon.contains(neighbor_point):
c = c + 1
HH_homphily = HH_homphily + jaccard_similarity(
attr_self, attr_neighbor)
else:
pass
agent.h**o = total_homophily
agent.shomo = HH_homphily / c
def step(self):
#print (self.schedule.time)
self.datacollector.collect(self)
self.schedule.step()
self.grid.create_rtree()
self.calculate_homophily()
