def radius_of_gyration(self):
""" R_g based on edge distances
"""
clon = np.average([coord[0] for coord in self.coordinates])
clat = np.average([coord[1] for coord in self.coordinates])
return np.average([greate_circle_distance(clon, clat, coord[0], coord[1]) for coord in self.coordinates])
def convert2graph(self, road_network=None, directed=True,
edge_weighted_by_distance=True,
node_weighted_by_dwelling=True):
""" Return a graph representation of human mobility, one which
is weighted by traveling distance for edge and dwelling time for node.
**PerfStat** (PersonNum,Calls,AccTime): 100,1519,54.191s
"""
graph = seq2graph(self.coordinates, directed)
if edge_weighted_by_distance:
for edge in graph.edges_iter():
if road_network:
dist = road_network.shortest_path_distance(edge[0], edge[1])
else:
dist = greate_circle_distance(edge[0][0], edge[0][1], edge[1][0], edge[1][1])
graph.edge[edge[0]][edge[1]]['weight'] = dist
if edge in self.freq:
graph.edge[edge[0]][edge[1]]['frequency'] = self.freq[edge]
else:
graph.edge[edge[0]][edge[1]]['frequency'] = 1
if node_weighted_by_dwelling:
for node in graph.nodes_iter():
graph.node[node]['weight'] = self.accdwelling.get(node)
return graph
def transtime(a, b):
dist = greate_circle_distance(a[0], a[1], b[0], b[1])
if dist <= 5: # km
speed = 5
elif dist <= 15:
speed = 20
else:
speed = 30
return 1.0 * dist / speed * 3600
def transtime(a, b):
dist = greate_circle_distance(a[0], a[1], b[0], b[1])
if dist <= 5: # km
speed = 5
elif dist <= 15:
speed = 20
else:
speed = 30
return 1.0 * dist / speed * 3600
def travel_dist(self):
""" Calculate the travelling distance totally.
"""
if len(self.coordinates) < 2:
return 0
total = 0
for i in range(0, len(self.coordinates)-1):
lon1, lat1 = self.coordinates[i]
lon2, lat2 = self.coordinates[i+1]
total += greate_circle_distance(lon1, lat1, lon2, lat2)
return total
def __init__(self, shapefile, edge_weighted_by_distance=True):
g = nx.read_shp(shapefile)
mg = max(nx.connected_component_subgraphs(g.to_undirected()), key=len)
if edge_weighted_by_distance:
for n0, n1 in mg.edges_iter():
path = np.array(json.loads(mg[n0][n1]['Json'])['coordinates'])
distance = np.sum(
greate_circle_distance(path[1:, 0], path[1:, 1],
path[:-1, 0], path[:-1, 1]))
mg.edge[n0][n1]['distance'] = distance
self.graph = mg
self._cache = {}
self._cache_nn = {}
def __init__(self, shapefile, edge_weighted_by_distance=True):
g = nx.read_shp(shapefile)
mg = max(nx.connected_component_subgraphs(g.to_undirected()), key=len)
if edge_weighted_by_distance:
for n0, n1 in mg.edges_iter():
path = np.array(json.loads(mg[n0][n1]['Json'])['coordinates'])
distance = np.sum(
greate_circle_distance(path[1:,0],path[1:,1], path[:-1,0], path[:-1,1])
)
mg.edge[n0][n1]['distance'] = distance
self.graph = mg
self._cache = {}
self._cache_nn = {}
