def test_binary_point_coverage(self):
binary_coverage_point = pyqgis_analysis.generate_binary_coverage(
self.demand_point_fl, self.facility_service_areas_fl, "Population",
"GEOID10", "ORIG_ID")
binary_coverage_point2 = pyqgis_analysis.generate_binary_coverage(
self.demand_point_fl, self.facility2_service_areas_fl,
"Population", "GEOID10", "ORIG_ID")
self.assertEqual(self.binary_coverage_point, binary_coverage_point)
self.assertEqual(self.binary_coverage_point2, binary_coverage_point2)
def test_binary_point_coverage(self):
binary_coverage_point = pyqgis_analysis.generate_binary_coverage(self.demand_point_fl,
self.facility_service_areas_fl,
"Population",
"GEOID10", "ORIG_ID")
binary_coverage_point2 = pyqgis_analysis.generate_binary_coverage(self.demand_point_fl,
self.facility2_service_areas_fl,
"Population",
"GEOID10", "ORIG_ID")
self.assertEqual(self.binary_coverage_point, binary_coverage_point)
self.assertEqual(self.binary_coverage_point2, binary_coverage_point2)
def run_example():
providers = QgsProviderRegistry.instance().providerList()
for provider in providers:
print(provider)
print(QgsApplication.showSettings())
demand_points = QgsVectorLayer("/Users/andrewlaird/Desktop/QGIS Mapping/points_2.shp", "demand_points", "ogr")
print(demand_points.isValid())
service_areas = QgsVectorLayer("/Users/andrewlaird/Desktop/QGIS Mapping/zones.shp", "service_areas", "ogr")
print(service_areas.isValid())
binary_coverage_polygon = pyqgis_analysis.generate_binary_coverage(demand_points, service_areas,
"od_rate", "point_id", "zone_id")
print(binary_coverage_polygon)
# Create the mclp model
# Maximize the total coverage (binary polygon) using at most 5 out of 8 facilities
logger.info("Creating MCLP model...")
mclp = covering.create_threshold_model(binary_coverage_polygon, 100.0)
# Solve the model using GLPK
logger.info("Solving MCLP...")
mclp.solve(pulp.GLPK())
# Get the unique ids of the 5 facilities chosen
logger.info("Extracting results")
ids = utilities.get_ids(mclp, "zones")
# Generate a query that could be used as a definition query or selection in arcpy
select_query = pyqgis_analysis.generate_query(ids, unique_field_name="zone_id")
logger.info("Output query to use to generate maps is: {}".format(select_query))
# Determine how much demand is covered by the results
service_areas.setSubsetString(select_query)
total_coverage = pyqgis_analysis.get_covered_demand(demand_points, "od_rate", "binary",
service_areas)
logger.info("{0:.2f}% of demand is covered".format((100 * total_coverage) / binary_coverage_polygon["totalDemand"]))
#make a grid layer
grid_layer = make_point_grid(kerrisdale_layer, county_bounds, GRID_CELL_SIZE)
print(len(list(grid_layer.getFeatures())))
grid_layer = delete_points_outside_polygon(grid_layer, kerrisdale_layer)
QgsVectorFileWriter.writeAsVectorFormat(grid_layer, GRID_LAYER_SHP, "System", grid_layer.crs(), "ESRI Shapefile")
#make a service area layer
service_area_layer = make_service_area_layer(grid_layer)
print(len(list(service_area_layer.getFeatures())))
QgsVectorFileWriter.writeAsVectorFormat(service_area_layer, SERVICE_AREA_SHP, "System", service_area_layer.crs(), "ESRI Shapefile")
run_example()
grid_layer = QgsVectorLayer(GRID_LAYER_SHP, "grid_layer", "ogr")
service_area_layer = QgsVectorLayer(SERVICE_AREA_SHP, "service_area_layer", "ogr")
binary_coverage_polygon = pyqgis_analysis.generate_binary_coverage(grid_layer, service_area_layer,
"demand", "point_id", "area_id")
print(binary_coverage_polygon)
# Create the mclp model
logger.info("Creating MCLP model...")
mclp = covering.create_threshold_model(binary_coverage_polygon, 100.0)
# Solve the model using GLPK
logger.info("Solving MCLP...")
mclp.solve(pulp.GLPK())
logger.info("Extracting results")
ids = utilities.get_ids(mclp, "areas")
# Generate a query that could be used as a definition query or selection in arcpy
select_query = pyqgis_analysis.generate_query(ids, unique_field_name="area_id")
logger.info("Output query to use to generate maps is: {}".format(select_query))
# Determine how much demand is covered by the results
service_area_layer.setSubsetString(select_query)
# Demand polygon shapefile has 212 polygons each where each feature has a demand (population) and unique identifier (GEOID10)
# Read the layers
demand_polygon_fl = qgis.core.QgsVectorLayer(
r"../sample_data/demand_polygon.shp", "demand_polygon_fl", "ogr")
# Read the facility service area layer
# Facility service area polygon layer has 8 polygons, where each feature has a unique identifier (ORIG_ID)
facility_service_areas_fl = qgis.core.QgsVectorLayer(
r"../sample_data/facility_service_areas.shp",
"facility_service_areas_fl", "ogr")
# Create binary coverage (polygon) dictionary structure
# Use population of each polygon as demand,
# Use GEOID as the unique field
# Ue ORIG_ID as the unique id for the facilities
binary_coverage_polygon = pyqgis_analysis.generate_binary_coverage(
demand_polygon_fl, facility_service_areas_fl, "Population", "GEOID10",
"ORIG_ID")
# Create the mclp model
# Maximize the total coverage (binary polygon) using at most 5 out of 8 facilities
logger.info("Creating MCLP model...")
mclp = covering.create_mclp_model(binary_coverage_polygon, {"total": 5})
# Solve the model using GLPK
logger.info("Solving MCLP...")
mclp.solve(pulp.GLPK())
# Get the unique ids of the 5 facilities chosen
logger.info("Extracting results")
ids = utilities.get_ids(mclp, "facility_service_areas")
# Generate a query that could be used as a definition query or selection in arcpy
select_query = pyqgis_analysis.generate_query(ids,
unique_field_name="ORIG_ID")
logger.addHandler(sh)
# Read the demand polygon layer
# Demand polygon shapefile has 212 polygons each where each feature has a demand (population) and unique identifier (GEOID10)
# Read the layers
demand_polygon_fl = qgis.core.QgsVectorLayer(r"../sample_data/demand_polygon.shp", "demand_polygon_fl", "ogr")
# Read the facility service area layer
# Facility service area polygon layer has 8 polygons, where each feature has a unique identifier (ORIG_ID)
facility_service_areas_fl = qgis.core.QgsVectorLayer(r"../sample_data/facility_service_areas.shp",
"facility_service_areas_fl", "ogr")
# Create binary coverage (polygon) dictionary structure
# Use population of each polygon as demand,
# Use GEOID as the unique field
# Ue ORIG_ID as the unique id for the facilities
binary_coverage_polygon = pyqgis_analysis.generate_binary_coverage(demand_polygon_fl, facility_service_areas_fl,
"Population", "GEOID10", "ORIG_ID")
# Create the mclp model
# Maximize the total coverage (binary polygon) using at most 5 out of 8 facilities
logger.info("Creating MCLP model...")
mclp = covering.create_mclp_model(binary_coverage_polygon, {"total": 5})
# Solve the model using GLPK
logger.info("Solving MCLP...")
mclp.solve(pulp.GLPK())
# Get the unique ids of the 5 facilities chosen
logger.info("Extracting results")
ids = utilities.get_ids(mclp, "facility_service_areas")
# Generate a query that could be used as a definition query or selection in arcpy
select_query = pyqgis_analysis.generate_query(ids, unique_field_name="ORIG_ID")
logger.info("Output query to use to generate maps is: {}".format(select_query))
# Determine how much demand is covered by the results