CS_Spatial_Modules
Create spatial indicators and mobility simulations for CityScope.
OpenCity
A tool for creating simple mobility simulations and vizualisations with minimal data inputs.
Data
The data required are:
- a shapefile of geometries in the study region
- an O-D matrix, optionally stratified by demographic attributes and/or industry. eg:
Home GEOID | Work GEOID | N_total | N_income_low | ... | N_NAICS_44-45 | ... |
---|---|---|---|---|---|---|
1 | 1 | 30 | 15 | ... | 5 | ... |
1 | 2 | 40 | 17 | ... | 9 | ... |
... | ... | ... | ... | ... | ... | ... |
For USA-based projects the data above can be easily obtained from public data sources using the tools in this repository.
Usage
Build geometry for state
Specify
- the state where the simulation will take place
- the geometry type ('block_group' (default) or 'block')
- the year to use for the geometry and commuting data
- Optionally: a center coordinate and radius to restrict the analysis to
fips=25
year=2017
geom_type='block'
centre= {'lat': 42.352927, 'lon': -71.059435}
centre_x_y=[centre['lon'], centre['lat']]
model_area_radius=5000
sim_area_radius=2000
state=OpenCity.US_State(state_fips=fips, year=year,
# geom_type=geom_type
)
state.get_geometry()
state.subset_geom_by_distance(centre_x_y, model_area_radius, 'model_area')
state.subset_geom_by_distance(centre_x_y, sim_area_radius, 'sim_area')
The state geometry can be accessed as a geopandas GeoDataFrame
all_zones=state.return_geometry()
sim_zones=state.return_geometry('sim_area')
model_zones=state.return_geometry('model_area')
Get commuting data for state from LEHD
state.get_lodes_data()
The commuting data can be used to build a simulated population
simpop_df=state.lodes_to_pop_table(model_subset_name='model_area',
sim_subset_name='sim_area')
Build mobility system for same area
Get the road network(s). These are used to create pandana network objects.
import pandana
import osmnet
networks={}
bbox=state.get_bounds(subset_name='model_area')
drive_nodes_df,drive_edges_df=osmnet.load.network_from_bbox(lat_min=bbox[1], lng_min=bbox[0], lat_max=bbox[3],
lng_max=bbox[2], bbox=None, network_type='drive',
two_way=True, timeout=180,
custom_osm_filter=None)
drive_edges_df['travel_time']=drive_edges_df['distance']*50000/3600
drive_net=pandana.Network(drive_nodes_df["x"], drive_nodes_df["y"], drive_edges_df["from"], drive_edges_df["to"],
drive_edges_df[["distance", "travel_time"]])
networks['drive']=OpenCity.PdnaNetwork(drive_net)
Define modes of transportation
drive_dict={
'target_network_id': 'drive',
'travel_time_metric': 'travel_time'}
modes={'drive': OpenCity.Mode(drive_dict)}
Create mobility system using the pandana network(s) and mode definition(s)
mob_sys=MobilitySystem(modes=modes,
networks=networks)
Create the Simulation Model
In order to reduct computational burden, the population can be further restricted to a maximum by using the "sample_N" parameter
sim=OpenCity.Simulation(simpop_df, mob_sys, model_zones)
simpop_df=sim.get_simpop_subset(simpop_df, sample_N=1000)
Simulate trips and trajectories
all_trips_df=sim.create_trip_table(simpop_df)
all_trips_df=sim.mode_chooser(all_trips_df)
route_table=sim.get_routes_table(all_trips_df)
Visualising Outputs
Get resulting trips as a GeoDataFrame of 'LineString's
route_gdf=sim.route_table_to_geo(route_table)
Get resulting trips as a geojsoon compatible with the kepler.gl Trips Layer. 'start_day_time_stamp' should be the timestamp in epoch seconds format at midnight at the beginning of the day being simulated.
start_day_time_stamp=
geo_dict=sim.route_gdf_to_trips_geojson(route_gdf, start_day_time_stamp)
CityScope Indicators
A set of modules which generate interactive indicators for CityScope project. Communication with cityIO is handled via the cs-brix library.
Data
The data required are:
- a shapefile of zone geometries in the study region. This should include columns indicating (i) counts of people living in each area by demographic categories and (ii) counts of people employed in each area by demographic categories.
GEOID | res_income_low | res_income_med | ... | emp_total | emp_income_low | ... | geometry |
---|---|---|---|---|---|---|---|
1 | 1000 | 500 | 300 | ... | 600 | ... | POLYGON(...) |
2 | 1200 | 600 | 200 | ... | 550 | ... | POLYGON(...) |
... | ... | ... | ... | ... | ... | ... | POLYGON(...) |
- the GEOGRID of the CityScope table (can be created using CityScopeJS)
- the simulated population (can be created from an O-D matrix using OpenCity as outlined above)
Usage
import CS_Indicators as CS from brix import Indicator, Handler import geopandas as gpd import pandas as pd
geogrid=gpd.read_file(SAVED_GEOGRID_LOCATION) zones=gpd.read_file(SAVED_ZONES_LOCATION) simpop_df=pd.read_csv(SAVED_SIMPOP_LOCATION)
table_name='cs_course_volpe'
H=Handler(table_name=table_name) H.reset_geogrid_data() # Optional
d=CS.Density_Indicator(zones=zones) p=CS.Proximity_Indicator(zones=zones, geogrid=geogrid) m=CS.Mobility_indicator(zones, geogrid, table_name, simpop_df) H.add_indicators([ d, p, m ])
H.listen()