City initialization and methods

[1]:

import os
import pandas as pd
import geopandas as gpd

example_data_path = "data"

Initialization

Use the results obtained from the blocksnet.preprocessing or use your own data.

[2]:

blocks = gpd.read_parquet(os.path.join(example_data_path, "blocks.parquet"))
acc_mx = pd.read_pickle(os.path.join(example_data_path, "acc_mx.pickle"))

[3]:

from blocksnet.models import City

city = City(
  blocks=blocks,
  acc_mx=acc_mx,
)

Print city model to get the information about existing service types, CRS and blocks count

[4]:

print(city)

CRS : EPSG:32636
Blocks : 16320
Service types : 0/66
Buildings : 0
Services : 0

Update layers

Update the information about services and buildings using .update_buildings() and .update_services().

It is IMPORTANT to update buildings first, because they serve as container for some kinds of services.

Specification for buildings is described in the blocksnet.models.city.Building

[5]:

buildings = gpd.read_parquet(os.path.join(example_data_path, "platform/buildings.parquet")).to_crs(city.crs)

[6]:

buildings = buildings.rename(columns={
  'population_balanced': 'population',
  'building_area': 'footprint_area',
})
buildings = buildings.fillna(0)
buildings['number_of_floors'] = buildings.apply(
  lambda x : x['storeys_count'] if x['storeys_count']>1 else 1,
  axis=1
)
buildings['build_floor_area'] = buildings['footprint_area']*buildings['number_of_floors']
buildings['business_area'] = buildings['build_floor_area'] - buildings['living_area']

[7]:

bad_buildings = city.update_buildings(buildings)

2024-09-23 01:08:01.221 | INFO     | blocksnet.models.city:update_buildings:1237 - Removing existing blocks from the model
2024-09-23 01:08:01.257 | INFO     | blocksnet.models.city:update_buildings:1241 - Joining buildings and blocks
2024-09-23 01:08:22.054 | WARNING  | blocksnet.models.city:update_buildings:1253 - 1721 buildings did not intersect any block
Update blocks buildings: 100%|██████████| 6109/6109 [00:19<00:00, 313.11it/s]

Visualize buildings that did not intersect any block

[8]:

ax = blocks.plot(color='#ddd', figsize=(10,10))
bad_buildings.plot(linewidth=1, edgecolor='red', color='red', ax=ax).set_axis_off()

Specification is described in the blocksnet.models.city.Service

[14]:

from tqdm import tqdm

for service_type in tqdm(city.service_types):
  try:
    services_gdf = gpd.read_parquet(os.path.join(example_data_path, f"platform/{service_type.name}.parquet")).to_crs(city.crs)
  except:
    continue
  services_gdf['area'] = services_gdf.geometry.area
  services_gdf.geometry = services_gdf.representative_point()
  if not 'capacity' in services_gdf.columns:
    services_gdf['capacity'] = 0
  else:
    services_gdf['capacity'] = services_gdf['capacity'].apply(lambda c : 0 if c is None else int(c))
  city.update_services(service_type, services_gdf[['geometry', 'area', 'capacity']])

100%|██████████| 66/66 [02:44<00:00,  2.50s/it]

Visualize the model using .plot() method. If you don’t have LandUse in your blocks, you can update the info later via .update_land_use() method.

[11]:

city.plot(max_travel_time=5)

Methods and other ways to operate the `City` model

Save city model to file with .to_pickle() so we can use it later

[15]:

city.to_pickle(os.path.join(example_data_path, 'model.pickle'))

Block within the city model can be obtained via id (int)

[ ]:

block = city[123]
block

Block(id=123, geometry=<POLYGON ((346460.223 6649125.497, 346465.461 6649128.123, 346465.462 664912...>, land_use=<LandUse.MIXED_USE: 'mixed_use'>, buildings=None, services={}, city=<blocksnet.models.city.City object at 0x7f12698ac670>)

Get indicators for the block.

[32]:

res = block.to_dict()
for key,value in res.items():
  if isinstance(value, float) : res[key]=round(res[key],2)
res

[32]:

{'id': 2,
 'geometry': <POLYGON ((353934.329 6625429.433, 353923.453 6625429.324, 353918.105 662542...>,
 'land_use': 'recreation',
 'is_living': True,
 'build_floor_area': 1173.87,
 'living_demand': 58.69,
 'living_area': 821.71,
 'share_living': 0.71,
 'business_area': 352.16,
 'share_business': 0.3,
 'site_area': 363005.82,
 'population': 14,
 'footprint_area': 1163.48,
 'fsi': 0.0,
 'gsi': 0.0,
 'l': 1.01,
 'osr': 308.25,
 'mxi': 0.7,
 'capacity_fuel': 501}

Get available ServiceTypes for current Block LandUse

[33]:

available_service_types = block.land_use_service_types
[st.name for st in available_service_types]

[33]:

['pitch',
 'swimming_pool',
 'stadium',
 'theatre',
 'museum',
 'cinema',
 'bowling_alley',
 'university',
 'beach',
 'train_building',
 'subway_entrance',
 'multifunctional_center',
 'park',
 'hotel',
 'circus',
 'post',
 'police',
 'dog_park',
 'hostel',
 'guest_house',
 'reserve',
 'sanatorium',
 'embankment',
 'wastewater_plant',
 'water_works',
 'substation',
 'train_station',
 'bus_station',
 'bus_stop',
 'pier']

ServiceType can be obtained the same way by name (str). The information about accessibility, demand, bricks can be obtained this way.

[34]:

service_type = city['school']
service_type

[34]:

ServiceType(code='3.5.1', name='school', accessibility=15, demand=120, land_use=[<LandUse.RESIDENTIAL: 'residential'>, <LandUse.BUSINESS: 'business'>], bricks=[ServiceBrick(capacity=250, area=3200.0, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=300, area=4000.0, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=600, area=8200.0, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=1100, area=13000.0, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=250, area=2200.0, is_integrated=True, parking_area=200.0), ServiceBrick(capacity=300, area=3600.0, is_integrated=True, parking_area=300.0), ServiceBrick(capacity=600, area=7100.0, is_integrated=True, parking_area=600.0)])

Add new ServiceType to the City model list

[35]:

from blocksnet import ServiceType

city.add_service_type(ServiceType(code='', name='internet_cafe', accessibility=30, demand=100, bricks=[]))
city['internet_cafe']

[35]:

ServiceType(code='', name='internet_cafe', accessibility=30, demand=100, land_use=[], bricks=[])

Get the distance (min) between two city Blocks

[36]:

city.get_distance(0, 1)

[36]:

7.4

Get blocks GeoDataFrame via .get_blocks_gdf() method. Use simplify=True to exclude information about services.

[37]:

blocks_gdf = city.get_blocks_gdf(simplify=True)
blocks_gdf.head()

[37]:

	geometry	land_use	is_living	build_floor_area	living_demand	living_area	share_living	business_area	share_business	site_area	population	footprint_area	fsi	gsi	l	osr	mxi
id
0	POLYGON ((354918.622 6625258.829, 354901.464 6...	None	True	43840.686518	50.641057	30688.480678	2.199357	13152.205840	0.942581	8.044667e+05	606	13953.390266	0.054497	0.017345	3.141938	18.031500	0.700000
1	POLYGON ((355412.142 6623378.149, 355411.700 6...	transport	True	10294.395525	53.114392	2177.690063	0.431105	8116.705462	1.606820	2.317313e+04	41	5051.410558	0.444238	0.217986	2.037925	1.760348	0.211541
2	POLYGON ((353934.329 6625429.433, 353923.453 6...	recreation	True	1173.871643	58.693582	821.710142	0.706252	352.161501	0.302679	3.630058e+05	14	1163.480697	0.003234	0.003205	1.008931	308.246934	0.700000
3	POLYGON ((355099.099 6623847.765, 355074.808 6...	residential	True	46303.954706	52.660044	30595.485797	2.760046	15708.468909	1.417075	1.964145e+05	581	11085.135352	0.235746	0.056437	4.177121	4.002452	0.660753
4	POLYGON ((352766.168 6621954.748, 352744.412 6...	recreation	True	153749.574111	53.097680	106407.750936	2.352959	47341.823175	1.046854	1.781752e+06	2004	45222.959595	0.086291	0.025381	3.399812	11.294526	0.692085

The same way buildings and services GeoDataFrames can be obtained with:

.get_buildings_gdf()
.get_services_gdf()

[38]:

buildings_gdf = city.get_buildings_gdf()
buildings_gdf.head()

[38]:

	block_id	geometry	population	footprint_area	build_floor_area	living_area	non_living_area	number_of_floors	is_living
id
158414	0	POLYGON ((354964.060 6625185.771, 354959.684 6...	0	15.030958	15.035234	10.524664	4.510571	1	True
150194	0	POLYGON ((355008.167 6625301.606, 355012.344 6...	0	22.185639	22.191954	15.534368	6.657586	1	True
58769	0	POLYGON ((354878.941 6625544.327, 354882.984 6...	0	31.264070	31.272943	21.891060	9.381884	1	True
158321	0	POLYGON ((355037.286 6624978.295, 355060.347 6...	0	158.324760	158.369888	110.858925	47.510963	1	True
36319	0	POLYGON ((354643.688 6625735.942, 354657.286 6...	1	192.709820	192.764221	134.934952	57.829269	1	True

[39]:

services_gdf = city.get_services_gdf()
services_gdf.head()

[39]:

	geometry	block_id	building_id	service_type	capacity	area	is_integrated
0	POINT (353603.237 6625736.175)	2	77507.0	fuel	501	80.0	True
1	POINT (355093.541 6624021.861)	3	NaN	pitch	75	8000.0	False
2	POINT (355171.604 6624273.791)	3	158369.0	fuel	261	80.0	True
3	POINT (353025.189 6622629.138)	4	NaN	pitch	165	8000.0	False
4	POINT (353416.159 6622235.257)	4	NaN	parking	52	1250.0	False

City initialization and methods

Initialization

Update layers

Methods and other ways to operate the City model

Methods and other ways to operate the `City` model