City initialization and methods

[1]:

import os
import pandas as pd
import geopandas as gpd

local_crs = 32636
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"))
adjacency_matrix = pd.read_pickle(os.path.join(example_data_path, "adjacency_matrix.pickle"))

[3]:

from blocksnet.models import City

city_model = City(
  blocks_gdf=blocks,
  adjacency_matrix=adjacency_matrix,
)

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

[4]:

print(city_model)

CRS:          : EPSG:32636
Blocks count  : 271
Service types :
    3.5.1      school               15 min     120/1000 population
    3.5.1      kindergarten         7 min      61/1000 population
    3.4.2      hospital             60 min     9/1000 population
    3.4.1      polyclinic           10 min     13/1000 population
    5.1.3      pitch                60 min     10/1000 population
    5.1.2      swimming_pool        30 min     8/1000 population
    5.1.1      stadium              30 min     10/1000 population
    3.6.1      theatre              60 min     6/1000 population
    3.6.1      museum               60 min     1/1000 population
    3.6.1      cinema               60 min     9/1000 population
    4.2        mall                 30 min     8/1000 population
    4.4        convenience          5 min      180/1000 population
    4.4        supermarket          15 min     900/1000 population
    3.7.1      religion             30 min     10/1000 population
    4.3        marketplace          30 min     800/1000 population
    4.8.1      bowling_alley        60 min     9/1000 population

Update layers

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

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

[5]:

school = gpd.read_parquet(os.path.join(example_data_path, "schools.parquet"))
kindergarten = gpd.read_parquet(os.path.join(example_data_path, "kindergartens.parquet"))
hospital = gpd.read_file(os.path.join(example_data_path, "hospitals.geojson"))
polyclinic = gpd.read_file(os.path.join(example_data_path, "policlinics.geojson"))

services = {"school": school, "kindergarten": kindergarten,
            "hospital": hospital,"polyclinic": polyclinic}

[6]:

for service_type, gdf in services.items():
  city_model.update_services(service_type, gdf)

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

[7]:

buildings = gpd.read_parquet(os.path.join(example_data_path, "buildings.parquet")).to_crs(local_crs)
buildings = buildings.rename(columns={
  'storeys_count': 'floors',
  'population_balanced': 'population',
  'total_area': 'area'
})

[8]:

city_model.update_buildings(buildings)

Methods and other ways to operate the City model

Plot city model layers with .plot()

[9]:

city_model.plot()
../_images/examples_city_16_0.png

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

[10]:

city_model.to_pickle(os.path.join(example_data_path, 'city_model.pickle'))

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

[11]:

block = city_model[123]
block

[11]:

Block(id=123, geometry=<POLYGON ((346460.223 6649125.497, 346465.461 6649128.123, 346465.462 664912...>, landuse=None, buildings=None, services={}, city=<blocksnet.models.city.City object at 0x7f43b08be020>)

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

[12]:

service_type = city_model['school']
service_type

[12]:

ServiceType(code='3.5.1', name='school', accessibility=15, demand=120, bricks=[ServiceBrick(capacity=250, area=1.2, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=300, area=1.1, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=600, area=1.3, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=1100, area=1.8, is_integrated=False, parking_area=0.0), ServiceBrick(capacity=250, area=0.2, is_integrated=True, parking_area=0.02), ServiceBrick(capacity=300, area=0.36, is_integrated=True, parking_area=0.03), ServiceBrick(capacity=600, area=0.8, is_integrated=True, parking_area=0.06), ServiceBrick(capacity=1100, area=1.1, is_integrated=True, parking_area=0.1)])

Add new ServiceType to the City model list

[13]:

from blocksnet import ServiceType

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

[13]:

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

Get the distance (min) between two city Blocks

[14]:

city_model.get_distance(0, 1)

[14]:

9.1

Get blocks GeoDataFrame via .get_blocks_gdf() method

[15]:

blocks_gdf = city_model.get_blocks_gdf()
blocks_gdf.head()

[15]:
geometry
id
0 POLYGON ((345370.504 6646430.885, 345370.505 6...
1 POLYGON ((345254.667 6647047.217, 345254.667 6...
2 POLYGON ((345055.642 6646758.330, 345055.643 6...
3 POLYGON ((344966.092 6647146.023, 344966.091 6...
4 POLYGON ((345082.562 6646776.418, 345082.561 6...

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

  • .get_buildings_gdf()

  • .get_services_gdf()

  • .get_service_type_gdf()

[16]:

buildings_gdf = city_model.get_buildings_gdf()
buildings_gdf.head()

[16]:
geometry population floors area living_area is_living
0 POINT (345625.313 6646379.081) 0.0 0.0 0.0000 0.0 False
1 POINT (345639.028 6646409.751) 0.0 1.0 575.4110 0.0 False
2 POINT (345619.009 6646431.188) 0.0 1.0 284.8310 0.0 False
3 POINT (345638.596 6646459.785) 0.0 1.0 16.2936 0.0 False
4 POINT (345452.265 6646403.874) 0.0 7.0 51867.5500 0.0 False 
[17]:

services_gdf = city_model.get_services_gdf()
services_gdf.head()

[17]:
geometry capacity service_type
0 POINT (344241.473 6648149.216) 1200 school
1 POINT (344950.101 6648432.999) 1244 school
2 POINT (344780.761 6648505.608) 1200 school
3 POINT (345204.730 6649377.764) 1200 school
4 POINT (345042.090 6649454.563) 1200 school 
[18]:

kindergarten_gdf = city_model.get_service_type_gdf('kindergarten')
kindergarten_gdf.head()

[18]:
geometry capacity service_type
0 POINT (30.23680 59.93402) 146 kindergarten
1 POINT (30.23554 59.93526) 280 kindergarten
2 POINT (30.23494 59.93640) 287 kindergarten
3 POINT (30.23266 59.93748) 192 kindergarten
4 POINT (30.21797 59.94301) 245 kindergarten