Annealing optimizer

The optimizer is used to optimize city blocks of certain land uses by building services of predefined capacity and area.

Prepare city

The method is time consuming and aimed to work with smaller territories, so we make a smaller model.

from blocksnet import City, AnnealingOptimizer

city = City.from_pickle('../data/model.pickle')

blocks = city.get_blocks_gdf(True)
nearest_blocks = blocks.sjoin_nearest(blocks[blocks.index == 1445], distance_col='distance')
nearest_blocks = nearest_blocks[nearest_blocks['distance']<=1000]
nearest_blocks.plot().set_axis_off()

city._blocks = {i:city[i] for i in nearest_blocks.index}

Method

import random
from blocksnet import LandUse
from blocksnet.method.land_use_optimizer import LU_INDICATORS

ao = AnnealingOptimizer(city_model=city)
blocks_lu = {block.id : random.choice(list(LandUse)) for block in city.blocks}
blocks_fsi = {b_id : LU_INDICATORS[lu].fsi_min for b_id, lu in blocks_lu.items()}
blocks_gsi = {b_id : LU_INDICATORS[lu].gsi_min for b_id, lu in blocks_lu.items()}
service_types = {st.name : 1/len(city.service_types) for st in city.service_types}
X, indicators, value, provisions = ao.calculate(blocks_lu, blocks_fsi, blocks_gsi, service_types, rate=0.9)

 50%|█████     | 505/1000 [01:12<01:10,  7.01it/s]

print('Best objective value:', value)

Best objective value: 0.5204066996619239

{st : round(value,2)  for st,value in provisions.items()}

{'school': 0.68,
 'kindergarten': 0.6,
 'hospital': 0.0,
 'polyclinic': 0.9,
 'pitch': 1.0,
 'swimming_pool': 1.0,
 'stadium': 0.0,
 'theatre': 1.0,
 'museum': 1.0,
 'cinema': 1.0,
 'mall': 1.0,
 'convenience': 0.56,
 'supermarket': 0.47,
 'cemetery': 0.0,
 'religion': 0.0,
 'market': 1.0,
 'bowling_alley': 1.0,
 'university': 1.0,
 'playground': 0.0,
 'pharmacy': 0.6,
 'fuel': 1.0,
 'beach': 0.0,
 'train_building': 1.0,
 'bank': 1.0,
 'lawyer': 1.0,
 'cafe': 0.0,
 'subway_entrance': 0.83,
 'multifunctional_center': 0.06,
 'hairdresser': 0.32,
 'restaurant': 1.0,
 'bar': 0.34,
 'park': 0.0,
 'government': 1.0,
 'recruitment': 0.0,
 'hotel': 1.0,
 'zoo': 0.0,
 'circus': 1.0,
 'post': 0.2,
 'police': 1.0,
 'dog_park': 1.0,
 'hostel': 0.79,
 'bakery': 1.0,
 'parking': 0.0,
 'guest_house': 0.0,
 'reserve': 0.0,
 'sanatorium': 0.79,
 'embankment': 0.0,
 'machine-building_plant': 0.0,
 'brewery': 0.29,
 'woodworking_plant': 1.0,
 'oil_refinery': 0.0,
 'plant_of_building_materials': 0.79,
 'wastewater_plant': 0.0,
 'water_works': 0.0,
 'substation': 0.01,
 'train_station': 1.0,
 'bus_station': 0.39,
 'bus_stop': 1.0,
 'pier': 0.71,
 'animal_shelter': 0.0,
 'military_kom': 1.0,
 'prison': 0.0,
 'landfill': 1.0,
 'plant_nursery': 0.0,
 'greenhouse_complex': 0.0,
 'warehouse': 0.0}

ao.to_bricks_df(X).head()

	block_id	service_type	is_integrated	area	capacity	count
16	39	supermarket	True	300.0	500	1
33	39	woodworking_plant	False	13000.0	500	1
76	40	military_kom	False	4000.0	200	1
88	40	hotel	False	24000.0	300	1
190	40	hostel	False	500.0	50	1

ao.to_gdf(X, indicators).head()

	population	train_station	military_kom	warehouse	fuel	water_works	kindergarten	plant_nursery	hotel	embankment	...	polyclinic	parking	park	school	landfill	hospital	wastewater_plant	train_building	geometry	land_use
block_id
39	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	POLYGON ((352493.392 6644174.577, 352480.316 6...	industrial
40	3113	0.0	200.0	0.0	0.0	0.0	0.0	0.0	300.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	POLYGON ((352846.893 6643739.658, 352854.917 6...	residential
41	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	POLYGON ((352713.398 6643353.609, 352656.125 6...	industrial
42	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	POLYGON ((352661.968 6643879.720, 352637.303 6...	industrial
43	0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	POLYGON ((353921.529 6644728.862, 353829.106 6...	recreation

5 rows × 69 columns