Socio-economic indicators evaluation

Read aggregated blocks spatial layer

[1]:

import pandas as pd

blocks_gdf = pd.read_pickle('./../../../data/saint_petersburg/blocks.pickle')

Read drive graph

[2]:

import pickle

with open('./../../../data/saint_petersburg/graph_drive.pickle', "rb") as f:
    graph = pickle.load(f)

Read drive accessibility matrix

[3]:

acc_mx = pd.read_pickle('./../../../data/saint_petersburg/accessibility_matrix_drive.pickle')

General indicators

[4]:

from blocksnet.analysis.indicators.socio_economic import calculate_general_indicators

general_indicators = calculate_general_indicators(blocks_gdf)
general_indicators

[4]:

{Area (km2): 1392.283137517296, Urbanization: 0.3640140273948924}

Demographic indicators

[5]:

from blocksnet.analysis.indicators.socio_economic import calculate_demographic_indicators

demographic_indicators = calculate_demographic_indicators(blocks_gdf)
demographic_indicators

[5]:

{Population: 5477076, Density (people/km2): 3933.880869782465}

Settlement indicators

[6]:

# not calculated within blocksnet

Transport indicators

[7]:

from blocksnet.analysis.indicators.socio_economic import calculate_transport_indicators

transport_indicators = calculate_transport_indicators(blocks_gdf, acc_mx, graph)
transport_indicators

[7]:

{Road network density (km/km2): 8.875121375198045,
 Settlements connectivity: 0.49947916666666664,
 Road network length (km): 12356.681834107552,
 Fuel stations count: 505,
 Average fuel station accessibility (h): 0.0911865234375,
 Railway stops count: 44,
 Average railway stop accessibility (h): 0.1654052734375}

Economic indicators

[8]:

# not calculated within blocksnet

Ecological indicators

[9]:

# not calculated within blocksnet

Engineering indicators

[10]:

from blocksnet.analysis.indicators.socio_economic import calculate_engineering_indicators

tmp_gdf = blocks_gdf.rename(columns={
    'count_school': 'count_treatment_facility',
    'count_kindergarten': 'count_water_intake',
    'count_hairdresser': 'count_gas_distribution',
    'count_fuel': 'count_power_plant',
    'count_park': 'count_reservoir'
})

engineering_indicators = calculate_engineering_indicators(tmp_gdf)
engineering_indicators

[10]:

{Power plant: 505,
 Water intake: 1541,
 Treatment facility: 880,
 Reservoir: 14255,
 Gas distribution: 3876,
 Infrastructure object: 21057}