7 Land-Use by Block – Urban Analysis & Spatial Science

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import pandas as pd
from geosnap import DataStore
from geosnap.io import get_acs
from tobler.area_weighted import area_interpolate
from tobler.dasymetric import extract_raster_features

OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

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%load_ext watermark
%watermark -a 'eli knaap' -u -d -iv

Author: eli knaap

Last updated: 2024-08-26

pandas: 2.2.2

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datasets = DataStore()

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dc_blocks = datasets.blocks_2020(states=["11"])

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dc_blocks.plot()

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nlcd_path = "https://spatial-ucr.s3.amazonaws.com/nlcd/landcover/nlcd_landcover_2021.tif"

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vals = pd.read_csv("../09_dynamics/nlcd_colors.csv")
codes = vals.code.unique().tolist()

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vals

	code	color	description
0	11	70,107,159	Open Water
1	12	209,222,248	Perennial Snow/Ice
2	21	222,197,197	Developed, Open Space
3	22	217,146,130	Developed, Low Intensity
4	23	235,0,0	Developed, Medium Intensity
5	24	171,0,0	Developed High Intensity
6	31	179,172,159	Barren Land (Rock/Sand/Clay)
7	41	104,171,95	Deciduous Forest
8	42	28,95,44	Evergreen Forest
9	43	181,197,143	Mixed Forest
10	52	204,184,121	Shrub/Scrub
11	71	223,223,194	Grassland/Herbaceous
12	81	220,217,57	Pasture/Hay
13	82	171,108,40	Cultivated Crops
14	90	184,217,235	Woody Wetlands
15	95	108,159,184	Emergent Herbaceous Wetlands

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dclu = extract_raster_features(
    dc_blocks, nlcd_path, pixel_values=codes, collapse_values=False
)

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dclu["value"] = dclu["value"].replace(
    dict(zip(vals.code.values, vals.description.values))
)

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crs = dc_blocks.estimate_utm_crs()

dc_blocks = dc_blocks.to_crs(crs)
dclu = dclu.to_crs(crs)

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dclu.plot("value", categorical=True)

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dclu

	value	geometry
0	Developed, Open Space	POLYGON ((323226.647 4318276.105, 323315.290 4...
1	Developed, Medium Intensity	POLYGON ((323272.435 4318205.034, 323301.983 4...
2	Developed, Open Space	POLYGON ((323361.078 4318185.376, 323390.626 4...
3	Developed, Open Space	POLYGON ((323183.792 4318224.692, 323213.340 4...
4	Developed, Low Intensity	POLYGON ((323354.424 4318156.393, 323413.519 4...
...	...	...
18301	Open Water	POLYGON ((322913.364 4295807.330, 322942.914 4...
18302	Developed High Intensity	POLYGON ((323031.565 4295781.132, 323061.115 4...
18303	Developed, Medium Intensity	POLYGON ((322942.914 4295800.780, 323031.565 4...
18304	Developed High Intensity	POLYGON ((322965.813 4295765.249, 323024.913 4...
18305	Open Water	POLYGON ((322922.960 4295713.836, 322952.511 4...

18306 rows × 2 columns

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lu_blks = area_interpolate(
    source_df=dclu,
    target_df=dc_blocks.set_index("geoid"),
    categorical_variables=["value"],
)
lu_blks.columns = lu_blks.columns.str.replace("value_", "")

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lu_blks.head()

	Developed, Open Space	Developed, Medium Intensity	Developed, Low Intensity	Deciduous Forest	Developed High Intensity	Mixed Forest	Woody Wetlands	Barren Land (Rock/Sand/Clay)	Pasture/Hay	Grassland/Herbaceous	Shrub/Scrub	Open Water	Cultivated Crops	Emergent Herbaceous Wetlands	Evergreen Forest	geometry
geoid
110019800001089	0.622183	0.076952	0.182508	0.000000	0.043109	0.000000	0.000000	0.000027	0.000000	0.000000	0.000000	0.0	0.075222	0.0	0.000000	POLYGON ((322969.039 4306546.708, 322970.142 4...
110010056012005	0.000000	0.659626	0.000000	0.000000	0.183696	0.000000	0.000000	0.156679	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	POLYGON ((321580.797 4307997.329, 321596.402 4...
110010005013003	0.013664	0.452591	0.111835	0.014578	0.407331	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	0.0	0.000000	POLYGON ((321758.637 4310250.787, 321759.503 4...
110010005011000	0.182705	0.110337	0.240769	0.305913	0.009644	0.041115	0.001609	0.078323	0.007111	0.005618	0.009364	0.0	0.000000	0.0	0.007491	POLYGON ((321810.700 4311101.634, 321883.434 4...
110010111003035	0.271752	0.200615	0.301211	0.106136	0.028303	0.000000	0.077833	0.000000	0.014151	0.000000	0.000000	0.0	0.000000	0.0	0.000000	POLYGON ((329084.877 4308909.414, 329085.096 4...

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lu_blks.explore("Developed High Intensity", tiles="CartoDB Positron")

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