Map tree visibility statistics per district

Author: Willeke A’Campo

Description: This notebooks shows the results for the tree visibility analysis for the trekroner municipalities: Bodø, Bærum, Kristiansand and Oslo.

Documentation:

Map the indicators for the 3-30-300-rule

import os
import leafmap
import pandas as pd

# TODO move
municipality = "kristiansand"
src_crs = "EPSG:25832"
reporting_dir = r"/workspaces/urban-climate/data/08_reporting/"

input_file = os.path.join(reporting_dir,f"{municipality}_district_treeVis_stat.parquet" )
# add layers to gdf for mapping
gdf_mapping = leafmap.read_parquet(
    input_file,
    return_type='gdf',
    src_crs= src_crs ,
    dst_crs="EPSG:4326"
    )

# Calculate the center of the study_area GeoDataFrame
center = gdf_mapping.geometry.unary_union.centroid

display(gdf_mapping[[
    "kommunenavn","grunnkretsnummer", "grunnkretsnavn",
    "n_trees", "n_bldg", "n_res_bldg",
    "n_trees_near_rbldg","perc_near_rbldg","labels_near_rbldg",
    "a_crown", "perc_crown", "labels_perc_crown",
    "n_res_bldg_near_gs","perc_near_gs", "labels_near_gs",
    "a_open_space", "a_private_space", "a_public_space"
    ]])

	kommunenavn	grunnkretsnummer	grunnkretsnavn	n_trees	n_bldg	n_res_bldg	n_trees_near_rbldg	perc_near_rbldg	labels_near_rbldg	a_crown	perc_crown	labels_perc_crown	n_res_bldg_near_gs	perc_near_gs	labels_near_gs	a_open_space	a_private_space	a_public_space
0	Kristiansand	42040701	Grim - Dueknipen	298	204	87	88	30	25-50%	24228.22	15.40	0-25%	61	70.0	50-75%	104499.24	32663.19	71273.56
1	Kristiansand	42040702	Grimsmyra	632	609	310	509	81	75-100%	39153.62	15.43	0-25%	286	92.0	75-100%	161599.53	83491.13	75987.51
2	Kristiansand	42040704	Møllevannet - Klappane	154	82	40	70	45	25-50%	9176.21	13.43	0-25%	40	100.0	75-100%	47883.75	16124.35	31553.15
3	Kristiansand	42040705	Enrum - Paradis	433	190	78	141	33	25-50%	35490.59	32.06	25-50%	77	99.0	75-100%	58644.02	27538.81	30578.40
4	Kristiansand	42040801	Kvadraturen Sørvest	440	307	72	108	25	0-25%	25854.21	5.59	0-25%	0	0.0	0-25%	205287.23	52343.63	153860.23
5	Kristiansand	42040802	Kvadraturen Nordvest	439	416	104	108	25	0-25%	31861.02	9.60	0-25%	0	0.0	0-25%	194937.12	47966.65	147339.89
6	Kristiansand	42040803	Kvadraturen Sørøst	432	375	170	165	38	25-50%	26190.16	4.59	0-25%	13	8.0	0-25%	190027.25	57009.83	132599.47
7	Kristiansand	42040804	Kvadraturen Nordøst	216	695	382	163	75	50-75%	9775.37	3.26	0-25%	54	14.0	0-25%	145466.62	60967.31	84394.45
8	Kristiansand	42040806	Eg	366	86	32	158	43	25-50%	23503.05	24.93	0-25%	32	100.0	75-100%	59291.48	16598.72	42233.00
9	Kristiansand	42040807	Sykehuset	1291	99	1	11	1	0-25%	96850.88	24.05	0-25%	1	100.0	75-100%	257912.45	159733.95	97249.30
10	Kristiansand	42040901	Galgeberg	333	285	129	173	52	50-75%	15768.14	6.06	0-25%	129	100.0	75-100%	118018.59	45730.00	71898.16
11	Kristiansand	42040902	Hamreheia	481	564	250	415	86	75-100%	23997.30	8.18	0-25%	248	99.0	75-100%	173022.23	90216.37	81218.94
12	Kristiansand	42040903	Kuholmen	332	395	160	228	69	50-75%	16465.98	5.34	0-25%	160	100.0	75-100%	188112.45	64551.17	122261.60
13	Kristiansand	42040904	Valhalla Sør	128	241	124	120	94	75-100%	4095.61	4.88	0-25%	114	92.0	75-100%	62455.76	34136.45	27904.97
14	Kristiansand	42040905	Valhalla Midt	276	499	261	242	88	75-100%	12991.49	7.35	0-25%	82	31.0	25-50%	124027.37	64567.89	58371.44
15	Kristiansand	42040907	Valhalla Nord - Kongsgård	439	303	155	328	75	50-75%	25669.20	12.04	0-25%	155	100.0	75-100%	148043.27	68896.92	77977.48
16	Kristiansand	42040908	Solbygg	122	98	50	63	52	50-75%	6955.10	6.78	0-25%	50	100.0	75-100%	73094.88	29007.49	43483.53
17	Kristiansand	42040909	Kjempegravane	47	132	61	45	96	75-100%	2604.27	5.22	0-25%	61	100.0	75-100%	34834.33	18410.68	15887.13
18	Kristiansand	42040910	Tobienborg	381	302	148	164	43	25-50%	23394.14	12.40	0-25%	148	100.0	75-100%	131510.67	50772.92	79382.28
19	Kristiansand	42040911	Nedre Lund	402	526	187	304	76	75-100%	21780.21	6.46	0-25%	177	95.0	75-100%	205347.41	89383.95	114498.01
20	Kristiansand	42040912	Oddemarka	118	108	31	65	55	50-75%	7577.51	10.27	0-25%	31	100.0	75-100%	54084.15	21627.48	32029.34
21	Kristiansand	42040913	Flaten	103	162	80	90	87	75-100%	5368.99	8.09	0-25%	80	100.0	75-100%	45788.73	23253.30	22286.44
22	Kristiansand	42040915	Gimlemoen - Jegersberg	580	40	1	5	1	0-25%	45532.23	21.19	0-25%	1	100.0	75-100%	145747.75	31836.04	113747.86
23	Kristiansand	42040918	Gimlevang	34	42	19	18	53	50-75%	1886.51	4.09	0-25%	19	100.0	75-100%	28194.61	10814.88	17202.92
24	Kristiansand	42040919	Gimle	1175	21	2	18	2	0-25%	89392.76	48.56	25-50%	2	100.0	75-100%	87002.41	15415.42	71380.45
25	Kristiansand	42040920	Marviksletta	48	6	0	0	0	0-25%	3426.57	18.12	0-25%	0	NaN	no data	12271.83	6491.21	5721.18
26	Kristiansand	42040921	Lund Industriområde	40	9	0	0	0	0-25%	2207.87	8.89	0-25%	0	NaN	no data	16471.13	11996.89	4519.13
27	Kristiansand	42041101	Nedre Kongsgård	1008	390	29	55	5	0-25%	57912.16	18.42	0-25%	29	100.0	75-100%	218743.47	87159.60	130853.74
28	Kristiansand	42041102	Kongsgard 1 - Vige	166	9	0	0	0	0-25%	11158.61	49.82	25-50%	0	NaN	no data	9656.72	4293.83	5215.83
29	Kristiansand	42041103	Bjørndalsheia	129	38	12	43	33	25-50%	8886.66	17.37	0-25%	12	100.0	75-100%	40086.45	5665.55	34368.30
30	Kristiansand	42041104	Vestre Bjørndalen	36	20	2	4	11	0-25%	1164.64	2.24	0-25%	2	100.0	75-100%	45113.70	11336.80	33673.90

Rule 3 | all residents should be able to see at least 3 trees from their home.

m = leafmap.Map()
m.set_center(center.x, center.y, zoom=13)
m.add_basemap("CartoDB.Positron")
m.add_data(
    gdf_mapping, column='perc_near_rbldg', scheme='Quantiles', cmap='Greens', legend_title='Number of Trees near Residential Buildings'
)
m

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
/usr/local/lib/python3.10/dist-packages/leafmap/common.py in ?(data, column, cmap, colors, labels, scheme, k, legend_kwds, classification_kwds)
   4949         import mapclassify
   4950     except ImportError:
-> 4951         raise ImportError(
   4952             "mapclassify is required for this function. Install with `pip install mapclassify`."

ModuleNotFoundError: No module named 'mapclassify'

During handling of the above exception, another exception occurred:

ImportError                               Traceback (most recent call last)
/tmp/ipykernel_591/161069249.py in ?()
      1 m = leafmap.Map()
      2 m.set_center(center.x, center.y, zoom=13)
      3 m.add_basemap("CartoDB.Positron")
----> 4 m.add_data(
      5     gdf_mapping, column='perc_near_rbldg', scheme='Quantiles', cmap='Greens', legend_title='Number of Trees near Residential Buildings'
      6 )
      7 m

/usr/local/lib/python3.10/dist-packages/leafmap/leafmap.py in ?(self, data, column, colors, labels, cmap, scheme, k, add_legend, legend_title, legend_position, legend_kwds, classification_kwds, layer_name, style, hover_style, style_callback, info_mode, encoding, **kwargs)
   3839             **kwargs: Additional keyword arguments to pass to the GeoJSON class, such as fields, which can be a list of column names to be included in the popup.
   3840
   3841         """
   3842
-> 3843         gdf, legend_dict = classify(
   3844             data=data,
   3845             column=column,
   3846             cmap=cmap,

/usr/local/lib/python3.10/dist-packages/leafmap/common.py in ?(data, column, cmap, colors, labels, scheme, k, legend_kwds, classification_kwds)
   4947
   4948     try:
   4949         import mapclassify
   4950     except ImportError:
-> 4951         raise ImportError(
   4952             "mapclassify is required for this function. Install with `pip install mapclassify`."
   4953         )
   4954

ImportError: mapclassify is required for this function. Install with `pip install mapclassify`.

Rule 30: all neighbourhoods should have 30% tree canopy cover.

m = leafmap.Map()
m.set_center(center.x, center.y, zoom=13)
m.add_basemap("CartoDB.Positron")
m.add_data(
    gdf_mapping, column='perc_crown', scheme='Quantiles', cmap='Greens', legend_title='Crown Area (%)'
)
m

Rule 300: all residents should be able to access a green area within 300 m from their home.

gdf_mapping_gs = gdf_mapping.dropna(subset=['n_res_bldg_near_gs_norm'])
m = leafmap.Map()
m.set_center(center.x, center.y, zoom=13)
m.add_basemap("CartoDB.Positron")
m.add_data(
    gdf_mapping_gs, column='perc_near_gs', scheme='Quantiles', cmap='Greens', legend_title='Number of Residential Buildings near green spaces'
)
m

TESTING OF LEAFMAP CUSTOM CATEGORICAL MAPPING

#gdf_mapping_gs = gdf_mapping.dropna(subset=['n_res_bldg_near_gs_norm'])
gdf_mapping_gs = gdf_mapping
# Define the bins
bins = pd.IntervalIndex.from_tuples([(-0.01, 25), (25, 50), (50, 75), (75, 100)])

# Create a new column 'categories' based on these bins
gdf_mapping_gs['bins'] = pd.cut(gdf_mapping_gs['n_res_bldg_near_gs_norm'], bins)
gdf_mapping_gs['bins'] = gdf_mapping_gs['bins'].astype(str)

# Define a color mapping for categories
labels = ["no residential buildings", "0-25%", "25-50%", "50-75%", "75-100%"]
color_dict = {"nan":"no residential buildings", "(-0.01, 25.0]": "0-25%", "(25.0, 50.0]": "25-50%", "(50.0, 75.0]": "50-75%", "(75.0, 100.0]": "75-100%"}

# Apply the color mapping to the 'color' column
gdf_mapping_gs['labels'] = gdf_mapping_gs['bins'].map(color_dict)

# Display the DataFrame
display(gdf_mapping_gs[[
    "kommunenavn","grunnkretsnummer", "grunnkretsnavn",
    "n_bldg", "n_res_bldg", "n_res_bldg_near_gs", "n_res_bldg_near_gs_norm",
    "bins", "labels"
]])

	kommunenavn	grunnkretsnummer	grunnkretsnavn	n_bldg	n_res_bldg	n_res_bldg_near_gs	n_res_bldg_near_gs_norm	bins	labels
0	Kristiansand	42040701	Grim - Dueknipen	204	87	61	70.0	(50.0, 75.0]	50-75%
1	Kristiansand	42040702	Grimsmyra	609	310	286	92.0	(75.0, 100.0]	75-100%
2	Kristiansand	42040704	Møllevannet - Klappane	82	40	40	100.0	(75.0, 100.0]	75-100%
3	Kristiansand	42040705	Enrum - Paradis	190	78	77	99.0	(75.0, 100.0]	75-100%
4	Kristiansand	42040801	Kvadraturen Sørvest	307	72	0	0.0	(-0.01, 25.0]	0-25%
5	Kristiansand	42040802	Kvadraturen Nordvest	416	104	0	0.0	(-0.01, 25.0]	0-25%
6	Kristiansand	42040803	Kvadraturen Sørøst	375	170	13	8.0	(-0.01, 25.0]	0-25%
7	Kristiansand	42040804	Kvadraturen Nordøst	695	382	54	14.0	(-0.01, 25.0]	0-25%
8	Kristiansand	42040806	Eg	86	32	32	100.0	(75.0, 100.0]	75-100%
9	Kristiansand	42040807	Sykehuset	99	1	1	100.0	(75.0, 100.0]	75-100%
10	Kristiansand	42040901	Galgeberg	285	129	129	100.0	(75.0, 100.0]	75-100%
11	Kristiansand	42040902	Hamreheia	564	250	248	99.0	(75.0, 100.0]	75-100%
12	Kristiansand	42040903	Kuholmen	395	160	160	100.0	(75.0, 100.0]	75-100%
13	Kristiansand	42040904	Valhalla Sør	241	124	114	92.0	(75.0, 100.0]	75-100%
14	Kristiansand	42040905	Valhalla Midt	499	261	82	31.0	(25.0, 50.0]	25-50%
15	Kristiansand	42040907	Valhalla Nord - Kongsgård	303	155	155	100.0	(75.0, 100.0]	75-100%
16	Kristiansand	42040908	Solbygg	98	50	50	100.0	(75.0, 100.0]	75-100%
17	Kristiansand	42040909	Kjempegravane	132	61	61	100.0	(75.0, 100.0]	75-100%
18	Kristiansand	42040910	Tobienborg	302	148	148	100.0	(75.0, 100.0]	75-100%
19	Kristiansand	42040911	Nedre Lund	526	187	177	95.0	(75.0, 100.0]	75-100%
20	Kristiansand	42040912	Oddemarka	108	31	31	100.0	(75.0, 100.0]	75-100%
21	Kristiansand	42040913	Flaten	162	80	80	100.0	(75.0, 100.0]	75-100%
22	Kristiansand	42040915	Gimlemoen - Jegersberg	40	1	1	100.0	(75.0, 100.0]	75-100%
23	Kristiansand	42040918	Gimlevang	42	19	19	100.0	(75.0, 100.0]	75-100%
24	Kristiansand	42040919	Gimle	21	2	2	100.0	(75.0, 100.0]	75-100%
25	Kristiansand	42040920	Marviksletta	6	0	0	NaN	nan	no residential buildings
26	Kristiansand	42040921	Lund Industriområde	9	0	0	NaN	nan	no residential buildings
27	Kristiansand	42041101	Nedre Kongsgård	390	29	29	100.0	(75.0, 100.0]	75-100%
28	Kristiansand	42041102	Kongsgard 1 - Vige	9	0	0	NaN	nan	no residential buildings
29	Kristiansand	42041103	Bjørndalsheia	38	12	12	100.0	(75.0, 100.0]	75-100%
30	Kristiansand	42041104	Vestre Bjørndalen	20	2	2	100.0	(75.0, 100.0]	75-100%

import leafmap

# Initialize the map and set the center
m = leafmap.Map()
m.set_center(center.x, center.y, zoom=13)

# Add the base map
m.add_basemap("CartoDB.Positron")

# Add the data to the map
m.add_data(
    data=gdf_mapping_gs,
    name="test",
    column="labels",
    colors=['#169200','#169200','#FFFFFF','#FFAA00','#FF5500'],
    labels=["no residential buildings", "0-25%", "25-50%", "50-75%", "75-100%"],
    legend_position='bottomright',
    style_kwds=dict(color="gray", LineWidth=10, weight=0.99),
    style={
        "stroke": True,
        "color": "gray",
        "weight": .5,
        "opacity": .5,
        "fill": True,
        "fillOpacity": 0.5,
    }
)

# Display the map
m

color_map =  {
        "no residential buildings": (0, 136, 55),
        "0-25%": (0, 136, 55),
        "25-50%": (127, 195, 28),
        "50-75%": (127, 195, 28),
        "75-100%": (127, 195, 28),
    }


leafmap.view_vector(gdf_mapping_gs, color_column='labels', color_map=color_map, opacity=0.5)
#leafmap.Legend(title="Wetland Type", legend_dict=color_map)
#display(gdf_mapping_gs)