Mapping the Urban Forest: Exploring Tree Data in Salinas, California 🌳

Urban trees are more than just greenery along our streets—they provide shade, improve air quality, and shape the character of a city. Thanks to open data initiatives, we can analyze and visualize tree inventories to better understand the composition and health of urban forests.

In this post, we’ll walk through an exploration of the City of Salinas, California’s tree inventory dataset, using Python tools like GeoPandas, Matplotlib, and Contextily to map and categorize the trees.

First we will import the packages used in this post. We can use either uv, pip, conda

import pandas as pd
import geopandas as gpd
import contextily as cx
from janitor import drop_constant_columns
import matplotlib.pyplot as plt

Data Importation

The data used in this analysis was downloaded from opendatasoft. We start by loading the tree inventory GeoJSON file with geopandas. Since many datasets contain placeholder values like N/A, we replace them with pandas actual NA value–pd.NA. To simplify the dataset, we also remove constant columns (columns with the same value for all rows).

inv_tbl_raw = gpd.read_file("data/tree-inventory-cityofsalinas.geojson")
inv_tbl = inv_tbl_raw.replace("N/A", pd.NA).drop_constant_columns()
inv_tbl.head()

Table 1: Data preview

	objectid	id	uniqueid	address	street	onstr	fromstr	tostr	side	site	...	spacesize	subzone	parkquad	zone	district	stmpvcnt	globalid	suffix	geo_point_2d	geometry
0	32031	30339	IH 20140709074052	283	CHAPARRAL ST	MARYAL DR	DEADEND	CHAPARRAL ST	Right	1	...	10.0	<NA>	<NA>	<NA>	4	No	{FFA7B88F-19F4-4FA2-9008-C6954AA0EB05}	None	{ "lon": -121.64605930227704, "lat": 36.703282...	POINT (-121.64606 36.70328)
1	32070	2496	BS 20140424130746	183	DENNIS AV	DENNIS AV	MIAMI ST	TAMPA ST	Front	1	...	5.0	<NA>	<NA>	<NA>	2	No	{E2350667-2941-42E7-A09B-A90151C81680}	None	{ "lon": -121.60828225058304, "lat": 36.675840...	POINT (-121.60828 36.67584)
2	32084	8825	BS 20140516133448	1271	MORENO DR	TOWT ST	MORENO WY	PASEO GRANDE	Rear	5	...	6.0	<NA>	<NA>	North/East Maintenance District	1	No	{37810329-E153-4E5B-9AFE-6F9CFD3C60BD}	None	{ "lon": -121.60240705321014, "lat": 36.687945...	POINT (-121.60241 36.68795)
3	32085	8833	BS 20140516134752	1255	MORENO DR	TOWT ST	MORENO WY	PASEO GRANDE	Rear	1	...	7.0	<NA>	<NA>	North/East Maintenance District	1	No	{C56D21E6-D6EA-4F76-941C-5C8CEBC53826}	None	{ "lon": -121.6030825123258, "lat": 36.6876225...	POINT (-121.60308 36.68762)
4	32092	9077	BS 20140519122755	1252	MORENO DR	MORENO DR	CAMARILLO CT	CAYUCOS CIR	Front	1	...	10.0	<NA>	<NA>	North/East Maintenance District	1	No	{DBDAD81C-8515-4AD0-A5EA-39291339AADD}	None	{ "lon": -121.60290566856168, "lat": 36.687211...	POINT (-121.60291 36.68721)

5 rows × 35 columns

We have interesting variables available in the dataset, but, we do not need all.

inv_tbl.columns

Index(['objectid', 'id', 'uniqueid', 'address', 'street', 'onstr', 'fromstr',
       'tostr', 'side', 'site', 'spp', 'dbh', 'stems', 'height', 'width',
       'condstruc', 'condcrwn', 'cond', 'ohutility', 'klir', 'inspect',
       'hdscape', 'vsiblty', 'sound', 'grow', 'spacesize', 'subzone',
       'parkquad', 'zone', 'district', 'stmpvcnt', 'globalid', 'suffix',
       'geo_point_2d', 'geometry'],
      dtype='object')

We’ll only make use of spp, dbh, geometry, and cond .

retain_col = [
    "spp", "dbh",
    "stems", "height", 
    "width", "cond", 
    "grow","geometry"
]
inv_tbl = inv_tbl.loc[:, retain_col]
inv_tbl.head()

Table 2: Data retained

	spp	dbh	stems	height	width	cond	grow	geometry
0	Fraxinus spp.	1.0	1.0	0 to 10	0 to 10	<NA>	Tree Lawn	POINT (-121.64606 36.70328)
1	Fraxinus oxycarpa	7.0	1.0	21 to 30	21 to 30	Fair	Parkway	POINT (-121.60828 36.67584)
2	Pyrus calleryana	10.0	1.0	21 to 30	21 to 30	Good	Back Up	POINT (-121.60241 36.68795)
3	Celtis sinensis	11.0	1.0	21 to 30	21 to 30	Fair	Back Up	POINT (-121.60308 36.68762)
4	Quercus ilex	11.0	1.0	21 to 30	21 to 30	Good	Tree Lawn	POINT (-121.60291 36.68721)

Table 2 shows the retained columns. The data is currently using the WGS 84 Geographic coordinate reference system.

inv_tbl.crs

<Geographic 2D CRS: EPSG:4326>
Name: WGS 84
Axis Info [ellipsoidal]:
- Lat[north]: Geodetic latitude (degree)
- Lon[east]: Geodetic longitude (degree)
Area of Use:
- name: World.
- bounds: (-180.0, -90.0, 180.0, 90.0)
Datum: World Geodetic System 1984 ensemble
- Ellipsoid: WGS 84
- Prime Meridian: Greenwich

Mapping All Trees

Let’s take a quick view of how the trees are planted in Salinas. We will convert to a mercator projector to work well with contextily.

inv_tbl = inv_tbl.to_crs(epsg=3857)

fig, ax = plt.subplots(figsize=(10, 6))
inv_tbl.plot(
    ax=ax, 
    color="red", 
    alpha=0.4, 
    markersize=2
)
cx.add_basemap(ax, source=cx.providers.OpenTopoMap)
plt.axis("off")
plt.tight_layout()

Figure 1: Tree distribution across Salinas. Trees are the red dots

Figure 1 gives us a city-wide snapshot of tree distribution in Salinas and we can tell that Salinas is quite green.

inv_tbl.explore(
    column="grow",
    categorical=True, 
)

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