Trees in Manhattan
Dashboard
Introduction
Background 📖
There is a nonprofit organization advising the planning department on ways to improve the quantity and quality of trees in New York City. The urban design team believes tree size (using trunk diameter as a proxy for size) and health are the most desirable characteristics of city trees. The city would like to learn more about which tree species are the best choice to plant on the streets of Manhattan. Here we who do the job to find the answer.
Goals and Objectives 🎯
We need make a report that covers the following:
- What are the most common tree species in Manhattan?
- Which are the neighborhoods with the most trees?
- A visualization of Manhattan's neighborhoods and tree locations.
- What ten tree species would you recommend the city plant in the future?
Data Source 💾
The team has provided access to the 2015 tree census and geographical information on New York City neighborhoods (trees, neighborhoods):
Tree Census
| Column | Description |
|---|---|
| tree_id | Unique id of each tree. |
| tree_dbh | The diameter of the tree in inches measured at 54 inches above the ground. |
| curb_loc | Location of the tree bed in relation to the curb. Either along the curb (OnCurb) or offset from the curb (OffsetFromCurb). |
| spc_common | Common name for the species. |
| status | Indicates whether the tree is alive or standing dead. |
| health | Indication of the tree's health (Good, Fair, and Poor). |
| root_stone | Indicates the presence of a root problem caused by paving stones in the tree bed. |
| root_grate | Indicates the presence of a root problem caused by metal grates in the tree bed. |
| root_other | Indicates the presence of other root problems. |
| trunk_wire | Indicates the presence of a trunk problem caused by wires or rope wrapped around the trunk. |
| trnk_light | Indicates the presence of a trunk problem caused by lighting installed on the tree. |
| trnk_other | Indicates the presence of other trunk problems. |
| brch_light | Indicates the presence of a branch problem caused by lights or wires in the branches. |
| brch_shoe | Indicates the presence of a branch problem caused by shoes in the branches. |
| brch_other | Indicates the presence of other branch problems. |
| postcode | Five-digit zip code where the tree is located. |
| nta | Neighborhood Tabulation Area (NTA) code from the 2010 US Census for the tree. |
| nta_name | Neighborhood name. |
| latitude | Latitude of the tree, in decimal degrees. |
| longitude | Longitude of the tree, in decimal degrees. |
Neighborhoods' geographical information
| Column | Description |
|---|---|
| ntacode | NTA code (matches Tree Census information). |
| ntaname | Neighborhood name (matches Tree Census information). |
| geometry | Polygon that defines the neighborhood. |
Executive Summary
The purpose of this notebook is to provide an executive summary of the analysis conducted in the following cells.
Analysis Overview
In this analysis, I explored the dataset to gain insights and make data-driven decisions. We performed various data cleaning, preprocessing, and exploratory data analysis tasks.
Key Findings
- The honeylocust is the most abundant tree in Manhattan, with a total of 13,176 scattered throughout the borough.
- The Upper West Side is home to an impressive array of trees, with a total of 5,807 specimens representing 73 different species. The most popular of these is the Honeylocust | No. | Species | Probability of Good | Probability of Fair | Probability of Poor | Number of Trees Planted | | --- | --- | :-: | :-: | :-: | --: | | 1 | honeylocust | 83% | 15% | 2% | 13,175 | | 2 | Callery pear | 74% | 20% | 6% | 7,297 | | 3 | ginkgo | 75% | 17% | 8% | 5,859 | | 4 | pin oak | 81% | 16% | 3% | 4,584 | | 5 | Sophora | 80% | 16% | 4% | 4,453 | | 6 | Japanese zelkova | 77% | 17% | 6% | 4,122 | | 7 | London planetree | 63% | 28% | 9% | 3,596 | | 8 | littleleaf linden | 62% | 24% | 14% | 3,333 | | 9 | American elm | 80% | 15% | 5% | 1,698 | | 10 | American linden | 64% | 24% | 12% | 1,583 |
Exploratory Data Analyst
import pandas as pd
import geopandas as gpd
# loading tree cencus data
tree_cencus = pd.read_csv('data/trees.csv')
# loading geospatial data
maps = gpd.read_file('data/nta.shp')tree_cencus.head()maps.head()import matplotlib.pyplot as plt
maps.plot()
plt.axis('off')
plt.show()tree_cencus.info()maps.info()Data Cleaning & Preprocessing 🧹
Null Value
# checking null values in tree_cencus
tree_cencus.isna().sum()[tree_cencus.isna().sum() > 0]