A choropleth map that divides a zipcode up based on areas that are predicted to have higher leaf fall.
A heatmap generated based on each tree's calculated risk.
The tool offers maps in 10+ color schemes
An example of fallen leaves that would contribute to clogged catch basins
Leafless is a tool that determines “hot spots” around the city that are more susceptible to street flooding and accidents as a result of fallen leaves.
How we built it
Using the 2015 Street Tree Census, this tool calculates an individual “risk” for each tree based on its dimensions, species, and health.
Additional information from the 311 Service Request dataset about clogged catch basins over the last 10 years are also used in calculating the riskiness of a tree based on the locations of nearby catch basins.
A Learning Experience
This was a first time experience for the entire team, with regards to coding in Python or working with open data.
What's next for Leafless
- Improve accuracy by incorporating weather & wind data
- Compatibility with NYC Department of Sanitation trucks to ensure live time tracking of clean-up efforts
About the Team
Leafless is a project by team Data Ninjas (Naiem Gafar, Alexander Tenf, Pablo Tenf, and Arthur Korchkov ) of the NYCOpenData Student Showcase program. A special thanks to the Tech Incubator at CUNY, Queens College, Crosscompute, and all of the mentors who have provided exceptional feedback and resources to the team.
Sample Results (based on NYC zip code 11419)
Copyright 2019 Leafless
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.