TREC

Allows users to efficiently assess the climate risk for transit stations within the context of the access it provides to vital services and regions.
https://github.com/tsdataclinic/TREC

Category: Climate Change
Sub Category: Natural Hazard and Storm

Keywords

climate-change data-science transit-data

Keywords from Contributors

measurements sanitation control training featured feature-flag feature-toggle

Last synced: about 6 hours ago
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Transit Resilience for Essential Commuting (TREC)

README.md

Transit Resilience for Essential Commuting (TREC)

In the fall of 2022, Data Clinic took part in The Opportunity Project, a semi-annual sprint organized by the U.S. Census in partnership with federal agencies to demonstrate the value of open data, as part of the Building Climate Change Resilience Through Public Transit sprint sponsored by the U.S. Department of Transportation.

Across our many conversations with transit officials, researchers, and community organizers from across the country about the climate-related challenges and opportunities transportation systems face, a recurring theme was the desire to enable a better understanding of climate's intersectional impact on both transit and communities. In other words, a flooded bus stop doesn't just mean that the bus and passengers can't access the stop, but it may also impede access to a hospital or community support, or to a large amount of essential jobs. How can we share that insight more effectively?

In response, we built Transit Resilience for Essential Commuting (TREC), an open source tool that allows users to efficiently assess the climate risk for transit stations within the context of the access it provides to vital services and regions. Initially focused on flooding, the most prevalent climate event facing transit officials across the country, and access to hospital and jobs, TREC allows users to explore our open data-derived, station-specific risk and access ratings, and easily filter those with the highest climate risk and highest importance for access.

Our hope is that this human-centered and geospatial approach to the intersectional impact of climate change on transit and communities will give transit planners a more holistic picture to inform their infrastructure improvement decision-making. Further, we hope that making localized climate resilience tools like this open source, user-friendly, and publicly available, will empower community organizations to advocate for their underserved constituents.

The climate crisis we face requires collective intelligence and creative problem solving, and democratizing access to these kinds of tools will be crucial in making progress.

Processed Data Files

Our app relies on two data files that we process using the data sources (listed below).

  • Stop Features: Stop level dervived metrics described in the data dictionary
  • Hospitals: Locations of hospitals within included cities

Derived Metrics Data Dictionary

Variable Description Type License Source
stop_id GTFS feed stop id str Apache 2.0 GTFS Feeds
stop_name GTFS feed stop names str Apache 2.0 GTFS Feeds
routes_serviced List of all routes servicing a stop list Apache 2.0 GTFS Feeds
climate_risk_category Score 0/1/2 indicating low/medium/high climate risk around transit stop int CC BY-NC-SA 4.0 First Street Climate-Adjusted Flood Risk,GTFS Feeds
hospital_access_cateogory Score 0/1/2 indicating low/medium/high hospital access from transit stop int Apache 2.0 Geographic Names Information System National File 2021,GTFS Feeds
job_access_category Score 0/1/2 indicating low/medium/high number of jobs around transit stop int Apache 2.0 LEHD Origin-Destination Statistics,GTFS Feeds
vulnerable_worker_category Score 0/1/2 indicating low/medium/high vulnerability of people working around transit stop int Apache 2.0 LEHD Origin-Destination Statistics,OpenStreetMap,CDC/ATSDR Social Vulnerability Index,GTFS Feeds
geometry Latitude/longitude point location of stop wkt Apache 2.0 GTFS Feeds

Contributing

To contribute to this project, refer to more details on

  • setting-up the Data pipeline in analysis
  • running the web-app locally in app

You can also submit Bug reports or Feature requests with Github issues using the respective templates.

To discuss tailored adaptations of TREC to your team/city, please email us at dataclinic@twosigma.com

Data Sources

All data accessed as of June 26th, 2023.

For list of GTFS feeds used and ther respective terms, refer to the file.

Data Clinic

Data Clinic is the data and tech-for-good arm of Two Sigma, a financial sciences company headquartered in NYC. Since Data Clinic was founded in 2014, we have provided pro bono data science and engineering support to mission-driven organizations around the world via close partnerships that pair Two Sigma's talent and way of thinking with our partner's rich content-area expertise. To scale the solutions and insights Data Clinic has gathered over the years, and to contribute to the democratization of data, we also engage in the development of open source tooling and data products.


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Last synced: 8 days ago

Total Commits: 480
Total Committers: 11
Avg Commits per committer: 43.636
Development Distribution Score (DDS): 0.525

Commits in past year: 150
Committers in past year: 3
Avg Commits per committer in past year: 50.0
Development Distribution Score (DDS) in past year: 0.193

Name Email Commits
Indraneel Purohit i****t@t****m 228
Kaushik Mohan k****n@t****m 86
CanyonFoot c****t@t****m 76
Kaushik Mohan k****n@n****u 48
Juan Pablo Sarmiento p****o@t****m 34
bewouk b****g@t****m 3
Govind Lahoti 1
Govind Lahoti g****d@t****m 1
Govind Lahoti g****2@g****m 1
Indraneel Purohit i****l@g****m 1
els2171 e****1@c****u 1

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Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 36
Total pull requests: 41
Average time to close issues: about 2 months
Average time to close pull requests: 6 days
Total issue authors: 2
Total pull request authors: 5
Average comments per issue: 0.19
Average comments per pull request: 0.1
Merged pull request: 39
Bot issues: 0
Bot pull requests: 0

Past year issues: 0
Past year pull requests: 1
Past year average time to close issues: N/A
Past year average time to close pull requests: less than a minute
Past year issue authors: 0
Past year pull request authors: 1
Past year average comments per issue: 0
Past year average comments per pull request: 0.0
Past year merged pull request: 1
Past year bot issues: 0
Past year bot pull requests: 0

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/tsdataclinic/TREC

Top Issue Authors

  • indraneel (22)
  • kaushik12 (14)

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  • kaushik12 (20)
  • CanyonFoot (13)
  • indraneel (4)
  • govindlahoti (3)
  • bewouk (1)

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Dependencies

app/package.json npm
  • autoprefixer ^10.4.13 development
  • postcss ^8.4.18 development
  • tailwindcss ^3.2.1 development
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  • @testing-library/jest-dom ^5.14.1
  • @testing-library/react ^13.0.0
  • @testing-library/user-event ^13.2.1
  • @types/geojson ^7946.0.10
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  • @types/react ^18.0.0
  • @types/react-dom ^18.0.0
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  • classnames ^2.3.2
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  • react ^18.2.0
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  • react-map-gl ^7.0.19
  • react-router-dom ^6.4.5
  • react-scripts 5.0.1
  • styled-components ^5.3.6
  • typescript ^4.4.2
  • web-vitals ^2.1.0
app/yarn.lock npm
  • 1385 dependencies
Pipfile pypi
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  • ipywidgets *
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  • openpyxl *
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  • pandas *
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  • plotly *
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  • scikit-learn *
app/docker-compose.yml docker
  • postgis/postgis 15-master
app/requirements.txt pypi
  • anyio ==3.7.0
  • click ==8.1.3
  • exceptiongroup ==1.1.1
  • fastapi ==0.97.0
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  • pydantic ==1.10.9
  • sniffio ==1.3.0
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  • typing_extensions ==4.6.3
  • uvicorn ==0.22.0

Score: 5.655991810819852