https://github.com/alan-turing-institute/uatk-spc
Synthetic Population Catalyst
Science Score: 33.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
○.zenodo.json file
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○DOI references
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✓Academic publication links
Links to: zenodo.org -
✓Committers with academic emails
6 of 9 committers (66.7%) from academic institutions -
○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (16.4%) to scientific vocabulary
Repository
Synthetic Population Catalyst
Basic Info
- Host: GitHub
- Owner: alan-turing-institute
- License: mit
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://alan-turing-institute.github.io/uatk-spc/
- Size: 101 MB
Statistics
- Stars: 23
- Watchers: 6
- Forks: 12
- Open Issues: 15
- Releases: 4
Metadata Files
README.md
Synthetic Population Catalyst
The Synthetic Population Catalyst (SPC) makes it easier for researchers to work with synthetic population data in England. It combines a variety of data sources and outputs a single file in protocol buffer format, describing the population in a given study area, with a particular focus on socio-economic characteristics and interactions between individuals. It is therefore well suited to create inputs for models studying the spreading of a pandemic or segregation (e.g.). The tool provides methods to export the outcome in diferent formats often use for researchers like CSV or JSON.
The input of the SPC tool is a list of the Middle Layer Super Output Area (MSOAs) where you want to create a spatially enriched sythetic population to feed other dynamic models. SPC includes a script to assist you with the proper list of the MSOAs by defining a Local Authority District area in England. Get started to download SPC data or run the tool in different MSOAs.
Lineage
The history of this project is as follows:
- The Dynamic Model for Epidemics (DyME), originally written in R, then later converted to Python and OpenCL was first written: https://github.com/Urban-Analytics/RAMP-UA
- The "ecosystem of digital twins" branch heavily refactored the code to support running in different study areas and added a new seeding and commuting modelling: https://github.com/Urban-Analytics/RAMP-UA/tree/Ecotwins-withCommuting
- This separate repository was created to port the initialisation logic to Rust, following the above branch.
There are many contributors to the project through these different stages; the version control history can be seen on Github in the other repositories.
Ethical considerations
Synthetic data may propagate biases existing in the real data it is based on, introduce new ones, or remove useful outliers. See ONS ethical guidance for more details. SPC is based on a collection of different 'modelling modules', including some developed externally by other researchers. Each module is validated independently. Validation for newly created methods and links to previous projects can be found in the modelling methods.
Owner
- Name: The Alan Turing Institute
- Login: alan-turing-institute
- Kind: organization
- Email: info@turing.ac.uk
- Website: https://turing.ac.uk
- Repositories: 477
- Profile: https://github.com/alan-turing-institute
The UK's national institute for data science and artificial intelligence.
GitHub Events
Total
- Issues event: 1
- Watch event: 2
- Delete event: 1
- Issue comment event: 8
- Push event: 3
- Pull request event: 1
- Create event: 1
Last Year
- Issues event: 1
- Watch event: 2
- Delete event: 1
- Issue comment event: 8
- Push event: 3
- Pull request event: 1
- Create event: 1
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Dustin Carlino | d****r@g****m | 440 |
| Hadrien Salat | h****t@t****k | 101 |
| Sam Greenbury | s****y@t****k | 66 |
| HSalat | h****t@u****k | 30 |
| mfbenitezp | f****z@t****k | 24 |
| mfbenitezp | F****z@s****k | 13 |
| Jonathan Yong | y****e@g****m | 9 |
| Nick Malleson | n****n@l****k | 2 |
| Sam Greenbury | 5****y | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 39
- Total pull requests: 32
- Average time to close issues: about 2 months
- Average time to close pull requests: 30 days
- Total issue authors: 7
- Total pull request authors: 8
- Average comments per issue: 3.62
- Average comments per pull request: 2.13
- Merged pull requests: 29
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 2
- Pull requests: 2
- Average time to close issues: 8 days
- Average time to close pull requests: 28 minutes
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 4.5
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- dabreegster (17)
- nickmalleson (6)
- sgreenbury (5)
- HSalat (5)
- darribas (3)
- mfbenitezp (2)
- lenkahas (1)
Pull Request Authors
- sgreenbury (10)
- mfbenitezp (7)
- dabreegster (7)
- HSalat (3)
- nickmalleson (2)
- yongrenjie (1)
- gmingas (1)
- darribas (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- 260 dependencies
- anyhow 1.0.51
- bincode 1.3.3
- bytes 1.1.0
- cap 0.1.0
- clap 3.0.0
- csv 1.1.6
- derive_more 0.99.17
- enum-map 1.1.1
- flate2 1.0.22
- fs-err 2.6.0
- futures-util 0.3.19
- geo 0.20.0
- geojson 0.22.3
- ndarray 0.15.4
- ndarray-npy 0.8.1
- ndarray-rand 0.14.0
- ordered-float 2.8.0
- proj 0.25.2
- prost 0.9.0
- rand 0.8.4
- rand_distr 0.4.2
- rayon 1.5.1
- reqwest 0.11.8
- rstar 0.8.4
- serde 1.0.132
- serde_json 1.0.73
- shapefile 0.3.0
- tar 0.4.38
- tokio 1.15.0
- tracing 0.1.31
- tracing-subscriber 0.2.25
- typed-index-collections 3.0.3