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flood-data-ecosystem-himachal-pradesh

The repository contains codes to extract relevant datasets and the modelling approach used to calculate Risk Scores in Himachal Pradesh.

https://github.com/civicdatalab/flood-data-ecosystem-himachal-pradesh

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ○
    DOI references
  • ○
    Academic publication links
  • ○
    Academic email domains
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.8%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

The repository contains codes to extract relevant datasets and the modelling approach used to calculate Risk Scores in Himachal Pradesh.

Basic Info
  • Host: GitHub
  • Owner: CivicDataLab
  • License: agpl-3.0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 608 MB
Statistics
  • Stars: 1
  • Watchers: 3
  • Forks: 0
  • Open Issues: 5
  • Releases: 0
Created about 2 years ago · Last pushed 11 months ago
Metadata Files
Readme License Citation

README.md

flood-data-ecosystem-Himachal-Pradesh

The repository contains codes to extract relevant datasets and the modelling approach used to calculate Risk Scores in Himachal Pradesh.

Repository Structure

  • Maps/: Contains geospatial data and map visualizations related to flood risk in Himachal Pradesh.
  • Sources/: Includes raw data sources and scripts for data extraction pertinent to the project.
  • requirements.txt: Lists the Python dependencies required to run the scripts in this repository.
  • CITATION.cff: Provides citation information for referencing this repository in academic works.
  • LICENSE: Details the licensing information for the repository.
  • README.md: Offers an overview of the project, including its purpose and structure.

Procurement Datasets

The repository includes scripts to extract and process various datasets essential for modeling flood risk in Himachal Pradesh. Below are some key datasets:

Flood Procurement Data

  • Flood Tenders Data:

    • Contains procurement data related to flood activities in Himachal Pradesh for the financial years 2019 to 2024.

  • Monthly Procurement Data:

    • Contains monthly procurement data for Himachal Pradesh from FY 2019 to 2024.

Sources Directory Structure

data/

Other Procurement Data and Geotagged Files

  • Variables Folder:
    • Includes processed datasets and supporting variables.

  • Geotagged Files:

    • Datasets include geotagged procurement data at the sub-district, tehsil, and district levels:
    • floodtenders_SDgeotagged.csv
    • floodtenders_Tehsil-geotagged.csv
    • floodtenders_districtgeotagged.csv
    • Flood Tenders - All Combined ### scripts/

  • Contains scripts for data cleaning, transformation, and analysis to prepare the raw data for modeling and visualization.

Owner

  • Name: CivicDataLab
  • Login: CivicDataLab
  • Kind: organization
  • Email: info@civicdatalab.in
  • Location: India

Harnessing Data, Tech, Design and Social Science to strengthen the course of Civic Engagements in India.

GitHub Events

Total
  • Issues event: 3
  • Delete event: 1
  • Issue comment event: 2
  • Push event: 8
  • Pull request event: 2
  • Create event: 1
Last Year
  • Issues event: 3
  • Delete event: 1
  • Issue comment event: 2
  • Push event: 8
  • Pull request event: 2
  • Create event: 1

Issues and Pull Requests

Last synced: 12 months ago

All Time
  • Total issues: 8
  • Total pull requests: 10
  • Average time to close issues: N/A
  • Average time to close pull requests: 3 days
  • Total issue authors: 2
  • Total pull request authors: 3
  • Average comments per issue: 0.25
  • Average comments per pull request: 0.1
  • Merged pull requests: 10
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 8
  • Pull requests: 2
  • Average time to close issues: N/A
  • Average time to close pull requests: 9 minutes
  • Issue authors: 2
  • Pull request authors: 2
  • Average comments per issue: 0.25
  • Average comments per pull request: 0.0
  • Merged pull requests: 2
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • saurabhlevin (4)
  • aparnasr1 (1)
Pull Request Authors
  • ArcD7 (13)
  • saurabhlevin (3)
  • AakashGandhi1 (2)
Top Labels
Issue Labels
Pull Request Labels

Dependencies

requirements.txt pypi
  • Fiona ==1.9.4.post1
  • GDAL ==3.8.5
  • Jinja2 ==3.1.2
  • MarkupSafe ==2.1.3
  • Pillow ==10.0.0
  • PuLP ==2.8.0
  • PyDrive ==1.3.1
  • PySocks ==1.7.1
  • PyYAML ==6.0.1
  • Pygments ==2.15.1
  • Rtree ==1.2.0
  • access ==1.1.9
  • affine ==2.4.0
  • appnope ==0.1.4
  • asttokens ==2.2.1
  • attrs ==23.1.0
  • backcall ==0.2.0
  • beautifulsoup4 ==4.12.3
  • boto3 ==1.28.43
  • botocore ==1.31.43
  • bqplot ==0.12.40
  • branca ==0.6.0
  • cachetools ==5.3.1
  • certifi ==2023.7.22
  • cfgv ==3.4.0
  • cftime ==1.6.2
  • charset-normalizer ==3.2.0
  • click ==8.1.6
  • click-plugins ==1.1.1
  • cligj ==0.7.2
  • colour ==0.1.5
  • comm ==0.1.3
  • contourpy ==1.1.0
  • cycler ==0.11.0
  • debugpy ==1.6.7
  • decorator ==5.1.1
  • deprecation ==2.1.0
  • distlib ==0.3.7
  • earthengine-api ==0.1.360
  • eerepr ==0.0.4
  • esda ==2.5.1
  • et-xmlfile ==1.1.0
  • exceptiongroup ==1.1.2
  • executing ==1.2.0
  • factor_analyzer ==0.5.0
  • filelock ==3.13.1
  • folium ==0.14.0
  • fonttools ==4.41.1
  • future ==0.18.3
  • geemap ==0.24.4
  • geocoder ==1.38.1
  • geopandas ==0.13.2
  • giddy ==2.3.5
  • google-api-core ==2.11.1
  • google-api-python-client ==2.95.0
  • google-auth ==2.22.0
  • google-auth-httplib2 ==0.1.0
  • google-cloud-core ==2.3.3
  • google-cloud-storage ==2.10.0
  • google-crc32c ==1.5.0
  • google-resumable-media ==2.5.0
  • googleapis-common-protos ==1.59.1
  • gurobipy ==11.0.0
  • h11 ==0.14.0
  • httplib2 ==0.22.0
  • identify ==2.5.32
  • idna ==3.4
  • imbalanced-learn ==0.11.0
  • imblearn ==0.0
  • imdlib ==0.1.19
  • inequality ==1.0.1
  • ipyevents ==2.0.1
  • ipyfilechooser ==0.6.0
  • ipykernel ==6.25.0
  • ipyleaflet ==0.17.3
  • ipython ==8.14.0
  • ipytree ==0.2.2
  • ipywidgets ==8.0.7
  • jedi ==0.18.2
  • jenkspy ==0.4.0
  • jmespath ==1.0.1
  • joblib ==1.3.1
  • jupyter_client ==8.3.0
  • jupyter_core ==5.3.1
  • jupyterlab-widgets ==3.0.8
  • kiwisolver ==1.4.4
  • libpysal ==4.9.2
  • llvmlite ==0.42.0
  • mapclassify ==2.6.1
  • matplotlib ==3.7.2
  • matplotlib-inline ==0.1.6
  • mgwr ==2.2.1
  • momepy ==0.7.0
  • mpmath ==1.3.0
  • natsort ==8.4.0
  • nest-asyncio ==1.5.6
  • netCDF4 ==1.6.4
  • networkx ==3.2.1
  • nodeenv ==1.8.0
  • numba ==0.59.0
  • numdifftools ==0.9.41
  • numpy ==1.25.1
  • oauth2client ==4.1.3
  • openpyxl ==3.1.2
  • outcome ==1.2.0
  • packaging ==23.1
  • pandas ==2.0.3
  • parso ==0.8.3
  • patsy ==0.5.3
  • pexpect ==4.8.0
  • pickleshare ==0.7.5
  • platformdirs ==3.9.1
  • plotly ==5.15.0
  • pointpats ==2.4.0
  • pre-commit ==3.5.0
  • prompt-toolkit ==3.0.39
  • protobuf ==4.23.4
  • psutil ==5.9.5
  • ptyprocess ==0.7.0
  • pure-eval ==0.2.2
  • pyasn1 ==0.5.0
  • pyasn1-modules ==0.3.0
  • pyparsing ==3.0.9
  • pyperclip ==1.8.2
  • pyproj ==3.6.0
  • pyshp ==2.3.1
  • pytesseract ==0.3.10
  • python-box ==7.0.1
  • python-dateutil ==2.8.2
  • python-decouple ==3.8
  • pytz ==2023.3
  • pyzmq ==25.1.0
  • quantecon ==0.7.1
  • rasterio ==1.3.8
  • rasterstats ==0.19.0
  • ratelim ==0.1.6
  • requests ==2.31.0
  • rioxarray ==0.14.1
  • rsa ==4.9
  • s3transfer ==0.6.2
  • scikit-learn ==1.3.1
  • scipy ==1.11.2
  • scooby ==0.7.2
  • seaborn ==0.13.0
  • segregation ==2.5
  • selenium ==4.10.0
  • semopy ==2.3.10
  • shapely ==2.0.1
  • simplejson ==3.19.1
  • six ==1.16.0
  • sniffio ==1.3.0
  • snuggs ==1.4.7
  • sortedcontainers ==2.4.0
  • soupsieve ==2.5
  • spaghetti ==1.7.5.post1
  • spglm ==1.1.0
  • spint ==1.0.7
  • splot ==1.1.5.post1
  • spopt ==0.6.0
  • spreg ==1.4.2
  • spvcm ==0.3.0
  • stack-data ==0.6.2
  • statsmodels ==0.14.0
  • sympy ==1.12
  • tenacity ==8.2.2
  • threadpoolctl ==3.2.0
  • tobler ==0.11.2
  • tornado ==6.3.2
  • tqdm ==4.66.1
  • traitlets ==5.9.0
  • traittypes ==0.2.1
  • trio ==0.22.2
  • trio-websocket ==0.10.3
  • tzdata ==2023.3
  • uritemplate ==4.1.1
  • urllib3 ==1.26.16
  • virtualenv ==20.24.7
  • wcwidth ==0.2.6
  • widgetsnbextension ==4.0.8
  • wsproto ==1.2.0
  • xarray ==2023.7.0
  • xlrd ==2.0.1
  • xlwt-future ==0.8.0
  • xyzservices ==2023.7.0