https://github.com/niconoe/arabel
🕷️ Source code for the future Atlas of spiders of Belgium based on the ARABEL database
https://github.com/niconoe/arabel
biodiversity biodiversity-data biodiversity-informatics spiders
Last synced: 20 days ago
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🕷️ Source code for the future Atlas of spiders of Belgium based on the ARABEL database
- Host: GitHub
- URL: https://github.com/niconoe/arabel
- Owner: niconoe
- License: mit
- Created: 2021-02-01T10:14:25.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-05-26T14:58:12.000Z (over 4 years ago)
- Last Synced: 2025-01-11T20:31:38.990Z (9 months ago)
- Topics: biodiversity, biodiversity-data, biodiversity-informatics, spiders
- Language: Python
- Homepage:
- Size: 12 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# TODO
- occurrence search: proper message if empty table
- occurrence search: show numer of occurrences in ?
- occurrence search: (option to) group by family
# Arabel
Source code for the (future) Atlas of spiders (ARABEL Database).
Implemented with Python, Django and Vue.js
## Data import process
1) convert access database to CSV using `jetread`. All useful data is in the tables listed below:
```
./jetread spinnenatlas_20201007.mdb export "STAAL GEGEVENS" -fmt csv > staal_gegevens.csv
./jetread spinnenatlas_20201007.mdb export "LITERATUUR" -fmt csv > litteratuur.csv
./jetread spinnenatlas_20201007.mdb export "GEGEVENS" -fmt csv > gegevens.csv
./jetread spinnenatlas_20201007.mdb export "SOORTEN INFO" -fmt csv > soorten_info.csv
./jetread spinnenatlas_20201007.mdb export "Omschrijving Rode lijst categoriën" -fmt csv > redlist_categories.csv
```
2) Copy those 4 files to ./data (keep the same filenames)
3) Run the full import script:
$ python manage.py import_all_access
4) Make sure all grid data is in the database (see point below)
5) Reconcile Access data and grid data:
$ python manage.py join_station_squares
## Importing grid data
Grid data (=MGRS squares over Belgium) should be imported to the MgrsSquare model/table.
GeoDjango's layermapping utility can be used for that. Source data in `grid_data` directory:
- the mgrs5 file contains both 5km and 10mk squares all over Belgium (from https://github.com/BelgianBiodiversityPlatform/grids-belgium)
- the mgrs1 data is split in two files for zone 31U and 32U (downloaded from https://earth-info.nga.mil/GandG/update/index.php?dir=coordsys&action=mgrs-1km-polyline-dloads and manually clipped)
Example:
from django.contrib.gis.utils import LayerMapping
from website.models import *
mapping = {'name': 'code', 'gzd': 'gzd', 'poly': 'POLYGON'}
lm = LayerMapping(MgrsSquare, '/home/nnoe/Downloads/utm5_grid_belgium.polygon.gpkg', mapping)
lm.save(verbose=True, strict=True)