Lockdown-Backyard-Bioblitz-Kerala-2020

An Analysis of Data from Observations made under Lockdwon-Backyard-Bioblitz-Kerala project in iNaturalist between March to June 2020.

iNaturalist data can be accessed as a snapshot of research-grade only observations from GBIF or on-demand of all observations using REST APIs.

Below is an example to get Project specific observations.

curl --location --request GET 'https://www.inaturalist.org/observations/project/70438?page=36&per_page=200&order_by=date_added&order=desc&license=any' \
--header 'Content-Type: application/json'

iNaturalist APIs serve up to 200 records per response. You would have to paginate to get all observations and that's fairly easy to do programmatically. For Python users, pyinaturalist can be handy.

For manual one-off experiments, the export feature in iNat can be very useful.

Tools:

1. Postman
2. Tableau Public
3. Jupyter Notebook
4. Microsoft Excel

APIs:

1. LocationIQ
2. iNaturalist

Python packages:

1. pyinaturalist
2. pandas
3. location-iq

Animation Steps:

1. Using any RESTful client, get all observations in JSON format. cURL and Python would do just fine.
2. Create a dataset in Tableau Public and 'unionize' all observations into a table.
3. Export just the user_login, Observed_On, SUM([Id]) measures.
4. Fill gaps in time series using Jupyter Notebook.
5. Calculate running total using Excel.
6. Visualize using Tableau Public

Note A lot of these steps could possibly be simplified with better understanding of Tableau, Python and Excel. If you do, reach out to me.

Visualization

Bioblitz Animation

Citations:

GBIF.org (15 June 2020) GBIF Occurrence Download

Copyright

• Code and Visualization are publised under Apache License. For any information on derivative work, please contact Yugender Subramanian - checkout.yugimani@gmail.com.
• Source data is published under multiple licenses by iNaturalist. For any information on data, please contact iNaturalist

Credits

• Visualizations were inspired from TowardsDataScience and Reddit. Thanks to respective authors.
• Thanks to iNat community
• Thanks to Tableau community
• Thanks to LocationIQ
• Thanks to Reddit r/dataisbeautiful community