Telling a Story of Threatened Species in Victoria through Data Visualisations

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Attention!!!

This repository is a continuing part of https://github.com/poojaisabelle/Project-Two. The main purpose of this second repo is to deploy our app using a personal Heroku free account. For a full picture of our working history, please also refer here.

What is this project about?

Victoria is home to a number of threatened species. Some of these are rare to see in the wild but well worth the effort for avid animal lovers, families looking for an adventure, or those who are staycationing at Victoria's national parks, coastal towns, and regional towns.

Our web app is built up using fauna sightings data from 2015 to 2020 provided by Victorian Biodiversity Atlas (VBA). The VBA fauna observations are a foundation dataset that feeds into many biodiversity tools used in government's everyday decision making for nature conservation - showing where wildlife is now and how this has changed over time. This makes it a core input to the majority of the government's processes and programs that impact native species.

Our interactive map is designed to help you identify what type of threatened species has been seen in a particular area. If you want to learn more about some of the threatened species, please check out our dashboard and data pages.

Where is the data from?

1. VBA_FAUNA25 provided by Department of Environment, Land, Water & Planning

   This dataset is Victorian Biodiversity Atlas fauna records (unrestricted) for sites with high spatial accuracy. Although this is a point layer, the actual accuracy of the site can range from +/- 1m to +/- 500m.

   Its original file is in ESRI shapefile format. We converted it to csv using QGIS.

2. Threatened species images have been scraped from https://www.zoo.org.au/fighting-extinction/local-threatened-species/ and https://www.environment.vic.gov.au/conserving-threatened-species/threatened-species

   Please refer to our webscraping work here.

What did we do?

• Use IPython Jupyter notebook to extract, analyse and transform data

• Use Jupyter Notebook, BeautifulSoup, Pandas, and Splinter to scrape animal images and information

• Back end: Python Flask-powered RESTful API

• Front end: HTML, CSS (with Bootstrap), Javascript

  • Mapping: Mapbox and Leaflet

  • Dashboard: Plotly

• Database: MongoDB. Cloud database: MongoDB Atlas

• Deployment: Heroku

An overview of the web pages

TO DO:

1. Add map layers for local government area boundaries and national parks.

2. Explore other mapping choices and leaflet plugins, e.g. heatmap, animal markers, time dimension

3. Add scraped information on each animal on dashboarding page.

4. Create a multi-choice filter for the data page.

5. Add additional threatened animals (expand dataset).

6. Add more styles to our webpages

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We make no claims as the ownership of the data. Hence, please do what you'd love with the data but credit the appropriate people.