Modern tech applied to the longest-running community science bird project

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Photo by Ray Hennessy on Unsplash

The Christmas Bird Count has been a tradition for 120 years. Started by ornithologist Frank M. Chapman in 1900, it was intended to encourage conservation by counting birds rather than hunting them. One thing that hasn’t changed since then is that it involves a lot of manual paperwork.

I had the privilege of participating in my first annual Christmas Bird Count (CBC) in San Jose, CA this past winter (2019). One thing that surprised me was the number of paper forms that had to be filled out by each team. These were turned in to the CBC compiler for the county, who then entered this data manually into the Audubon site. …


Named Entity Recognition for taxonomy with the spaCy NLP library

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Photo by Jan Antonin Kolar on Unsplash

The Audubon Society’s Christmas Bird Count has been a tradition for 120 years. Started by ornithologist Frank M. Chapman in 1900, it was intended to encourage conservation by counting birds rather than hunting them. One thing that hasn’t changed much since then is that it involves a lot of manual paperwork.

As part of a larger effort to automate the annual Christmas Bird Count (CBC), I wanted to be able to create a definitive species list for a count circle given a PDF of a tally sheet, similar to this:


Simple daily weather summary with Python

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Photo by David Watkis on Unsplash

As part of a larger project to automate Audubon’s annual Christmas Bird Count, I wanted to produce a short summary of weather conditions on a particular day. Dating back to the original Christmas Bird Count in 1900, part of the information requested is the local weather:

Such reports should be headed by the locality, hour of starting and returning, character of the weather, direction and force of the wind, and the temperature; the latter taken when starting.

The end result ideally would be something like this:

Weather was partly cloudy with a low of 45º F and a high of 62º F, winds from the NW gusting to 12mph. …


Birding together by birding apart

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Or maybe a condor’s wingspan…

Santa Clara County is the heart of Silicon Valley and has some amazing birding spots. We are in the n-th week of a shelter-in-place directive with various birdathons coming up. My birding friends, the Sharpies, were thinking of how we could best cover our territory. Our outside activities have been restricted to within five miles of our houses, but 5MR birding (5 mile radius) is already a thing, so let’s run with it.

The Jupyter notebook for this project is a great tool for 5MR birding or planning Big Days and Christmas Bird Counts, whether you are birding together or apart. …


Basic Sleuthing with Pandas, Seaborn and Folium

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Photo by Ilze Lucero on Unsplash

One of the key attributes of a data scientist is an inquisitive mind. In this article, we will explore an interesting data set using some popular Python tools, but will also try to think about the data in unconventional terms. For areas like fraud detection, connecting the data in unexpected ways can tease out patterns, even if the data may seem incomplete. Ideally, you will come up with insights that will open up new business opportunities.

While looking around Kaggle for supply chain data sets, I came upon one called Supply Chain Data, with a subtitle of Import and Shipment Data. Scanning down the page, I see the data source is called walmart-import-data.csv. Hmmm, could be interesting. Uh oh, “About this file” says "No description yet". This is where the sleuthing starts — we don't even have a data dictionary.

Usually when you are doing exploratory data analysis, it is because you have a specific question that you are trying to answer using the data, e.g. how many cases of pineapple were shipped from the Bahamas last month, but here we will let our imagination run wild and see what secrets the data might reveal. …


Using Excel with iNaturalist and eBird

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Steven Wright @stevenwright via Unsplash

I recently gave an informal talk to a class of botany students at Gavilan College. The original topic was nature photography, but I also talked about the data science techniques that I used to create my recently completed photo book, Portraits of Birds: Shoreline Park.

The concept for the book was to try to personally take photos of all of the bird species in a particular area, in this case Shoreline at Mountain View Park in Mountain View, California, which I later expanded to include the Palo Alto Baylands. To enumerate the species that have been seen in this area, I turned to two citizen science sites, iNaturalist and eBird, both of which have application programmatic interfaces (APIs). …


Quick starts for your data science investigations

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Photo by Agto Nugroho on Unsplash

In a nicely written article, Set Your Jupyter Notebook up Right with this Extension [1], William Koerhsen describes how to use Jupyter notebook extensions to improve productivity by creating a notebook template. This template serves as the starting point for each of your data science journeys. We have all found ourselves typing the same boilerplate code at the start of a new investigation.

The essence of [1] is that you can put a Javascript file into a particular folder used by Jupyter’s nbextensions system, and it will be called by Jupyter each time a new notebook is created. In his article, he creates a new notebook with some basic, common Python boilerplate, and also enables a hook that forces you to rename the notebook from the default “Untitled.ipynb”. …

About

John Hurley

Mathematician, data scientist, equestrian, photographer, birder. I enjoy looking for patterns. https://www.linkedin.com/in/johnhurleyphd/

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