UFO and Bigfoot Sighting Analysis

Link to Interactive Tableau Dashboard

Project Description

Geographic comparison of UFO and bigfoot sighting reports in the United States. Data of bigfoot sighting reports and locations were aquired and merged to form one cohesive dataframe. UFO data was geocoded to obtain the longitude and latitude of each sighting location. Cleaned reports were then connected to a Tableau dashboard, in which the user is able to see on a map of the United States where each sighting was reported. Map is interactive, and the user is able to select individual map markers to read the report and date of the sighting. Analysis created to aid in the question: Where, in the United States, are UFOs and bigfoot mostly spotted? Do UFOs make more appearances in the northeast? Does bigfoot prefer warmer climates, like Texas or Florida? Let's find out!

Instructions to Run Project/Install Virtual Environment

1. Clone the repository by typing the following:

   git clone https://github.com/GraceEnsign/UFO_Bigfoot_Sighting_Analysis.git

2. Run the following to install virtual environment (if not already installed):

   pip install virtualenv

3. Create virtual environment by typing the following:

   virtualenv environment

4. To enter the virtual environment:

   source environment\Scripts\activate.bat

   This is the command to activate the virtual environment on Windows in PowerShell or command line. For a complete list of commands for any platform or shell click here.
5. Run the following to install required packages:

   pip install -r requirements.txt

6. Open the Analysis.ipynb once in the main directory with Jupyter Notebook, or open with editor.

Relevant Packages

Python 3.10.4
Jupyter Notebook
Pandas 1.4.2
NumPy 1.23.3
Geocoder 1.38.1
GeoPy 2.2.0
Openpyxl 3.0.9

Data Description

Three dataframes were initially acquired. Bigfoot sighting locations dataframe was a csv file and the reports dataframe was a json file. UFO dataframe was a json file. Due to the size of each dataframe, a SQL query was created to obtain a URL of each dataframe, and then read in as a csv.

Dataframe Sources

UFO Data
Both Bigfoot Datframes

Features Included

1. Loading data- Read in three data files (Cells 2-4)
2. Clean and operate the data while combining them- Performed Pandas merge on the two bigfoot dataframes (Cell 6) after cleaning column names to assist with the merge (Cell 5). Used Pandas and NumPy to split, rename, reformat, drop, and filter columns (Cells 7-22, 27). Used geocoding to acquire longitude and latitude for UFO dataframe (Cells 23-24). Exported cleaned dataframes into Excel worksheets (Cell 28).
3. Visualize/Present your data- Created interactive Tableau dashboard, which can be viewed here. All visualizations were made from the cleaned code exported at the end of the notebook.
4. Best practices- Utilized a virtual environment and included instructions in README.
5. Interpretation of your data- Added markdown cells in Jupyter Notebook (found between cells through notebook). Further analysis found in section below.

Analysis

UFO Analysis

Reported UFO sightings were more prevalent in the Pacific Coat, the Northeast, and Florida. The Great Plains reported the least amount of sightings, which was surprising. The probability of sightings would increase in less populated areas without buildings, lights, or landmarks to obstruct a view of the sky. However that same rational can be used for coastal cities, which reported more sightings that inland cities. It is worth noting that the three areas with the most reports (California, Florida, and New England coast) are also home to very active Air Force bases, in which aircrafts of varying shapes are frequently flown.

Bigfoot Analysis

Reported bigfoot sightings were more prevalent in the southern and midwest regions, as well as California and Colorado. Florida reported a high level of sightings for both UFOs and bigfoot. Middle American reported the least amount of UFO sightings. North and South Dakota had the least amount of sightings, if any. It's worth noting that both states have colder weather (averaging below 50 degrees seven months out of the year) which may lead to less time spent outdoors. Or bigfoot simply prefers warmer climates.

Changes from Project Plan

1. I initially planned to include annotations in Tableau, but decided to keep the dashboard as a simple interactive tool and add two additional maps. By adding additional maps I was able to add analysis of each dataframe separately.
2. For the best practices feature I intially planned to build a custom data dictionary. However, during the process of cleaning the data, it became apparent that a virtual environment would be a necessity to run the project.