The public webpage to this project can be found at https://imhkchow.github.io/

Screen Shot 2022-12-15 at 9.18.14 PM.png

Bigfoot Sightings Across North America¶

Grace Graves and Isabelle Chow

Background and Plan¶

This team will be investigating data sets related to Bigfoot (Sasquatch, Yeti, Yowie, Skunk ape, etc) a bipedal ape-like creature said to inhabit the forests of North America and potentially similar environments around the world. Tales of creatures similar to Bigfoot have been woven into the mythologies of indigineous people and detailed reports and investigations of its existence have occurred since the early 1700s. More information about the history of Bigfoot can be found here. Although the majority of mainstream scientests have historically declared the existence of Bigfoot a hoax, many sightings still occur to this day, and the existence of Bigfoot is a widely publicized and debated topic in pop culture and cryptozoology. In this project, the team wanted to examine this phenomenon by exploring data related to the location, time of year and day, classification, and context of the sighting as well as weather data considering temperature.

In this project, the team will consider the following questions:

What is the distribution of Bigfoot sightings across North America? Where are the sightings concentrated?
Was there a time (year or time of year) when Bigfoot was more active and what factors affected that time?
What were the weather conditions of the incidences and is there a correlation between certain weather conditions and a higher amount of sightings?
Do descriptions of the sightings indicate any patterns in how or where more bigfoots are found?

In this project, the team would like to analyze the sightings dataset by examining trends in the time and kind of sighting. We will also find dates of release of certain Bigfoot related media and see if this has a relationship with the sighting frequency as well. We will use a temperature dataset to see if there is a relationship between weather and sightings. This will continue to help us answer our questions on location and distribution of Sasquatch across North America, giving us an overall more holistic and detailed understanding of this mysterious creature, and look for patterns that might point us to optimal bigfoot sighting settings.

from google.colab import drive
drive.mount('/content/drive')
%cd /content/drive/MyDrive/BigfootDataScienceProject/

Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
/content/drive/.shortcut-targets-by-id/1WVWh0s9coKx-LzaErUE6N7YbcIg9B1aI/BigfootDataScienceProject

# Import all the libraries we will be using in the project

import pandas as pd
import numpy as np

! pip install geopandas
import geopandas as gpd
from shapely.geometry import Point
from geopandas import GeoDataFrame
! pip install geoplot
import geoplot as gplt

import plotly.express as px
import matplotlib.pyplot as plt
! pip install seaborn
import seaborn as sns

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Main Dataset: Bigfoot Sightings¶

This table is from the primary authority on Bigfoot sitings, the Bigfoot Field Researchers Organization, or BFRO. It is a "comprehensive database of credible sightings and related reports, maintained by an all volunteer network of bigfoot/sasquatch researchers, archivists, and investigators in the United States and Canada founded in 1995."(https://www.bfro.net/gdb). This dataset contains thousands of reports of Bigfoot sightings, including a short description of the circumstance of the sighting, a classification (A: Clear sighting, misinterpretation or misidentification can be ruled out with great confidence B: Possible sasquatch observed at a great distance or in poor lighting C: Second and third-hand reports), as well as a date and a time, latitude, and longitutde. This team chose this data set as it offers a fantastic base of every officially documented Bigfoot sighting as well as good base information that could help us easily answer questions and draw connections to other potential datasets we will load and organize in the future. Because Sasquatches are not scientifically confirmed to be a real species of animal, verification and reliability of data sources related to them are always going to be difficult, but this was the best option available to the team given the very large sample size and objective intentions of the BFRO as a continuously growing database in existence for over 20 years.

!pwd

/content/drive/.shortcut-targets-by-id/1WVWh0s9coKx-LzaErUE6N7YbcIg9B1aI/BigfootDataScienceProject

# Unzip dataset
#!mkdir -p ./data/archive
#!unzip ./data/archive.zip -d ./data/sightings

bf = pd.read_csv("./data/sightings/bfro-report-locations.csv")
bf

Difficulties with Data¶

Although the dataset contains some very useful information about sasquatch sightings, there could be problems in using the data in it's current format. For starters, the "timestamp" column contains very important information on the year, month, day, and time of the sightings but isn't going to be easy to use in it's current format: a string of values all in one column. According to the principles of tidy data, each column should represent a value in the observation. As it stands, the "timestamp" column contains multiple values, even though they are of the same category. The current format is also difficult to summarize using pandas summary functions. The set also duplicates some data. The columns "number" and "title" both have a report number in them. The "classification" column with values "Class A", "Class B", and "Class C" is a bit redundant. This team will resolve all of these issues in the code below.

bf[["report #", "description", "extra"]] = bf["title"].str.split(": ", expand=True) #split the title into report number and description of sighting
bf[["date", "time"]] = bf["timestamp"].str.split("T", expand=True) #split the timestamp into date and time columns
bf[["year", "month", "day"]] = bf["date"].str.split("-", expand=True) #split the date into year, month, and day
bf

# Map classes to letters for readability and ease of use

bf["class"] = bf["classification"].map({
    "Class A": "A",
    "Class B": "B",
    "Class C": "C"
})

# Delete duplicate or unnecessary rows

del bf["report #"] #duplicate
del bf["extra"] #unnecessary
del bf["timestamp"] #duplicate
del bf["title"] #duplicate
del bf["time"] #unnecessary (all values are exactly the same and therefore not accurate)
del bf["date"] #duplicate
bf

Columns were split to allow each variable of a report to be used to the fullest in the summary and interpretation.

# Reorder/rename columns

bf = bf[['number', 'description', 'class', 'longitude', 'latitude', 'year', 'month', 'day']]
bf.rename(columns= {"number":"report_number"}, inplace=True)
bf

/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:5039: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

bf.dtypes

report_number      int64
description       object
class             object
longitude        float64
latitude         float64
year              object
month             object
day               object
dtype: object

# Fix dtypes to accurately reflect types of data

bf["report_number"] = bf["report_number"].astype(str)
bf[["year", "month", "day"]] = bf[["year", "month", "day"]].astype(int)
bf

<ipython-input-59-62f8ed55b6ee>:3: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:3641: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

# Drop longitude and latitude values outside range of possibility

bf.drop(bf[(bf['longitude'] >180) | (bf['longitude'] < -180) | (bf['latitude'] < -90) | (bf['latitude'] > 90)].index, inplace=True)
bf

/usr/local/lib/python3.8/dist-packages/pandas/core/frame.py:4906: SettingWithCopyWarning:


A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

bf.describe()

Secondary Dataset: Average Temperatures¶

To simplify the weather data, we will scrape a list of average temperatures by US state. This will give us insight into the potential habitation preferences of Bigfoot and allow us to predict the best state to go to to spot bigfoot or what places abroad could be home to other sasquatches.

import requests

# scrape website to get html

headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'}

r = requests.get('https://www.currentresults.com/Weather/US/average-annual-state-temperatures.php', headers=headers, timeout=10)
print(r.status_code)

200

from bs4 import BeautifulSoup

# beautify the html for readability and extract table

soup = BeautifulSoup(r.content)
print(soup.find("table").prettify()[:600])

<table class="articletable tablecol-1-left">
 <caption>
  Average annual temperature for each state.
 </caption>
 <thead>
  <tr>
   <th class="left">
    State
   </th>
   <th>
    Avg °F
   </th>
   <th>
    Avg °C
   </th>
   <th>
    Rank
   </th>
  </tr>
 </thead>
 <tbody>
  <tr>
   <td>
    <a href="/Weather/Alabama/average-annual-temperatures.php">
     Alabama
    </a>
   </td>
   <td>
    62.8
   </td>
   <td>
    17.1
   </td>
   <td>
    7
   </td>
  </tr>
  <tr>
   <td>
    <a href="/Weather/Alaska/average-annual-temperatures.php">
     Alaska
    </a>
   </td>
   <td>
    26.6
   <

# Since the data we want is split we will make 
# every piece of the table we want into a dataframe then concatenate them

avg_weather_tables = []
for t in soup.findAll("table"):
    df_t = pd.read_html(str(t))
    avg_weather_tables.append(df_t[0])

avg_weather = pd.concat(avg_weather_tables)
avg_weather.reset_index(inplace=True)
display(avg_weather.head(10))

avg_weather.drop(["index", "Avg °C"], axis=1, inplace=True)
avg_weather.head(10)

# giving each state a code for mapping purposes

avg_weather["state_code"] = ["AL", "AK", "AZ", "AR", "CA", "CO", "CT", "DE",
                             "FL", "GA", "HI", "ID", "IL", "IN", "IA", "KS",
                             "KY", "LA", "ME", "MD", "MA", "MI", "MN", "MS",
                             "MO", "MT", "NE", "NV", "NH", "NJ", "NM", "NY",
                             "NC", "ND", "OH", "OK", "OR", "PA", "RI", "SC",
                             "SD", "TN", "TX", "UT", "VT", "VA", "WA", "WV",
                             "WI", "WY"]

avg_weather

avg_weather.dtypes

State          object
Avg °F        float64
Rank            int64
state_code     object
dtype: object

Exploratory Data Analysis¶

Light data analysis was performed on class of reports, year of most sightings, and time of year of most sightings were found. Class of reports is important because it tells the team how potentially accurate each sighting was, with class A being a definite clear sighting and C being a second or third-hand account. This also speaks to the reliability of the BFRO dataset as a whole, and because only 19 out of thousands of sightings were not entirely reliable C sightings, and over half of the total sightings were class A, definite clear sightings, we can determine that most of the data here was a sure sasquatch sighting.

Knowing the year where Bigfoot sightings is interesting as we could potentially link it to historical trends like the rise of information sharing on the internet or certain news trends/cycles that might influence how interested the general public is in Bigfoot. We also know early instances of recorded Bigfoot sightings (1869!) which is interesting to think about people hundreds of years ago recording seeing these creatures as well.

Knowing the time of year when most Bigfoot sightings occur is useful as in the future we could link this information to yearly camping/hunting statistics and see if there is any correlation between more people being out in the forest for a certain reason (hunting, camping, etc) and a greater frequency of seeing Bigfoot, or if Bigfoots are more attracted to urban infrastructures like national park facilities and roadways. According to our EDA, Bigfoots are more likely to be seein in the late summer and fall months with October, July, and August being in the top three months for sightings.

Further data analysis has now been done concerning location of sightings and temperature data for the US. The maps and charts below will assist this team in determining areas of focus for the questions we want to answer and models we plan to build. For example, the map of sightings has already clued this team into the possibility of a relationship between sasquatch sightings and elevation changes (the presence of mountains).

# What class of reports are most common?
bf["class"].value_counts()

A    1892
B    1890
C      19
Name: class, dtype: int64

Class A and B reports are most common.

Most reported sightings happened between 2003-2007. If we want to see this in a visual we can plot the values.

Frequency of Sightings by Year

# What year were sightings the most common in?
bf["year"].value_counts()

2004    250
2005    226
2006    188
2007    170
2003    154
       ... 
1921      1
1869      1
1937      1
1947      1
1930      1
Name: year, Length: 81, dtype: int64

bf.year.plot.hist(bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x7f8608fdba30>

This graph is interesting when compared to a few major events in Bigfoot culture history in the US: The Patterson Film(1967), releases of Bigfoot media in the 80s like the Harry and the Hendersons movie and similar films, and the release of the popular Animal Planet Show Finding Bigfoot(2011). The Patterson film is one of the most famous images and forms of evidence pointing to Bigfoot's existence in modern North American Bigfoot lore. Despite the release of this film and various other forms of media, there seems to be little correlation between the times of releases and the larger spikes in sightings over the years. This observation would suggest that the patters of sightings are more reliable as they are independent from any periods of media frenzy surrounding Bigfoot.

Month vs Sighting Frequency

# Which time of year is bigfoot most active?
bf["month"].value_counts()

10    496
7     481
8     458
9     401
11    364
6     344
5     257
1     246
4     214
12    209
3     171
2     160
Name: month, dtype: int64

bf
bf.month.plot.hist(alpha=.5, density=True, legend=True)

<matplotlib.axes._subplots.AxesSubplot at 0x7f8604bdc760>

Bigfoots were most likely to be spotted in the months of October to December. Here is to histograph showing their frequency of spotting and month on the x axis.

bf

Description Word Tokenization¶

An untapped resource we find in the dataset is the description. To gain insight into this data without reading every line, we tokenized the descriptions given by the observers. We can use this to analyze which words, whether they be objects, people or places had the most occurences throughout the data. This will help us further determine what circumstances bigfoots show up in frequently.

import nltk #Special thanks to Karthik for suggesting tokenizing the description and analyzing word instances
import re
from collections import Counter #used Counter collection to count instances of each word 

description = bf['description'].copy() #takes description just from the bf dataset
description.columns = ['DESCRIPTION']
desc = description.str.cat(sep= ' ') #concatenate all descriptions into one large string

desc
splitdesc = desc.split(' ') #split my large text list into individual words
splitdesc
WordCounts = Counter(splitdesc) #use counter to count instances of each word
print(WordCounts.most_common())

[('near', 1683), ('a', 1215), ('sighting', 1015), ('of', 902), ('in', 888), ('by', 749), ('the', 690), ('and', 608), ('on', 570), ('at', 495), ('Possible', 466), ('encounter', 375), ('has', 368), ('possible', 357), ('while', 323), ('vocalizations', 296), ('Lake', 290), ('outside', 286), ('night', 264), ('creature', 257), ('heard', 231), ('Man', 230), ('road', 225), ('River', 183), ('daylight', 181), ('large', 179), ('have', 176), ('animal', 176), ('hear', 173), ('hears', 165), ('hunter', 156), ('recalls', 152), ('with', 147), ('Two', 145), ('nighttime', 144), ('his', 143), ('to', 139), ('morning', 137), ('from', 136), ('National', 129), ('two', 129), ('Park', 127), ('found', 123), ('Forest', 123), ('along', 117), ('tracks', 114), ('crossing', 113), ('sees', 111), ('Woman', 111), ('Daylight', 110), ('home', 109), ('close', 107), ('late', 107), ('footprints', 107), ('motorist', 105), ('Motorist', 103), ('early', 102), ('State', 102), ('Memory', 100), ('told', 100), ('an', 98), ('strange', 96), ('figure', 96), ('hunters', 94), ('Campers', 94), ('find', 92), ('family', 90), ('Hunter', 90), ('north', 83), ('tall', 82), ('encounters', 82), ('describes', 82), ('Family', 81), ('daytime', 81), ('vocalization', 81), ('hairy', 80), ('seen', 80), ('Creek', 80), ('their', 75), ('see', 75), ('bipedal', 71), ('observes', 70), ('biped', 70), ('loud', 70), ('south', 70), ('bigfoot', 69), ('finds', 69), ('east', 68), ('sasquatch', 67), ('Nighttime', 67), ('driving', 66), ('walking', 65), ('as', 64), ('witness', 64), ('hunting', 64), ('screams', 64), ('area', 61), ('deer', 61), ('Early', 60), ('camping', 60), ('time', 60), ('Young', 59), ('A', 58), ('Late', 58), ('Couple', 57), ('evening', 56), ('Sighting', 56), ('upright', 56), ('man', 53), ('west', 53), ('witnesses', 52), ('her', 52), ('Mountain', 52), ('Night', 52), ('Large', 52), ('incidents', 51), ('afternoon', 49), ('son', 49), ('wood', 49), ('dusk', 48), ('off', 48), ('snow', 47), ('large,', 47), ('rural', 47), ('Deer', 47), ('motorists', 47), ('young', 47), ('woods', 46), ('Hikers', 46), ('scream', 45), ('', 45), ('experience', 45), ('Witness', 45), ('footprint', 45), ('across', 44), ('fishing', 44), ('childhood', 44), ('Wilderness', 44), ('tree', 43), ('behind', 43), ('knocks', 43), ('stalking', 42), ('City', 41), ('campers', 40), ('activity', 40), ('rock', 40), ('sounds', 40), ('Daytime', 39), ('Area', 39), ('While', 39), ('unknown', 39), ('observe', 38), ('through', 38), ('over', 38), ('sightings', 38), ('reports', 38), ('dark', 37), ('are', 37), ('Road', 37), ('couple', 37), ('into', 37), ('remembers', 37), ('being', 37), ('Bigfoot', 36), ('observed', 36), ('Hiker', 35), ('fisherman', 35), ('property', 35), ('during', 34), ('running', 34), ('Three', 34), ('Fort', 34), ('camp', 34), ('cross', 34), ('experiences', 34), ('window', 33), ('farm', 33), ('men', 33), ('unusual', 33), ('Hwy', 32), ('hikers', 32), ('Valley', 32), ('seeing', 32), ('Camper', 32), ('just', 31), ('wife', 31), ('Hunters', 31), ('for', 31), ('Fisherman', 31), ('driver', 30), ('several', 30), ('Mt.', 30), ('car', 29), ('between', 29), ('around', 29), ('incident', 29), ('Mountains', 29), ('is', 29), ('Driver', 28), ('Highway', 28), ('Springs', 28), ('roadside', 28), ('&', 28), ('report', 28), ('miles', 28), ('foot', 28), ('recounts', 27), ('Wildlife', 27), ('Father', 27), ('Vocalizations', 27), ('Motorists', 26), ('observation', 26), ('Multiple', 26), ('front', 26), ('fishermen', 26), ('camper', 26), ('Creature', 26), ('howls', 26), ('throwing', 26), ('field', 25), ('riding', 25), ('town', 25), ('thrown', 25), ('standing', 25), ('Canyon', 25), ('Big', 25), ('cabin', 25), ('covered', 25), ('out', 25), ('after', 24), ('Recollection', 24), ('edge', 24), ('years', 24), ('Evening', 23), ('Hwy.', 23), ('Camp', 23), ('up', 23), ('three', 23), ('Morning', 23), ('County', 22), ('white', 22), ('face', 22), ('down', 22), ('trail', 22), ('Falls', 22), ('hair', 22), ('hiking', 22), ('very', 22), ('dog', 22), ('Rock', 22), ('track', 22), ('man-like', 21), ('rocks', 21), ('boys', 21), ('woman', 21), ('Boy', 21), ('Pass', 21), ('father', 21), ('forest', 21), ('knocking', 21), ('looking', 21), ('Fork', 21), ('Tracks', 20), ('highway', 20), ('North', 20), ('resident', 20), ('Mother', 20), ('Trail', 20), ('something', 20), ('St.', 19), ('Preserve', 19), ('walk', 19), ('next', 19), ('describe', 19), ('chased', 19), ('multiple', 19), ('Lake.', 19), ('watching', 19), ('daughter', 19), ('intimidation', 19), ('BFRO', 19), ('approach', 19), ('them', 19), ('side', 18), ('back', 18), ('Route', 18), ('Husband', 18), ('about', 18), ('Island', 18), ('he', 18), ('creek', 18), ('Mount', 18), ('Sasquatch', 17), ('New', 17), ('that', 17), ('headlights', 17), ('group', 17), ('hiker', 17), ('above', 17), ('creatures', 17), ('boy', 17), ('old', 17), ('black', 17), ('River.', 17), ('Elk', 17), ('friends', 17), ('run', 17), ('Close', 17), ('watches', 17), ('experienced', 17), ('Unusual', 17), ('truck', 17), ('other', 17), ('hearing', 17), ('-', 16), ('like', 16), ('Youth', 16), ('backyard', 16), ('Bow', 16), ('Reservoir', 16), ('yard', 16), ('human-like', 16), ('youth', 16), ('lake', 16), ('visitor', 16), ('remote', 16), ('Strange', 16), ('Loud', 16), ('howl', 16), ('footsteps', 15), ('was', 15), ('teen', 15), ('Grove', 15), ('small', 15), ('prints', 15), ('railroad', 15), ('crosses', 15), ('children', 15), ('Indian', 15), ('Encounter', 15), ('startled', 15), ('spots', 15), ('brief', 15), ('vocals', 15), ('Beach', 15), ('Bear', 15), ('East', 14), ('Four', 14), ('line', 14), ('stand', 14), ('ape-like', 14), ('him', 14), ('road-crossing', 14), ('boat', 14), ('South', 14), ('Teen', 14), ('Near', 14), ('San', 14), ('training', 14), ('logging', 14), ('they', 14), ('people', 14), ('Ridge', 14), ('series', 14), ('were', 14), ('behavior', 14), ('tall,', 13), ('then', 13), ('dirt', 13), ('dogs', 13), ('Sulphur', 13), ('tells', 13), ('Dusk', 13), ('Campground', 13), ('Pine', 13), ('riders', 13), ('US', 13), ('campsite', 13), ('away', 13), ('Hill', 13), ('border', 12), ('vocalizations,', 12), ('bike', 12), ('Spring', 12), ('mountain', 12), ('Management', 12), ('Afternoon', 12), ('range', 12), ('dawn', 12), ('Brothers', 12), ('Meadows', 12), ('Reservation', 12), ('recall', 12), ('high', 12), ('Ft.', 12), ('day', 12), ('Red', 12), ('bi-pedal', 12), ('far', 12), ('animals', 12), ('residents', 12), ('observing', 12), ('Swamp', 12), ('Several', 12), ('river', 12), ('heavy', 12), ('followed', 12), ('Footprints', 12), ('one', 12), ('awakened', 12), ('house', 11), ('repeated', 11), ('reported', 11), ('trees', 11), ('Teenager', 11), ('school', 11), ('working', 11), ('bridge', 11), ('White', 11), ('Huntington', 11), ('Point', 11), ('Flat', 11), ('Fishermen', 11), ('newspaper', 11), ('officer', 11), ('when', 11), ('Bay', 11), ('tent', 11), ('elk', 11), ('huge', 11), ('West', 11), ('Grand', 11), ('not', 11), ('approached', 11), ('teenagers', 11), ('Ocala', 11), ('frightening', 11), ('playing', 11), ('watch', 11), ('Houston', 11), ('unseen', 11), ('spot', 10), ('brown', 10), ('eyes', 10), ('teens', 10), ('possibly', 10), ('Nightime', 10), ('road.', 10), ('Truck', 10), ('teenager', 10), ('vehicle', 10), ('Brief', 10), ('Old', 10), ('Lakes', 10), ('visual', 10), ('Oregon', 10), ('Tall', 10), ('Various', 10), ('saw', 10), ('Peak', 10), ('Cypress', 10), ('Woods', 10), ('girl', 10), ('Seneca', 10), ('night-time', 10), ('police', 10), ('country', 10), ('odor', 10), ('four', 10), ('Salt', 10), ('screaming', 10), ('Rural', 10), ('Sam', 10), ('whoops', 10), ('Witnesses', 9), ('Green', 9), ('trip', 9), ('ATV', 9), ('hair-covered', 9), ('7', 9), ('fence', 9), ('Blue', 9), ('before', 9), ('traveling', 9), ('Tahoe', 9), ('Girl', 9), ('Teens', 9), ('Hunting', 9), ('High', 9), ('legs', 9), ('child', 9), ('Great', 9), ('The', 9), ('Bowhunter', 9), ('Colorado', 9), ('long', 9), ('it', 9), ('campground', 9), ('Canton', 9), ('later', 9), ('parked', 9), ('southeast', 9), ('Homeowner', 9), ('Child', 9), ('shaking', 9), ('bank', 9), ('Texas', 9), ('calls', 9), ('smell', 9), ('Provincial', 9), ('deep', 8), ('red', 8), ('within', 8), ('chasing', 8), ('stop', 8), ('Creek.', 8), ('horseback', 8), ('Strawberry', 8), ('passenger', 8), ('Twain', 8), ('3', 8), ('rider', 8), ('article', 8), ('Port', 8), ('mile', 8), ('Lake,', 8), ('Jacksonville', 8), ('or', 8), ('Middleburg', 8), ('squirrel', 8), ('residence', 8), ('living', 8), ('Mountain.', 8), ('steps', 8), ('Ohio', 8), ('2', 8), ('under', 8), ('Forest.', 8), ('scouting', 8), ('hill', 8), ('site', 8), ('taken', 8), ('print', 8), ('startles', 8), ('night,', 8), ('land', 8), ('well', 8), ('sets', 8), ('Sabine', 8), ('Horse', 8), ('Long', 8), ('Rainier', 8), ('Trout', 8), ('frightened', 8), ('Wood', 8), ('sound', 8), ('Screams', 8), ('husband', 7), ('described', 7), ('hairy,', 7), ('way', 7), ('work', 7), ('same', 7), ('below', 7), ('bottoms', 7), ('bedroom', 7), ('friend', 7), ('views', 7), ('soldier', 7), ('city', 7), ('chase', 7), ('Eagle', 7), ('Friends', 7), ('ridge', 7), ('Service', 7), ('Teenagers', 7), ('mountains', 7), ('guide', 7), ('investigator', 7), ('Bridge', 7), ('trucker', 7), ('10', 7), ('year', 7), ('Childhood', 7), ('recount', 7), ('Little', 7), ('area.', 7), ('worker', 7), ('leaves', 7), ('Junction', 7), ('Marine', 7), ('brothers', 7), ('Series', 7), ('sitting', 7), ('Park.', 7), ('Everglades', 7), ('scat', 7), ('Refuge', 7), ('juvenile', 7), ('School', 7), ('evidence', 7), ('stalked', 7), ('figures', 7), ('ranch', 7), ('nightime', 7), ('occurrences', 7), ('Illinois', 7), ('Columbia', 7), ('cornfield', 7), ('taking', 7), ('separate', 7), ('Cemetery', 7), ('call', 7), ('Knocks', 7), ('shaken', 7), ('returning', 7), ('brush', 7), ('Nat.', 7), ('Black', 7), ('Wayne', 7), ('Cave', 7), ('mine', 7), ('park', 7), ('relates', 7), ('hours', 7), ('Gorge', 7), ('get', 7), ('nights', 7), ('Trinity', 7), ('late-night', 7), ('Morton', 7), ('barefoot', 7), ('Rocks', 7), ('fresh', 7), ('Heard', 7), ('knock', 7), ('past', 6), ('Passenger', 6), ('wading', 6), ('moving', 6), ('clearing', 6), ('swamp', 6), ('Union', 6), ('ape', 6), ('Youths', 6), ('wooded', 6), ('Animal', 6), ('Base', 6), ('she', 6), ('Retired', 6), ('Army', 6), ('Scout', 6), ('few', 6), ('Myers', 6), ('Shasta', 6), ('Nevada', 6), ('teenage', 6), ('girls', 6), ('Happy', 6), ('Jasper', 6), ('smells', 6), ('Leadville', 6), ('encounter.', 6), ('shaggy', 6), ('bus', 6), ('Children', 6), ('form', 6), ('finding', 6), ('U.S.', 6), ('5', 6), ('Gainesville', 6), ('John', 6), ('approaching', 6), ('I-75', 6), ('River,', 6), ('outskirts', 6), ('Jackson', 6), ('NW', 6), ('Clark', 6), ('another', 6), ('corn', 6), ('Resident', 6), ('photos', 6), ('summer', 6), ('look', 6), ('Local', 6), ('porch', 6), ('Oak', 6), ('party', 6), ('Uwharrie', 6), ('mother', 6), ('jump', 6), ('Cuyahoga', 6), ('low', 6), ('nest', 6), ('Pigeon', 6), ('consecutive', 6), ('ongoing', 6), ('photographs', 6), ('mud', 6), ('Day', 6), ('Baker', 6), ('Lewis', 6), ('Helens', 6), ('Greenwater', 6), ('Camping', 6), ('Bend', 6), ('Cle', 6), ('Elum', 6), ('Yellowstone', 6), ('Appalachian', 6), ('(w/photos)', 6), ('Sugar', 6), ('loud,', 6), ('what', 6), ('crashing', 6), ('walks', 5), ('sighting,', 5), ('story', 5), ('eastern', 5), ('/', 5), ('Mississippi', 5), ('Vernon', 5), ('bow', 5), ('Sightings', 5), ('Cedar', 5), ('parking', 5), ('Duck', 5), ('Air', 5), ('Harrison', 5), ('squatting', 5), ('Officer', 5), ('base', 5), ('scary', 5), ('Harte', 5), ('massive', 5), ('employee', 5), ('Have', 5), ('Has', 5), ('Meadow', 5), ('northwest', 5), ('binoculars', 5), ('Gold', 5), ('students', 5), ('sighted', 5), ('6', 5), ('big', 5), ('shore', 5), ('part', 5), ('Trucker', 5), ('Carson', 5), ('Lost', 5), ('pre-dawn', 5), ('Former', 5), ('women', 5), ('Scouts', 5), ('Rd.', 5), ('picking', 5), ('witness.', 5), ('woods.', 5), ('runs', 5), ('crew', 5), ('41', 5), ('Prairie', 5), ('adult', 5), ('interrupted', 5), ('hog', 5), ('target', 5), ('shooting', 5), ('almost', 5), ('Large,', 5), ('seven', 5), ('Hills', 5), ('Nashville', 5), ('Helen', 5), ('middle', 5), ('up-close', 5), ('days', 5), ('primate', 5), ('homeowner', 5), ('than', 5), ('abandoned', 5), ('Turkey', 5), ('SR', 5), ('food', 5), ('local', 5), ('eating', 5), ('Soldier', 5), ('path', 5), ('glimpse', 5), ('mining', 5), ('Interstate', 5), ('Bipedal', 5), ('apart', 5), ('firewood', 5), ('exploring', 5), ('Pleasant', 5), ('had', 5), ('Virginia', 5), ('gathering', 5), ('bed', 5), ('A.', 5), ('gravel', 5), ('light', 5), ('backpackers', 5), ('pasture', 5), ('Group', 5), ('turkey', 5), ('strip', 5), ("witness's", 5), ('Ongoing', 5), ('movement', 5), ('sons', 5), ('Hog', 5), ('Washington', 5), ('Hood', 5), ('human', 5), ('Rt.', 5), ('Brazos', 5), ('In', 5), ('kill', 5), ('Olympic', 5), ('Footprint', 5), ('"', 5), ('five', 5), ('(w/', 5), ('Ocean', 5), ('passes', 5), ('come', 5), ('powerful', 5), ('intimidated', 5), ('Graham', 5), ('terrifying', 5), ('Hears', 5), ('Vacationer', 5), ('Chilliwack', 5), ('tracks,', 5), ('stick', 5), ('--', 5), ('Military', 5), ('records', 5), ('moaning', 5), ('camped', 5), ('staying', 5), ('identical', 5), ('Heaven', 5), ('Cushman', 5), ('water', 4), ('object', 4), ('also', 4), ('where', 4), ('Lady', 4), ('duck', 4), ('Roadside', 4), ('Talladega', 4), ('private', 4), ('Kansas', 4), ('remembered', 4), ('community', 4), ('Fouke', 4), ('Benton', 4), ('spooked', 4), ('Coon', 4), ('Squirrel', 4), ('El', 4), ('Force', 4), ('moonlit', 4), ('Pre-dawn', 4), ('beside', 4), ('Robinson', 4), ('inside', 4), ('30', 4), ('Arizona', 4), ('northeast', 4), ('gray', 4), ('commuter', 4), ('Hollow', 4), ('Kennedy', 4), ('Grass', 4), ('50', 4), ('Albion', 4), ('Hiking', 4), ('Sierra', 4), ('briefly', 4), ('7-8', 4), ('Laurel', 4), ('Mammoth', 4), ('French', 4), ('Backpacker', 4), ('backpacker', 4), ('Observes', 4), ('bipeds', 4), ('sleeping', 4), ('Mills', 4), ('Vancouver', 4), ('4', 4), ('mountainside', 4), ('Southern', 4), ('Fishing', 4), ('reddish', 4), ('Pike', 4), ('retired', 4), ('wildlife', 4), ('trailer', 4), ('Buffalo', 4), ('southwest', 4), ('Glenwood', 4), ('Backpackers', 4), ('Rd', 4), ('creature.', 4), ('Five', 4), ('six', 4), ('open', 4), ('carrying', 4), ('College', 4), ('Huge', 4), ('Florida', 4), ('Orlando', 4), ('garbage', 4), ('accounts', 4), ('Florence', 4), ('Atlanta', 4), ('hiding', 4), ('route', 4), ('thermal', 4), ('Top', 4), ('Cleveland', 4), ('Late-night', 4), ('village', 4), ('horses', 4), ('each', 4), ('More', 4), ('sounds,', 4), ('Newspaper', 4), ('staring', 4), ('distance', 4), ('midnight', 4), ('heading', 4), ('Van', 4), ('Boone', 4), ('rabbit', 4), ("7'", 4), ('lengthy', 4), ('owner', 4), ('recent', 4), ('Branch', 4), ('observations', 4), ('motorcyclist', 4), ('Nature', 4), ('Sandy', 4), ('Run', 4), ('occasions', 4), ('tracks.', 4), ('Photographer', 4), ('leave', 4), ('BF', 4), ('unidentified', 4), ('frozen', 4), ('arms', 4), ('strong', 4), ('make', 4), ('Madison', 4), ('horse', 4), ('8', 4), ('Blaine', 4), ('Small', 4), ('walked', 4), ('Franklin', 4), ('leaving', 4), ('Tree', 4), ('window.', 4), ('Greenville', 4), ('Houghton', 4), ('window,', 4), ('Hairy', 4), ('neighborhood', 4), ('shocked', 4), ('Manistee', 4), ('interactions', 4), ('moonlight', 4), ('discovered', 4), ('Missouri', 4), ('Home', 4), ('Springfield', 4), ('Mushroom', 4), ('Sullivan', 4), ('James', 4), ('Reserve', 4), ('photo', 4), ("Nat'l", 4), ('Boys', 4), ('crouching', 4), ('memory', 4), ('Teenage', 4), ('Bragg', 4), ('Barrens', 4), ('Mescalero', 4), ('head', 4), ('pond', 4), ('Aurora', 4), ('Beaver', 4), ('screams,', 4), ('Midnight', 4), ("see's", 4), ('Mill', 4), ('similar', 4), ('involving', 4), ('Encounters', 4), ('college', 4), ('Game', 4), ('Resort', 4), ('reddish-brown', 4), ('Canadian', 4), ('Clear', 4), ('Tillamook', 4), ('Silver', 4), ('beach', 4), ('nearby', 4), ('approaches', 4), ('Diamond', 4), ('Native', 4), ('Stevens', 4), ('upright,', 4), ('members', 4), ('ago', 4), ('Monte', 4), ('Cristo', 4), ('home,', 4), ('game', 4), ('witnessed', 4), ('spotted', 4), ('Chehalis', 4), ('Lone', 4), ('Packwood', 4), ('subject', 4), ('Fish', 4), ('Holden', 4), ('display', 4), ('gets', 4), ('Ashford', 4), ('Indianola', 4), ('Dam', 4), ('Spanaway', 4), ('course', 4), ('investigation', 4), ('Phillips', 4), ('Monongahela', 4), ('33', 4), ('setting', 4), ('workers', 4), ('Uinta', 4), ('screamed', 4), ('whooping', 4), ('Emigrant', 4), ('Loud,', 4), ('Oroville', 4), ('Sounds', 4), ('foul', 4), ('footprints,', 4), ('etc.,', 4), ('growling', 4), ('stride', 4), ('Recreation', 4), ('Lumby', 4), ('Vocalization', 4), ('photographed', 4), ('noted', 4), ('responses', 4), ('deck', 4), ('vocal', 4), ('Salmon', 4), ('noticed', 4), ('Loop', 4), ('chatter', 4), ('characteristic', 4), ('all', 4), ('Shawnee', 4), ('Granite', 4), ('events', 4), ('Unknown', 4), ('coon', 4), ('record', 4), ('distant', 4), ('Snowmobiler', 3), ('fishing,', 3), ('visits', 3), ('creature,', 3), ('youths', 3), ('Huntsville', 3), ('Kayaker', 3), ('first', 3), ('Bigfoots', 3), ('spotlight', 3), ('top', 3), ('following', 3), ('sisters', 3), ('checking', 3), ('cattle', 3), ("8'-9'", 3), ('against', 3), ('Eureka', 3), ('Dorado', 3), ('two-legged', 3), ('Cove', 3), ('scope', 3), ('Knob', 3), ('military', 3), ('Monument', 3), ('Show', 3), ('Prescott', 3), ('Sound', 3), ('Alturas', 3), ('Desolation', 3), ('Car', 3), ('299', 3), ('Sister', 3), ('La', 3), ('Feather', 3), ('Law', 3), ('Enforcement', 3), ('Santa', 3), ('Northern', 3), ('California', 3), ('sightings,', 3), ('Stumpy', 3), ('aggressive', 3), ('Coast', 3), ('Independence', 3), ('101', 3), ('Orick', 3), ('Gulch', 3), ('Multi-witness', 3), ('footage', 3), ('Face', 3), ('ravine', 3), ('Yosemite', 3), ('complex', 3), ('Pacific', 3), ('law', 3), ('enforcement', 3), ('patrol', 3), ('Klamath', 3), ('bottom', 3), ('16', 3), ('lying', 3), ('20', 3), ('climbing', 3), ('40', 3), ('Road.', 3), ('BC', 3), ('sighitng', 3), ('Nelson', 3), ('outing', 3), ('Ontario', 3), ('tourist', 3), ('Sioux', 3), ('Frances', 3), ('Westport', 3), ('Crater', 3), ('giant', 3), ('wilderness', 3), ('thing', 3), ('peeking', 3), ('Fork,', 3), ('Conejos', 3), ('1', 3), ('account', 3), ('inch', 3), ('biologist', 3), ('Mtn.', 3), ('strides', 3), ('sighting.', 3), ('leader', 3), ('apparent', 3), ('Deputy', 3), ('Surprise', 3), ('FL', 3), ('Security', 3), ('duty', 3), ('Six', 3), ('Upright', 3), ('investigated', 3), ('Federal', 3), ('humanoid', 3), ('half', 3), ('hunched', 3), ('Ruskin', 3), ('home.', 3), ('be', 3), ('road-cross', 3), ('Head', 3), ('Rainbow', 3), ('Land', 3), ('female', 3), ('Homestead', 3), ('stands', 3), ('Weekend', 3), ('Blanding', 3), ('I-10', 3), ('cutting', 3), ('rest', 3), ('comes', 3), ('Marys', 3), ('Women', 3), ('Bulow', 3), ('stories', 3), ('directly', 3), ('Myakka', 3), ('Harbor', 3), ('Georgia', 3), ('Dark', 3), ('Chattahoochee', 3), ('county', 3), ('Riverdale', 3), ('Stewart', 3), ('numerous', 3), ('Memories', 3), ('Cedartown', 3), ('surprises', 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('Brattleboro', 1), ('Missing', 1), ('Cattle', 1), ('Milk', 1), ('Hoodsport', 1), ('answer', 1), ('Blocking', 1), ('Okanogon', 1), ('rose', 1), ('Guler', 1), ('"roars",', 1), ('clear-cut', 1), ('odors', 1), ('odd,', 1), ('17-18', 1), ('Montesano', 1), ('trail.', 1), ('Rooks', 1), ('Investigation', 1), ('echoed', 1), ('mountainside.', 1), ('unsusual', 1), ('exchanges', 1), ('guttral,', 1), ('sceams.', 1), ('Barefoot', 1), ('whistling', 1), ('earlier', 1), ('Worse', 1), ('"Brinson', 1), ('striking', 1), ('answered', 1), ('source', 1), ('terrify', 1), ('sticks', 1), ('beaten', 1), ('1)', 1), ('2)', 1), ('harassed', 1), ('19"', 1), ('25-50', 1), ('intimidating', 1), ('beating', 1), ('guides', 1), ('wake', 1), ('Environmental', 1), ('Engineer', 1), ('responding', 1), ('Spelunkers', 1), ('garbled', 1), ('nest-like', 1), ('dense', 1), ('Townsend', 1), ('Clayton,', 1), ('Hollywood', 1), ('Multible', 1), ('lake.', 1), ('canyons', 1), ('Coulee', 1), ('growled', 1), ('woodworking', 1), ('Wahkiacus', 1), ('Sol', 1), ('Duc', 1), ('footfalls,', 1), ('clicking', 1), ('foorprints', 1), ('Alder', 1), ('Stevenson', 1), ('Hartstene', 1), ('homeowners', 1), ('Alpental', 1), ('walkers', 1), ('Outdoorsman', 1), ('Tinkham', 1), ('Jim', 1), ('daybreak,', 1), ('Sutherland', 1), ('repair', 1), ('stop.', 1), ('Mossy', 1), ('noisy', 1), ('Thumps', 1), ('Prior', 1), ('Joyce', 1), ('repeatedly', 1), ("Deadman's", 1), ('expeditioners', 1), ('Peninsula.', 1), ('Stick', 1), ('Throwing', 1), ('neighbors', 1), ('mournful', 1), ('Nason', 1), ('Rain', 1), ('rock/stick', 1), ('drainage', 1), ('Muckleshoot', 1), ('Scenic', 1), ('interrupt', 1), ('camper’s', 1), ('quiet', 1), ('Gifford', 1), ('Pinchot', 1), ('NF,', 1), ('Tarbell', 1), ('Brown', 1), ('rainforest,', 1), ('Kulshan', 1), ('Train', 1), ('engineer', 1), ('prospector', 1), ('Metaline', 1), ('Tonasket', 1), ('Okanogan', 1), ('guttural', 1), ('Winthrop', 1), ('Redmond', 1), ('pigeon', 1), ('shooters', 1), ('girlfriend,', 1), ('Cispus,', 1), ('Angler', 1), ('senses', 1), ('ominous', 1), ('Blyn', 1), ('Sequim', 1), ('"Ohio"', 1), ('Harstine', 1), ('Humptulips', 1), ('diverts', 1), ('scream-howl', 1), ('cannot', 1), ('identify', 1), ('alone', 1), ('Camano', 1), ('reoccurring', 1), ('sit', 1), ('launch', 1), ('monkey-like', 1), ('Tieton', 1), ('Malo', 1), ('dashing', 1), ('Poodle', 1), ('Hitchhiker', 1), ('Ilwaco', 1), ('rock-throwing,', 1), ('Elbe', 1), ('Mossyrock', 1), ('Napavine', 1), ('instructing', 1), ('"Chimp-like"', 1), ('horseman', 1), ('Dow', 1), ('Raymond', 1), ('killing', 1), ('Elwha', 1), ('quadruped', 1), ('Moclips', 1), ('Colville', 1), ('confluence', 1), ('Boss', 1), ('shoved', 1), ('Steilacoom', 1), ('race', 1), ("'Hide", 1), ('Go', 1), ("Seek'", 1), ('Vigorous', 1), ('unnerve', 1), ('Packwood.', 1), ('Pasayten', 1), ('Marymoor', 1), ('109', 1), ('frequent', 1), ('someone', 1), ('"Screaming', 1), ('Heather', 1), ('97', 1), ('solo', 1), ('Goat', 1), ('trips', 1), ('having', 1), ('Steelhead', 1), ('Duckabush', 1), ('elevated', 1), ('bushes', 1), ('Lena', 1), ('growls', 1), ('spook', 1), ('carpenters', 1), ('trainer', 1), ('Shelton', 1), ('touched', 1), ('For', 1), ('Brinnon', 1), ('backpacking,', 1), ('Natches', 1), ('Wilkeson', 1), ('"weird', 1), ('exchange"', 1), ('unknowns', 1), ('smoke', 1), ('alarmed', 1), ('origin', 1), ('Mica', 1), ('Target', 1), ('(see', 1), ('video)', 1), ('tree-breaks', 1), ('Aeneas', 1), ('roads', 1), ('Snowplow', 1), ('Mazama', 1), ('Gibberish-like', 1), ('DuPont', 1), ('Addy', 1), ('day-time', 1), ('Moses', 1), ("'70s", 1), ('unnerves', 1), ('meal', 1), ('Smackout', 1), ('Someone', 1), ('Dodgeville.', 1), ('suspected', 1), ('Grantsburg', 1), ('Menomonee', 1), ('footsteps,', 1), ('inexplicable', 1), ('Vilas', 1), ('Chequamegon', 1), ('carcass', 1), ('nabbed', 1), ('pickup', 1), ('Erin', 1), ('167', 1), ('Way)', 1), ('attending', 1), ('Namekagon', 1), ('continuing', 1), ('hike,', 1), ('Bonita', 1), ('Kettle', 1), ('Moraine', 1), ('cleaning', 1), ('ditch', 1), ('Woodruff', 1), ('buildings', 1), ('arrives', 1), ('Avoca', 1), ('Kiel', 1), ('151', 1), ('Gorilla-like', 1), ('Mauston', 1), ('Horicon', 1), ('Boscobel', 1), ('Drummond', 1), ('dive', 1), ('Washburn', 1), ('Charateristic', 1), ('Winter,', 1), ('Minden', 1), ('"people', 1), ('backwards."', 1), ('Many', 1), ('On-going', 1), ('cautious', 1), ('Lesage', 1), ('imprint', 1), ('menacing', 1), ('Beckley', 1), ('Pt.Pleasant', 1), ('Bottom', 1), ('Judy', 1), ('Onego', 1), ('expedition,', 1), ('2005.', 1), ('throwing,', 1), ('Participant', 1), ('WV', 1), ('Circleville', 1), ('Tygart', 1), ('Sistersville', 1), ('Frozen', 1), ('Arnoldsberg', 1), ('Morgantown', 1), ('Glade', 1), ('Pond,', 1), ('Monon.', 1), ('raccoon', 1), ('explorers', 1), ('bizarre', 1), ('reveals', 1), ('Pennsboro', 1), ('Iaeger', 1), ('Lavalette', 1), ('wakes', 1), ('falls', 1), ('Durbin', 1), ('boulders', 1), ('recovered', 1), ('Wellsburg', 1), ('include', 1), ('Pax', 1), ('Hedgesville', 1), ('occassions', 1), ('bait', 1), ('Sundance', 1), ('Felsenthal', 1), ('Writer', 1), ('Discusses', 1), ('Her', 1), ('Pursuing', 1), ('Legends', 1), ('100', 1), ('Fayette', 1), ('Counties', 1), ('"Big', 1), ('Muddy', 1), ('flap', 1), ("'huge'", 1), ('throws', 1), ('Chittyville.', 1), ('Beige', 1), ('1000', 1), ('Kell.', 1), ('describing', 1), ('sized', 1), ('Edwardsville.', 1), ('Vocalizing,', 1), ('note', 1), ('high-pitched,', 1), ('glowing', 1), ('Second-hand', 1)]

Description Analysis

Aside from conjunction words and words that don't really tell us any details about the sighting ("near", "sighting", "Possible", "encounter") the most common words in the descriptions are "vocalizations" "Lake" "night" and "outside", suggesting that many of these sightings vocalizations were heard. If you wanted to see a bigfoot, maybe standing by a lake at night outside would be more probable to encounter one as there are more descritions with these words. Another interesting observation is that the word "Man" has 230 counts whereas "Woman" only has 111, suggesting that more men see Bigfoot than women by a significant margin.

desc.count('Campers') + desc.count('campers') + desc.count('campsite') + desc.count('tent') #use the .count function to count instances of relevant or interesting words

165

desc.count('Forest') + desc.count('forest') + desc.count('woods') + desc.count('Woods') + desc.count('tree')

305

Lots of mentions of woods and camping, as expected. Bigfoot is most clasically seen in the woods.

desc.count('highway') + desc.count('Highway') + desc.count('Freeway') + desc.count('freeway')

52

desc.count('Desert') + desc.count('desert') + desc.count('hot') + desc.count('arid')

57

Less mentions of bigfoot near highways or freeways and in deserts or hot and arid places.

desc.count('Sasquatch') + desc.count('sasquatch') + desc.count('Squatch')

87

desc.count('Bigfoot') + desc.count('bigfoot')

113

desc.count('Yowie') + desc.count('Yeti')+ desc.count('Skunk-Ape')

1

Bigfoot is most commonly referred to as Bigfoot or Squatch/Sasquatch. Other names for bigfoot are less used in this dataset.

Sightings vs Latitude over Year and Month Scatter Plot

Location of the sightings is of the utmost importance as we focus in on one of our main questions: does the weather/temperature affect the probability of seeing Bigfoot. Below we will plot the longitude and latitude of each sighting on a map to visualize the distribution of sightings in North America. Note that this includes sightings overtime. Future plots contrasting sighting location over time could prove useful in determining potential migration habits or simply the spreading of the Bigfoot rumor.

bf.plot.scatter(x="longitude", y="latitude", c="year", cmap="plasma", alpha=.5)

<matplotlib.axes._subplots.AxesSubplot at 0x7f8604b9e4f0>

bf.plot.scatter(x="longitude", y="latitude", c="month", cmap="plasma", alpha=.5)

<matplotlib.axes._subplots.AxesSubplot at 0x7f86048bc670>

We plotted the sightings across the scatter plot by latitude and longitude with the color variations being by month. Bigfoots show no annual migratory patterns across North America as there are no patterns in the sightings, month, and geographical location. This rules out any potential confusion we could have experience in considering all of the years data at once. Since there is no clear change in pattern across time, we can use all the data at once.

Sightings over a Map of North America

# Techniques used to load and size this plot derived from https://geopandas.org/en/stable/docs/user_guide/mapping.html
# and https://gis.stackexchange.com/questions/375582/zoom-to-a-plot-with-geopandas-based-on-data-from-csv-and-shapefile

geometry = [Point(xy) for xy in zip(bf['longitude'], bf['latitude'])]
gdf = GeoDataFrame(bf, geometry=geometry)   

#this is a simple map that goes with geopandas
world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
ax = gdf.plot(ax=world.plot(figsize=(20, 6)), marker='o', color='orange', markersize=15, aspect='auto', alpha=.6);
minx, miny, maxx, maxy = gdf.total_bounds
ax.set_xlim(minx-5, maxx+5)
ax.set_ylim(miny-5, maxy+5)

(20.141, 74.5)

Heatmap of Temperature across the United States Divided by State

# Techniques used to load and size this plot derived from https://towardsdatascience.com/simplest-way-of-creating-a-choropleth-map-by-u-s-states-in-python-f359ada7735e
fig = px.choropleth(avg_weather,
                    locations='state_code', 
                    locationmode="USA-states", 
                    scope="usa",
                    color='Avg °F',
                    color_continuous_scale="plasma", 
                    )
fig.show()

Following the Location of the sightings, climate data across the region of interest can be visualized above. As you can see (and probably intuit), temperature varies mainly from North to South. The sighting map does not seem to portray the same pattern at first glance.

Answering the Question¶

So does Bigfoot have a preference according to temperature. The graphs below attempt to answer this question.

The bar graphs show variance in sighting frequency across longitude and latitude. The first (longitude) shows two spikes potentially indicating a correlation between longitudinal factors and bigfoot. However, the latitude bar graph shows a much weaker correlation to sighting frequency, pushing away the idea that Bigfoot prefers certain temperatures.

bf.longitude.plot.hist(alpha=.5, density=True, legend=True, bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x7f8604866700>

bf.latitude.plot.hist(alpha=.5, density=True, legend=True, bins=50)

<matplotlib.axes._subplots.AxesSubplot at 0x7f860473bfa0>

Now, we can create a heatmap to more concisely view the variation in frequency accross both longitude and latitude simultaneously.

bf_freq_base = {
    "longitude" : ["25-30", "25-30", "25-30", "25-30", "30-35", "30-35", "30-35", "30-35", "35-40", "35-40", "35-40", "35-40", "40-45", "40-45", "40-45", "40-45", "45-50", "45-50", "45-50", "45-50"],
    "latitude" : ["60-80", "80-100", "100-120", "120-140", "60-80", "80-100", "100-120", "120-140", "60-80", "80-100", "100-120", "120-140", "60-80", "80-100", "100-120", "120-140", "60-80", "80-100", "100-120", "120-140"],
    "sightings" : [bf[(bf.latitude > 25) & (bf.latitude < 30) & (bf.longitude < -60) & (bf.longitude > -80)].report_number.count(),
                   bf[(bf.latitude > 25) & (bf.latitude < 30) & (bf.longitude < -80) & (bf.longitude > -100)].report_number.count(),
                   bf[(bf.latitude > 25) & (bf.latitude < 30) & (bf.longitude < -100) & (bf.longitude > -120)].report_number.count(),
                   bf[(bf.latitude > 25) & (bf.latitude < 30) & (bf.longitude < -120) & (bf.longitude > -140)].report_number.count(),
                   bf[(bf.latitude > 30) & (bf.latitude < 35) & (bf.longitude < -60) & (bf.longitude > -80)].report_number.count(),
                   bf[(bf.latitude > 30) & (bf.latitude < 35) & (bf.longitude < -80) & (bf.longitude > -100)].report_number.count(),
                   bf[(bf.latitude > 30) & (bf.latitude < 35) & (bf.longitude < -100) & (bf.longitude > -120)].report_number.count(),
                   bf[(bf.latitude > 30) & (bf.latitude < 35) & (bf.longitude < -120) & (bf.longitude > -140)].report_number.count(),
                   bf[(bf.latitude > 35) & (bf.latitude < 40) & (bf.longitude < -60) & (bf.longitude > -80)].report_number.count(),
                   bf[(bf.latitude > 35) & (bf.latitude < 40) & (bf.longitude < -80) & (bf.longitude > -100)].report_number.count(),
                   bf[(bf.latitude > 35) & (bf.latitude < 40) & (bf.longitude < -100) & (bf.longitude > -120)].report_number.count(),
                   bf[(bf.latitude > 35) & (bf.latitude < 40) & (bf.longitude < -120) & (bf.longitude > -140)].report_number.count(),
                   bf[(bf.latitude > 40) & (bf.latitude < 45) & (bf.longitude < -60) & (bf.longitude > -80)].report_number.count(),
                   bf[(bf.latitude > 40) & (bf.latitude < 45) & (bf.longitude < -80) & (bf.longitude > -100)].report_number.count(),
                   bf[(bf.latitude > 40) & (bf.latitude < 45) & (bf.longitude < -100) & (bf.longitude > -120)].report_number.count(),
                   bf[(bf.latitude > 40) & (bf.latitude < 45) & (bf.longitude < -120) & (bf.longitude > -140)].report_number.count(),
                   bf[(bf.latitude > 45) & (bf.latitude < 50) & (bf.longitude < -60) & (bf.longitude > -80)].report_number.count(),
                   bf[(bf.latitude > 45) & (bf.latitude < 50) & (bf.longitude < -80) & (bf.longitude > -100)].report_number.count(),
                   bf[(bf.latitude > 45) & (bf.latitude < 50) & (bf.longitude < -100) & (bf.longitude > -120)].report_number.count(),
                   bf[(bf.latitude > 45) & (bf.latitude < 50) & (bf.longitude < -120) & (bf.longitude > -140)].report_number.count(),]
}

bf_freq = pd.DataFrame(bf_freq_base)
bf_freq

bf_freq_pivot = bf_freq.pivot_table(
    index= ["latitude"], columns = ["longitude"], 
    values = "sightings", aggfunc=np.max
)
bf_freq_pivot = bf_freq_pivot.reindex(["60-80", "80-100", "100-120", "120-140"], axis=0)
bf_freq_pivot

sns.heatmap(bf_freq_pivot)

<matplotlib.axes._subplots.AxesSubplot at 0x7f8604693580>

Conclusions¶

Upon starting this project, this team set out to learn more about the subject and patterns of the infamous yeti/sasquach/yowie known as Bigfoot, a wildly popular topic in cryptozoology. By extracting data from the world's largest and most reputable source of bigfoot sighting data, the Bigfoot Reasearchers Organization, we hoped to uncover correlations or lack thereof that might hint towards the creatures existence or the possibility of a hoax. Chief among our many many questions was the relationship between Bigfoot and his environment: do certain weather or climate factors determine whether Bigfoot is present/is more likely to be spotted? Through our exploratory data analysis, we explored all apsects of the bigfoot sighting data and attempted to eliminated future sources of confusion by ruling out correlations between date and location. We also examined patterns found in the description of the sighting and tried to find common themes in the descriptions. Our original hypothesis was that there will be a correlation between sighting location and average temperature potentially indicating bigfoot climate preference. We had hoped that this might lead to a predictive model of where you are more likely to spot bigfoot based on weather. However, our findings did not support this as temperature generally varies along changes in latitude and bigfoot sightings show no such pattern. In fact, frequency varies far more across longitude. As an interesting sidenote and a place for potential continued analysis, our map of bigfoot sightings is eerily congruent with maps of population density such as one found here. This has a variety of explanations ranging from "Bigfoot does not show preference for climate as a similar animal would so he isn’t real, people fake sightings everywhere" to "species of bigfoot have adapted to various climates." As you can see, it is easy to explain away correlations in either direction, making Bigfoot data difficult to deal with in terms of concrete conclusions.

#Convert to HTML

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	number	title	classification	timestamp	latitude	longitude
0	637	Report 637: Campers' encounter just after dark...	Class A	2000-06-16T12:00:00Z	61.50000	-142.90000
1	2917	Report 2917: Family observes large biped from car	Class A	1995-05-15T12:00:00Z	55.18720	-132.79820
2	7963	Report 7963: Sasquatch walks past window of ho...	Class A	2004-02-09T12:00:00Z	55.20350	-132.82020
3	9317	Report 9317: Driver on Alcan Highway has noon,...	Class A	2004-06-18T12:00:00Z	62.93750	-141.56670
4	13038	Report 13038: Snowmobiler has encounter in dee...	Class A	2004-02-15T12:00:00Z	61.05950	-149.78530
...	...	...	...	...	...	...
3805	4159	Report 4159: Campers hear unusual grunting sou...	Class C	1995-11-15T12:00:00Z	31.22333	-94.96611
3806	175	Report 175: Campers encounter large animal nea...	Class C	2000-07-30T12:00:00Z	47.47745	-121.04150
3807	945	Report 945: Creature with red glowing eyes seen	Class C	1999-03-19T12:00:00Z	47.57778	-124.29330
3808	1387	Report 1387: Father and son hear heavy footste...	Class C	1983-08-17T12:00:00Z	47.51030	-122.04560
3809	3044	Report 3044: Second-hand report of creature lo...	Class C	1975-09-10T12:00:00Z	43.98694	-91.33662

	number	title	classification	timestamp	latitude	longitude	report #	description	extra	date	time	year	month	day
0	637	Report 637: Campers' encounter just after dark...	Class A	2000-06-16T12:00:00Z	61.50000	-142.90000	Report 637	Campers' encounter just after dark in the Wran...	None	2000-06-16	12:00:00Z	2000	06	16
1	2917	Report 2917: Family observes large biped from car	Class A	1995-05-15T12:00:00Z	55.18720	-132.79820	Report 2917	Family observes large biped from car	None	1995-05-15	12:00:00Z	1995	05	15
2	7963	Report 7963: Sasquatch walks past window of ho...	Class A	2004-02-09T12:00:00Z	55.20350	-132.82020	Report 7963	Sasquatch walks past window of house at night	None	2004-02-09	12:00:00Z	2004	02	09
3	9317	Report 9317: Driver on Alcan Highway has noon,...	Class A	2004-06-18T12:00:00Z	62.93750	-141.56670	Report 9317	Driver on Alcan Highway has noon, road encount...	None	2004-06-18	12:00:00Z	2004	06	18
4	13038	Report 13038: Snowmobiler has encounter in dee...	Class A	2004-02-15T12:00:00Z	61.05950	-149.78530	Report 13038	Snowmobiler has encounter in deep snow near Po...	None	2004-02-15	12:00:00Z	2004	02	15
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3805	4159	Report 4159: Campers hear unusual grunting sou...	Class C	1995-11-15T12:00:00Z	31.22333	-94.96611	Report 4159	Campers hear unusual grunting sound from behin...	None	1995-11-15	12:00:00Z	1995	11	15
3806	175	Report 175: Campers encounter large animal nea...	Class C	2000-07-30T12:00:00Z	47.47745	-121.04150	Report 175	Campers encounter large animal near Cle Elum	None	2000-07-30	12:00:00Z	2000	07	30
3807	945	Report 945: Creature with red glowing eyes seen	Class C	1999-03-19T12:00:00Z	47.57778	-124.29330	Report 945	Creature with red glowing eyes seen	None	1999-03-19	12:00:00Z	1999	03	19
3808	1387	Report 1387: Father and son hear heavy footste...	Class C	1983-08-17T12:00:00Z	47.51030	-122.04560	Report 1387	Father and son hear heavy footsteps made by an...	None	1983-08-17	12:00:00Z	1983	08	17
3809	3044	Report 3044: Second-hand report of creature lo...	Class C	1975-09-10T12:00:00Z	43.98694	-91.33662	Report 3044	Second-hand report of creature looking in at w...	None	1975-09-10	12:00:00Z	1975	09	10

	report_number	description	class	longitude	latitude	year	month	day
0	637	Campers' encounter just after dark in the Wran...	A	-142.90000	61.50000	2000	06	16
1	2917	Family observes large biped from car	A	-132.79820	55.18720	1995	05	15
2	7963	Sasquatch walks past window of house at night	A	-132.82020	55.20350	2004	02	09
3	9317	Driver on Alcan Highway has noon, road encount...	A	-141.56670	62.93750	2004	06	18
4	13038	Snowmobiler has encounter in deep snow near Po...	A	-149.78530	61.05950	2004	02	15
...	...	...	...	...	...	...	...	...
3805	4159	Campers hear unusual grunting sound from behin...	C	-94.96611	31.22333	1995	11	15
3806	175	Campers encounter large animal near Cle Elum	C	-121.04150	47.47745	2000	07	30
3807	945	Creature with red glowing eyes seen	C	-124.29330	47.57778	1999	03	19
3808	1387	Father and son hear heavy footsteps made by an...	C	-122.04560	47.51030	1983	08	17
3809	3044	Second-hand report of creature looking in at w...	C	-91.33662	43.98694	1975	09	10

	report_number	description	class	longitude	latitude	year	month	day
0	637	Campers' encounter just after dark in the Wran...	A	-142.90000	61.50000	2000	6	16
1	2917	Family observes large biped from car	A	-132.79820	55.18720	1995	5	15
2	7963	Sasquatch walks past window of house at night	A	-132.82020	55.20350	2004	2	9
3	9317	Driver on Alcan Highway has noon, road encount...	A	-141.56670	62.93750	2004	6	18
4	13038	Snowmobiler has encounter in deep snow near Po...	A	-149.78530	61.05950	2004	2	15
...	...	...	...	...	...	...	...	...
3805	4159	Campers hear unusual grunting sound from behin...	C	-94.96611	31.22333	1995	11	15
3806	175	Campers encounter large animal near Cle Elum	C	-121.04150	47.47745	2000	7	30
3807	945	Creature with red glowing eyes seen	C	-124.29330	47.57778	1999	3	19
3808	1387	Father and son hear heavy footsteps made by an...	C	-122.04560	47.51030	1983	8	17
3809	3044	Second-hand report of creature looking in at w...	C	-91.33662	43.98694	1975	9	10

	report_number	description	class	longitude	latitude	year	month	day
0	637	Campers' encounter just after dark in the Wran...	A	-142.90000	61.50000	2000	6	16
1	2917	Family observes large biped from car	A	-132.79820	55.18720	1995	5	15
2	7963	Sasquatch walks past window of house at night	A	-132.82020	55.20350	2004	2	9
3	9317	Driver on Alcan Highway has noon, road encount...	A	-141.56670	62.93750	2004	6	18
4	13038	Snowmobiler has encounter in deep snow near Po...	A	-149.78530	61.05950	2004	2	15
...	...	...	...	...	...	...	...	...
3805	4159	Campers hear unusual grunting sound from behin...	C	-94.96611	31.22333	1995	11	15
3806	175	Campers encounter large animal near Cle Elum	C	-121.04150	47.47745	2000	7	30
3807	945	Creature with red glowing eyes seen	C	-124.29330	47.57778	1999	3	19
3808	1387	Father and son hear heavy footsteps made by an...	C	-122.04560	47.51030	1983	8	17
3809	3044	Second-hand report of creature looking in at w...	C	-91.33662	43.98694	1975	9	10

	longitude	latitude	year	month	day
count	3801.000000	3801.000000	3801.000000	3801.000000	3801.000000
mean	-97.999788	39.757491	1997.426467	7.207577	14.202315
std	16.965645	6.070532	14.078200	3.090785	8.619828
min	-167.131000	25.141000	1869.000000	1.000000	1.000000
25%	-118.375000	35.474500	1988.000000	5.000000	7.000000
50%	-92.304600	39.492650	2002.000000	8.000000	15.000000
75%	-83.282650	44.705960	2007.000000	10.000000	20.000000
max	-63.836830	69.500000	2053.000000	12.000000	31.000000

	index	State	Avg °F	Avg °C	Rank
0	0	Alabama	62.8	17.1	7
1	1	Alaska	26.6	-3.0	50
2	2	Arizona	60.3	15.7	10
3	3	Arkansas	60.4	15.8	9
4	4	California	59.4	15.2	12
5	5	Colorado	45.1	7.3	39
6	6	Connecticut	49.0	9.4	29
7	7	Delaware	55.3	12.9	16
8	8	Florida	70.7	21.5	1
9	9	Georgia	63.5	17.5	5

	longitude	latitude	sightings
0	25-30	60-80	0
1	25-30	80-100	221
2	25-30	100-120	1
3	25-30	120-140	0
4	30-35	60-80	13
5	30-35	80-100	556
6	30-35	100-120	77
7	30-35	120-140	1
8	35-40	60-80	126
9	35-40	80-100	709
10	35-40	100-120	219
11	35-40	120-140	97
12	40-45	60-80	152
13	40-45	80-100	458
14	40-45	100-120	99
15	40-45	120-140	159
16	45-50	60-80	15
17	45-50	80-100	137
18	45-50	100-120	137
19	45-50	120-140	526

	State	Avg °F	Rank	state_code
0	Alabama	62.8	7	AL
1	Alaska	26.6	50	AK
2	Arizona	60.3	10	AZ
3	Arkansas	60.4	9	AR
4	California	59.4	12	CA
5	Colorado	45.1	39	CO
6	Connecticut	49.0	29	CT
7	Delaware	55.3	16	DE
8	Florida	70.7	1	FL
9	Georgia	63.5	5	GA
10	Hawaii	70.0	2	HI
11	Idaho	44.4	40	ID
12	Illinois	51.8	23	IL
13	Indiana	51.7	25	IN
14	Iowa	47.8	36	IA
15	Kansas	54.3	19	KS
16	Kentucky	55.6	15	KY
17	Louisiana	66.4	3	LA
18	Maine	41.0	48	ME
19	Maryland	54.2	20	MD
20	Massachusetts	47.9	35	MA
21	Michigan	44.4	40	MI
22	Minnesota	41.2	47	MN
23	Mississippi	63.4	6	MS
24	Missouri	54.5	18	MO
25	Montana	42.7	45	MT
26	Nebraska	48.8	30	NE
27	Nevada	49.9	28	NV
28	New Hampshire	43.8	42	NH
29	New Jersey	52.7	22	NJ
30	New Mexico	53.4	21	NM
31	New York	45.4	37	NY
32	North Carolina	59.0	13	NC
33	North Dakota	40.4	49	ND
34	Ohio	50.7	26	OH
35	Oklahoma	59.6	11	OK
36	Oregon	48.4	33	OR
37	Pennsylvania	48.8	30	PA
38	Rhode Island	50.1	27	RI
39	South Carolina	62.4	8	SC