Meteorite Landings
Michael Maynard
2025-02-25
Introduction This dataset contains detailed records of 34,513 known meteorite landings worldwide, compiled by The Meteoritical Society and hosted on NASA’s Open Data Portal. It includes essential information about meteorites, such as their classification, mass, location, and whether they were observed falling or found later.
Step 1:Ask I found this data set to be interesting, and was intrigued to take a deeper dive into what I could find. I am searching to see if there is a more common time or area that meteorites have landed. Coming into this case study, I am aware the majority happen in the ocean.
Step 2:Data Collection
The data was retrieved from Kaggle.com( https://www.kaggle.com/datasets/nafayunnoor/meteorite-landings-on-earth-data/data)
This dataset provides a comprehensive collection of meteorite landings worldwide, compiled by The Meteoritical Society and made available through NASA’s Open Data Portal. It includes 34,513 recorded meteorites with key details such as location, type, mass, fall status (whether the meteorite was observed falling or found later), and geographical coordinates. The data has been updated to reflect new meteorite discoveries and includes fields like:
Name & Type: Meteorite classification Mass (grams): Weight of the meteorite Fell or Found: Whether it was seen falling or later discovered Year: The year of discovery or fall Location Data: Latitude, longitude, and geo-coordinates
Source: Original Data Provider: The Meteoritical Society
Hosted by: NASA Open Data Portal (data.nasa.gov)
Dataset Link: Meteorite Landings on Data.gov
Choose Datasets After previewing the dataset using the code below, several points are addressed below:
Installed Libraries
# install packages
install.packages("tidyverse")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("ggplot2")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("dplyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("tidyr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("lubridate")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("readr")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)install.packages("gridExtra")## Installing package into '/cloud/lib/x86_64-pc-linux-gnu-library/4.4'
## (as 'lib' is unspecified)# load library
library("tidyverse")## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.4 ✔ tidyr 1.3.1
## ✔ purrr 1.0.4## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary("ggplot2")
library("dplyr")
library("tidyr")
library("lubridate")
library("readr")
library("gridExtra")##
## Attaching package: 'gridExtra'
##
## The following object is masked from 'package:dplyr':
##
## combineUploaded the data set
Meteorite_Landings <-read.csv("Meteorite_Landings.csv")summary(Meteorite_Landings)## name id nametype recclass
## Length:45716 Min. : 1 Length:45716 Length:45716
## Class :character 1st Qu.:12689 Class :character Class :character
## Mode :character Median :24262 Mode :character Mode :character
## Mean :26890
## 3rd Qu.:40657
## Max. :57458
##
## mass..g. fall year reclat
## Min. : 0 Length:45716 Min. : 860 Min. :-87.37
## 1st Qu.: 7 Class :character 1st Qu.:1987 1st Qu.:-76.71
## Median : 33 Mode :character Median :1998 Median :-71.50
## Mean : 13278 Mean :1992 Mean :-39.12
## 3rd Qu.: 203 3rd Qu.:2003 3rd Qu.: 0.00
## Max. :60000000 Max. :2101 Max. : 81.17
## NA's :131 NA's :291 NA's :7315
## reclong GeoLocation X
## Min. :-165.43 Length:45716 Mode:logical
## 1st Qu.: 0.00 Class :character NA's:45716
## Median : 35.67 Mode :character
## Mean : 61.07
## 3rd Qu.: 157.17
## Max. : 354.47
## NA's :7315colnames(Meteorite_Landings)## [1] "name" "id" "nametype" "recclass" "mass..g."
## [6] "fall" "year" "reclat" "reclong" "GeoLocation"
## [11] "X"Step3:Process
As you can see in the summary, there are 45,716 files, with the available fields to work with being “name,”id”,“nametype”,“recclass”,“mass”,“fall”,year”,“reclat”,“reclong”, and”GeoLocation”.
Step4:Analyze
{r} world_map <- Meteorite_Landings(“GeoLocation”)
Plot the map and add data points
ggplot() + geom_polygon(data = world_map, aes(x = long, y = lat,
group = group), fill = “lightgray”, color = “white”) + geom_point(data =
geo_data, aes(x = Longitude, y = Latitude), color = “red”, size = 3) +
theme_minimal()
## Including Plots
You can also embed plots, for example:
Interesting enough, the majority of the meteorites appear to fall above the equator. The equator also appears to not be hit very often. This data does not appear to include any water landings, so that can skew the data, but it is noticeable how few hits happen near the equator. As expected, the poles also do not receive many hits.
step 5: Share Findings In conclusion, the analysis of this comprehensive dataset of 34,513 recorded meteorite landings has offered intriguing insights into their global distribution. While the initial assumption of the majority of meteorites landing in the ocean couldn’t be directly assessed due to the absence of water landing data in this dataset, the geographical visualization revealed a notable pattern. Specifically, a higher concentration of recorded meteorite landings appears to occur above the equator, with a seemingly lower frequency of impacts near the equator itself and the polar regions. This observation, although potentially influenced by the lack of oceanic data and biases in meteorite recovery efforts, suggests a non-uniform distribution of recorded meteorite strikes across the Earth’s landmasses. Further research, potentially incorporating data on found meteorites in previously unsearched regions and considering the biases inherent in meteorite collection, could provide a more complete understanding of meteorite landing patterns.