tPP Preliminary Statistical Report #2 of 5

The following report was completed by statistics students utilizing a version of tPP dataset as of March 13, 2019. These analyses are focused on developing models for future use, and the interpretations and conclusions they contain reflect a dataset still in development, and only a superficial engagement with the wider literature on political violence. We continue to expand, improve and refine the data, and as such, these analyses should be seen as preliminary and subject to change. This views expressed in these reports belong solely to the authors, and do not necessarily reflect the findings of tPP team and are subject to further inquiry and revision.



Below you will find the non-technical analytical summary and selected visualizations for Team #2 (Ideological Analysis) of 5.

This report was authored by Lesi Wei, Lexi Gelinas, Siqi Zhang & Yiduo Yang. To download the complete report, click here.



Introduction

The Prosecution Project (tPP) has collected data on cases in which individuals or groups engage in political violence that results in a felony or has been described through State speech as having a connection to a terrorist or extremist group with a political agenda. Specifically, this analysis is looking at several key variables in the relationship between ideology and the political violence itself.

Results

Ideology and Lethality

There are more instances of political violence that do not result in a death, but of the ones that do, Rightist groups commit more of these attacks than other groups.

Ideology and People vs Property

Salafi, Jihadist, or Islamic groups commit more attacks against no direct target than any other group. Rightist groups have more cases in which they attack property than people.

Tactic and Physical Target

Threat/support of an organization is the most used tactic and has the most cases in the online community and against unknown targets.

Ideology and Ideological Target

Salafi, Jihadist, or Islamic groups have more cases in which they attack unspecified ideological targets more than any other groups.

Ideology and State Speech

No group affiliation and Leftist groups have more cases in which they use state speech than the other groups

Tactic and Group Affiliation & FTO Affiliation

 

Salafi, Jihadist, or Islamist individuals tend to have strong tactic of threat/support of an organization, and the rightist tend to external device as their tactic. And group that affiliation with an FTO, individuals tend to provide material/financial support to the terrorist organization. No affiliation with an FTO, leads to more use of an external device.

Ideology and Location

Salafi, Jihadist, or Islamist Individuals commit more attacks in the East Coast, West Coast, and Midwest areas in the United States. Rightist groups commit more attacks in the Central area of United States. Leftist only have two states in which they commit the most political violence.

Conclusions

Not all groups of categorical variables have obvious trends, only few categories have some significant trends under each variable based on the plots. The deeper analysis will examine this in the technical report part.

References

McHugh, M. (2013). The Chi-square test of independence. Biochemia Medica 23 (2) 143-149.

R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ .

tPP Preliminary Statistical Report #1 of 5

The following report was completed by statistics students utilizing a version of tPP dataset as of March 13, 2019. These analyses are focused on developing models for future use, and the interpretations and conclusions they contain reflect a dataset still in development, and only a superficial engagement with the wider literature on political violence. We continue to expand, improve and refine the data, and as such, these analyses should be seen as preliminary and subject to change. This views expressed in these reports belong solely to the authors, and do not necessarily reflect the findings of tPP team and are subject to further inquiry and revision.



Below you will find an analytical summary and selected visualizations for Team #1 (Descriptive Analysis) of 5.

This report was authored by Emma Ellis, Sikai Huang, Haiduan Tao & Haosen Yang. To download the complete report, click here.



The main question answered in this report is: How does the US legal system prosecute acts of political violence (descriptive) and how has this changed over time and space?

First, the data was mined and edited using RStudio. The final format had 1280 observations. The only observations that were removed from the data set were cases that had ‘pending’ as values because these had no information and would negatively impact the descriptive statistics that were created. Each of the variables chosen had a table created. These tables looked at Category, Number of Observations, Average Prison Sentence Length, Percentage of Life Sentences, and Percentage of Death Sentences. Multiple tables had a lot of zeros under the death sentence column.

After tables were initially created it was decided that the combination of some categories depending on the variable would occur. The only variable that did not have a table created was the location. That is because a geomap was found to be more beneficial as a visualization. The geomap showed that states with higher populations also had a higher amount of life and death sentences.

The white color states (Wyoming, Nebraska, Rhode Island, and Hawaii) have no information in the data provided in the project. New York has the largest prosecution count number, far more than other states. Overall, about 87% states’ length of prison sentences is fewer than 200 months. Oklahoma and New Hampshire have longer prison sentence than other states, but they have few prosecution counts. Texas, California and New York also have relatively longer prison sentence with more prosecution count. Oklahoma has the largest percentage of life sentence and death sentence. Nearly half of the states have life sentences and 23% of states have death sentences.

Since this analysis is wholly descriptive there can be no definite conclusions drawn for predicting the length of a prison sentence. From the tables that were created and the geomap, there are some trends that were found in regards to life and death sentences.

One major finding is that there were no death sentences given to any case where the criminal was not of U.S. Citizenship.

Another notable find was that if there were no deaths involved there was no death sentence given, the most interesting part of this is that there were over 1,000 observations of zero killed.

The last notable find was that if an informant was present there were no cases that resulted in the death penalty. This can be explained by a crime being able to be stopped if the police were informed beforehand.

References

Hadley Wickham (2017). tidyverse: Easily Install and Load the ‘Tidyverse’. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse

Garrett Grolemund, Hadley Wickham (2011). Dates and Times Made Easy with lubridate. Journal of Statistical Software, 40(3), 1-25. URL http://www.jstatsoft.org/v40/i03/ .

David M Diez, Christopher D Barr and Mine Cetinkaya-Rundel (2017). openintro: Data Sets and Supplemental Functions from ‘OpenIntro’ Textbooks. R package version 1.7.1. https://CRAN.R-project.org/package=openintro

Paolo Di Lorenzo (2018). usmap: US Maps Including Alaska and Hawaii. R package version 0.4.0. https://CRAN.R-project.org/package=usmap

Carson Sievert (2018) plotly for R. https://plotly-book.cpsievert.me

So what do tPP team members do?

As we begin recruiting for the next class of tPP students, I have been receiving a lot of emails asking what exactly being part of the team entails. Well, in the fall, tPP will be ran through SJS497 where we will learn data science and methodology skillsets in the classroom each Tuesday, and then practice them in the classroom each Thursday. For example, on a Tuesday we may learn how to verify a newspaper story via locating and interpreting a criminal indictment, and on Thursday, use that approach to verify and complete various cases under analysis.

Throughout the semester we plan to cover a wide range of tasks, including but now limited to:

  • Coding cases: This is one of the main tasks of tPP. This involves studying a particular criminal case, collecting the necessary source documents (e.g. Case Docket, Indictment, Criminal Complaint, Plea Agreement, Sentencing Memorandum, newspaper article) and then translating these texts into codes from our code book. For a bizarre cartoon explaining Qualitative Coding, check this out. Like all tPP skills, this will be taught in class and then practiced in a workshop style
  • Checking, improving and verifying cases already in our system. This is especially important as cases change–defendants are sentenced, fugitives are captured and tried, and arrests continue to occur
  • Helping to identify new cases for inclusion through reviewing and monitoring services of the Department of Justice, US Attorney’s Office, FBI and others.
  • ‘Scraping’ and ‘mining’ texts from large documents to help locate new cases for inclusion and to ensure all appropriate cases are counted
  • Evaluating cases marked for exclusion through investigating the facts of the cases and working them through a decision tree
  • Evaluating documents for accuracy, authenticity and reliability; rep-lacing poorly scoring sources with better sources
  • Reviewing the work of your fellow coders, providing peer-review and intercoder reliability and helping to refine the code book
  • Refining the data for analysis which involves ‘cleaning’ the data, shifting its format, exporting/importing and learning how to work with the materials in SPSS, R, Tableu, GIS and a variety of other tool suites.

So if this sounds like you, get in touch with us. Check out this post for information on SJS497 and the application process.