Mahnoor's Statistics Site

• I want to perform “Geospatial Patterns of Protest Activities During Election Cycles”.
• I want to investigate how protest occurrences vary geographically during different phases of election cycles, such as pre-election campaigns, election days, and post-election periods.
• Further, want to apply some machine learning algorithm to predict the hotspot regions in next elections cycle.
• I have got 1 source “United States Election Assistance Commission” https://www.eac.gov/research-and-data, and I am exploring it, if it provides the elections data.
• If anyone knows the exact source, it will make my life easy :).

• ACLED data has large number of columns with multiple unique values in each column.
• To better understand the data, I went through the documentation of data on ACLED website, https://acleddata.com/knowledge-base/guide-to-2023-acled-column-changes/.
• I am going through the documentation to get better understanding of the data and then come up with some questions to perform on this.

• Completed the programming part of the project for problem statement “A Racial Disparity in Police Shootings Across US Counties.”
• Racial Disparity in this project is defined as a race has more share in shooting as compared to the county population.
• I have found Baltimore, Cook, Fulton counties where Black race has face continuous disparity in shooting throughout the data being recorded 2015-2023.
• Despite being may be 1/3rd of the population, they were among the majority or sometimes the only people being shot by police.
• I made another analysis and find out counties where at least 2 races have faced disparity for at least 6 years, and I found the following results.
  • Orange- B, H
  • El Paso- B, W
  • Miami Dade- B, W
  • Maricopa- B, N
  • San Bernardino- B, W
  • Alameda- B, H

All the above analysis are dynamic, and we can find the counties where a continuous disparity was observed within a certain time period i.e., 2020-2024, 2015-2019.
Also, we can find counties where multiple races have face disparity in a non-continuous manner.

• I wrote python script to fetch Ethnicity and Race data of all counties for year 2015-2023 from data.census.gov using API key.
• I transformed the data and calculated race distribution of races in each county per year.
• In Washington police shooting data, there were 4692 records where county was missing. I impute the counties with the help of “geopy” library with city and state fields in data.
• I transformed the Washington police shooting data and calculated shot race distribution of races in each county per year.
• Next steps will be working on how to compare them to have a better idea of counties where a particular racial group experiences a disproportionate number of shootings despite not being the majority of the population.

• I have to go through extensive the documentation of United States Census Bureau https://data.census.gov/ for fetching data.
• It took me so much time, to understand the metadata and the specific codes by which the data is provided for races and ethnicities.
• I have made a python program to fetch data using API, for specific county, year combination and also modifying the code to fetch data for all countries from 2015-2023 time period.
• I want to identify counties where a particular racial group experiences a disproportionate number of shootings despite not being the majority of the population.

• I want to do the comparison of %age of races being shoot vs %age of races presents in each county of US.
• I want to identify counties where a particular racial group experiences a disproportionate number of shootings despite not being the majority of the population
• For this analysis, I need the population by race and year.  I have found out a site https://data.census.gov/, that provides data on different topics like
  • Business and Economy 
  • Education 
  • Employment 
  • Families and Living Arrangements 
  • Government 
  • Health 
  • Housing 
  • Income and Poverty 
  • Populations and People 
  • Race and Ethnicity 

    With respect to different geographies:

    • Nation 
    • State 
    • County 
    • County Subdivision 
    • Place 
    • ZIP Code Tabulation Area 
    • Metropolitan/Micropolitan Statistical Area 
    • Census Tract 
    • Block 
    • Block Group 
    • All Geographies 
• Census.gov has also provided an API to access the data programmatically, here I am provided the link to the documentation:
  API Documentation: https://www.census.gov/data/developers/guidance/api-user-guide.html
• Currently, I am exploring the API documentation and one more direction does income, and poverty has any effect on county where non-majority races are shot more often.
• I found 1 record disturbing, that a young black woman who was identified as mentally ill, unarmed and was not trying to flee was alsosh shot in Illinois. The agency that shot the woman has just 1 record of shooting only.