In 2023, the District of Columbia had the highest reported violent crime rate in the United States, with 1,150.9 violent crimes per 100,000 of the population. Maine had the lowest reported violent crime rate, with 102.5 offenses per 100,000 of the population. Life in the District The District of Columbia has seen a fluctuating population over the past few decades. Its population decreased throughout the 1990s, when its crime rate was at its peak, but has been steadily recovering since then. While unemployment in the District has also been falling, it still has had a high poverty rate in recent years. The gentrification of certain areas within Washington, D.C. over the past few years has made the contrast between rich and poor even greater and is also pushing crime out into the Maryland and Virginia suburbs around the District. Law enforcement in the U.S. Crime in the U.S. is trending downwards compared to years past, despite Americans feeling that crime is a problem in their country. In addition, the number of full-time law enforcement officers in the U.S. has increased recently, who, in keeping with the lower rate of crime, have also made fewer arrests than in years past.

These data are part of NACJD's Fast Track Release and are distributed as they there received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except of the removal of direct identifiers. Users should refer to the accompany readme file for a brief description of the files available with this collections and consult the investigator(s) if further information is needed. This study examines municipal crime levels and changes over a nine year time frame, from 2000-2008, in the fifth largest primary Metropolitan Statistical Area (MSA) in the United States, the Philadelphia metropolitan region. Crime levels and crime changes are linked to demographic features of jurisdictions, policing arrangements and coverage levels, and street and public transit network features.

This study was designed to explore school culture and climate and their effects on school disorder, violence, and academic performance on two levels. At the macro level of analysis, this research examined the influences of sociocultural, crime, and school characteristics on aggregate-level school violence and academic performance measures. Here the focus was on understanding community, family, and crime compositional effects on disruption and violence in Philadelphia schools. This level included Census data and crime rates for the Census tracts where the schools were located (local data), as well as for the community of residence of the students (imported data) for all 255 schools within the Philadelphia School District. The second level of analysis, the intermediate level, included all of the variables measured at the macro level, and added school organizational structure and school climate, measured with survey data, as mediating variables. Part 1, Macro-Level Data, contains arrest and offense data and Census characteristics, such as race, poverty level, and household income, for the Census tracts where each of the 255 Philadelphia schools is located and for the Census tracts where the students who attend those schools reside. In addition, this file contains school characteristics, such as number and race of students and teachers, student attendance, average exam scores, and number of suspensions for various reasons. For Part 2, Principal Interview Data, principals from all 42 middle schools in Philadelphia were interviewed on the number of buildings and classrooms in their school, square footage and special features of the school, and security measures. For Part 3, teachers were administered the Effective School Battery survey and asked about their job satisfaction, training opportunities, relationships with principals and parents, participation in school activities, safety measures, and fear of crime at school. In Part 4, students were administered the Effective School Battery survey and asked about their attachment to school, extracurricular activities, attitudes toward teachers and school, academic achievement, and fear of crime at school. Part 5, Student Victimization Data, asked the same students from Part 4 about their victimization experiences, the availability of drugs, and discipline measures at school. It also provides self-reports of theft, assault, drug use, gang membership, and weapon possession at school.

This project used a 2019 crimes dataset (crimes which are dangerous to the victims) to create a hotspot map for dangerous crimes in Philadelphia to see the geographic locations that have more violent crime. A hotspot map was also made for shooting victims in Philadelphia to give more weight for fatal crimes. Theoretically, the places where the two of these overlap would be the most dangerous portions of the city. The different census tracts of the city are then enriched to determine where areas of lower income (and therefore lower housing cost) would be. Finally, buffers are created around the University of Pennsylvania, Temple, and La Salle University for evaluating safety.Notable Modules Used: Python: pandas, numpy, matplotlib ArcGIS: create_buffers, find_hot_spots, enrich_layer

• p<0.05**p<0.01***p<0.0011. All estimates include controls for median age, median household income, percent of the population with less than a high school-level education, and percent of households earning less than the federal poverty standard. 2. IRR: Incidence Rate Ratio; ratio of incidence rate of crimes per square mile at the treatment site to incidence rate of crimes per square mile at the control site 3. SE: Standard ErrorAdjusted Difference-in-Differences Estimates of Violation Compliance on Point-Level Crime Outcomes, by City Section, Philadelphia, PA, January 2010 –April 20131.

Context

Real estate data set of Philly.

Content

Data set included Addresses, sales price, crime rate and rank by zipcode, school ratings and rank by zipcode, walkscore and rank by zip code, approximate rehab cost,

Acknowledgements

Data from phila.gov and other sites

Inspiration

Find out how data could impact house price.

• p<0.05**p<0.01***p<0.0011.All estimates include controls for median age, median household income, percent of the population with less than a high school-level education, and percent of households earning less than the federal poverty standard. 2. IRR: Incidence Rate Ratio; ratio of incidence rate of crimes per square mile at the treatment site to incidence rate of crimes per square mile at the control site 3. SE: Standard Error4. “-”indicates that numbers are too small to report.Adjusted Difference-in-Differences Estimates of Renovation Permit on Census-Tract Level Crime Outcomes, by City Section, Philadelphia, PA, January 2010 –April 20131.

V6 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. V5 release notes: Changes the word "larceny" to "theft" in column names - eg. from "act_larceny" to "act_theft."Fixes bug where state abbrebation was NA for Washington D.C., Puerto Rico, Guam, and the Canal Zone.Fixes bug where officers_killed_by_accident was not appearing in yearly data. Note that 1979 does not have any officers killed (by felony or accident) or officers assaulted data.Adds aggravated assault columns to the monthly data. Aggravated assault is the sum of all assaults other than simple assault (assaults using gun, knife, hand/feet, and other weapon). Note that summing all crime columns to get a total crime count will double count aggravated assault as it is already the sum of existing columns. Reorder columns to put all month descriptors (e.g. "jan_month_included", "jan_card_1_type") before any crime data.Due to extremely irregular data in the unfounded columns for New Orleans (ORI = LANPD00) for years 2014-2016, I have change all unfounded column data for New Orleans for these years to NA. As an example, New Orleans reported about 45,000 unfounded total burglaries in 2016 (the 3rd highest they ever reported). This is 18 times largest than the number of actual total burglaries they reported that year (2,561) and nearly 8 times larger than the next largest reported unfounded total burglaries in any agency or year. Prior to 2014 there were no more than 10 unfounded total burglaries reported in New Orleans in any year. There were 10 obvious data entry errors in officers killed by felony/accident that I changed to NA.In 1974 the agency "Boston" (ORI = MA01301) reported 23 officers killed by accident during November.In 1978 the agency "Pittsburgh" (ORI = PAPPD00) reported 576 officers killed by accident during March.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by accident during June.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during April.In 1978 the agency "Bronx Transit Authority" (ORI = NY06240) reported 56 officers killed by felony during June.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by accident during May.In 1978 the agency "Queens Transit Authority" (ORI = NY04040) reported 56 officers killed by felony during May.In 1996 the agency "Ruston" in Louisiana (ORI = LA03102) reported 30 officers killed by felony during September.In 1997 the agency "Washington University" in Missouri (ORI = MO0950E) reported 26 officers killed by felony during March.V4 release notes: Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Makes all column names lowercase.Change some variable namesMakes values in character columns lowercase.Adds months_reported variable to yearly data.Combines monthly and yearly files into a single zip file (per data type).V3 release notes: fixes a bug in Version 2 where 1993 data did not properly deal with missing values, leading to enormous counts of crime being reported. Summary: This is a collection of Offenses Known and Clearances By Arrest data from 1960 to 2016. Each zip file contains monthly and yearly data files. The monthly files contain one data file per year (57 total, 1960-2016) as well as a codebook for each year. These files have been read into R using the ASCII and setup files from ICPSR (or from the FBI for 2016 data) using the package asciiSetupReader. The end of the zip folder's name says what data type (R, SPSS, SAS, Microsoft Excel CSV, Stata) the data is in. The files are lightly cleaned. What this means specifically is that column names and value labels are standardized. In the original data column names were different between years (e.g. the December burglaries cleared column is "DEC_TOT_CLR_BRGLRY_TOT" in 1975 and "DEC_TOT_CLR_BURG_TOTAL" in 1977). The data here have standardized columns so you can compare between years and combine years together. The same thing is done for values inside of columns. For example, the state column gave state names in some years, abbreviations in others. For the code uses to clean and read the data, please see my GitHub file h

This dataset shows the means of transportation to work reported by females. The information is mapped according to place of residence. The data is part of the Census Transportation Planning Package (CTPP), and is the result of a cooperative effort between various groups including the State Departments of Transportation, U.S. Census Bureau, and the Federal Highway Administration. The data is a special tabulation of responses from households completing the decennial census long form. The data was collected in 2000 and is shown at tract level. This data can be found at http://www.transtats.bts.gov/Fields.asp?Table_ID=1338.