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Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program...
- openicpsr.org
Updated Jan 16, 2021+ more versions
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EmailClick to copy linkLink copiedCiteJacob Kaplan (2021). Jacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: County-Level Detailed Arrest and Offense Data [Dataset]. https://www.openicpsr.org/openicpsr/project/108164/viewDataset updatedJan 16, 2021Dataset provided byUniversity of PennsylvaniaAuthorsJacob KaplanLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyArea coveredCounties in the United StatesDescriptionVersion 5 release notes:- Changes release notes description, does not change data.
Version 4 release notes:- I am retiring this dataset - please do not use it.
- The reason that I made this dataset is that I had seen a lot of recent articles using the NACJD version of the data and had several requests that I make a concatenated version myself. This data is heavily flawed as noted in the excellent Maltz & Targonski's (2002) paper (see PDF available to download here and important paragraph from that article below) and I was worried that people were using the data without considering these flaws. So the data available here had the warning below this section (originally at the top of these notes so it was the most prominent thing) and had the Maltz & Targonski PDF included in the zip file so people were aware of it.
- There are two reasons that I am retiring it.
- First, I see papers and other non-peer reviewed reports still published using this data without addressing the main flaws noted by Maltz and Targonski. I don't want to have my work contribute to research that I think is fundamentally flawed.
- Second, this data is actually more flawed that I originally understood. The imputation process to replace missing data is based off of a bad design, and Maltz and Targonski talk about this in detail so I won't discuss it too much. The additional problem is that the variable that determines whether an agency has missing data is fatally flawed. That variable is the "number_of_months_reported" variable which is actually just the last month reported. So if you only report in December it'll have 12 months reported instead of 1. So even a good imputation process will be based on such a flawed measure of missingness that it will be wrong. How big of an issue is this? At the moment I haven't looked into it in enough detail to be sure but it's enough of a problem that I no longer want to release this kind of data (within the UCR data there are variables that you can use to try to determine the actual number of months reported but that stopped being useful due to a change in the data in 2018 by the FBI. And even that measure is not always accurate for years before 2018.).
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Hate Crimes by County and Bias Type: Beginning 2010
- catalog.data.gov
- data.ny.gov
- +2more
Updated Nov 10, 2023+ more versionsShareFacebookTwitterEmailClick to copy linkLink copiedCitedata.ny.gov (2023). Hate Crimes by County and Bias Type: Beginning 2010 [Dataset]. https://catalog.data.gov/dataset/hate-crimes-by-county-and-bias-type-beginning-2010Dataset updatedNov 10, 2023Dataset provided bydata.ny.govDescriptionUnder New York State’s Hate Crime Law (Penal Law Article 485), a person commits a hate crime when one of a specified set of offenses is committed targeting a victim because of a perception or belief about their race, color, national origin, ancestry, gender, religion, religious practice, age, disability, or sexual orientation, or when such an act is committed as a result of that type of perception or belief. These types of crimes can target an individual, a group of individuals, or public or private property. DCJS submits hate crime incident data to the FBI’s Uniform Crime Reporting (UCR) Program. Information collected includes number of victims, number of offenders, type of bias motivation, and type of victim.
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Data from: Marginal Structural Models to Estimate Causal Effects of...
- tandf.figshare.com
txtUpdated May 30, 2023ShareFacebookTwitterEmailClick to copy linkLink copiedCiteWillem M. Van Der Wal (2023). Marginal Structural Models to Estimate Causal Effects of Right-to-Carry Laws on Crime [Dataset]. http://doi.org/10.6084/m9.figshare.20771246.v1txtAvailable download formatsUnique identifierhttps://doi.org/10.6084/m9.figshare.20771246.v1Dataset updatedMay 30, 2023Dataset provided byTaylor & FrancisAuthorsWillem M. Van Der WalLicenseAttribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automaticallyDescriptionRight-to-carry (RTC) laws allow the legal carrying of concealed firearms for defense, in certain states in the United States. I used modern causal inference methodology from epidemiology to examine the effect of RTC laws on crime over a period from 1959 up to 2016. I fitted marginal structural models (MSMs), using inverse probability weighting (IPW) to correct for criminological, economic, political and demographic confounders. Results indicate that RTC laws significantly increase violent crime by 7.5% and property crime by 6.1%. RTC laws significantly increase murder and manslaughter, robbery, aggravated assault, burglary, larceny theft and motor vehicle theft rates. Applying this method to this topic for the first time addresses methodological shortcomings in previous studies such as conditioning away the effect, overfit and the inappropriate use of county level measurements. Data and analysis code for this article are available online.
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FacebookTwitterEmailJacob Kaplan's Concatenated Files: Uniform Crime Reporting (UCR) Program Data: County-Level Detailed Arrest and Offense Data
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
- Changes release notes description, does not change data.
- I am retiring this dataset - please do not use it.
- The reason that I made this dataset is that I had seen a lot of recent articles using the NACJD version of the data and had several requests that I make a concatenated version myself. This data is heavily flawed as noted in the excellent Maltz & Targonski's (2002) paper (see PDF available to download here and important paragraph from that article below) and I was worried that people were using the data without considering these flaws. So the data available here had the warning below this section (originally at the top of these notes so it was the most prominent thing) and had the Maltz & Targonski PDF included in the zip file so people were aware of it.
- There are two reasons that I am retiring it.
- First, I see papers and other non-peer reviewed reports still published using this data without addressing the main flaws noted by Maltz and Targonski. I don't want to have my work contribute to research that I think is fundamentally flawed.
- Second, this data is actually more flawed that I originally understood. The imputation process to replace missing data is based off of a bad design, and Maltz and Targonski talk about this in detail so I won't discuss it too much. The additional problem is that the variable that determines whether an agency has missing data is fatally flawed. That variable is the "number_of_months_reported" variable which is actually just the last month reported. So if you only report in December it'll have 12 months reported instead of 1. So even a good imputation process will be based on such a flawed measure of missingness that it will be wrong. How big of an issue is this? At the moment I haven't looked into it in enough detail to be sure but it's enough of a problem that I no longer want to release this kind of data (within the UCR data there are variables that you can use to try to determine the actual number of months reported but that stopped being useful due to a change in the data in 2018 by the FBI. And even that measure is not always accurate for years before 2018.).