Version 5 release notes:Adds 2016 dataStandardizes the "group" column which categorizes cities and counties by population.Arrange rows in descending order by year and ascending order by ORI. Version 4 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. Version 3 Release Notes:Merges data with LEAIC data to add FIPS codes, census codes, agency type variables, and ORI9 variable.Change column names for relationship variables from offender_n_relation_to_victim_1 to victim_1_relation_to_offender_n to better indicate that all relationship are victim 1's relationship to each offender. Reorder columns.This is a single file containing all data from the Supplementary Homicide Reports from 1976 to 2015. The Supplementary Homicide Report provides detailed information about the victim, offender, and circumstances of the murder. Details include victim and offender age, sex, race, ethnicity (Hispanic/not Hispanic), the weapon used, circumstances of the incident, and the number of both offenders and victims. All the data was downloaded from NACJD as ASCII+SPSS Setup files and cleaned using R. The "cleaning" just means that column names were standardized (different years have slightly different spellings for many columns). Standardization of column names is necessary to stack multiple years together. Categorical variables (e.g. state) were also standardized (i.e. fix spelling errors, have terminology be the same across years). The following is the summary of the Supplementary Homicide Report copied from ICPSR's 2015 page for the data.The Uniform Crime Reporting Program Data: Supplementary Homicide Reports (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.

Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. The data sets here combine all data from the years 1992-2015 into a single file. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data. The zip file contains the data in the following formats and a codebook: .dta - Stata.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

For any questions about this data please email me at jacob@crimedatatool.com. If you use this data, please cite it.Version 5 release notes:Adds data in the following formats: SPSS, SAS, and Excel.Changes project name to avoid confusing this data for the ones done by NACJD.Adds data for 1991.Fixes bug where bias motivation "anti-lesbian, gay, bisexual, or transgender, mixed group (lgbt)" was labeled "anti-homosexual (gay and lesbian)" prior to 2013 causing there to be two columns and zero values for years with the wrong label.All data is now directly from the FBI, not NACJD. The data initially comes as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. Version 4 release notes: Adds data for 2017.Adds rows that submitted a zero-report (i.e. that agency reported no hate crimes in the year). This is for all years 1992-2017. Made changes to categorical variables (e.g. bias motivation columns) to make categories consistent over time. Different years had slightly different names (e.g. 'anti-am indian' and 'anti-american indian') which I made consistent. Made the 'population' column which is the total population in that agency. Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data.

One the most obvious difficulties in comparing the influence of landscape on crop production across studies is the choice of landscape metric. There exist countless metrics of landscape composition—the categories of land cover found on a landscape—and landscape configuration—the spatial organization of these categories. Common landscape composition metrics include measures of diversity—such as the Shannon Diversity Index or the Simpson Diversity Index—and measures of land cover composition—such as the percent of the landscape classified as natural cover. Common landscape configuration metrics include measures of patch size (contiguous areas of the same land cover) and mixing as well as edge length (linear length of patch boundaries/perimeter) and fragmentation. Even just considering diversity metrics, numerous options to select from can be found in the literature. Each one of these metrics has its own particularities in terms of sensitivity to scale, rare categories, and boundaries that can significantly alter the conclusions of studies examining the relationship between landscape characteristics and crop production. To address this challenge, we assess the sensitivity of our model results to a number of indicators of landscape composition and configuration using the USDA NASS Cropland Data Layer (CDL) as our indicator of land cover. This dataset classifies land cover at a 30-meter resolution nationwide from 2008 to present using satellite imagery and extensive ground truth data. While the 30-meter spatial resolution of this land cover data cannot accurately represent very small or narrow patches of land cover including shelterbelts and wildflower strips, given its relatively high resolution, full coverage, and historical availability, it is the best data for understanding land cover across agricultural landscapes in the U.S. We extract landscape indices from the CDL data using the landscapemetrics package in R, which considers all land cover in each county’s bounding box with the exception of open water and null categories. We measure compositional complexity using a set of six common landscape metrics associated with the number or the predominance of land cover categories across a landscape. Five of these metrics—Shannon Diversity Index, Simpson Diversity Index, Richness, Shannon Evenness Index, and Simpson Evenness Index—can be considered measures of land cover diversity. The sixth metric–Percent Natural Cover–is a simple measure of the predominance of undeveloped and uncultivated land cover classes (such as wetlands, grasslands, and forests) on a landscape. All of the compositional complexity metrics are aspatial, in that their calculation is not contingent on how land cover categories are arranged within the landscape. Configurational complexity is measured using four landscape metrics associated with the size of land cover patches (continuous areas of a single land cover category), shape of land cover patches, or mixing of land cover categories across the landscape. The metrics Mean Patch Area and Largest Patch Index are most strongly associated with patch size, the Contagion metric is a measure of land cover category mixing and strongly related to patch size, and the Edge Density metric is related to patch size and shape. Unlike the landscape composition metrics, the four landscape configuration metrics are spatially explicit and depend on the arrangement of land cover categories across the landscape. All code used to build data can be found here: https://github.com/katesnelson/aglandscapes-what-or-how Resources in this dataset:

Resource Title: County-level Estimates of Landscape Complexity and Configuration in the Coterminous US File Name: landscape_panel.txt Resource Description: GEOID: State and county FIPS codes in format SSCCC YEAR: Year in which CDL data was collected VALUE: Index value INDEX_NAME: Indices with _AG were computed for the subset of agricultural lands in a county. Indices with _ALL were computed for the entire landscape (agricultural and nonagricultural lands) in a county. LSM_AREA_MN_AG/ALL: Mean patch area, a measure of patch structure. Approaches 0 if all patches are small. Increases, without limit, as the patch areas increase. Higher values generally indicate lower complexity. LSM_CONTAG_AG/ALL: Contagion, a measure of dispersion and interspersion of land cover classes where a high proportion of like adjacencies and an uneven distribution of pairwise adjacencies produces a high contagion value. Range of 0 to 100. Higher values generally indicate lower complexity. LSM_ED_AG/ALL: Edge density, a measure of the patchiness of the landscape. Equals 0 if only one land cover is present and increased without limit as more land cover patches are added. Higher values generally indicate higher complexity. LSM_LPI_AG/ALL: Largest patch index, a measure of patch dominance representing the percentage of the landscape covered by the single largest patch. Approaches 0 when the largest patch is becoming small and equals 100 when only one patch is present. Higher values generally indicate lower complexity. LSM_RICH_AG/ALL: Richness, a measure of the abundance of categories. Higher values generally indicate higher complexity. LSM_SHDI_AG/ALL: Shannon Diversity Index, a measure of the abundance and evenness of land cover categories. This index is sensitive to rare land cover categories. Typical values are between 1.5 and 3. Higher values indicate higher complexity. LSM_SHEI_ALL: Simpson Evenness Index, a measure of diversity or dominance calculated as the ratio between the Shannon Diversity Index and the theoretical maximum of the Shannon Diversity Index. Shannon Evenness Index = 0 when there is only one land cover on the landscape and equals 1 when all land cover classes are equally distributed. Higher values generally indicate higher complexity. LSM_SIDI_ALL: Simpson Diversity Index, a diversity measure that considers the abundance and evenness of land cover categories. This index is not sensitive to rare land cover categories. Values range from 0 to 1. Higher values generally indicate higher complexity MODE_AG : Most dominant agricultural land use type found in the data (mode of agricultural CDL categories) MODE_ALL : Most dominant land use type found in the data (mode of all land use categories) PNC : Percent natural cover

Resource Title: Technical Validation File Name: technical_validation.txt

Version 3 release notes: Adds data for 2016.Order rows by year (descending) and ORI.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Hate Crime data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about hate crimes reported in the United States. The data sets here combine all data from the years 1992-2015 into a single file. Please note that the files are quite large and may take some time to open.Each row indicates a hate crime incident for an agency in a given year. I have made a unique ID column ("unique_id") by combining the year, agency ORI9 (the 9 character Originating Identifier code), and incident number columns together. Each column is a variable related to that incident or to the reporting agency. Some of the important columns are the incident date, what crime occurred (up to 10 crimes), the number of victims for each of these crimes, the bias motivation for each of these crimes, and the location of each crime. It also includes the total number of victims, total number of offenders, and race of offenders (as a group). Finally, it has a number of columns indicating if the victim for each offense was a certain type of victim or not (e.g. individual victim, business victim religious victim, etc.). All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The only changes I made to the data are the following. Minor changes to column names to make all column names 32 characters or fewer (so it can be saved in a Stata format), changed the name of some UCR offense codes (e.g. from "agg asslt" to "aggravated assault"), made all character values lower case, reordered columns. I also added state, county, and place FIPS code from the LEAIC (crosswalk) and generated incident month, weekday, and month-day variables from the incident date variable included in the original data. The zip file contains the data in the following formats and a codebook: .csv - Microsoft Excel.dta - Stata.sav - SPSS.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

Version 3 release notes: Add data for 2016.Order rows by year (descending) and ORI.Removed data from Chattahoochee Hills (ORI = "GA06059") from 2016 data. In 2016, that agency reported about 28 times as many vehicle arsons as their population (Total mobile arsons = , population = 2754.Version 2 release notes: Fix bug where Philadelphia Police Department had incorrect FIPS county code. The Arson data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains information about arsons reported in the United States. The data sets here combine all data from the years 2001-2015 into a single file. The year 2006 is not available. Please note that the files are quite large and may take some time to open.The raw data that I downloaded from NACJD has monthly data. The data here is yearly and was created by adding all the monthly columns together for each variable. The format is similar to the UCR's Offenses Known data where each row is an agency-year and columns are crime counts for various crimes. Instead of various crimes, here it is the type of arson such as arson of a single occupancy building, a storage building, or a motor vehicle. Like the Offenses Known data it has the number of reports found to have actually occurred ("actual"), be unfounded, cleared, and cleared with an arrestee under the age of 18. There are also columns for the total number of arsons reported to police, total number of arsons of uninhabited buildings, and estimated damage from the arson.About 30% of the rows were from agencies that did not report any months of data. I removed these rows to reduce file size. I did not make any changes to the data other than the following: Change some column names, reorder columns, and spell out the month in the months reported variable (originally some months were abbreviated). Years 2001 and 2002 had "1" and "2" as their reported years which I changed to "2001" and "2002". I deleted the agency of Oneida, New York (ORI = NY03200), since they had multiple years that reported single arsons costing over $700 million. I also added state, county, and place FIPS code from the LEAIC (crosswalk).When an arson is determined to be unfounded the estimated damage from that arson is added as negative to zero out the previously reported estimated damages. This occasionally leads to some agencies have negative values for arson damages. You should be cautious when using the estimated damage columns as some values are quite large. Negative values in other columns are also due to adjustments (zeroing out the error) from month to month. Negative values are not meant to be NA in this data set. All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. The zip file contains the data in the following formats and a codebook: .csv - Microsoft Excel.dta - Stata.sav - SPSS.rda - RIf you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

Version 5 release notes:

• Removes support for SPSS and Excel data.
• Changes the crimes that are stored in each file. There are more files now with fewer crimes per file. The files and their included crimes have been updated below.
  
• Adds in agencies that report 0 months of the year.
• Adds a column that indicates the number of months reported. This is generated summing up the number of unique months an agency reports data for. Note that this indicates the number of months an agency reported arrests for ANY crime. They may not necessarily report every crime every month. Agencies that did not report a crime with have a value of NA for every arrest column for that crime.
• Removes data on runaways.
  

Version 4 release notes:

• Changes column names from "poss_coke" and "sale_coke" to "poss_heroin_coke" and "sale_heroin_coke" to clearly indicate that these column includes the sale of heroin as well as similar opiates such as morphine, codeine, and opium. Also changes column names for the narcotic columns to indicate that they are only for synthetic narcotics.
  

Version 3 release notes:

• Add data for 2016.
• Order rows by year (descending) and ORI.

Version 2 release notes:

• Fix bug where Philadelphia Police Department had incorrect FIPS county code.

The Arrests by Age, Sex, and Race data is an FBI data set that is part of the annual Uniform Crime Reporting (UCR) Program data. This data contains highly granular data on the number of people arrested for a variety of crimes (see below for a full list of included crimes). The data sets here combine data from the years 1980-2015 into a single file. These files are quite large and may take some time to load.

All the data was downloaded from NACJD as ASCII+SPSS Setup files and read into R using the package asciiSetupReader. All work to clean the data and save it in various file formats was also done in R. For the R code used to clean this data, see here. https://github.com/jacobkap/crime_data. If you have any questions, comments, or suggestions please contact me at jkkaplan6@gmail.com.

I did not make any changes to the data other than the following. When an arrest column has a value of "None/not reported", I change that value to zero. This makes the (possible incorrect) assumption that these values represent zero crimes reported. The original data does not have a value when the agency reports zero arrests other than "None/not reported." In other words, this data does not differentiate between real zeros and missing values. Some agencies also incorrectly report the following numbers of arrests which I change to NA: 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 99999, 99998.

To reduce file size and make the data more manageable, all of the data is aggregated yearly. All of the data is in agency-year units such that every row indicates an agency in a given year. Columns are crime-arrest category units. For example, If you choose the data set that includes murder, you would have rows for each agency-year and columns with the number of people arrests for murder. The ASR data breaks down arrests by age and gender (e.g. Male aged 15, Male aged 18). They also provide the number of adults or juveniles arrested by race. Because most agencies and years do not report the arrestee's ethnicity (Hispanic or not Hispanic) or juvenile outcomes (e.g. referred to adult court, referred to welfare agency), I do not include these columns.

To make it easier to merge with other data, I merged this data with the Law Enforcement Agency Identifiers Crosswalk (LEAIC) data. The data from the LEAIC add FIPS (state, county, and place) and agency type/subtype. Please note that some of the FIPS codes have leading zeros and if you open it in Excel it will automatically delete those leading zeros.

I created 9 arrest categories myself. The categories are:

• Total Male Juvenile
• Total Female Juvenile
• Total Male Adult
• Total Female Adult
• Total Ma