Bubbles85 said:

It's time we stopped being so politically correct and just admit that this multicultural experiment our politicians have embarked on is a complete and utter failure. Not because of skin colour, but because many of the cultures and ideology's now here, are simply not compatible with our own culture, or each others and never will be.

---

XUL said:

What studies?

Show us. And please make sure the methodology is included -- otherwise it's hard to digest.

---

Falcon91Wolvrn03 said:

XUL said:

What studies?

Show us. And please make sure the methodology is included -- otherwise it's hard to digest.

---

Any of these on Google.

And if you're too lazy to look through the google results, here's 18 of them from the very first Google hit.

---

Quote:

U.S. Police Shootings Data.

Data on police shootings were accessed in December 2014, from the U.S. Police Shootings Database [1] compiled by Kyle Wagner. The data can be accessed and improved at http://goo.gl/Su60Cm.

From the total set of police shooting data, records were selected only when: 1) a suspect’s status as either armed or unarmed was clearly described (sometimes by searching for subsequent media reports), 2) a suspect’s race/ethnicity could be clearly assigned, 3) the year of the shooting was no earlier than 2011 and no later than 2014, and 4) a county name could be assigned. The data were then error-checked by removing listings for the same suspect/victim name; duplicates under spelling variations of suspect/victim name were found and eliminated by checking for multiple cases of police shootings with the same victim year, age, race/ethnicity, and county. In total, 721 cases of police shootings were analyzed. About five percent (N = 35) of records were randomly spot-checked by using a random number generator to sort the cleaned Excel file containing the data; the urls cited in the USPSD were then checked to ensure validity of the data. In each case the urls were valid (but see below) and the data accurately described. In a small number of cases (N = 3) the original url returned an error and www.archive.org was used to retrieve the original web-page, which, in each case, was archived under the url provided in the USPSD and included a full event description. The raw data of the USPSD in its entirety can be found at http://goo.gl/QrylkY. Additionally, the cleaned data modeled in this paper are included in the the Data folder of S1 File. Finally, county-level raw data, covariates, and modeling results are included in the Data folder of S1 File in a geographically resolved format linked to a shapefile that can be used for geographic data visualization in the R software environment.

Methodologically, users are asked to adhere to the following protocol in data collection (see [1], direct quotation follows):

    Using Google’s search tools, isolate a single day (e.g. Jan. 1, 2011, to Jan. 1, 2011) and search for the term “police involved shooting” (don’t use quotation marks). Use Chrome’s Incognito mode when searching to ensure you aren’t getting local results.
    Read each link on the first 10 pages of results; for any instances of shootings involving a police officer, log them in the form.
    We’re looking at 2011, 2012, [2013 and 2014], and tracking date, name, age, gender, race/ethnicity, injured/killed, and a number of other fields. Please be as thorough as possible with each incident, and provide links to where you found the information (this will be crucial during verification). Often, the first day of reports will not have personal details, and a second search of subsequent days will fill in more of the story.
    Before starting in, take a look at the submissions here and pick a day that no one has begun (“Not Checked” in the third sheet). Remember, we’re starting off by looking at just the past three years.
    A later death, after a person is hospitalized in a police-involved shooting, is considered a death for our purposes.
    We are looking for any incidence of a police officer shooting and hitting another person. This can be off-duty if the officer was acting in a law-enforcement capacity.
    We are not looking for incidences of police officers discharging their weapons and hitting no one. In a perfect world these would be tracked, since often the only difference is that the shot missed, but these incidents are not as thoroughly reported and would probably bias the data.
    Please keep the data as neat as possible. Work within specific months, make sure you’re in the correct year, keep the columns clean, add peripheral information in the “Summary” portion, etc.

Census and Covariate Data.

County-level census population data and county-level covariates on income and inequality were accessed from AIDSvu [54], an open access database available through the Rollins School of Public Health at Emory University.
Google Racial Animus Data.

The racial animus data were generously provided by Seth Stephens-Davidowitz [29] at the level of the DMA (designated market area), and were translated to county-level data using FIPS codes and a cross-walk provided by the ICPSR at http://www.icpsr.umich.edu/icpsrweb/DSDR/studies/22720.
Crime Rate Data.

Race-specific assault- and weapons-related arrest data compiled by the United States Department of Justice were accessed from the Inter-University Consortium for Political and Social Research [55]. Arrests were summed within counties.
Geographic Data.

Geographic shapefiles were downloaded from GADM [56], an open access database of Global Administrative Areas.
Bayesian Modeling
Estimating Risk Ratios for Police Shooting as a Function of Race and Arms Status.

County-level police shooting rates are estimated using binomial probabilities, and a prior, estimated from the data, under hierarchical partial pooling. Hierarchical pooling allows information collected in other counties within the United States to partially inform the parameter estimates in a focal county, which improves out-of-sample predictive inference globally [57], and regularizes otherwise undefined posterior predictive risk ratios. Prior to the introduction of multi-level modeling methods, relative risk ratios at local levels were very hard to infer. For example, if no unarmed whites but three unarmed black individuals were killed in a county, the estimated relative risk ratio would be: yielding some undefined relative risk ratio. The multi-level Bayesian methods used here, partially (rather than fully) pool information across counties, allowing for more stable estimates in relative risk ratios (by regularizing the denominator of the above equation away from zero and toward the global mean for that value), while still allowing for, and representing, heterogeneity across counties.

To begin the model, the race-specific probabilities of being shot by police at the county-level are estimated under two conditions (armed and unarmed), in all counties with at least a single incidence of a police-involved shooting between the years of 2011–2014. These probabilities are then used to estimate the relative risk of being the victim of a police shooting, conditional on being armed or unarmed, and race/ethnicity.

For example, the probability, , in county c of a black (B) and unarmed (U) individual being shot by police can be estimated from the county-level census data, , and the count of shootings, , as: (1)

Eq (1) serves as a template for each of the main probability statements in the model, where a probability unique to the interaction of race/ethnicity (B = black, H = hispanic, and W = white) and arms status (A = armed and U = unarmed) is estimated. Note that superscripts are labels, and do not indicate exponentiation. The subsequent main probability statements run as follows: (2) (3) (4) (5) (6) Thus, in each county, there are D = 6 probability parameters to be estimated. These parameters are organized into a vector: (7) The probability parameter vector θ[c] ∈ (0,1)D is transformed to a parameter vector on the unconstrained scale Θ[c] ∈ ℝD using an inverse logit transformation: (8) Then, vectors unique to each county c, are estimated from a higher-level distribution in a multi-level Bayesian framework: (9) The Cholesky factor, L, of the model’s covariance matrix is formed from Cholesky decomposition of the correlation matrix ρ multiplied by a diagonal matrix of scale parameters, σ: (10) Each element of the mean vector, μ, is given a weakly regularizing normal prior: (11) which places weakly regularizing prior support over the plausible range of values on the probability scale. Each element of the scale vector, σ, is given a vague half-Cauchy prior: (12)

The correlation matrix is given an implicit uniform prior over its support. The parameters of the model are estimated using Hamiltonian Markov Chain Monte Carlo simulation, implemented in the Stan version 2.5 C++ library [58].

The raw probability estimates at the county-level are surely underestimates, because the U.S. Police Shootings Database has still only documented a fraction of the police shootings since 2011. However, since the data are collected by users selecting a given day, and then recording information on police shootings documented on that day, the data are randomly collected with respect to the suspect’s race/ethnicity, location, and status as armed versus unarmed. As such, the relative risk ratios of U.S. citizens to police shooting as a function of race/ethnicity, location, or armed status should not be especially biased. Relative risk ratios are notated as below: (13) The symbol reads as: “The relative risk in county c of being black, unarmed, and shot by police, to being white, unarmed, and shot by police.”
Modeling Risk Ratios for Police Shooting as a Function of County-Level Covariates.

In each county, the relevant risk ratio, R[c], is modeled as a function of county-level covariates. To begin the model, each posterior sample of R[c] from the above described Bayesian model is log-transformed and the distribution is summarized by calculating the mean, μR[c] = mean(log(R[c])), and standard deviation, σR[c] = sd(log(R[c])) over the MCMC samples. Uncertainty about these estimates is propagated using a Bayesian measurement error model: (14) (15) where μ[c] is defined for several candidate models (M1, M2, … M25), using a standard regression equation. The most basic, intercept only, model is defined as: (16) and various, increasingly complex, multivariate models are fit, up to the most complex: (17) where: N[c] is the total population in county c, P[c] is the percentage of black individuals in county c, I[c] is the median income of county c, G[c] is the Gini coefficient of county c, H[c] is the Google search data proxy for racist norms in county c, and are the assault-related arrest rates for whites and blacks, respectively, in county c, and and are the weapons-related arrest rates for whites and blacks, respectively, in county c. The covariates included in models M2–M24 are indicated in Tables 1 and 2 in the Results section.

Each element of the regression parameter vector is given a vague prior: (18) and the scale parameter is given a weakly regularizing prior: (19)

---

Quote:

Law enforcement agencies across the country have failed to provide us with even basic information about the lives they have taken. And while the recently signed Death in Custody Reporting Act mandates this data be reported, its unclear whether police departments will actually comply with this mandate and, even if they do decide to report this information, it could be several years before the data is fully collected, compiled and made public.

We cannot wait to know the true scale of police violence against our communities. And in a country where at least three people are killed by police every day, we cannot wait for police departments to provide us with these answers. The maps and charts on this site aim to provide us with the answers we need. They include information on 1,131 known police killings - including 1,067 arrest-related deaths (according to Bureau of Justice Statistics definitions) as well as 64 unintentional, off-duty and/or in-custody deaths - that occurred in 2014. They also include information on 1,080 police killings in 2013, 1,131 in 2015, and 1,129 police killings in 2016. 93 percent of the killings in our database occurred while a police officer was acting in a law enforcement capacity. Importantly, these data do not include killings by vigilantes or security guards who are not off-duty police officers.

This information has been meticulously sourced from the three largest, most comprehensive and impartial crowdsourced databases on police killings in the country: FatalEncounters.org, the U.S. Police Shootings Database and KilledbyPolice.net. We've also done extensive original research to further improve the quality and completeness of the data; searching social media, obituaries, criminal records databases, police reports and other sources to identify the race of 91 percent of all victims in the database.

We believe the data represented on this site is the most comprehensive accounting of people killed by police since 2013. A recent report from the Bureau of Justice Statistics estimated approximately 1,200 people were killed by police between June, 2015 and May, 2016. Our database identified 1,179 people killed by police over this time period. While there are undoubtedly police killings that are not included in our database (namely, those that go unreported by the media), these estimates suggest that our database captures 98% of the total number of police killings that have occurred since 2013. We hope these data will be used to provide greater transparency and accountability for police departments as part of the ongoing campaign to end police violence in our communities.
Definitions

Police Killing: A case where a person dies as a result of being chased, beaten, arrested, restrained, shot, pepper sprayed, tasered, or otherwise harmed by police officers, whether on-duty or off-duty, intentional or accidental.

A victim was coded as Unarmed in the database if they were one or more of the following:

    not holding any objects or weapons when killed
    holding household/personal items that were not used to attack others (cellphone, video game controller, cane, etc.)
    holding a toy weapon (BB gun, pellet gun, air rifle, toy sword)
    an innocent bystander or hostage killed
    a pedestrian or motorist accidentally hit by a police car or passengers in a vehicle chased by police with no weapon on them
    drivers or passengers accidentally hit by a police car
    a person who dies in police custody after a police use of force or police neglect of their medical needs
    alleged to be armed by the police, but multiple independent witnesses maintain the person was unarmed, video evidence shows that the person was unarmed, and/or circumstances indicate it was physically impossible for that person to be armed (i.e. claiming a person shot themselves with their own gun while handcuffed and under surveillance in police custody after being searched for weapons)

A victim was coded as having a Vehicle as a weapon if they were one or more of the following:

    a driver who was killed while hitting, dragging or driving towards officers or civilians
    a driver being chased at high speed by police when police killed them (they could be considered a threat to others by driving at such reckless speeds)
    people who were killed by a civilian driver or crashed without being hit directly by police during a police pursuit are not included in the database. Note that an estimated 400 people are killed in police pursuits each year and only a small proportion of these cases are included in the database (most deadly pursuits end after the driver crashes themselves into something or hits a civilian vehicle without being directly rammed/hit by police).

A person was coded as Allegedly Armed in the database if they:

    were alleged to have possessed objects or weapons in circumstances other than those stated above

---

Quote:

The law firm working groups developed and employed robust processes for their investigations. They
formulated investigation plans that identified witnesses and documents relevant to each topic of inquiry
and implemented specific interview protocols that provided for sharing of information with each group
before and after interviews. By the end of their investigations, the working groups had interviewed more
than 100 witnesses, including the Chief of Police, current and former SFPD officers and employees, every
sitting police commissioner, numerous former police commissioners, high-level city officials, staff from a
number of city agencies, policy and law enforcement experts, and community leaders.
14
The groups also reviewed and analyzed thousands of pages of documentary evidence, including publicly available policies,
procedures, and reports, and additional documents and data requested from relevant public agencies.
As an independent advisory body, the Panel did not have any power to compel testimony or subpoena
documents. All interviews were voluntary. Working groups received periodic feedback from the judges
on their investigations, as well as from Veritas Assurance Group, a consulting firm specializing in law
enforcement practices retained by the Panel through a donation to PolicyLink.
The Panel also held five hearings that were open to the public. At the first public hearing, held on
December 15, 2015, the working groups updated the judges on their investigative plans and progress and
received feedback. The second and third hearings, held on January 14, 2016, and February 22, 2016,
respectively, were devoted to taking public testimony from key witnesses. The working groups determined
which witnesses to call and conducted the initial questioning of each witness, after which the Panel’s
judges also questioned the witnesses. The following witnesses presented at these hearings:
•
Jeff Adachi, San Francisco Public Defender
•
George Gascón, San Francisco District Attorney
•
Jeff Godown, former SFPD Interim Chief of Police
•
Joyce M. Hicks, Director of the Office of Citizen Complaints
•
Tonia Lediju, Audit Director at the Office of the Controller
•
Suzy Loftus, Police Commission President
•
Allison Macbeth, Assistant District Attorney
•
Dr. Joe Marshall, Police Commissioner
•
Lt. Joe Reilly, Secretary to the Police Commission (Ret.)
•
Greg P. Suhr, former SFPD Chief of Police
•
Rev. Arnold Townsend, Vice President of the San Francisco NAACP
•
Sergeant Yulanda Williams, President of Officers for Justice
•
Rebecca Young, Assistant Public Defender
The Panel’s fourth hearing, held on March 21, 2016, in the Bayview, was devoted exclusively to
community feedback and perspectives. At its fifth and final hearing, held on May 9, 2016, the Panel’s
working groups presented preliminary findings and recommendations for consideration and feedback by
the public before finalizing its report.

---

The Ecstatic said:

But even if he did, you cant take arrest rates and equate that with criminal acts.

I commit crimes every single day and dont get arrested for it.

---

XUL said:

Falcon91Wolvrn03 said:

XUL said:

Falcon91Wolvrn03 said:

Blacks are more likely to be treated unjustly than whites, according to many studies.

---

What studies?

Show us. And please make sure the methodology is included -- otherwise it's hard to digest.

---

Any of these on Google.

And if you're too lazy to look through the google results, here's 18 of them from the very first Google hit.

---

You can't hand me a list of articles and tell me to research your claim in order to ascertain if it's true or not.

The burden of proof is on you. Don't be intellectually lazy.

---

qman said:

Falcon91Wolvrn03 said:

No one's saying whites are treated fairly.  They're saying whites often get treated better, that's all.

---

How are they treated better if they're also victims of the same injustices from the criminal justice system? That doesn't make any sense.

---

Quote:

You can't hand me a list of articles and tell me to research your claim in order to ascertain if it's true or not.

The burden of proof is on you. Don't be intellectually lazy.

---

ballsalsa said:

materials and methods section of any paper will tell you the answers to the questions you seek.

1:

Quote:

U.S. Police Shootings Data.

Data on police shootings were accessed in December 2014, from the U.S. Police Shootings Database [1] compiled by Kyle Wagner. The data can be accessed and improved at http://goo.gl/Su60Cm.

From the total set of police shooting data, records were selected only when: 1) a suspect’s status as either armed or unarmed was clearly described (sometimes by searching for subsequent media reports), 2) a suspect’s race/ethnicity could be clearly assigned, 3) the year of the shooting was no earlier than 2011 and no later than 2014, and 4) a county name could be assigned. The data were then error-checked by removing listings for the same suspect/victim name; duplicates under spelling variations of suspect/victim name were found and eliminated by checking for multiple cases of police shootings with the same victim year, age, race/ethnicity, and county. In total, 721 cases of police shootings were analyzed. About five percent (N = 35) of records were randomly spot-checked by using a random number generator to sort the cleaned Excel file containing the data; the urls cited in the USPSD were then checked to ensure validity of the data. In each case the urls were valid (but see below) and the data accurately described. In a small number of cases (N = 3) the original url returned an error and www.archive.org was used to retrieve the original web-page, which, in each case, was archived under the url provided in the USPSD and included a full event description. The raw data of the USPSD in its entirety can be found at http://goo.gl/QrylkY. Additionally, the cleaned data modeled in this paper are included in the the Data folder of S1 File. Finally, county-level raw data, covariates, and modeling results are included in the Data folder of S1 File in a geographically resolved format linked to a shapefile that can be used for geographic data visualization in the R software environment.

Methodologically, users are asked to adhere to the following protocol in data collection (see [1], direct quotation follows):

    Using Google’s search tools, isolate a single day (e.g. Jan. 1, 2011, to Jan. 1, 2011) and search for the term “police involved shooting” (don’t use quotation marks). Use Chrome’s Incognito mode when searching to ensure you aren’t getting local results.
    Read each link on the first 10 pages of results; for any instances of shootings involving a police officer, log them in the form.
    We’re looking at 2011, 2012, [2013 and 2014], and tracking date, name, age, gender, race/ethnicity, injured/killed, and a number of other fields. Please be as thorough as possible with each incident, and provide links to where you found the information (this will be crucial during verification). Often, the first day of reports will not have personal details, and a second search of subsequent days will fill in more of the story.
    Before starting in, take a look at the submissions here and pick a day that no one has begun (“Not Checked” in the third sheet). Remember, we’re starting off by looking at just the past three years.
    A later death, after a person is hospitalized in a police-involved shooting, is considered a death for our purposes.
    We are looking for any incidence of a police officer shooting and hitting another person. This can be off-duty if the officer was acting in a law-enforcement capacity.
    We are not looking for incidences of police officers discharging their weapons and hitting no one. In a perfect world these would be tracked, since often the only difference is that the shot missed, but these incidents are not as thoroughly reported and would probably bias the data.
    Please keep the data as neat as possible. Work within specific months, make sure you’re in the correct year, keep the columns clean, add peripheral information in the “Summary” portion, etc.

Census and Covariate Data.

County-level census population data and county-level covariates on income and inequality were accessed from AIDSvu [54], an open access database available through the Rollins School of Public Health at Emory University.
Google Racial Animus Data.

The racial animus data were generously provided by Seth Stephens-Davidowitz [29] at the level of the DMA (designated market area), and were translated to county-level data using FIPS codes and a cross-walk provided by the ICPSR at http://www.icpsr.umich.edu/icpsrweb/DSDR/studies/22720.
Crime Rate Data.

Race-specific assault- and weapons-related arrest data compiled by the United States Department of Justice were accessed from the Inter-University Consortium for Political and Social Research [55]. Arrests were summed within counties.
Geographic Data.

Geographic shapefiles were downloaded from GADM [56], an open access database of Global Administrative Areas.
Bayesian Modeling
Estimating Risk Ratios for Police Shooting as a Function of Race and Arms Status.

County-level police shooting rates are estimated using binomial probabilities, and a prior, estimated from the data, under hierarchical partial pooling. Hierarchical pooling allows information collected in other counties within the United States to partially inform the parameter estimates in a focal county, which improves out-of-sample predictive inference globally [57], and regularizes otherwise undefined posterior predictive risk ratios. Prior to the introduction of multi-level modeling methods, relative risk ratios at local levels were very hard to infer. For example, if no unarmed whites but three unarmed black individuals were killed in a county, the estimated relative risk ratio would be: yielding some undefined relative risk ratio. The multi-level Bayesian methods used here, partially (rather than fully) pool information across counties, allowing for more stable estimates in relative risk ratios (by regularizing the denominator of the above equation away from zero and toward the global mean for that value), while still allowing for, and representing, heterogeneity across counties.

To begin the model, the race-specific probabilities of being shot by police at the county-level are estimated under two conditions (armed and unarmed), in all counties with at least a single incidence of a police-involved shooting between the years of 2011–2014. These probabilities are then used to estimate the relative risk of being the victim of a police shooting, conditional on being armed or unarmed, and race/ethnicity.

For example, the probability, , in county c of a black (B) and unarmed (U) individual being shot by police can be estimated from the county-level census data, , and the count of shootings, , as: (1)

Eq (1) serves as a template for each of the main probability statements in the model, where a probability unique to the interaction of race/ethnicity (B = black, H = hispanic, and W = white) and arms status (A = armed and U = unarmed) is estimated. Note that superscripts are labels, and do not indicate exponentiation. The subsequent main probability statements run as follows: (2) (3) (4) (5) (6) Thus, in each county, there are D = 6 probability parameters to be estimated. These parameters are organized into a vector: (7) The probability parameter vector θ[c] ∈ (0,1)D is transformed to a parameter vector on the unconstrained scale Θ[c] ∈ ℝD using an inverse logit transformation: (8) Then, vectors unique to each county c, are estimated from a higher-level distribution in a multi-level Bayesian framework: (9) The Cholesky factor, L, of the model’s covariance matrix is formed from Cholesky decomposition of the correlation matrix ρ multiplied by a diagonal matrix of scale parameters, σ: (10) Each element of the mean vector, μ, is given a weakly regularizing normal prior: (11) which places weakly regularizing prior support over the plausible range of values on the probability scale. Each element of the scale vector, σ, is given a vague half-Cauchy prior: (12)

The correlation matrix is given an implicit uniform prior over its support. The parameters of the model are estimated using Hamiltonian Markov Chain Monte Carlo simulation, implemented in the Stan version 2.5 C++ library [58].

The raw probability estimates at the county-level are surely underestimates, because the U.S. Police Shootings Database has still only documented a fraction of the police shootings since 2011. However, since the data are collected by users selecting a given day, and then recording information on police shootings documented on that day, the data are randomly collected with respect to the suspect’s race/ethnicity, location, and status as armed versus unarmed. As such, the relative risk ratios of U.S. citizens to police shooting as a function of race/ethnicity, location, or armed status should not be especially biased. Relative risk ratios are notated as below: (13) The symbol reads as: “The relative risk in county c of being black, unarmed, and shot by police, to being white, unarmed, and shot by police.”
Modeling Risk Ratios for Police Shooting as a Function of County-Level Covariates.

In each county, the relevant risk ratio, R[c], is modeled as a function of county-level covariates. To begin the model, each posterior sample of R[c] from the above described Bayesian model is log-transformed and the distribution is summarized by calculating the mean, μR[c] = mean(log(R[c])), and standard deviation, σR[c] = sd(log(R[c])) over the MCMC samples. Uncertainty about these estimates is propagated using a Bayesian measurement error model: (14) (15) where μ[c] is defined for several candidate models (M1, M2, … M25), using a standard regression equation. The most basic, intercept only, model is defined as: (16) and various, increasingly complex, multivariate models are fit, up to the most complex: (17) where: N[c] is the total population in county c, P[c] is the percentage of black individuals in county c, I[c] is the median income of county c, G[c] is the Gini coefficient of county c, H[c] is the Google search data proxy for racist norms in county c, and are the assault-related arrest rates for whites and blacks, respectively, in county c, and and are the weapons-related arrest rates for whites and blacks, respectively, in county c. The covariates included in models M2–M24 are indicated in Tables 1 and 2 in the Results section.

Each element of the regression parameter vector is given a vague prior: (18) and the scale parameter is given a weakly regularizing prior: (19)

---

2: seems to be based off the same study as 1

3:

Quote:

Law enforcement agencies across the country have failed to provide us with even basic information about the lives they have taken. And while the recently signed Death in Custody Reporting Act mandates this data be reported, its unclear whether police departments will actually comply with this mandate and, even if they do decide to report this information, it could be several years before the data is fully collected, compiled and made public.

We cannot wait to know the true scale of police violence against our communities. And in a country where at least three people are killed by police every day, we cannot wait for police departments to provide us with these answers. The maps and charts on this site aim to provide us with the answers we need. They include information on 1,131 known police killings - including 1,067 arrest-related deaths (according to Bureau of Justice Statistics definitions) as well as 64 unintentional, off-duty and/or in-custody deaths - that occurred in 2014. They also include information on 1,080 police killings in 2013, 1,131 in 2015, and 1,129 police killings in 2016. 93 percent of the killings in our database occurred while a police officer was acting in a law enforcement capacity. Importantly, these data do not include killings by vigilantes or security guards who are not off-duty police officers.

This information has been meticulously sourced from the three largest, most comprehensive and impartial crowdsourced databases on police killings in the country: FatalEncounters.org, the U.S. Police Shootings Database and KilledbyPolice.net. We've also done extensive original research to further improve the quality and completeness of the data; searching social media, obituaries, criminal records databases, police reports and other sources to identify the race of 91 percent of all victims in the database.

We believe the data represented on this site is the most comprehensive accounting of people killed by police since 2013. A recent report from the Bureau of Justice Statistics estimated approximately 1,200 people were killed by police between June, 2015 and May, 2016. Our database identified 1,179 people killed by police over this time period. While there are undoubtedly police killings that are not included in our database (namely, those that go unreported by the media), these estimates suggest that our database captures 98% of the total number of police killings that have occurred since 2013. We hope these data will be used to provide greater transparency and accountability for police departments as part of the ongoing campaign to end police violence in our communities.
Definitions

Police Killing: A case where a person dies as a result of being chased, beaten, arrested, restrained, shot, pepper sprayed, tasered, or otherwise harmed by police officers, whether on-duty or off-duty, intentional or accidental.

A victim was coded as Unarmed in the database if they were one or more of the following:

    not holding any objects or weapons when killed
    holding household/personal items that were not used to attack others (cellphone, video game controller, cane, etc.)
    holding a toy weapon (BB gun, pellet gun, air rifle, toy sword)
    an innocent bystander or hostage killed
    a pedestrian or motorist accidentally hit by a police car or passengers in a vehicle chased by police with no weapon on them
    drivers or passengers accidentally hit by a police car
    a person who dies in police custody after a police use of force or police neglect of their medical needs
    alleged to be armed by the police, but multiple independent witnesses maintain the person was unarmed, video evidence shows that the person was unarmed, and/or circumstances indicate it was physically impossible for that person to be armed (i.e. claiming a person shot themselves with their own gun while handcuffed and under surveillance in police custody after being searched for weapons)

A victim was coded as having a Vehicle as a weapon if they were one or more of the following:

    a driver who was killed while hitting, dragging or driving towards officers or civilians
    a driver being chased at high speed by police when police killed them (they could be considered a threat to others by driving at such reckless speeds)
    people who were killed by a civilian driver or crashed without being hit directly by police during a police pursuit are not included in the database. Note that an estimated 400 people are killed in police pursuits each year and only a small proportion of these cases are included in the database (most deadly pursuits end after the driver crashes themselves into something or hits a civilian vehicle without being directly rammed/hit by police).

A person was coded as Allegedly Armed in the database if they:

    were alleged to have possessed objects or weapons in circumstances other than those stated above

---

4:

Quote:

The law firm working groups developed and employed robust processes for their investigations. They
formulated investigation plans that identified witnesses and documents relevant to each topic of inquiry
and implemented specific interview protocols that provided for sharing of information with each group
before and after interviews. By the end of their investigations, the working groups had interviewed more
than 100 witnesses, including the Chief of Police, current and former SFPD officers and employees, every
sitting police commissioner, numerous former police commissioners, high-level city officials, staff from a
number of city agencies, policy and law enforcement experts, and community leaders.
14
The groups also reviewed and analyzed thousands of pages of documentary evidence, including publicly available policies,
procedures, and reports, and additional documents and data requested from relevant public agencies.
As an independent advisory body, the Panel did not have any power to compel testimony or subpoena
documents. All interviews were voluntary. Working groups received periodic feedback from the judges
on their investigations, as well as from Veritas Assurance Group, a consulting firm specializing in law
enforcement practices retained by the Panel through a donation to PolicyLink.
The Panel also held five hearings that were open to the public. At the first public hearing, held on
December 15, 2015, the working groups updated the judges on their investigative plans and progress and
received feedback. The second and third hearings, held on January 14, 2016, and February 22, 2016,
respectively, were devoted to taking public testimony from key witnesses. The working groups determined
which witnesses to call and conducted the initial questioning of each witness, after which the Panel’s
judges also questioned the witnesses. The following witnesses presented at these hearings:
•
Jeff Adachi, San Francisco Public Defender
•
George Gascón, San Francisco District Attorney
•
Jeff Godown, former SFPD Interim Chief of Police
•
Joyce M. Hicks, Director of the Office of Citizen Complaints
•
Tonia Lediju, Audit Director at the Office of the Controller
•
Suzy Loftus, Police Commission President
•
Allison Macbeth, Assistant District Attorney
•
Dr. Joe Marshall, Police Commissioner
•
Lt. Joe Reilly, Secretary to the Police Commission (Ret.)
•
Greg P. Suhr, former SFPD Chief of Police
•
Rev. Arnold Townsend, Vice President of the San Francisco NAACP
•
Sergeant Yulanda Williams, President of Officers for Justice
•
Rebecca Young, Assistant Public Defender
The Panel’s fourth hearing, held on March 21, 2016, in the Bayview, was devoted exclusively to
community feedback and perspectives. At its fifth and final hearing, held on May 9, 2016, the Panel’s
working groups presented preliminary findings and recommendations for consideration and feedback by
the public before finalizing its report.

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you get the idea.

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What idea?  I don't see what point you're even attempting to make here.

The Ecstatic said:

qman said:

Falcon91Wolvrn03 said:

No one's saying whites are treated fairly.  They're saying whites often get treated better, that's all.

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How are they treated better if they're also victims of the same injustices from the criminal justice system? That doesn't make any sense.

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If i punch fal twice and you 26 times, whats the difference? Why complain? I punched both of yall.

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Falcon91Wolvrn03 said:

Balls was responding to XUL's request for the methodology used in the studies provided.

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qman said:

Do non-whites have the exclusive rights of being victims of police misconduct?  I don't think so.

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qman said:

Falcon91Wolvrn03 said:

qman said:

ballsalsa said:

materials and methods section of any paper will tell you the answers to the questions you seek.
.
.
.
you get the idea.

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What idea?  I don't see what point you're even attempting to make here.

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Balls was responding to XUL's request for the methodology used in the studies provided.

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I understand that fact, that doesn't change my question.

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Falcon91Wolvrn03 said:

qman said:

Do non-whites have the exclusive rights of being victims of police misconduct?  I don't think so.

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Did anyone claim that?

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qman said:

Falcon91Wolvrn03 said:

qman said:

Do non-whites have the exclusive rights of being victims of police misconduct?  I don't think so.

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Did anyone claim that?

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Does BLM or the NFL players kneeling concern themselves with police misconduct against whites? No they don't, they separate the issue in terms of race.

Aren't people in this thread attempting to justify that fact?  Absolutely.

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Falcon91Wolvrn03 said:

qman said:

ballsalsa said:

materials and methods section of any paper will tell you the answers to the questions you seek.
.
.
.
you get the idea.

---

What idea?  I don't see what point you're even attempting to make here.

---

Balls was responding to XUL's request for the methodology used in the studies provided.

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qman said:

Does BLM or the NFL players kneeling concern themselves with police misconduct against whites? No they don't, they separate the issue in terms of race.

Aren't people in this thread attempting to justify that fact?  Absolutely.

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