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Baton Rouge Murder Stats · Gil I Am

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  Speaker 1: Okay, when someone brings up crime stats by race, you
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  Speaker 1: know as well as I do that things get heated
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  Speaker 1: before a single number is even spoken out loud. Some
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  Speaker 1: folks use these stats to just confirm their existing beliefs,
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  Speaker 1: while others completely refuse to discuss them, worrying about where
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  Speaker 1: the conversation might lead. But in this explainer, we're looking
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  Speaker 1: at the numbers directly, because numbers shouldn't be feared, they
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  Speaker 1: should be understood. Today we're objectively unpacking the data from
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  Speaker 1: Baton Rouge, Louisiana. We aren't here to condemn a community
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  Speaker 1: or turn suffering into some sort of political weapon. We're
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  Speaker 1: simply going to examine what the available statistics actually show,
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  Speaker 1: what they don't show, and why that difference matters so much.
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  Speaker 1: To get a full accurate picture. Here is our roadmap
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  Speaker 1: for today. We'll start with the population baseline, move into
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  Speaker 1: victims and suspects, look at the limitations of the data,
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  Speaker 1: dive into root causes and realities, and finally wrap up
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  Speaker 1: with a concept called holding two truths. Let's get right
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  Speaker 1: into it. Okay, let's dive into this section one, the
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  Speaker 1: population baseline. Before we look at a single crime stat
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  Speaker 1: absolutely need a baseline. Without knowing the actual makeup of
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  Speaker 1: a city, raw numbers just mean nothing. So Baton Rouge
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  Speaker 1: has about two hundred and twenty three thousand residents. Roughly
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  Speaker 1: fifty point five percent identifies black, about thirty five point
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  Speaker 1: eight percent is White, four percent is Asian, and six
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  Speaker 1: percent is two or more races, and about six point
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  Speaker 1: two percent identify as Hispanic or Latino. Now why does
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  Speaker 1: this baseline matter? Well, if one group represents about half
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  Speaker 1: the population, like the black population here, we naturally expect
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  Speaker 1: them to represent a significant chunk of the data. The
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  Speaker 1: real question we're asking is does the crime data match
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  Speaker 1: this baseliner? Is it dramatically different? I want you to
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  Speaker 1: keep that fifty percent anchor right in the back of
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  Speaker 1: your mind. Moving right along to Section two, Victims and Suspects,
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  Speaker 1: Now we can examine a highly detailed data set, the
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  Speaker 1: twenty twenty two Baton Rouge Group Violence Assessment. They looked
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  Speaker 1: closely at one hundred and five homicides and two hundred
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  Speaker 1: and seventeen non fatal shootings. Among homicide victims where race
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  Speaker 1: was recorded, nearly eighty five percent were black, White, and
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  Speaker 1: Latino victims each accounted for about seven point six percent.
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  Speaker 1: Think back to that baseline we just set. Black residents
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  Speaker 1: make up roughly fifty percent of the city but suffered
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  Speaker 1: nearly eighty five percent of the homicides. That is an
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  Speaker 1: enormous disparity, and this brilliantly illustrates a really critical point.
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  Speaker 1: This is fundamentally a profound victimization statistic. We're talking about
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  Speaker 1: lives lost, families devastated. When people discuss racial disparities in crime,
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  Speaker 1: they almost always focus on the offenders, but the reality
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  Speaker 1: is that these communities are suffering the absolute highest levels
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  Speaker 1: of violent victimization. That exact same assessment also reviewed identified
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  Speaker 1: homicide suspects. Here the data shows approximately seventy nine point
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  Speaker 1: five percent of identified suspects were black, thirteen point six
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  Speaker 1: percent were Latino, and six point eight percent were white.
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  Speaker 1: So once again, the statistical disparity continues on the suspects
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  Speaker 1: side of the ledger, sitting well above that roughly fifty
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  Speaker 1: percent population baseline. We actually see an even larger disparity
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  Speaker 1: when we look at identified suspects in non fatal shootings
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  Speaker 1: eighty eight percent black, eight percent white, and four percent Latino.
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  Speaker 1: But hey, as focus learners, we absolutely have to look
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  Speaker 1: at the boundaries of this data. Race information was missing
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  Speaker 1: in several records. A lot of incidents had no identified
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  Speaker 1: suspect at all, or victims simply refused to cooperate. So
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  Speaker 1: while these percentages accurately describe the known information, they do
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  Speaker 1: not represent every single shooting, shooter or victim in the city.
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  Speaker 1: Let's move to and see how this builds in Section
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  Speaker 1: three Limitations of the data. This brings us to a
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  Speaker 1: crucial part of any data analysis, understanding what the numbers
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  Speaker 1: don't say. The word suspect is incredibly important here. A
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  Speaker 1: suspect is just someone identified through witness statements, leads, or
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  Speaker 1: maybe some video footage. Sure some are eventually arrested, prosecuted,
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  Speaker 1: or convicted, but others they might be clear completely or
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  Speaker 1: literally never charged at all. Because of that, we can
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  Speaker 1: never ever treat suspect statistics as identical to conviction statistics.
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  Speaker 1: An identified suspect is really just a data point in
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  Speaker 1: an ongoing investigation, whereas a convicted offender has actually been
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  Speaker 1: proven guilty in a court of law. Blurring those two
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  Speaker 1: lines together leads to bad data analysis and really unfair assumptions.
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  Speaker 1: This distinction matters legally, morally, and statistically. To put these
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  Speaker 1: data limitations into perspective, I want you to look at
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  Speaker 1: this massive number twenty six thousand and eleven. That is
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  Speaker 1: the total number of reported offenses in Baton Rouge in
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  Speaker 1: twenty twenty three. That spans over six thousand crimes against persons,
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  Speaker 1: seventeen thousand property crimes, and twenty four hundred crimes against
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  Speaker 1: society like drug offenses. Now compare that staggering scale, that
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  Speaker 1: massive ocean of total crime, to the one hundred and
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  Speaker 1: five homicides we were just looking at.
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  Speaker 2: This highlights a massive statistical trap that's so easy to
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  Speaker 2: fall into. The published Baton Rouge totals do not provide
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  Speaker 2: a complete race by race breakdown for all twenty six
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  Speaker 2: thy eleven of those offenses. Therefore, we cannot dishonestly take
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  Speaker 2: the narrow demographic percentages from homicides and just automatically apply
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  Speaker 2: them to shoplifting, fraud or vehicle theft. Going beyond what
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  Speaker 2: the evidence supports is super dangerous. Furthermore, twenty eleven offenses
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  Speaker 2: does not mean twenty six thy eleven different criminals. Think
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  Speaker 2: about it, A single incident like a home break in
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  Speaker 2: and assault and a stolen car generates multiple offenses. Offenses, incidents,
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  Speaker 2: and offenders are entirely different measurements. Our right shifting gears
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  Speaker 2: to Section four root causes and realities. So if we
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  Speaker 2: have accurate data on violent crime disparities, we have to
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  Speaker 2: move past the statistical symptoms and start examining the structural diseases.
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  Speaker 2: Let's be absolutely clear here, no serious analysis out there
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  Speaker 2: suggests that skin color itself causes criminal behavior. Race is
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  Speaker 2: not a motive. Race does not pull a trigger. A
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  Speaker 2: person who commits a violent crime is solely responsible for
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  Speaker 2: that decision, and examining root causes does not excuse individual
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  Speaker 2: accountability for a second. Instead of race, criminologists look at
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  Speaker 2: highly relevant environmental factors concentrated poverty, unstable housing, family disruption,
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  Speaker 2: illegal drug markets, gang conflicts, and deep distrust between residents
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  Speaker 2: and law enforcement. Because of long term economic and social developments,
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  Speaker 2: these conditions often become heavily concentrated in very specific neighborhoods.
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  Speaker 2: If our actual goal is to prevent the next violent crime,
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  Speaker 2: we have to understand the environment where that violence is
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  Speaker 2: brewing in the first place, and that leads us right
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  Speaker 2: to one of the most vital realities of crime data.
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  Speaker 2: Serious violence is heavily concentrated in relatively small social networks
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  Speaker 2: and geographic areas. It is definitely not evenly distributed across
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  Speaker 2: an entire racial population. Looking at homicide statistics and concluding
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  Speaker 2: that Black residents in general are criminals is entirely false.
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  Speaker 2: The overwhelming majority in Baton Rouge are going to work,
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  Speaker 2: raising their families and serving their communities. In fact, they're
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  Speaker 2: often the victims and the community leaders who are out
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  Speaker 2: there demanding accountability. The statistics describe concentration, not the moral
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  Speaker 2: character of a race. Finally, we arrive at section five
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  Speaker 2: holding two truths. So the crucial point is that we
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  Speaker 2: need a framework for analyzing this kind of complex social data,
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  Speaker 2: and it's all about holding two realities in our minds
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  Speaker 2: at the exact same time. Umber one is that Baton
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  Speaker 2: Rouge's data shows a severe, undeniable racial disparity among homicide
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  Speaker 2: victims and identified shooting suspects. That shouldn't be ignored. But
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  Speaker 2: truth number two is that these statistics do not justify
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  Speaker 2: treating every member of a racial group as suspicious or violent.
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  Speaker 2: That reality cannot be denied either. True honesty requires acknowledging
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  Speaker 2: both of these facts simultaneously. If we can hold those
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  Speaker 2: two truths together, we can move from just arguing over
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  Speaker 2: percentages to actually finding community solutions. The goal here is
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  Speaker 2: fewer funerals and fewer traumatized children.
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  Speaker 1: Right. This requires focusing law enforcement on those small networks
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  Speaker 1: driving the violence, solving homicides for grieving families, intervening in
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  Speaker 1: conflicts early on, and strengthening social supports like schools and
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  Speaker 1: addiction treatment. And crucially, it requires agencies to publish transparent,
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  Speaker 1: complete data so residents aren't left stringing together fragments and
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  Speaker 1: making bad assumptions. What the Baton Row statistics ultimately reveal
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  Speaker 1: is a profound crisis of violence, victimization, and fractured communities.
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  Speaker 1: We absolutely must tell the truth about the nun and
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  Speaker 1: the truth about their limitations. But most importantly, we must
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  Speaker 1: never ever forget that behind every single percentage on a
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  Speaker 1: chart is a human being, a victim, a grieving family.
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  Speaker 1: Statistics are powerful tools that can start a conversation. So
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  Speaker 1: as we leave this explainer today, consider this. Are we
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  Speaker 1: willing to look past the politics of a percentage and
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  Speaker 1: rely on truth, accountability, and compassion to actually save the
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  Speaker 1: human beings behind the numbers.