You said earlier that, "The whole traffic study is comparing stops to census data, which is a widely discredited benchmark for a mobile population." But the study you linked to references using census data as one of the data points so it is hardly widely discredited. It sounds more like census data has limitations, like any benchmark. |
Reread Phase II of the analysis they perform. |
Nowhere does it say or imply that using Census data is "widely discredited". It acknowledges both benefits and limitations of Census data. |
It says not to use it alone. They perform multiple analyses with different benchmarks and compare. "I" am saying it's widely discredited. 2.3.9 Types of Benchmarks One of the most problematic aspects of stop data analysis is the construction of an accurate benchmark or baseline. 9° The difficulty is in determining the population that is at-risk of being stopped. This is sometimes called the "denominator problem." One recent study concluded that racially biased policing cannot be "proven" because it is currently infeasible to accurately determine who is eligible to be stopped. 9j This conclusion stemmed from the generally accepted belief that measures of resident population (i.e., census data) are a poor indicator of the population at risk of being stopped. One study verified this assertion by cross-checking census data with information gathered from an observational study. 92 Several benchmarks for motor vehicle stops have been discussed in the literature and implemented in studies of actual stop data. 93 These include census data, adjusted census data, data collected by the Department of Motor Vehicles (DMV), data gathered from blind enforcement mechanisms, data from observational studies, crime data, traffic accident data, and survey data. https://www.ojp.gov/pdffiles1/Digitization/215460NCJRS.pdf The problem, however, is that that the demographic characteristics of the people living at any one location at ten year intervals has nothing to do with the driving population in a given place, nor who is breaking the law in any specific area. We use our vehicles to travel to places away from our homes, as people generally do not work, shop, or recreate in their homes. Several studies illustrate this well. https://www.dolanconsultinggroup.com/news/racial-profiling-or-bad-research/ Back then, and even as this document goes to press, most agencies were and still are conducting “census benchmarking.” In census benchmarking agencies compare the demographic profile of the drivers stopped by police to the demographic profile of the residents of the jurisdiction as determined by the U.S. Census. For a variety of reasons, such a comparison is of no scientific value for purposes of trying to measure racial bias in policing and, in fact, has very often resulted in misleading and unsupported findings. https://www.policeforum.org/assets/docs/Free_Online_Documents/Racially-Biased_Policing/by%20the%20numbers%20-%20a%20guide%20for%20analyzing%20race%20data%20from%20vehicle%20stops%202004.pdf In spite of is accessibility, many researchers, law enforcement officials, and various other groups are highly critical of the use of residential Census data as a benchmark in stops analyses. While there are nuances to these critiques, they all center on one central argument: that residential population data does not accurately reflect the driving population or population at risk of encountering law enforcement. For example, many researchers argue that the residential population of a given area is an inappropriate benchmark for the driving population because it cannot account for daily inflows and outflows of individuals from the area under examination. These inflows and outflows can occur for numerous reasons; whether they are caused by commuters entering a city for work on a daily basis, individuals driving to an area for entertainment and shopping excursions, or the fact that the driving population in an area sitting along a major interstate will likely be impacted by flow-through traffic. Relatedly, seasonal population changes can also be problematic, whether driven by tourism during certain months or the significant changes that some municipalities with universities experience when students return to campus for classes. https://www.oregon.gov/cjc/stop/Documents/Traffic_Stop_Research_Memo_Final_Draft-10-16-18.pdf |
What this tells me is that some people, particularly law enforcement officials, 9 Object to seeing census benchmarks because it doesn't "prove" bias. That's ridiculous, the purpose of a benchmark is not to prove bias. None of this suggests that using it is "widely discredited" in fact it's clear it's still being used by many. |
This is hilarious. One poster is citing nuanced studies that show and measure about 10 different data points. The other is going "cops are bad" Incredible. |
Those nuanced studies clearly show that using census data is not "widely discredited". I certainly agree it is not a perfect benchmark. As one of the studies cited by PP shows, it also has clear benefits. I also pointed out many of the criticisms come form law enforcement. That is just a true statement. I am not sure how you get "cops are bad" from that. |
| She's like a parody of a woke liberal at this point: https://twitter.com/KristinMink_/status/1624499637645193217 |
There is literally nothing that distinguishes her in that social media post than any other councilmember. They are all stereotypical TPSS Twitter progressives. For better or for worse. |
She spells "folks" with an x on the end. Does that make it more gender inclusive? JFC. https://twitter.com/KristinMink_/status/1624500376647999492 |
interesting tweet. that person Carlean is not on the council staff. so I'm puzzled how they are her public safety policy director. I think Kristin just hasn't figured out how to transition from activist to legislator yet |
How census benchmarking is done in Montgomery County Black residents are 18% of the population Black drivers are 31% of the population No other analysis. Then they conclude that since these numbers are disparate, there must be racism. But association is not causation. I can't stress that enough. More They don't take into account any confounding factors. Location, age, number of miles driven, time of day, road conditions, license status, intoxication status, vehicle condition, etc. So most people who don't understand how to work with data will believe what they want to believe. Which is that police are the most significant or even sole driver of those disparate outcomes. Why does this matter? 1) Because if they don't understand the real drivers of disparities, they can't easily fix them. 2) If they just try to close the gap by modifying police operations, they risk significant unintended consequences. And if they are going to hang onto association = causation, then they need to consider this: Traffic enforcement has fallen by 2/3 since the pandemic, and traffic/pedestrian fatalities have increased. That's one of the potential unintended consequences that the council needs to consider before they do something stupid like mess with traffic enforcement. |
| I live down the block from Kristin. She's incredibly obnoxious. Most of us on the block do not like her. She puppets a lot of political talking points, even in private conversations. |
You can fault her about a lot of things and rightfully so. But no one can faulted her for not genuinely believing what she says. |
Even if she should silly and dumb? Which is a lot of the time. -Kristin's neighbor |