Norman Rockwells also hate victims

In our prior article (with >500 likes/shares and in MSM), regarding broad-based and significantly rising hate crime, we noticed some comments from skeptical readers concerning things from the basic data, to what the mathematics meant.  The data is as clean as it gets for the U.S., and generally it is better to offer alternative insights to critique one’s research (other than an outright dismissal based on an anecdote).  We’ll also go through some additional probability ideas here, and leverage frightening comments yesterday from the Washington police department concerning hate crimes in 2016 versus before.  The bottom line though is that 100% of us are unswervingly impacted by hate crime, even those who look like they are from an illustration of 20th century American-culturist Norman Rockwell.  It is not something that simply exists in seclusion on some other side of a chasm from most Americans.  Clearly there are many complex concerns that each American faces (let alone those in other countries), and this is another one of those concerns.  We bring with us different biases and sincere experiences, but we can also all benefit from the consideration of new data and analysis, which is the purpose for this article.

The District of Columbia is the first of the (quasi) states to provide some insights into their reporting of hate crimes in 2016.  It also happens to be a sparsely populated, democrat-leaning region and hence one whose hate crime data may be surprising in relation to national trends.  In 2015 hate crime in our nation’s capital fell 5%.  But in 2016, the number of hate crimes there surged 62%!

Hate crimes also rose in virtually every category, so against people who are or based on: Jews, Muslims, other minority religions, race&ethnicity, disabilities, political orientation, sexual orientation, and gender identity.  Again, immigrant bias hate crime is not a stand-alone category, and there is no double-counting among categories (another topic we discuss below).  Also, like previous research, we show the raw data but the statistics are still worsening at an alarming rate, even if one takes population growth into account (or take extreme assumptions on the level of bogus claims and unreported claims into account!) 

There is every indication that 2017 will show significant increase in hate crime versus 2016.  Some readers are also curious of course about how hate crimes contrasts with other crimes, and as noted by the Washington police chief, we compute that hate crimes account for only 0.2% of all crimes in 2015.  However, given the drop in population and rapid acceleration in the hate, hate crimes accounted for 0.3% of all crimes in 2016, and on track for 0.4% in 2017 (double the 2015 levels!).

The other topic revolves around the demographic categorizations of the targets of hate crime.  The reporting of these hate crimes is mutually exclusive and completely exhaustive into these categories, even though all of us have different and overlapping categorical values based on our many dimensions.  Clearly there can not be negative correlation among more than two variables (link), and given different category sizes (e.g., more heterosexual people than Whites) there is not perfect positive correlation either.  For example, one can be a Christian disabled person, or a White transgender.  Implying somewhat of independent relationship between one trait and another.

The categories we have enforced in these articles were four, one for each dimension of the spider web: (1) Religion, (2) Race/Ethnicity, (3) Disability/Other, and (4) Sexual orientation.  All of us may have a small probability of have an uncommon trait in one of these dimensions, and therefore the probability of having the majority trait on these dimensions (the Norman Rockwell type of person) becomes smaller than one might realize.

To see these several dimensions becomes tricky in simple and traditional 2-dimensional computer screens or Venn-diagrams.  However, with some creativity it is possible as we re-illustrate a general multivariate normal distribution plot below (bear in mind that colors and bubble-sizes add dimensionality!)

We should approximate the portion of all Americans who fit into one of these major human traits, which includes those who even have overlapping multiple minority traits.  This is a more advanced theoretical model than the simple Pareto principle described in the earlier article, though the results are analogous over the broader curve.  We’ll then map this to hate crime reports further below.  For now:
weight_{minority religion}⁴

+ weight_{minority race}⁴

+ weight_{disability/other}⁴

+ weight_{minority sexual orientation}⁴

+ weight_{multiple minority traits}⁴

+ weight_{all majority traits}⁴

= 100%/a_scaler

And solving for the major aspects so that the crime data and Census demographic data comport to one another, here we have the following:

Demographic population trait	Portion of population		Hate crime trait	FBI portion of hate crimes
Judaism, Islam	3%		Religious bias	20%
LGBTQ	4%		Sexual orientation	20%
Blacks	13%		Race/ethnicity bias	58%
Disability/Other	19%		Disability/other bias	2%
Netting for multiple/overlapping minority characteristics above	~-11%
Other "minority characteristics" (e.g., Buddhists, Agnostics, Most immigrants, etc.)	~35%
All majority traits (Norman Rockwells)	~37%
Total	100%		Total	100%

And that then leads to the following assessment of how much hate crime impacts different portions of the population, based on justice department reporting.  We also see two different ways that everyone is affected, not simply the certain minorities we sometimes hear about in the news (who incidentally offer this country superb diversity!)  Including Norman Rockwells who represent 37% of America, since they stomach nearly 5% of all hate crime (less than implied using the term “reverse hate crime” by President Trump).  Also, even if they experience 0 hate crime directly, with >29% of the country represented by the 4 repressed minority clusters above (and most immigrants are not even in those 4 clusters!), there is near certain chance that all Norman Rockwells (and nearly all Americans) know someone close to them who is (here, here) one of these members of our national community and therefore it impacts all of us just the same.