Compared To What
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"Compared to What" was written by American singer and songwriter Gene McDaniels.[3] It was copyrighted in 1966.[4] The lyrics contain a "topical rant" against the Vietnam War and the then President of the United States Lyndon B. Johnson,[5] and include the lines: "The president, he's got his war / Folks don't know just what it's for / Nobody gives us rhyme or reason / Have one doubt, they call it treason".[6] In 1976, the popular American music critic B. Lee Cooper suggested that the song "of social criticism attacked a variety of social practices as being based on hypocritically 'unreal values'" and contrasted "the social myth of equality and the economic reality of poverty in the stratified American society."[7]
There was more to come! In 1982, Morton's group made another path analysis [27] of a somewhat larger dataset of IQ correlations among American family members. This time (and without citing their earlier estimates of 0.75 and 0.67) their estimate of genetic heritability, h2, was 0.31 with 0.42 for cultural heritability, c2. Thirteen years later, Otto et al. [28] applied the path analysis method to sixteen familial correlations for IQ published by Bouchard & McGue [29]. The estimated heritability depended on the type of assortative mating (social homogamy or phenotypic homogamy) and whether cultural transmission was direct or indirect. The estimates of h2 varied from 0.29 to 0.42, while that of c2 was close to 0.27.
Missing compared to what In the first part of this note, we discussed the limitations of estimates of heritability from familial correlations, in particular their reliance on linear models and irrelevance with respect to potential environmental interventions. Why, then, should such heritabilities be the standards relative to which GWA-based variance analyses are compared By including those polymorphisms that failed to be significant in GWA studies, analyses of new linear models [2,66] have produced increased estimates of the variance fraction explained by genomic variation. However, it almost always remains below that estimated from familial analyses.
We find that although crime is more concentrated at addresses than other spatial units, this is due to the fact that more addresses have no crime than is true of larger units. When only places with one or more crimes are examined, place crime is no more concentrated than other spatial unit crime. Crime appears to be concentrated at places at about the same level as it is concentrated among offenders or victims. And crime concentration does not appear to be peculiarly concentrated compared to non-crime related phenomena.
With so many phenomena, across so many fields of research, showing concentration,Footnote 2 perhaps rather than ask, how concentrated is crime at places, we should ask, how concentrated is crime at places compared to other phenomena Is it more concentrated Is it less concentrated Or is it about the same level of concentration as most other phenomena If crime is about as concentrated at places as other phenomena are concentrated, then the explanation for crime concentration will require us to look at explanations for concentration in general. If crime is more or less concentrated than other phenomena, then the explanation involves looking for something special about places and crime.
We then turn to comparing place concentration to concentration of crime among victims and offenders. This is the core of our study. Well over a quarter of a century ago, Spelman and Eck (1989) compared the relative concentration of crime along these three critical dimensions of crime. They found crime more concentrated at places than among offenders or victims. Given the passage of time and accumulation of many more studies, it is important to check whether their findings are valid. We make use of three systematic reviews to compare findings from three sizable bodies of literature. We also look at evidence about place concentration found in some repeat victimization studies. Within the limits of the way data on place, offender, and victim concentration has been measured, we find that there is little evidence to suggest that crime is substantially more concentrated at places than among victims or offenders.
The most obvious comparison of crime concentration at places is to crime concentration at other spatial aggregations. The relative concentration of crime at places, compared to other geographic units, matters for three reasons. First, if crime is equally concentrated among places and neighborhoods, then this has important implications for theory. It implies that the geographic scale of analysis is irrelevant. Stated another way, crime is geographically scale invariant.
There has been a modest amount of research on the relative concentration of crime across different spatial units. It consistently shows that smaller spatial areas are more concentrated than larger ones. Andresen and Malleson (2010) examine the stability of crime concentration at the street segment level over time in Vancouver, British Columbia. They show that crime concentration is more stable at this level than at a larger area level. This is true when they examine all segments and areas, and when they examine only those segments and areas with crime (eliminating segments and areas that have no crime). Johnson (2010) compared burglary concentration at the street segment level to the same 12,806 burglaries grouped by census areas for a U.K. county. He found that crime is more concentrated at the segment level. Recently, Steenbeek and Weisburd (2016) reported very similar results for The Hague, in the Netherlands.
Our results are substantially different from what we discovered when looking at all units, regardless of crime involvement. When examining only units with crime (eliminating neighborhoods and places with no crime), four things change. First, the Gini coefficients decline substantially, except for neighborhoods. This exception is easily accounted for: all neighborhoods have at least one crime, so no neighborhood is excluded. Second, the difference between the largest and smallest Gini is half that of the difference when all units are examined. When all units are examined the difference between the largest and smallest Gini is .46. When uninvolved units are eliminated, the difference is .23. Third, the ordering of the Gini coefficients appears arbitrary, rather than systematic. In Fig. 1 we saw a logical ordering: as the geographic unit shrank, the Gini rose. In Fig. 2, the smallest Gini is for addresses, then natural neighborhoods, then segments, and then artificial neighborhood grid cell. Finally, if we look at the percentage of crime in the most crime-afflicted units (the top 10%), we see that this drops and the differences among the units is a paltry 7% (compared to 42% when uninvolved units are included).
In this paper we set out to establish a context for interpreting the concentration of crime at places. We did this through three sets of comparisons: (1) crime concentration at places to crime concentration at larger geographic units, (2) place crime concentration compared to the concentration of crime among offenders and victims, and (3) place crime concentration compared to non-crime phenomena concentration. We have already provided conclusions at each step, so here we will summarize them.
Although the city recognizes 52 neighborhoods in most databases, it also recognizes a number of subneighborhoods or larger areas. This provides 71 neighborhood areas and reduces the disparity in neighborhood size, somewhat.
We will focus in what follows on two simple measures of warning skill: the probability of detection (POD; the percentage of all tornadoes for which a warning was issued ahead of time) and the false alarm ratio (FAR; the percentage of all tornado warnings within which no tornado was reported for the duration of the warning). These two metrics provide the basis for a performance diagram (Roebber 2009) that graphically depicts both axes of forecast skill. In addition, a performance diagram depicts the critical success index [CSI (Schaefer 1990); the CSI is a function of POD and FAR] as curves.
Metrics based on an environment-specific baseline such as ESSP and ESSF can serve as a useful post-event evaluation tool: as an example, performance data for the major tornado outbreak that occurred on 27 April 2011 (Knupp et al. 2014) are depicted in Fig. 6. Note that for the event as a whole, POD and FAR are both a substantial improvement over what we would have expected based on the near-storm environment alone (ESSP = +0.396 and ESSF = +0.111). Even separating the event into daytime, early evening transition, and nocturnal tornadoes (Fig. 6a), we still see a general pattern of great improvement in POD and FAR, with only a slight dip in POD for daytime events. The improvement in POD is common across both RMS and QLCS tornadoes (Fig. 6b) as well as across all (E)F scales (Fig. 6c). Hence, while it may have been straightforward to recognize that this event was well forecast, the environmental framework has allowed us to state with some authority that the high skill was not, for instance, entirely due to anomalously high MLCAPE and SHR6 making for a perhaps less difficult forecasting problem; other important factors contributed to the high skill in this event.
Thus, life has been compared to a pilgrimage, to a drama, to a battle; Congress may be compared with the British Parliament. Paris has been compared to ancient Athens; it may be compared with modern London.
Bryan Garner, Garner's Modern American Usage, fourth edition (2016) provides what I take to be the current (and traditional) formal prescriptivist view among U.S. usage authorities of when to use compered with and when to use compared to:
compare with; compare to. The usual phrase has for centuries been compare with, which means "to place side by side, noting differences and similarities between" let us compare his goals with his actual accomplishments. Compare to = to observe or point only to likenesses between he compared her eyes to limpid pools. 59ce067264