TODO: Predicting University Shootings: Are Geographical Statistical Dimensions Applicable

I am interest in populating a number-crunching formula using R programming language that includes a number of factors related to recent and consistent statistical frequency and geographical distance of university shootings to serve as a predictor of likelihood patterns. A csv file of every university in the united states including zip code linked below can be adapted to include such campus violence variables in order to predict, much like a traveling salesman formula, to indicate via statistical population, the distribution of such acts in a likelihood indicator:


3 responses to this post.

  1. another interesting parameter that may be useful in predicting campus violence gun crime is twofold:

    1- time of the month
    2- mean span of time between incidents

    the time of the month is an interesting parameter for the domestic legal system. first of all, petty crime in poor, migrant or black neighborhoods tends to occur and be enforced hence appear in the courts toward the end of the month, when monthly welfare checks are sent, while organized crime, and orchestrated raids tend to occur in the beginning or middle of the month for the sake of capacity slack because of the resources assigned to the end of the month capacity.

    the mean span of time between such incidents is interesting because of the cyclical nature of the press treatment where press conferences are held with university leaders and area police, calls for gun control, and even public address by the president himself. the iniquity of the crime tends to be difficult to cop, however, it appears to occur within a cyclical span of time and i would like to measure the mean (average) amount of time between such incidents of campus violence going back historically up to a decade.


  2. Posted by jadeowl on October 10, 2015 at 1:16 pm

    TODO perhaps expand university zip code to include zip codes where such mass shootings have occurred at large. also interesting is a profile of shooters that can infer of predict. the ancillary post regarding urban predictors of homeless rates is an interesting corollary study as it may be an interesting co-predictor for campus or mass shooting incident as well


  3. implying that the collection or receipt of cyclically dispersed income suggest as in 1 timothy 6:10 indicating that “money is the root of all evil”


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