Stone Village Data Blog

DataXploits Jeff Jenkins
Jeff Jenkins Founder of DataXploits, MBA, Data Scientist
  • Data Science and Analysis
  • Data Visualizations
  • Social Listening / Monitoring

See something cool? Something you can use?

  •    Find the R code at the github link at bottom of page. Or,
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  • Family Incomes by Income Tax Tiers - Nov. 2016

    Check out this snapshot of family incomes from the Census Bureau. I set out looking into correlations between federal income tax rates, GDP growth and US Budget deficits - until I started wading into the morass of shifting tax rate tiers -- what a mess! Look for another blog post when I get that sorted.
    Early years show gradual income growth (in adjusted dollars), then there seems to be an inflection point for higher income tiers around 1982. I'm still working on making tax rate tables compare-able, but I do see that the top federal income tax rate dropped by 29% (from 70% to 50%) in 1982. Btw, there was another 30% reduction in max income tax level in 1964 (from 91% to 70%).
    Are there conclusions that these trend lines show us? Dunno. The flatter growth rates for lower income tiers and their disparity in growth with higher tiers may be a factor in a feeling of being left behind by the system. Or - an opposite explanation - there may be a progression of taxpayers upward through these tiers, bearing out the American dream. After getting a feel for the data, next comes creating/testing a hypothesis to find the cause(s).

    DataXploits MeanFamilyIncomeLevels
    Income in 2015 CPI-U-RS adjusted dollars Source: U.S. Census Bureau, Current Population Survey, Annual Social and Economic Supp lements. For information on confidentiality protection, sampling error, nonsampling error, and definitions, see //

    DataXploits Income Growth Rates
    Growth rate by tier before/after the reduced maximum rate, across 50-year period

    I'd love your comments...I'll post them here on the site

    Straight Data on Federal Spending - July 2016
    Have you heard political arguments about the preference of one party or the other on social spending -vs- defense spending? May be interesting to look at some data! Each blog post is (aims to be) around interesting data sets or twists on visualizations or interpretation. Here are some visualizations of data on tax/deficit levels, sources of US gov't revenue and expenditures.
    - first a quick sidenote on levels of US taxation on Gross Domestic Product (GDP)
    Fed'l tax receipts lagged GDP growth (output to be taxed) by 8.65% 1961 - 2015
    (US OMB & World Bank data)
    Note the trend in tax receipts growth showing similar variance but with much greater magnitude than before 2000. Reverting in recent years to an earlier normal.
    DataXploits TaxSources
    DataXploits TaxSources
    Sources of Federal Revenue as a % of total US revenue; categories shown as used by US government.

    Federal Spending - Social Security included

    Note some large shifts - Social Security starts in the 1940s and continues to expand, Medicare begins early 1960's.

    DataXploits TaxReceipts percentage
    Spending levels, by US Office of Management and Budget.

    Federal Spending with Social Security netted out, % by Category

    Looking at the above chart of Federal Spending, I thought I had a good picture of the split between defense/social spending categories

    Clearly Defense spending as a % of Total Federal spending is declining, Social spending is increasing.
    The scale of the spending was equalized across the long time period by using % of Federal Spending. Perfectly valid way to avoid adjustments in the data for inflation...but it occurred to me that large changes in the spending allocations (rise of Social Security as a source of tax collections and largest single outlay) might be skewing the message.
    Looking at this unique category, I see that inflow/outflow for Social Security money is nearly a wash in the federal budget, only slightly more is paid out than is collected each year. If I just pull Social Security out of the data, the net impact being very small...what is the picture like?

    Federal Spending - Social Security excluded

    (excluded because there is nearly equivalent offsetting revenue for US govt so ins/outs become 'noise' in this analysis)
    DataXploits FedlOutlay_netted

    This better description of Spending shows the scale/relationship of spending categories with less distortion

    Defense spending has come down to 26% of total (non-Social Security) govt spending, down from a staggering percentage in earlier decades.
    Social spending continues expanding and in 2015 was about 50% of US govt spending. 1993 was the turning point when Social spending surpassed Defense spending as a % of US tax receipts. Some amount of Medicare expense should be subtracted from this 50%, the offsetting income was obscured.
    I think further digging into sub-categories of gov't spending will show that the real driver of the expansion in Social spending is cost of medical care.

    I'd love your comments...I'll post them here on the site

    Map Your Data for Valuable Insight - May 2016 post
    Data sets of public interest are increasingly available for download. Roughly 22 police departments across the country have so far started posting Crime data for public usage.
    Mapping is such a powerful visualization tool that it frequently brings out insight that would be hard to access otherwise. This example uses Crime Report data from Austin Texas, but the same approach applied to your business data - Sales, Revenue, Addressable Market, etc. - can enable valuable understanding. Two types of maps are shown - all data in one map and faceted, the two reporting years separate. These map arrangements compare two points in time...other categories and more time periods can be depicted; area included in the maps can be zoomed in or scaled outward to include relevant geographic areas.
    This data is a sampling of the Austin Crime with so many data sets, clean up is necessary Only ~8% of entries in the raw data have geo coordinates. Other important factors must usually be accomodated; for example, from 2008 to 2015 (years whose stats are available), Austin had substantial population growth. Further analysis with this data set might call for some processing such as converting instances to crimes per 100,000 residents (cluster in the center of map is most population-dense sector). Another caveat is that the classification method for sexual assaults changed during this period, making 2008 stats not totally comparable to 2015.
    Using R, I have categorized a sampling of the crime reports; and filtered to one crime type - Drugs. Next post, I'll be exploring a method to generate a dashboard that allows crime type to be selected and (hopefully) allows map to scale at the push of a button.

    I'm generating this visualization using R. Another great tool for generating map visualizations (or a wide range of other useful visualizations) is Tableau. Tableau is easy to use but medium expensive; on the other hand, R is free but does require some investment of time to build capability.

    DataXploits ATX crime DataXploits ATX crime

    I'd love your comments...I'll post them here on the site

    Seeing something cool? Something you can use? You can find the code at the github link below. Or,

    Data Analytics services are available through

    DataXploits code to reproduce visualizations

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