As Aristotle explored in his Metaphysics: Book Delta, the parts of something, say the parts of a city, are divisions of the whole that can be differentiated from one another by quantification or by qualification. In the sense of quantifying, North Minneapolis can be differentiated from Minneapolis by observational data, for example, unemployment rates, education rates, and wages.
In the sense of qualifying, North Minneapolis can be differentiated by recognition of area. But it should be noted that the geography of North Minneapolis is still the geography of Minneapolis. It is just a recognition of a specified area, which is not Northeast Minneapolis, South Minneapolis, or Southwest Minneapolis.
Furthermore, North Minneapolis is broken down further by quantification and qualification into area codes: 55411 and 55412. Thus, the 55411 and 55412 zip codes are distinguishable by name and specific geography, this is obvious, and by observational data.
For example, previous articles in this blog have shown the 55411 zip code to be the zip code with the highest number of reported crimes in North Minneapolis; whereas, previous articles in this blog have shown the 55412 zip code to be the zip code with the highest number of foreclosures over the past decade.
Utilizing this systemic approach, the wages between Minneapolis and North Minneapolis, specifically the 55411 zip code, can be differentiated and analyzed.
Thus, are the dynamics of the wages (how wages change over time) shown to be relatively equal to one another? Are the dynamics of the wages of the 55411 zip code shown to be greater than Minneapolis? Or are the dynamics of the wages of the 55411 zip code shown to be less than Minneapolis?
As Graph 1 illustrates, we can see that the wage rate of Minneapolis is steeper than the wage rate of the 55411 zip code in Graph 2. And we’re not just eyeing this. We can see this distinctly via the linearization equations in Graph 1 and Graph 2.
The linearization equation in Graph 1 (y = 6.4152x + 1083.1) shows a rate of 6.4 and the linearization equation in Graph 2 (y = 2.2805x + 823.6) shows a rate of 2.3, if both rates of change are rounded-off. Obviously, 6.4 is greater than 2.3, and by quite a bit. Why is this important?
Dynamically (how wages change over time), this shows the wages of Minneapolis are growing at a greater rate than the wages of the 55411 zip code. Of course, these equations also show that the average weekly wages of Minneapolis are between $250 and $300 higher than the 55411 zip code.
This little bit of information ought to provide policy makers with some much-needed direction to create and apply economic policy. Of course the operative modal verb is “ought to.”
So do you think local policy makers would consider differentiating between the part and the whole when creating economic policy? Or do you think local policy makers would just create and apply the same policy for both the part and the whole?
Nothing brings visitors and views to mainstream media websites quite like homicides in the windy city. When homicides are up, mainstream visitor traffic is good.
But don’t expect to hear from the mainstream media anytime soon. This is because homicides are down from last year at this time. Yes! It’s true.
According to data compiled by the Chicago Tribune, there were 260 homicides through the month of May in 2016. That was up significantly from 2015. In contrast, there have been 235 homicides so far this year. For those keeping count, that’s a reduction of 9.6 percent from last year.
Hopefully, Memorial Day weekend will stay relatively quiet this year and homicides will remain at 235.
Of course, the warm summer months are always the busiest time for crime in general, including homicides. Historically this has been the trend, and this is exactly what the data sets are saying.
If the trends hold, then homicides in Chicago should follow a normal distribution, i.e., a bell-curve, although the 2016 distribution of homicides skewed left.
This means that roughly about 68 percent of the homicides should happen within the warm months of the summer, or one standard deviation from the mean as the normal distribution above illustrates.
Moreover, about 32 percent of the homicides should occur outside of the one standard deviation, or outside the warmer months.
Does this mean the warm months of Chicago in 2017 will see more homicides than the warm months of Chicago in 2016? It does not.
So far, homicides are down from 2016 and if this trend continues throughout the summer months, then homicides should remain down. But the reader should keep in mind that homicides are very difficult to predict.
The only reason it is being suggested that homicides may trend below last year is because homicides are down. If they were up, then the prediction would be the opposite. Of course, this method is an archaic form of bayesian statistics, so take it with a grain of salt.
What do you think? Do you think homicides will remain lower than last year? Or do you think homicides will explode over the summer months? Either way, please provide your reasons and explanations below.
The economy is a hot topic in the presidential debates and is among the top public concerns. But the “economy” is a loose and hazy notion and, for politicians, a convenient place to make promises.
Even the solutions are pitched at a high level of abstraction. On the Republican side, the common answer is to reduce taxes, which also has the obvious attraction of aiding their donor class, and to cut back on government regulations. On the Democratic side, one response is to increase taxes on the wealthy, with the precise causal mechanism never explained or demonstrated.
The reality is rather more daunting and the answers could lie in place few politicians discuss explicity: cities.
Given that growth projections are limited, we need to be thinking more about productivity gains. That means we need to make our economy more efficient – generating more economic value with the same inputs. And one way to do this is to improve the productivity of our cities in various ways, including better land use, beefed-up infrastructure and smarter technology.
Metro areas in the U.S. now house 83 percent of the population and are the main site for innovation and job growth. The 100 largest metro areas hold 69 percent of all jobs and are responsible for three-quarters of the nation’s GDP.
If we are serious about growing our economy, then getting our cities to work better is just as important as tax reform or wage policy. The problem is that cities tend to be discussed in terms of redistributional issues, such as welfare or race relations, but rarely as a platform for addressing the “economy.”
Consider just some of the traditional inputs of land, labor and energy. Cities use enormous amounts of energy. So policies about urban energy use and urban transportation are not just urban concerns, they are matters of national economic concern.
In other countries, there is a closer connection in political discourse between the economy and the city. In Australia, where close to half of the population lives in the five largest cities, the idea of improving living standards and competitiveness by increasing urban productivity is now part of political discussions.
Traditional economics is not much help, as productivity is generally used with reference to individual firms or workers. Rarely is it used to measure the productivity of cities. Even when they do look at cities, economic theorists rarely move on from noting that large cities achieve agglomeration economies through the clustering of activities, labor pooling and knowledge spillovers.
This explains an economic rationale for cities but does not help us make cities more productive. How can we do that?
Consider transport. There are significant cost savings in increasing the ridership of mass transit systems compared with constructing expensive new systems. Even small-scale policy changes have rolling consequences. Improving traffic light sequencing, for example, reduces travel times, emissions, fuel consumption and road accidents.
Meanwhile, encouraging telecommuting, while reducing the benefits of face-to-face contact in real time, generates savings in terms of time and energy costs as well as the wear and tear on commuters slogging their way through traffic. The collective gain is a more efficient city and greater economic productivity.
Also, a single government authority in a large city is more efficient than a multiplicity of municipal governments. One study of cities across five countries found that a metro region with many municipal governments, has, on average, six percent lower productivity than a city with one metropolitan authority.
Cities are a target-rich environment for improving productivity because they are places where public policies have leverage. Dysfunction at the federal level, likely to halt any ambitious proposals discussed in the presidential elections, does not stop experiments at the city level. And here a combination of nonpartisan federal and local policies can achieve savings.
For example, new federal legislation has allowed companies to provide the same level of benefits for mass transit users and carpoolers as it did for parkers. Against this background, city authorities can enable more carpooling by setting aside designated spots for informal carpools.
Productivity has a cold-blooded sound to it, as if citizens are imagined just as labor inputs to be trained and moved around to increase efficiencies. But there is a meshing of economic and social concerns.
A more efficient land use and transportation system, for example, means people spend less time and money commuting. I was reminded of this when seeing the route map of a low-income worker in Atlanta, Georgia, whose two-hour journey to work involves 118 bus stops and a nine-minute train ride.
Can technology make a difference? We now have lots of data on the flows of energy, people, goods, capital and ideas. While big data on its own does not provide the solution, the intelligent use of these data can provide us with a real-time handle on urban productivity to provide benchmarks of performance and measures of progress. And once urban productivity is measured, it can be improved.
Big data could also help improve our infrastructure, which would aid productivity and reduce economic losses. Many bridges need renovation and replacement. But if we use good-quality data on how much repair they need as well as how much traffic they support, we would be in a better position to prioritize our infrastructure funds so that the most dangerous and the most frequented were targeted first.
We are still at a very early stage of using big urban data to provide smarter, safer, more efficient and more socially just cities. An important start is that we realize that more of our economic activity takes place in cities and improving urban economic performance is the road to economic growth and social justice.
Analyzing data always provides interesting insights. For example, a simple analysis of establishment (business) data from the Minnesota Department of Employment and Economic Development (DEED) reveals some fascinating insights into the systems dynamics – a system changing over time – of the Minneapolis marketplace with respect to business firms.
As the data, Graph 1, reveals, the number of establishments, or businesses, in Minneapolis has been decreasing for at least the past 10 years. Why is this so? This blog will not venture into such speculation. This is because the system’s perspective is limited to only establishment data. A multivariate perspective (multiple perspectives) is needed to find such possible reasons.
As Graph 1 illustrates, the number of firms per quarter has been decreasing since at least 2006. And although this rate has been variable, which is to be expected because the marketplace is probabilistic, the overall trend has been negative.
Furthermore, this overall negative trend can be shown in a couple of different ways. First, it can be illustrated via linearization. As Graph 2 shows, the overall trend is negative. That is, the Minneapolis marketplace decreased in the total number of establishments between the 1st Quarter of 2006 and the 3rd Quarter of 2016.
It should be noted that the linearization seen here is not the same linearization as in dynamical systems. In dynamical systems, linearization is an approximation “to a function at a given point.” Obviously this is not the case here.
Again, the main idea to take away from linearization, in the way it is used here, is the overall trend of the graph – did the marketplace gain businesses over the period stated in Graph 2, did the marketplace lose businesses over the period stated in Graph 2, or did the marketplace remain about the same over the period stated in Graph 2?
And finally, the marketplace behavior of business establishments in Minneapolis can be illustrated through Vector Algebra. Yes! That’s right – Vector Algebra. In this case, there will be no math included, just an illustration of direction via Graph 3, so there is no reason to be alarmed.
As Graph 3 shows, the overall dynamics, or vector, of the marketplace is negative in regards to the number of establishments from the 1st Quarter of 2006 through the 3rd Quarter of 2016. And the vectors, those letter “a’s” with the hats over them, further illustrate a greater decrease in total establishment between the 1st Quarter of 2006 and the 3rd Quarter of 2010 than between the 3rd Quarter of 2010 and the 3rd Quarter of 2016.
Of course, these vectors could further be broken into smaller vectors. But the way the algebra works, each vector that is computed in this system should add up to the overall vector, which is negative. Thus, this decomposition of the system behavior provides a more conclusive way of viewing the dynamics of this particular system than how linearization is being used here. And the vector idea, along with the math, supports the initial observation. That is, the total number of establishments in the Minneapolis marketplace has decreased since at least the 1st Quarter of 2006.
So how does this market behavior compare to the county or state level? How does Minneapolis compare to the zip codes that reside within it?
And another interesting question to ask one’s self is, has employment increased, decreased, or stayed the same in Minneapolis? And what does this mean for the number of employees per establishment?
Matt Johnson is a writer for the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.
You can connect with him directly in the comments section, and follow him on LinkedIn or Facebook.