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.
Making distinctions between different levels of a system is an important first step to thinking about systems in a systematic way. But how can this be accomplished?
This can be accomplished by utilizing Diagram 1 as a visual aide. As Diagram 1 illustrates, the United States is the primary system, or general system, whereas the Region, Division, State, County, City, Zip Code, Census Track, and Block Group are all sub-systems of the United States.
And in this blog, distinctions will be made between the labor forces in the Minnesota system, the Hennepin County System, and Minneapolis system. Making these distinctions will help partition out where these respective systems reside in the grand scheme of things, and how their respective labor forces differentiate from each other. But first, two terms will be defined: labor force and system.
What is a labor force?
According to the Bureau of Labor Statistics, a labor force is a population of workers who are either working in the marketplace or who are actively looking for work in the marketplace.
Indeed, we should note a labor force does not account for those persons not participating in the marketplace. The point here is we will be looking at those citizens who are actively engaged in the marketplace via the Minnesota labor force, the Hennepin County labor force, and the Minneapolis labor force.
What is a system?
The simplest definition contains three parts, or three conditions: a system contains elements, these elements interact, and a function is produced from this interaction. These elements could be a small group of elements or a large group of elements. Of course how elements exist in the system is either observable or unobservable (we will not address the unobservable or uncountable in this blog).
This means a person could observe nine baseball players in dark-blue jerseys on a baseball diamond. These baseball players would then be the elements of the system. Furthermore, these nine baseball players in dark-blue jerseys would be interacting with each other, while out in the field or while hitting, throughout the nine innings of the game. And the interactions in this small system would produce an outcome for the baseball team in dark-blue jerseys (possible outcomes produced would be a win or a loss).
For purposes of this blog, we will assume these three conditions are satisfied.
The Labor Force
To recall, we will focus on three levels of the nine-level system presented in Diagram 1: state, county, and city. Before proceeding, we should note that the systems levels of metro area, district/ward, and neighborhood were not included in Diagram 1 for brevity (those levels of the system will be examined in future blogs).
First, and moving forward, what kind of systems behavior should we see in the state labor force? That is, should we see positive, negative, or no growth since 2006?
As we can see, the labor force of Minnesota has been trending upwards since at least the 1st Quarter of 2006. Indeed, we also see that the market has fluctuated quite a few times, but it’s important that we understand that this fluctuation is normal behavior for a stochastic (probabilistic) system such as a labor force. So when we say the labor force of Minnesota has been trending upwards since at least the 1st Quarter of 2006, we are saying the overall behavior of the system has been positive.
Second, what kind of systems behavior should we see in the county labor force? That is, should we see positive, negative, or no growth since 2006?
Much like the Minnesota labor force, we can see in Graph 2 that the Hennepin County labor force has been trending upwards since 2006 as well. Sure! It to has fluctuated throughout, but again, that’s to be expected in a probabilistic system such as a marketplace.
Third, what kind of systems behavior should we see in the city labor force? That is, should we see positive, negative, or no growth since 2006?
In the observations of the three levels of the Minnesota system, we see that the Minneapolis labor force has been trending upwards since 2006 as well. Again, we observe peaks and valleys in the data, but the overall behavior has been positive. Thus we have seen positive growth over a ten-year period at the state, county, and city levels of the system, and making these distinctions has enlightened us by delving a bit deeper into the economic system of Minnesota.
Here are some questions we might want to ask ourselves. Would we continue to see this positive labor force growth over the past 10 years if we examined various zip codes in Minneapolis? By making distinctions and partitioning out say the 55411 and the 5549, would we see similar growth in both zip codes, for example? Would we see this same positive behavior if we examined various Minneapolis neighborhoods like Seward, Fulton, or Jordan, or would we see differences? And finally, would we see this same positive behavior if we examined various areas – a census track or block group – located inside various Minneapolis neighborhoods?
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 Facebook.
Cassini is the most sophisticated space probe ever built. Launched in 1997 as a joint NASA/European Space Agency mission, it took seven years to journey to Saturn. It’s been orbiting the sixth planet from the sun ever since, sending back data of immense scientific value and images of magnificent beauty.
Cassini now begins one last campaign. Dubbed the Grand Finale, it will end on Sept. 15, 2017 with the probe plunging into Saturn’s atmosphere, where it will burn up. Although Saturn was visited by three spacecraft in the 1970s and 1980s, my fellow scientists and I couldn’t have imagined what the Cassini space probe would discover during its sojourn at the ringed planet when it launched 20 years ago.
A huge storm churning across the face of Saturn. At the time this image was taken, 12 weeks after the storm began, it had completely wrapped around the planet.
Massive storms periodically appear in Saturn’s cloud tops, known as Great White Spots, observable by Earthbound telescopes. Cassini has a front-row seat to these events. We have discovered that just like Earth’s thunderstorms, these storms contain lightning and hail.
Cassini has been orbiting Saturn long enough to observe seasonal changes that cause variations in its weather patterns, not unlike the seasons on Earth. Periodic storms often appear in late summer in Saturn’s northern hemisphere.
In 2010, during northern springtime, an unusually early and intense storm appeared in Saturn’s cloud tops. It was a storm of such immensity that it encircled the entire planet and lasted for almost a year. It was not until the storm ate its own tail that it eventually sputtered and faded. Studying storms such as this and comparing them to similar events on other planets (think Jupiter’s Great Red Spot) help scientists better understand weather patterns throughout the solar system, even here on Earth.
Saturn’s six-sided vortex at Saturn’s north pole known as ‘the hexagon.’ This is a superposition of images taken with different filters, with different wavelengths of light assigned colors. NASA/JPL-Caltech/SSI/Hampton University, CC BY
Cassini has also confirmed the existence of a bizarre hexagon-shaped polar vortex originally glimpsed by the Voyager mission in 1981. The vortex, a mass of whirling gas much like a hurricane, is larger than the Earth and has top wind speeds of 220 mph.
Home to dozens of diverse worlds
Cassini discovered that Saturn has 45 more moons than the 17 previously known – placing the total now at 62.
The largest, Titan, is bigger than the planet Mercury. It possesses a dense nitrogen-rich atmosphere with a surface pressure one and a half times that of Earth’s. Cassini was able to probe beneath this moon’s cloud cover, discovering rivers flowing into lakes and seas and being replenished by rain. But in this case, the liquid is not water, but rather liquid methane and ethane.
False-color image of Ligeia Mare, the second largest known body of liquid on Saturn’s moon Titan. It’s filled with liquid hydrocarbons. NASA/JPL-Caltech/ASI/Cornell, CC BY
That’s not to say that water is not abundant there – but it’s so cold on Titan (with a surface temperature of -180℃) that water behaves like rock and sand. Although it has all the ingredients for life, Titan is essentially a “frozen Earth,” trapped at that moment in time before life could form.
The sixth-largest moon of Saturn, Enceladus, is an icy world about 300 miles in diameter. And for me, it’s the site of the Mission’s most spectacular finding.
The discovery started humbly, with a curious blip in magnetic field readings during the first flyby of Enceladus in 2004. As Cassini passed over the moon’s southern hemisphere, it detected strange fluctuations in Saturn’s magnetic field. From this, the Cassini magnetometer team inferred that Enceladus must be a source of ionized gas.
Intrigued, they instructed the Cassini navigators to make an even closer flyby in 2005. To our amazement, the two instruments designed to determine the composition of the gas that the spacecraft flies through, the Cassini Plasma Spectrometer (CAPS) and the Ion and Neutral Mass Spectrometer (INMS), determined that Cassini was unexpectedly passing through a cloud of ionized water. Emanating from cracks in the ice at Enceladus’ south pole, these water plumes gush into space at speeds up to 800 mph.
I am on the team that made the positive identification of water, and I have to say it was the most thrilling moment in my professional career. As far as Saturn’s moons were concerned, everyone thought all of the action would be at Titan. No one expected small, unassuming Enceladus to harbor any surprises.
Geologic activity happening in real time is quite rare in the solar system. Before Enceladus, the only known active world beyond Earth was Jupiter’s moon Io, which possesses erupting volcanoes. To find something akin to Old Faithful on a moon of Saturn was practically unimaginable. The fact that it all started with someone noticing an odd reading in the magnetic field data is a wonderful example of the serendipitous nature of discovery.
The geyser basin at the south pole of Enceladus, with its water plumes illuminated by scattered sunlight. NASA/JPL-Caltech/Space Science Institute, CC BY
The story of Enceladus only becomes more extraordinary. In 2009, the plumes were directly imaged for the first time. We now know that water from Enceladus comprises the largest component of Saturn’s magnetosphere (the area of space controlled by Saturn’s magnetic field), and the plumes are responsible for the very existence of Saturn’s vast E-ring, the second outermost ring of the planet.
More amazingly, we now know that beneath the crust of Enceladus is a global ocean of liquid saltwater and organic molecules, all being heated by hydrothermal vents on the seafloor. Detailed analysis of the plumes show they contain hydrocarbons. All this points to the possibility that Enceladus is an ocean world harboring life, right here in our solar system.
NASA at Saturn: Cassini’s Grand Finale.
When Cassini plunges into the cloud tops of Saturn later this year, it will mark the end of one of the most successful missions of discovery ever launched by humanity.
Scientists are now considering targeted missions to Titan, Enceladus or possibly both. One of the most valuable lessons one can take from Cassini is the need to continue exploring. As much as we learned from the first spacecraft to reach Saturn, nothing prepared us for what we would find with Cassini. Who knows what we will find next?
Geomagnetically Induced Currents, or GICs, can result from geomagnetic storms — a type of space weather event in which Earth’s magnetic field is rattled by incoming magnetic solar material. The quick-changing magnetic fields create GICs through a process called electromagnetic induction. GICs can flow through railroad tracks, underground pipelines and power grids.
NASA has long been a leader in understanding the science of space weather, including research into the potential for induced electrical currents to disrupt our power systems. Last year, NASA scientists worked with scientists and engineers from research institutions and industry during a pair of intensive week-long workshops in order to assess the state of science surrounding this type of space weather. This summary was published Jan. 30, 2017, in the journal Space Weather.
Storms from the sun can affect our power grids, railway systems, and underground pipelines through a phenomenon called geomagnetically induced currents, or GICs. The sun regularly releases a constant stream of magnetic solar material called the solar wind, along with occasional huge clouds of solar material called coronal mass ejections. This material interacts with Earth’s magnetic field, causing temporary changes. That temporary change to the magnetic field can create electric currents just under Earth’s surface. These are GICs.
Long, thin, metal structures near Earth’s surface — such as underground pipelines, railroads and power lines — can act as giant wires for these currents, causing electricity to flow long distances underground. This electric current can cause problems for all three structures, and it’s especially difficult to manage in power systems, where controlling the amount of electric current is key for keeping the lights on. Under extreme conditions, GICs can cause temporary blackouts, which means that studying space weather is a crucial component for emergency management.
“We already had a pretty good grasp of the key moving pieces that can affect power systems,” said Antti Pulkkinen, a space weather researcher at NASA’s Goddard Space Flight Center in Greenbelt, Maryland. “But this was the first we had solar experts, heliospheric scientists, magnetospheric physicists, power engineers and emergency management officials all in a room together.”
Though GICs can primarily cause problems for power systems, railroads and pipelines aren’t immune.
“Researchers have found a positive correlation between geomagnetic storms and mis-operation of railway signaling systems,” said Pulkkinen, who is also a member of the space weather research-focused Community Coordinated Modeling Center based at Goddard.
This is because railway signals, which typically control traffic at junctures between tracks or at intersections with roads, operate on an automated closed/open circuit system. If a train’s metal wheels are on the track near the signal, they close the electrical circuit, allowing electrical current to flow to the signal and turn it on.
“Geomagnetically induced currents could close that loop and make the system signal that there’s a train when there isn’t,” said Pulkkinen.
Similarly, current flowing in oil pipelines could create false alarms, prompting operators to inspect pipelines that aren’t damaged or malfunctioning.
In power systems, the GICs from a strong space weather event can cause something called voltage collapse. Voltage collapse is a temporary state in which the voltage of a segment of a power system goes to zero. Because voltage is required for current to flow, voltage collapse can cause blackouts in affected areas.
Though blackouts caused by voltage collapse can have huge effects on transportation, healthcare, and commerce, GICs are unlikely to cause permanent damage to large sections of power systems.
“For permanent transformer damage to occur, there needs to be sustained levels of GICs going through the transformer,” said Pulkkinen. “We know that’s not how GICs work. GICs tend to be much more noisy and short-lived, so widespread physical damage of transformers is unlikely even during major storms.”
The scientists who worked on the survey, part of the NASA Living With a Star Institute, also created a list of the key unanswered questions in GIC science, mostly related to computer modeling and prediction. The group members’ previous work on GIC science and preparedness has already been used to shape new standards for power companies to guard against blackouts. In September 2016, the Federal Energy Regulatory Commission, or FERC, released new standards that require power companies to assess and prepare for potential GIC disruptions.
“We’re really proud that our team members made major contributions to the updated FERC standards,” said Pulkkinen. “It also shows that the U.S. is actively working to address GIC risk.”