# Tag: Data Analysis

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.

Photo credit: The Systems Scientist

# A quick view of an economic system

By Matt Johnson

In this short blog, I will illustrate one way an urban dynamicist, i.e., systems scientist, looks at an economic system and its data.

Diagram 1 is hierarchical, derives from the U.S. Census Bureau, and represents a few of the many levels of an economic system. Moreover, each level of the economic system in Diagram 1 is further a sub-system, or sub-economy, of the general United States economy.

This means that a zip code, for example, can be examined as an economic system, and then it can be compared and contrasted with a city’s economic system. And this examination will illustrate similarities and differences between a sub-system, a zip code, and a general system, a city, for instance.

Thus, an urban dynamicist can partition out each level of the economic system and analyze each level as a distinct entity, although one system is still a sub-system of the one superior to it in the hierarchy. Within each level, differences, relationships, perspectives, dynamics, and models can be examined through data.

As stated before, each level of the system can be analyzed against the other levels of the system through data, because data provides a picture at each level of the system. For example, the State can be illustrated and compared to the Division, Zip Code, or Census Tract via crime densities, demographic comparisons and migration patterns, and economic variables such as median household incomes, unemployment rates, the labor force and labor participation rates.

Here is the stochastic (probabilistic) behavior of the labor force in Minneapolis over the past 10 years as seen here in Graph 1.

And here is the stochastic (probabilistic) behavior of the Minnesota labor force over the past 10 years as illustrated in Graph 2.

Future articles will delve deeper into the specifics of the behavior and dynamics of these two systems and their respective data sets. For now, the main point is that data can provide a picture of the economic systems at their respective levels of the system.

One last thought, Diagram 1 does not illustrate the interactions or dynamics that take place within each level of the system by itself, nor does it account for a lot of things. This is why the data is needed. So assumptions and conclusions should be limited.

As this focus on data continues, I will be utilizing the hierarchical model and other systems models to help illustrate and explain how economic systems can be better understood. In addition, I will be using systems theory along with applied mathematics to explore the complexity of systems. But I will also be working diligently and meticulously to convey this information to you the best I can.

As I get better at explaining this stuff to you, I hope your knowledge of systems, mathematics, and economics increases as well.

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.

Photo credit: Pixabay

# In 2016, crime was ‘Up’ overall in Minneapolis but…

By Matt Johnson

After a first pass through the Minneapolis crime data, it appears reported crimes in Minneapolis increased in 2016. However, they didn’t increase by much. In total numbers, reported crimes increased from 21,341 in 2015 to 21,485 in 2016. That’s 144 reported crimes.

As a percent, that’s less than 1 percent. But of course, this crime data only tells us about the total number of crimes for the city of Minneapolis. It doesn’t tell us anything about where the majority of these crimes happened nor does it tell us anything about the types of crimes that are most prominent in these locations. And of course, it really does depend on location.

For example, North Minneapolis has some of the highest crime rates per square mile in the city. But not all neighborhoods and zip codes are created equal when it comes to crime. There are certain neighborhoods that experience much higher crime rates than others.

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The Jordan neighborhood, which resides in the central part of North Minneapolis in the 5th Ward, experienced the highest number of crimes and the highest number of crimes per square mile on the north side in 2015 and 2016. But the number of crimes and the crimes per square mile in the Jordan neighborhood decreased in 2016. In other words, they were higher in 2015.

The Jordan neighborhood is the only neighborhood in Minneapolis that is predominantly black.

In contrast, the crimes per square mile on average are much lower in the Harrison and Sumner Glenwood neighborhoods in the 5th Ward on the north side. And of course, the 4th Ward, which also resides on the north side, has its neighborhoods that are relatively quiet when it comes to crime and others that are active with higher numbers of crimes.

Harrison and Sumner Glenwood are predominantly white.

As my regular readers know, crime is usually associated with other adverse socio-economic factors such as higher rates of unemployment, lower rates of education, and housing issues. Sometimes this is referred to as urban decay or urban blight. But in the case of my research, I am using mathematical methods to lead me to this knowledge of urban environments and their respective discrepancies. But there are instances where I have found crime does exist on its own.

For Minneapolis, this happens in downtown Minneapolis, specifically in the Downtown West neighborhood, which experiences the highest number of crimes in the city month after month.

Why is this so? Well this is a question I will leave for you to ponder. Other questions you might think about as well are, why do these adverse socio-economic factors exist together with very few exceptions? Are policy makers aware of these facts? And if they are, why haven’t they done anything about it?

Matt Johnson is a writer for The Systems Scientist and the Urban Dynamics blog; and is a mathematical scientist. He has also contributed to the Iowa State Daily and Our Black News.