Month: December 2015

Patterns of the 5th Ward: “Race”

By Matt Johnson

Over the next five articles, I am going to share with you some interesting patterns I’ve discovered in the 5th ward. In this first article, we well look at the difference in the population percentage with respect to the top four “racial” groups in Minneapolis – Asian, Black, Latino, and White.

In the second article, we will look at the unemployment rates between the zip codes of the 5th Ward – 55401, 55405, and 55411. In the third, we will look at the contrast of foreclosures between the 55401, 55405, and 55411 zip codes, and there is quite a difference. In the fourth article, we will look at the education levels between the three zip codes. And finally in the fifth and last article, we will bring everything together to illustrate these clear and striking differences and what they may tell us about the patterns of the 5th Ward with respect to the sub-system of the 5th Ward and the general system of Minneapolis.

Figure 1
Data Provided by – Data Compiled and Computed by Urban Dynamics – Figure 1

As Figure 1 illustrates, there is a noticeable difference between a zip code and the group with the largest population. For example, In both the 55401 and 55405, we see that the vast majority of residents are “white.” After that, the two zip codes diverge in regards to what other group is second, third, and fourth, respectively.

In contrast, we see that “black” residents are the largest population in the 55411. And drawing on our knowledge from previous articles, we know that the percent of median household incomes greater than $75 thousand is much much lower for “black” residents than it is for “white” residents overall in the City of Minneapolis.

We also know that the 55411 has the lowest percentage of median household income earners greater than $75 thousand in the City of Minneapolis. We also know from American history that “black” Americans were partitioned into undesired neighborhoods in the northern industrial cities during the Great Migration from about 1920 to 1940.

And we also know that for the vast majority of American history, “black” Americans have been partitioned from economic and political opportunities by bondage, redlining policies including parks, schools, and other city services, and so on and so forth. So given this information, what should we expect to find when we look at a depressed part of an American city?

Should we find a difference between the number of “black” and “white” residents? Should a depressed part of town have a higher percentage of American citizens who are “black?” Should this same part of town have higher foreclosure rates when compared to other more affluent parts of town? It wouldn’t be unreasonable for these more affluent parts of town to be mostly “white,” would it?

Should we expect to find a difference in unemployment rates? Should we find that “black” unemployment is higher than “white” unemployment? Should we see a correlation between the unemployment rates, foreclosure rates, and what group has the highest percentage of residents? In other words, should a higher percentage of “black” residents correlate with higher unemployment rates, and higher foreclosure rates?

Furthermore, should we expect to see a difference in education levels? Based off this one graph, which zip code would you expect to have the highest percentage of bachelor degrees or higher? Would it be the 55401, the 55405, or the 55411?

And finally, should we expect to see a correlation between a higher percentage of “black” citizens, higher unemployment rates, higher foreclosure rates, and lower education levels? Post your comments, thoughts, and predictions below.

And remember, you can refresh your memory on some of these topics with the following articles: Comparing Zip Codes | Median Household Income for Minneapolis and Foreclosure Rates: Wards 2, 4, and 5 from 2006 to 2015.

A Starting Point for Systems Language

By Matt Johnson

One thing I haven’t done very well on this blog is talk about systems and the language of systems. This is important because it provides a common language for those who come from different backgrounds and political affiliations. It is no secret that there is a political divide in this country and it has existed for sometime now.

Liberals view the world one way and conservatives view the world another way. This in turn influences policy decisions and applications. But systems are neither liberal or conservative. Systems do not care if you like Star Wars or if you like Star Trek (you’re writer likes both). Systems do not care if you like chocolate ice cream or vanilla ice cream. The point is that systems have no persona and no agenda. Systems are self-differentiating, complex entities. And very few people understand systems in general.

Why is a common language important? It’s important because it helps to clarify what is meant by “system,” “structure,” and “environment.” We often hear terms like “systemic oppression,” “systemic racism,” and “the patriarchy,” which is another way of saying, “It’s the system.” These terms, when used and maybe for the best of intentions, really just muddy the waters. There is no corollary or definition behind them. They can mean anything. They are ambiguous.

But it makes sense that the current systems language in everyday use is ambiguous. This is because everyday citizens have been forced to adapt and improve in their language. They see something and it’s complex. How does one explain something that seems to possess an absurd amount of variables (or things going on)? So people have to improvise. They have to make sense of it somehow.

Another reason why systems language is ambiguous is because the scientific version of it just doesn’t exist, at least not at the level of physics, chemistry, or biology. Those sciences have been around for so long that the lexicon of those sciences has had time to migrate out to the populace. For example, people use infinity, quantum, calculus, derivatives, atoms, electrons, planets, comets, black holes, gravity, psychoanalysis, and the list goes on and on. These things make at least some sense because there have been scientific practitioners to help aid in the dissemination and understanding of such scientific language, for instance, Carl Sagan and Neil deGrasse Tyson. But systems science has not had this relationship with the general public, nor has it had its Michio Kaku or Bill Nye.

The science of systems is young. It’s origins can be traced back to Ludwig Von Bertalanffy and his book General Systems Theory (see the Mark Twain page for the link) in the middle of the 20th century. Compared to physics and mathematics, this is an extremely young science. But that’s one of the reasons why this blog exists.

It’s here to provide corollary (a proposition established from truth) and definition to the conversation of systems science. It’s here to explain what a system is and what it does. It’s here to explain the difference between what a system is, what a structure is, and what an environment is. And it’s here to explain why the vast majority of systems discussed on Urban Dynamics are probabilistic systems and not causal. It’s here to provide access to an otherwise esoteric field of science.

I will do my best to make sense of these ideas I have shared with you. But it will take time for these ideas to make sense. and I won’t promise that it will happen over night. Habits will need to be broken and some knowledge will need to be accumulated on the part of the reader. But in the end, it will payoff because I will have provided you with a new way to look at the world and a language to describe it. 





Some Thoughts on Systems

By Matt Johnson

We’ve spent some time talking about elements of systems, but what do we mean by the elements of system? And what are they?

Well, we’ve discussed the foreclosure rates, condemned and vacant buildings (CVBs), the median household incomes, and unemployment rates in North and South Minneapolis, and Minneapolis in general. These are elements of the system of Minneapolis. But more importantly, these are, as this systems scientist believes, the most important elements in the system that have been identified thus far and as of this writing. This is because they play a significant role in the lives of residents (please note that I am a scientist, so I open to changing my mind as the data is presented to me).

And what have we learned from these elements? Well in the case of the 4th Ward, specifically 55412, we’ve learned that the foreclosures rates are higher relative to the rest of the city except when compared to the 55411. 55412 also has a higher than average population of “black” residents, it has a lower than average level of median household income, the education level is lower than average when compared to the rest of the city, and the unemployment rate is higher than average. According to City Data, the unemployment rate was 16 percent in 2013, only second to the 55411’s 21.9 percent.

In contrast, these elements look very different in other parts of the city. As we have learned from the data, the 2nd Ward in Southeast Minneapolis and the 10th Ward in Southwest Minneapolis have much lower foreclosure numbers and percentages than Wards 4 and 5 in North Minneapolis. These areas are also mostly populated by “white” residents and have higher levels of education. In the case of the 2nd Ward, there is an anomaly. The zip code 55455 is in the 2nd Ward and it has an unemployment level of more than 33 percent. But it also has a poverty level of less than 1 percent. What the…???

This is because this is one of the zip codes for the University of Minnesota. They are students my dear Watson. And yes, we should expect that type of rate with a large population of students, which is why we throw it out as an anomaly.

The element of the median household income also looks different in the 55406 and 55419 in South Minneapolis. In 2013, almost 40 percent of households in the 55406 had a median household income greater than $75 thousand and just under 60 percent of the households in the 55419 had a median household income greater than $75 thousand.

In that same year, less than 25 percent of households in the 55412 had a median household income greater than $75 thousand and a bit more than 16 percent of the households in the 55411 had a median household income greater than $75 thousand. Obviously the median household income element shows different faces in different wards and zip codes.

We’ve taken our first step in understanding systems. To recap, we know what the primary elements of the system are for this particular system and we know that they show different faces in different parts of the system. We must also understand that they perpetuate different interactions with other elements of the system, which gives rise to the purpose of this particular system. but that is another discussion for another day.

We are indeed moving towards a greater understanding of systems and Urban Dynamics my dear readers.

Minneapolis: The Numbers Within the 4th Ward

By Matt Johnson

Figure 1
Data Provided by the City of Minneapolis – Data Compiled and Computed by Urban Dynamics – Figure 1

As Figure 1 illustrates, the 4th Ward has had the highest percentage of foreclosures in the City of Minneapolis since at least the fourth quarter of 2006 with the exception of the second quarter of 2007 when the 5th Ward held the highest frequency.

But is every zip code and neighborhood created equal when it comes to the numbers and percentages within the 4th Ward?

As we can see from Figure 2, there are distinct differences between the zip codes of the 4th Ward in North Minneapolis – 55411, 55412, and 55430. It is clear that 55412 contains the highest numbers of foreclosures from quarter to quarter throughout 2014.

And the distinction is fantastically highlighted when we look at the differences in relative frequency. As Figure 3 indicates, between 70 to 80 percent of the foreclosures in the 4th Ward were concentrated in the 55412 zip code throughout the respective quarters of 2014.

Figure 2
Data Provided by the City of Minneapolis – Data Compiled and Computed by Urban Dynamics – Figure 2

But there are a couple of differences to keep in mind while we continue to analyze these three zip codes over the course of Urban Dynamic’s analysis of the north side.

55412 is the largest zip code by area in the 4th Ward at 3.6 square miles and it comprises most of the 4th Ward’s land area.  55430 comprises the next most land area for the 4th Ward at 1.23 square miles. This is because only 22.07 percent of 55430 resides in Minneapolis. But there are some other differences between 55430 and 55412. According to City Data, 55430 has a higher proportion of “white” to “black” residents, a lower unemployment rate, and a higher level of education from high school through graduate school than 55411, 55412. How much of this difference between “white” and “black” residents in the 55430 zip code exists? That’s an additional question that will require a bit more digging.

Continuing our decomposition of the 4th Ward, 55411 is the smallest zip code in area in the 4th Ward. This is because most of 55411 resides in the 5th Ward, so in this analysis, 55411 and 55412 won’t be compared.

Figure 1
Data Provided by the City of Minneapolis – Data Compiled and Computed by Urban Dynamics – Figure 3

55411 will be considered in future articles when we analyze the 5th Ward and when we directly compare the 4th Ward to the 5th Ward. However, it should be noted that 55411 and 55412 contribute most of the foreclosures in the 4th and 5th Wards.

Moreover, future articles will explain how the number and percent of foreclosures in North Minneapolis increase as one moves from north to south (or from south to north depending on your reference point); that is, from 55430 to 55412 to 55411 and to 55405 and 55401. Future articles will also attempt to illustrate this same thinking and pattern for unemployment, median household income, education level, and “race.” Remember, as each article is published, another part of the painting is created.

The Median Household Income for St. Paul for 2013

As we saw in the previous article The Median Household Income for Minneapolis in 2013, Minneapolis differed greatly between the median household incomes for Asian, “black,” Hispanic/Latino, and “white” households. In a twist of fate, St. Paul is eerily similar to Minneapolis in its own discrepancy. But before we get to Figure 1, let’s recall the Minneapolis numbers.

Median Household Income of St. Paul for 2013
Data Collected from – Data Organized and Presented by Urban Dynamics – Figure 1

42 percent of “white” households had a median household income greater than $75 thousand; 26 percent of Asian and Hispanic/Latino households had a median household income greater than $75 thousand; and 11 percent of “black” households had a median household income greater than $75 thousand. These numbers were for 2013.

Looking at St. Paul, we see about 39 percent of “white” households had a median household income greater than $75 thousand for 2013; about 23 percent of Asian households had a median household income greater than $75 thousand for 2013; about 22 percent of Hispanic/Latino households had a median household income greater than $75 thousand for 2013; and a little less than 11 percent of “black” households had a median household income greater than $75 thousand for 2013.

2013 Median Household Income for Minneapolis greater than $75 K
Data Collected from – Data Organized and Presented by Urban Dynamics – Figure 1

Comparing the two cities, we see that “white” households in both Minneapolis and St. Paul are fairly similar at 42 percent and 39 percent, respectively. We also see similar differences between Asian and Hispanic/Latino households in Minneapolis and St. Paul. As Figure 2 illustrates, Asian and Hispanic/Latino households are performing at 26 percent each, while Asian households are doing a percentage point better than Hispanic/Latino Households in St. Paul.

And finally, “black” households are basically separated by a half a percentage point between Minneapolis and St. Paul. Either way, “black” households are being severely out performed by all other groups in both cities. And as the reader knows, Urban Dynamics has been focusing on such a discrepancy in the Minneapolis system.

Why are the numbers for “black” households in Minneapolis and St. Paul similar? That’s the question. Minneapolis and St. Paul are difference cities with different, but yet intertwining histories. It is easy to get caught up in speculation, but the bottom line is that we just don’t know. What we know about Minneapolis thus far is what we gleaned from the already established data. What we know about St. Paul is exactly this set of data, nothing else.

Moving forward, we will learn more about St. Paul and why the current numbers read the way they do. We will learn something about its Urban Dynamics.

The Median Household Income for Minneapolis in 2013

In this short article, we will move away from focusing on comparing parts of Minneapolis and instead zoom out to see how different groups in Minneapolis are doing in general. We want to add to our knowledge of Minneapolis by seeing what group is doing better economically. Thus, we will use median household income as our metric for this posting.

This is a bit of a silly question, but without looking at Figure 1 (if you haven’t looked at it yet and I won’t blame you if you did), and considering the past few articles and the metric we’re using for this post, which group do you think is doing the best economically in Minneapolis?

2013 Median Household Income for Minneapolis greater than $75 K
Data Collected from – Data Organized and Presented by Urban Dynamics – Figure 1

If you guessed “white” households, then you are correct. I’m sure there was some unfortunate intuition in your reasoning. But nonetheless and as the graph shows us, 42 percent of “white” households in 2013 had a median household income greater than $75 thousand, which was the largest proportion of the four groups.

Following “white” households, about 26 percent of both Asian and Hispanic/Latino households had a median household income greater than $75 thousand in 2013. And last, about 11 percent of “black” households had a median household income greater than $75 thousand in that same year.

As we can see, there was quite a difference between “white” households and Asian and Hispanic/Latino households, and a remarkable difference between “white” households and “black” households. 16 percent and 31 percent, respectively, is not exactly close.

Adding to our already established knowledge of Minneapolis, we are beginning to see some trends emerge in our work. Information like this will eventually allow us to be able to analyze the Urban Dynamics of Minneapolis.

The Colors, Data, and Economy of Los Angeles

Los Angeles is a large American city with millions of people. In 2013, it had 3,857,799 residents to be exact. Of that more than 3.8 million, almost 2 million were Hispanic or Latino, or 49.3 percent of the total population; more than a million were “white,” or 28.2 percent of the total population; about 435,000 were Asian, or 11.2 percent of the total population; and less than 333,000 were “black,” or 8.6 percent of the total population.

But although the majority of the residents were Hispanic or Latino in 2013, some familiar economic activity took place. As Figure 1 illustrates, the group with the largest percentage of median household incomes over $75 thousand in 2013 were “whites,” while “blacks” trailed behind “whites” in the same category by exactly 18 percent – 21.9 percent to 39.9 percent. But there was also a bit of a surprise, or maybe not?

2013 Median Household Income for Los Angeles Greater Than $75 K
Data Collected from – Data Organized and Presented by Urban Dynamics – Figure 1

In 2013, the two of the four largest groups in Los Angeles had the lowest rates of median household income; that is, 21.9 percent of the “black” households and 19.6 percent of Latino households in Los Angeles had a median household income of more than $75 thousand. However, the other two of the four largest groups in Los Angeles had the highest rates of median household income. “Whites” were one of those groups and Asians were the other group. In that year, the percentage of the median household income over $75 thousand for Asians was 38.4 percent, which was a difference of only 1.5 percent below that of whites. What may explain the economic success of Asians in Los Angeles?

Traditionally, “whites” have been the dominant population by economic, political, and social standards. They have also been the dominant group by population. But clearly they are not the dominant group by population in Los Angeles, although they are still dominant by median household income.

If “whites” are still prominant economically but Latinos are not, then what explains a minority group with more economic power. How is this possible? Is it because of the economic, political, and social headstart that Tim Wise alludes to in his writing and speaking? And how does Los Angeles compare to other American cities such as Minneapolis, Boston, Dallas, New York, and Seattle?

This is just one of the many the questions that will be asked and answered as this analysis of Los Angeles economic system and general system continues.