Month: March 2018

1. Redlining highly influenced segregation in Chicago. What other features may have historically influenced this demographic distribution?

The guest lecturer on Monday mentioned a number of other factors, besides redlining, that historically have influenced the demographic distribution in Chicago. Amongst these various factors were education, property values, and economic opportunity. Property values and education are intimately connected, as the guest lecturer emphasized that education funding is based on property values. Since the properties in the hazardous redlining areas tend to be of lower value, and individuals in these areas tend to have trouble getting loans, the low property value assessments in these areas means that these areas education systems get very little funding. Consequently, the children that grow up here are almost certainly more predisposed to a lower quality education, and hence have less total lifetime economic opportunity. Additionally, various studies support that the less education one has, the more they are predisposed to crime (U.S. Department of Housing and Urban Development). The combination of these factors, poor education, low property values, and low lifetime economic opportunity work in conjugation with redlining boundaries to help influence prolonged racial segregation in Chicago.

 

2. Are these specific to Chicago? Would similar patterns exist in Omaha? What data would we need to make this connection?

The aforementioned influences that help reinforce redlining based segregation are certainly not confined to Chicago alone. Education funding schemes like those utilized in Chicago are used throughout the country, including Omaha, hence the same factors that are contributing to continued segregation in Chicago, without a doubt also exist within Omaha, and almost all other American cities.

3. How might we use crime statistics in conjunction with demographic data to evaluate the cause and consequences of segregation? What additional data would you like to have to make a richer cartographic connection?

Looking at arrests in conjugation with demographic data can provide some interesting insights into the potential consequences of HOLC induced segregation. The two maps below show an interesting correlation between population in various neighborhoods, the ethnicity of those neighborhoods, where the neighborhoods are in relation to HOLC status, and the relative crime rate (only narcotic crimes visualized to help cut down on data and make it legible). In analyzing these maps it’s very obvious that narcotic arrests are more prevalent in the poorly classified, ethnic neighborhoods. This is not entirely surprising as these poorly classified, segregated neighborhoods tend to be quite poor due to their historical organs, and in general “substance abuse is more prevalent among families living in poverty” (DualDiagnosis.org). Thus, it appears that at least one consequence of HOLC backed segregation practices has been to induce more drug use, and consequently more crime. However, it should be noted, that it is entirely possible that this data is presenting false correlations due to other conflicting variables. For example, potential neighborhood specific “broken windows” policing could be responsible for making it appear that crime in one neighborhood is higher than it is in other neighborhoods, if the particular neighborhood experiences a much higher degree of police scrutiny compared to its counterparts. I think having the “stop and frisk” data set that the guest lecturer presented on Monday would be very useful for helping identify and weed out these potential confounding variables. Also, other thing to note is the fact that most crimes now appear to be taking place in neighborhoods defined as class three yellow areas by the HOLC, not the hazardous, red, class four neighborhoods. This is consistent with the guest lecturer’s assertion that the poorer populations have begun migrating away from Chicago’s interior since the time the HOLC map was produced.

 

4. Would the data you have available reveal whether minorities are likely to experience disproportionate surveillance? If not, what additional data would you like to obtain?

The data we have at our fingertips can provide us potential hints that certain populations receive more surveillance than others, however, without further data it is impossible to firmly assert this. Looking at the data we do have, if we took crime rates in ethnic neighborhoods, and compared them to crime rates in white neighborhoods, it may provide us with some information that could be used to argue that minority neighborhoods receive a higher degree of surveillance (i.e. more arrests per capita in minority neighborhoods). However, these conclusions certainly could be flawed, as a higher crime rate in minority areas, could have a variety of factors contributing to it, aside from simply more vigilant police. As alluded to in the above response, I think one of the most interesting and important data sets in revealing potential disproportionate surveillance levels across minorities would be the stop and frisk data set that the guest lecturer had. If we could get ahold of this and overlay it onto the maps we have produced, we could then look at how stop and frisk occurrences related to different neighborhoods, and if some neighborhoods experienced a higher per-capita rate of stop and frisk occurrences.

1. 1. 1. In addition to redlining, I think that accessibility to decent jobs and decent education greatly impacted the demographic distribution. Because minorities were not loaned money to buy homes in desirable areas, the property that they did buy was not worth very much. This lack of home equity leads to lower property taxes, which leads to less funds towards the area’s public schools. Essentially, this lack of education has failed in providing the means and the tools for children to rise out of their current situations. Further, the lack of education did not prepare people with the means to obtain high paying jobs (although racial hiring practices also contribute to the lack  of accessibility to decent jobs).
      2. These patterns are not specific to Chicago, and definitely impact other metropolitan areas such as Omaha. We would need to obtain census data, unemployment data, bank loan records, and school records.
      3. We could use both the crime statistics and demographic data to evaluate the practices that occurred previously and map the change over time. I would want the additional resources of unemployment data, school funds, school records to better understand poverty, crime, lack of education, and its impact on crime statistics over time.
      4. Yes. Dr. McHendry introduced the topic of “broken window policy,” which is essentially when cops investigate or warrant action due to minor infractions, as these minor infractions often lead to major infractions. Unfortunately, these redlined areas were undesirable and not worth a lot, and did not give minorities a chance to move up financially or move out geographically. So, with less money there will be less money to upkeep houses, leading to more broken windows, and a disproportionate level of surveillance in these areas. I’m not entirely sure if this was a category of
         “crime,” but I would just be curious if there is any public data related to speeding tickets/traffic tickets. I think this would be a relevant example of how, “broken window,” or not, there is a disproportionate level of surveillance on minorities.

Demographic distributions are shaped by economic, social, political and environmental historical factors. These factors are not unique to Chicago, but  shape other cities too, such as Omaha. The final project we are completing for this class is going to present the data necessary to make the connections between the demographic distribution of Omaha and the the historical economic, social, political and environmental factors, which caused the distribution. Crime statistics can be used in conjunction with demographic data to evaluate the causes and consequences of segregation, but to do so, it is is also necessary to include data about economic opportunity, healthcare availability, and education system viability to fully understand the consequences of segregation. To reveal whether minorities are more likely to experience disproportionate surveillance, I would need to obtain data on police presence and surveillance camera placement and operation.

Dr. Guy McHendry argued in his presentation that redlining was an act of surveillance because you had to collect and record information about the city and its residents. Therefore, redlining allows police to survey distinguished areas defined by HOLC maps. Dr. McHendry said that this surveying makes people into docile bodies.  Additionally, he discussed the broken windows policing, which assumes that if you pay attention to the most minute infractions, you can improve the quality of a neighborhood. This is the dominant philosophy of policing in America. Police often look for any small sign of people being up to no good, so they can stop and frisk people for anything illegal. This tactic is often used in areas of minorities and data shows that the amounts of times leading to an arrest occurs more with minorities than whites. Dr. McHendry used chicago’s stop and frisk data as an example of this. The data shows that over time, redlined areas experience more stopping and frisking and policing in white areas is much less than in minority areas. In conclusion, crime data would be a helpful avenue in exploring the relationships between surveillance, redlining, and minorities.

Redlining highly influenced segregation in Chicago. What other features may have historically influenced this demographic distribution?

 

There are many different factors that influenced segregation in Chicago. First, business tend to not be developed in poorer neighborhoods. They tend to not start and if they do they tend to have a harder time of being successful. This is because there is less economic opportunity for these businesses. Small businesses are very important to neighborhoods because they provide a lot of opportunity for the communities and help them grow. When there is less opportunity, entrepreneurs tend to move into other neighborhoods to have more opportunity. This just digs a deeper hole for these communities.

 

Are these specific to Chicago? Would similar patterns exist in Omaha? What data would we need to make this connection?

This is something that is not just specific to Chicago. This is common in all major cities in the United States. These patterns are also similar in Omaha. You can see in more segregated areas in Omaha that there are less small business thriving. You would need to find data on what business are in specific areas of the city. It would also be nice to have data on how much money is going into these business.

How might we use crime statistics in conjunction with demographic data to evaluate the cause and consequences of segregation? What additional data would you like to have to make a richer cartographic connection?

I think that we could use the crime statistics because if there is less economic opportunity you will see more crime. I think that there will be a strong correlation between this demographic data and crime statistics. I think it would be useful to also have statistics on household income.

Would the data you have available reveal whether minorities are likely to experience disproportionate surveillance? If not, what additional data would you like to obtain?

I think that it would show disproportionate surveillance because there will be more surveillance with higher crime rates. There is not a need for more surveillance in less crime areas normally.