Detroit is a big place, and the demand for schools can’t be the same equally across the city, especially since there are such large differences between thriving and disinvested areas. Because the education landscape continues to be a topic of much discussion, we recently put an approximated student location data set to good use by creating an interpolated model showing where Detroit public school students are concentrated. The model indicates that there are several areas of Detroit with higher concentrations of students attending public schools (defined here as local, charter and Education Achievement Authority).
Using interpolation to create a modeled surface entails approximating values of certain characteristics at all locations between known observations. In this case, our observations were the number of students per block, and that number was spatially located at the center of each block. Between the centers of each census block, we applied a mathematical formula that models the likely value at each point, taking into consideration the values of student counts in nearby blocks. The interpolation process is based on the premise that observations that are close in space are likely to have similar value, so in-between values are estimated based on nearby actual observations.
The resulting model can’t be used to determine actual counts at any one location. For example, the model erroneously shows students residing on Belle Isle, which is the result of Belle Isle being a large census block. In addition, the model could look considerably different depending on the assumptions made (the size of the grid or the weighting, for instance). Instead of regarding these values as actual observations, the model should be regarded as showing a spatial trend. While not precise, this smoothed spatial trend allows us to see areas of generalized higher concentration.
Modeled map showing where students are concentrated.
In the map above, orange and red indicate the highest numbers of students who attend public Detroit schools per area. As you can see, concentrations of these students exist around Lafayette Park, within the apartment complexes along 1-75 between Mack and Warren, and in Southwest Detroit, with smaller concentrations in Warrendale and around East English Village, on the far east side.
Conversely, large sections of the interior of Detroit, especially east and southeast of Hamtramck, show the fewest students. This situation isn’t completely surprising, given that this area of the city has high vacancy rates, large numbers of vacant lots and some of the worst building conditions in the city, according to the Motor City Mapping survey. The map below shows numbers of existing structures as a percentage of parcels per block group. The lighter colors represent areas of the city that have a higher percentage of vacant lots. While not perfectly accurate, these areas line up fairly well with the areas in the previous map showing the lowest concentrations of students — with the exception of Southwest Detroit. Southwest Detroit, with its large Hispanic population, is known to have higher birth rates than other parts of the city. These birth rates would explain the higher concentrations of students despite a higher number of vacant lots.
Percent Existing Structures, by Block Group, Winter 2013
Generally speaking, this model illustrates the areas of the city where a relatively higher school capacity (higher number of student “seats”) is needed and areas where less capacity may be warranted. Of course, there are other criteria to evaluate in addition to proximity to students. Factors such as the number and quality of existing nearby schools, physical barriers such as highways, and societal barriers such as language and ethnicity are all important considerations as well.
While these data and maps are not prescriptive by themselves, they represent an important tool for policymakers working toward improving the climate for education in Detroit. Further layers of data would need to be added to this analysis to paint a more precise picture, but, optimistically speaking, the data and tools are becoming available to make data-driven decisions on education.
Since the 2011-12 school year, Data Driven Detroit (D3) has created a series of maps that illustrate the spatial patterns related to where students from different areas in Detroit attend school or where students from different schools live (see our previous blog post introducing the project). This year’s data, from the October 2013 student count, allowed us to compile maps for almost all publicly funded schools in the city, leaving out only a few that were incorrectly identified to be outside the city. Using the 2013-14 Student Dispersion Tool that D3 has published, interested parties can view and download maps showing the dispersion patterns of each public school in the city to see where its students live. Additionally, with the filters provided, users can easily contrast results between schools in the same category or at the same level for a more appropriate comparison.
In this most recent update of the data and maps, the category breaks are consistent across all maps for comparison’s sake. The same color blue in one map represents the same number of students in all the maps. However, since schools are often very different in the size of their student bodies, we also created maps showing percentages of students living in a tract. In this post, we’ve selected a few maps from the 2013-14 collection, from across the spectrum of school types, to highlight. We encourage you to view all the schools with the new tool.
Cass Technical High School
Cass Tech is Detroit’s original and most recognized magnet school. It is a citywide school with a competitive application and examination process. It is located just north of the Chrysler Freeway and downtown Detroit in a brand new, modern glass and steel building. Cass Tech boasts an impressive list of notable alumni and has a strong educational reputation.
Number of Students Who Attend Cass Tech, by Tract
The first thing you notice when you look at Cass Tech’s dispersion map is that a lot of students travel very long distances to attend the school, including many students who reside outside the city’s borders. While it is somewhat expected at a magnet school that students would travel farther to get to school, it does raise the question of whether those distances cause academic problems or strains on the student’s family. In addition, the larger series of dispersion maps shows that a fairly long commute is common for Detroit students.
Students Attending Cass Tech, By Tract, as a Percentage of Student Body
Because Cass Tech is a competitive school, one might expect that students from wealthier areas of Detroit would send larger numbers of students there. However, looking at the dispersion of total students, it is unclear whether there are areas with disproportionate numbers of students attending Cass. There are good showings from some typically strong neighborhoods such as the University District (7 mile and Livernois) and Grandmont/Rosedale (Grand River and M-39) but nothing that looks extremely uneven. In fact, the largest numbers of students from any tract are to the north and east of Hamtramck, which does not boast fine housing stock or higher salaries. In fact, even tracts in those areas with a high degree of abandonment and disinvestment, such as those neighborhoods east of Hamtramck, have a decent representation.
The map showing the percentage of students attending Cass Tech by tract shows the equal distribution of students even more plainly. No tract in Detroit houses more than 4.9 percent of the Cass Tech student body. Cass, at first glance, appears to truly serve the whole city.
Phoenix Elementary-Middle School
In contrast to the even distribution of Cass Tech students, Phoenix Elementary in Southwest Detroit shows a highly concentrated dispersion pattern. While elementary students typically don’t travel as far as older students, Phoenix’s students are more local than most. Phoenix is an Education Achievement Authority school. As such, it was in the lowest 5 percent in performance for all schools statewide when the state took over administration of the school. There has been some speculation ever since the state created the EAA district that the worst-performing schools (including EAA schools by rule) would retain the least mobile students with the fewest resources because those students who had the ability to attend better-performing schools farther away would likely do so. In addition, Detroit educators often say that strong ethnic bonds in Southwest Detroit cause students from Southwest Detroit to tend to stay at schools in that area with high numbers of Latino students. Both of these reasons, and probably others, likely contribute to Phoenix’s tight distribution of students, though we can’t say that with certainty. Further study is needed.
Number of Students Who Attend Phoenix Elementary, By Tract
Students Attending Phoenix Elementary, By Tract, as a Percentage of Student Body
Warrendale Charter Academy
Warrendale Charter Academy is a mid-sized K-8 charter school on Detroit’s west side, near Dearborn. This charter school is interesting because it draws students mostly from the west side of Detroit and a lot of students from adjacent tracts. In fact, the percentage maps shows that at least 40 percent of its students come from adjacent tracts. Also, even though it is close to the border of other cities, the school draws almost exclusively from Detroit (this is less the case with other charters). So why is it that some schools, charter or not, draw from so much farther away and even from other cities? It may be a deliberate recruitment strategy, or it may be based on performance or reputation or other factors. Perhaps there are stronger neighborhood bonds in this area than in other areas of Detroit. More study is certainly needed.
Number of Students Who Attend Warrendale Charter Academy, By Tract
Students Attending Warrendale Charter Academy, By Tract, as a Percentage of Student Body
Denby High School
Denby High School is an Educational Achievement Authority school on the east side of Detroit. The dispersion map for Denby, in comparison to Cass Tech, or even Western International, shows a student population highly concentrated within east-side neighborhoods. In addition, very few students who attend Denby live outside the city, despite the fact that the school is relatively close to the city border. While it is no surprise that few, if any, students attend Denby from the nearby “Pointes,” because of the strong schools systems and different demographics in those areas, it is mildly surprising that there are not more students coming from Eastpointe and Warren. The neighborhood directly around Denby is still in pretty good shape, according to Motor City Mapping results. However, just a few blocks west, across Hayes Road, is a highly disinvested area where vacant lots outnumber homes. Since this area likely has fewer school-age children, perhaps it limits the reach of Denby into other parts of the city. Denby’s limited reach helps to reinforce the trend that EAA schools on average tend to draw students from a smaller geographic area.
Number of Students Who Attend Denby High School, By Tract
Students Attending Denby High School, By Tract, as a Percentage of Student Body
There’s a Lot More to See!
As you can see from this post, each school we’ve highlighted has a unique pattern. Data Driven Detroit has maps for each publicly funded school in Detroit and has made them available online through an interactive graphic. The maps are very interesting to look at. Check them out at:
Next Up in This Series
We’ll take a more in-depth look at average commuting distances per school.
“Data Highlights” are a new D3 blog feature focused on sharing some of the interesting trends and patterns we’ve noticed over the course of our work! We hope that these insights give a unique perspective into our work, and increase interest in using data to make informed decisions.
In an influential January 30, 2014 Detroit News article entitled “Detroit is Deadliest City for Children,” the author, Karen Bouffard, wrote, “In 2010, Detroit (population about 713,000) and Cleveland (population about 390,000) had the highest infant mortality rates of Big City America: 13.5 deaths for every 1,000 live births — higher than in Panama, Romania and Botswana. The measure includes deaths from all causes in a child’s first 12 months….”  After 2010, Detroit’s rate dipped a bit to 12.6/1,000 in 2011 but then jumped to 15.0/1,000 in 2012, the latest year for which statistics are available. The grim reality this article highlighted prompted Detroit policy makers to take action, and they joined other efforts underway in the city and at the state level to combat infant mortality. For example, one of these programs is the Detroit Regional Infant Mortality Task Force’s program Sew Up the Safety Net for Women and Children.
This three-part blog series examines one factor, low birth weight (“LBW”), which is closely associated with higher infant mortality rates. We look at information recorded on the infant’s birth certificate in an effort to understand whether there are demographic and socioeconomic characteristics of the mother that can help identify women most at risk of having a LBW infant. We are not examining medical conditions of the mother – that’s beyond our expertise—nor are we including women who gave birth to two or more babies (e.g., twins or triplets) because infants in multiple births have a known high risk of low birth weight.
In this first blog of the series we focus on Detroit exclusively, investigating the associations between the rate of low birth weight and (1) the mother’s age at the birth of the child; (2) her educational attainment; (3) her marital status; (4) her race; (5) her ethnicity; (6) the adequacy of the prenatal care she received; and (7) the distribution of LBW infants within the city of Detroit (by census tract of mother’s residence).
The second blog focuses on the same factors for the metro Detroit region, which for the purpose of this blog we define as Wayne County outside of Detroit, Oakland County, and Macomb County (“Metro Region”). We compare the associations found for that region with those found for Detroit as a way of asking, “Does place matter?”
If we find that place does matter (and it seems to), we ask in the third blog whether there is something else besides place of residence that can contribute to our understanding of differences in low birth weight rates. In particular we investigate the effect of race and place together.
As we conducted these analyses, we found that the results kept raising more questions than we could answer, so we consider this series to be a starting point for deeper investigations. We hope the report motivates readers to offer their insights and findings and suggest other ways of looking at the birth record data or other sources to better understand factors influencing low birth weight. A further blog series will examine correlates of inadequate prenatal care, another factor associated with higher infant mortality.
Source of data
The data for this study came from birth certificate records of babies born in 2010, 2011, and 2012 with the mother’s residence in Wayne, Oakland, or Macomb Counties. No names or home addresses were included in the records, but we were able to identify the census tract of the mother’s residence. Census tracts with fewer than six births during the three year period were suppressed. Low birth weight is defined as less than 2500 grams. In all the analyses, missing data have been eliminated. Of the total number of women with single births (women with multiple births were excluded) in Detroit during this period, 11 records had missing birth weight information and have been eliminated from all the analyses. Individual variables had differing numbers of records with missing data. In cases where the percentage of missing data was 1% or greater of the total records, we note the percentage of records with missing data. We used three years of data to smooth the year-to-year fluctuations.
Over the three-year period from 2010-2012 there were 30,244 single births (“singletons”) to mothers living in Detroit (30,233 with birth weight on the birth certificate). 88% of these 30,233 were of normal birth weight, while 12% (3,478) were of low birth weight. This is an average of 1,192 LBW singleton babies per year from 2010 – 2012.
Table 1: Percentage of low birth weight singleton births by mother’s age, Detroit, 2010-2012
We looked first at the relationship between mother’s age and the birth weight of the infant. We divided the ages of the mothers into five groups: teens (younger than 20); 20-24; 25-29; 30-34; 35+. As Table 1 shows, 11 to 12% of the age groups through age 34 had LBW babies. At ages 35 and higher, this percentage increased to 15%.
For women giving birth in Detroit during this three-year period, age did not affect the likelihood of having a low birth weight baby until age 35 and above.
Next we turned to the effect of educational attainment on the likelihood of having a low birth weight baby.
Table 2: Percentage of LBW singleton births by mother’s educational attainment, Detroit, 2010-2012
Figure 1 illustrates the perhaps surprising finding that women with an eighth grade education or less had the lowest rate of LBW babies. As we discuss further in the blog, this is due at least in part to the characteristics of the women in this educational category, but we leave this until we have looked at other factors.
Figure 1: Percentage of LBW singleton births by mother’s educational attainment, Detroit, 2010-2012
There were few women in this Detroit group with a Master’s degree or a doctorate or professional degree (data not shown). The larger group of women who held a bachelor’s degree or higher had a low birth weight rate of 9%, two or three percentage points lower than women with less education.
Table 3: Percentage of LBW singleton births by mother’s marital status, Detroit, 2010-2012
We divided the mother’s marital status into three categories: never married, married, and widowed or divorced. 2% of the Detroit women (509) were divorced or widowed. Because this is such a small group and one with two different marital situations, we have excluded them from the analysis. Table 3 illustrates that married women had a three percentage point lower rate of low birth weight babies than women who had never been married.
Race and Ethnicity
Table 4: Percentage of LBW singleton births by mother’s race, Detroit, 2010-2012
Race on the birth certificate is coded into 17 different categories. We collapsed the 17 categories into three: Black, White, and Other. Of the 30,000+ women, 81% were classified as Black, 8% as White, and 10% as Other. The Other category includes all those women who were not classified as Black or White (3,092). Because 96% of Other Race was Hispanic, we have eliminated them from the analysis in order to not overlap with the Hispanic identity analysis.
Figure 2: Percentage of LBW singleton births by mother’s ethnicity and race, Detroit, 2010-2012
There was a gap of five percentage points in percentage of singleton LBW babies between Black (13%) and White (8%) groups. If Blacks had had the White rate of 8% instead of 13%, there would have been more than 1,100 fewer Black infants of low birth weight during the three-year period.
Table 5: Percentage of LBW singleton births by mother’s ethnicity, Detroit, 2010-2012
Table 5 and Figure 2 show that Hispanic women had a substantially lower LBW rate than Non-Hispanic women. Now we can start to tease out why women with an eighth grade education or less had a much lower rate of low birth weight than women with higher educational attainment. First, 59% of the 1,180 women with an 8th grade education or less were Hispanic in contrast to the 10% of the total population of 30,244 women who were Hispanic. Second, those women with an 8th grade education or less were more likely to be married (51%) than the total population of women (19%), and marriage also conferred a birth weight advantage on the infant.
Adequacy of prenatal care
Table 6: Percentage of LBW singleton births by mother’s level of prenatal care, Detroit, 2010-2012
The Kessner Index assigns a value of adequate, intermediate, or inadequate to the level of prenatal care a woman received. The Michigan Department of Community Health Division of Vital Records and Health Statistics explains the Index as, “… a classification of prenatal care based on the month of pregnancy in which prenatal care began, the number of prenatal visits and the length of pregnancy (i.e. for shorter pregnancies, fewer prenatal visits constitute adequate care).”
As is evident from Table 6, both adequate and intermediate prenatal care had similar rates of low birth weight, even if not precisely the same percentage, while inadequate prenatal care, which included no prenatal care, was associated with a five to six percentage points higher rate. Note that fully 18% of the women had inadequate prenatal care.
Geographical distribution of low birth weight babies in Detroit
Figure 3: Geographical distribution of LBW singleton births in Detroit, 2010-2012
Figure 3 displays low birth weight rates (grouped into ranges) for all census tracts in Detroit. Except for areas of Southwest Detroit (which have a high Hispanic population), there does not appear to be any readily-discernible grouping of census tracts by birth weight.
This first blog examined social and demographic correlates of the birth weight of babies born in 2010-2012 to women residing in Detroit. We looked at the associations between birth weight and the mother’s age, her education, marital status, ethnicity, race, and the level of prenatal care she received.
Overall, ethnicity, race, and level of prenatal care had the greatest effect on birth weight. The Hispanic low birth weight rate was six percentage points lower than the rate for Non-Hispanics, and the rate for Whites was five percentage points lower than the rate for Blacks. Adequate and intermediate levels of prenatal care were associated with a five to six point advantage in birth weight rates over inadequate prenatal care.
Education and marital status were less strongly related to birth weight, although being married versus being unmarried conferred a three percentage point advantage, and having a bachelor’s degree or higher was associated with a two percentage point lower rate of low birth weight than having less education. Age made little difference until age 35 when the rate of LBW babies increased by three-to-four percentage points over younger ages.
In the next blog, we look at the same factors in the “Metro Region”; that is, Wayne County outside of Detroit, Oakland County, and Macomb County considered as one region. We contrast the findings for the Metro Region with what we saw for Detroit to ask, “Does place matter?”
 Bouffard, Karen. “Detroit is Deadliest City for Children, “ The Detroit News, January 30, 2014
 Source: 1989-1999 Michigan Death Certificate Registries; 1999-2011 Geocoded Michigan Death Certificate Registries; 2012 Michigan Death Certificate Registry., 1989-1999 Michigan Birth Certificate Registries;2000-2012 Geocoded Michigan Birth Certificate Registries., Vital Records and Health Statistics Section, Division for Vital Records and Health Statistics, Michigan Department of Community Health
 Brown, Sally. “Detroit Task Force to Reduce Infant Mortality,” Henry Ford Health System News and Research, October 19, 2011.
 Very low birth weight is defined as less than 1500 grams. In this analysis, very low and low are aggregated as “low” birth weight.
 1.5% of the records were missing educational attainment.
 7.4% of the group of 30,000+ women had missing data on this variable.
This post is the second in a series focused on employment and commuting patterns in Detroit and the surrounding region.
In the first post of the “Uphill Both Ways” series, we looked at employment of lower-earning residents in Detroit, Hamtramck and Highland Park in communities that have opted out of the SMART public transportation service. We found a spatial mismatch between jobs and transportation service for these lower-earning residents. More than 10,000 of these workers commuted daily to jobs with limited or no access to public transportation, in an environment that ranks as one of the most expensive in which to own a car. These added costs likely compound the burden faced by individuals earning near or below the poverty level, making it more difficult for them to attain more stable earnings and employment.
From that targeted focus, we’ll now broaden our lens to look at the overall employment patterns in Detroit and its suburbs. For this post, we’ll define the “suburbs” as the Detroit Metropolitan Statistical Area, which includes Wayne County outside of Detroit, along with Lapeer, Livingston, Macomb, Oakland, and St. Clair counties. As before, we’ll use 2011 data from the Census Bureau’s Longitudinal Employer-Household Dynamics Program (LEHD) to track the inflows and outflows of workers between the city and the surrounding municipalities. To compare job patterns in Detroit and the metro region, we’ve divided workers in these areas into two categories – Detroit and outside Detroit.
Overall, the shift of population from Detroit to the surrounding suburbs has been accompanied by even greater changes in the location of jobs. In 1970, the city of Detroit had a population of 1.5 million, which accounted for 35.1 percent of the 4.3 million people living in the metropolitan area. Detroit also held 42 percent of the metropolitan area jobs at that time. By 2011, Detroit contained just 16.2 percent of the metropolitan area population and 9 percent of its primary jobs. During these four decades, the total population of the metropolitan area changed very little; Detroit’s losses of population and jobs have essentially been its suburbs’ gains.
According to the LEHD data, Detroit housed almost 232,000 primary jobs in 2011. Some 52 percent of these jobs paid more than $40,000 a year, the highest wage category reported. About one-third of the jobs paid between $15,000 and $40,000; less than one in six paid less than $15,000. Detroit jobs had an estimated median wage rate of just over $40,000, compared to the metropolitan area median of about $36,000.
At 232,000, the number of primary job opportunities in Detroit was substantially higher than the number of employed Detroit residents (169,000). At first glance, these numbers might indicate a strong labor market with low unemployment. Unfortunately, however, this was not the case; only 65,000 (27.3 percent) of the jobs in Detroit were filled by Detroit residents. At every wage level, the majority of Detroit jobs were held by people who did not live in Detroit (classified here as “commuters”).
These commuters were much more likely than Detroiters to have higher-earning jobs as well. At just over $30,000, the median wage for Detroiters working in Detroit was about $10,000 lower than the median wage paid to all workers in the city. While there are almost 1.5 commuters working in Detroit for each Detroit resident at the lowest wage rate, this ratio rises to 4.5-to-1 at the highest wage rate ($40,000 per year or more).
Where Do Suburban Workers Live?
The surrounding suburbs contained five times as many employment opportunities as the city of Detroit, and more than 43 percent of these jobs were in the highest wage category. Some 104,000 Detroiters commuted to the suburbs for these jobs, representing just 9 percent of the more than 1.6 million suburban primary jobs (Figure 2). Detroit residents were over-represented in low- and middle-wage jobs, with more than 38,000 Detroit residents commuting to the suburbs for jobs that paid less than $15,000 a year. Detroiters held just 22,000 of the suburban primary jobs that paid more than $40,000 annually.
Overall, the data confirm that the challenges posed by Detroit’s spatial employment mismatch extend far beyond those communities that have opted out of having public transportation service. Nearly two-thirds of working Detroiters commute to jobs in the suburbs, and in both the suburbs and the city Detroiters are underrepresented in the highest-earning wage category. These trends reinforce isolation of Detroit from its surrounding communities and create additional obstacles to improving the challenging conditions faced by so many Detroit residents.
* * *
Thank you for reading “Uphill Both Ways.” In the next post in the series, we’ll look at Detroit in comparison with several other cities across the country with similar populations, to get a broader picture of how the city’s employment climate compares at the national level.
Today, Detroit Mayor Mike Duggan made a monumental announcement, releasing the City’s first Open Data Portal. The portal will provide access to many datasets that have never been released publicly for download, including data on building permits and crime. This announcement marks an important development in city policies that encourage transparency and accessibility. It also coincides with this weekend’s International Open Data Day on Saturday, February 21.
For the last six years, Data Driven Detroit (D3) has been committed to providing accessible, high-quality data to drive informed decision-making, and we are excited to embrace the city’s new platform.
Mayor Duggan’s announcement significantly affects the open data landscape in Detroit, in which D3 has been actively involved since our inception. The addition of city datasets that are regularly updated will be an enormous asset in understanding local issues and promoting data-driven outcomes.
Since December 2013, we have worked with Esri, the supplier of ArcGIS and ArcGIS Online products, to create an Open Data Portal that houses much of our rich data inventory. Since then, we have uploaded more than 70 datasets, ranging from transit stops and routes to detailed parcel information to locations of amenities such as schools and libraries. The site includes a helpful interactive mapping feature, but for those who would like to dig into the data, all datasets are available for download in CSV, KML and Shapefile formats, along with an API feature that allows for integration with third-party applications. We see both portals working together, providing much needed data to those who need it.
As a member of the Urban Institute’s National Neighborhood Indicators Partnership (NNIP), D3 has benefitted from learning from other cities that have implemented open data policies and platforms. The NNIP network wholeheartedly believes that open data:
- Increases transparency to help citizens hold government accountable,
- Encourages civic engagement in government decision-making,
- Enables access to a public good funded by taxpayers and
- Generates economic value that fuels private sector activity.
Read NNIP’s July 2014 assessment of the open data landscape in Detroit here.
D3 has had the privilege of working with all types of organizations, from larger institutions to neighborhood block clubs, and we know how important good data is. We look forward to using our expertise in weaving together data sources to illustrate complex relationships and reveal the stories of our city.
This is the first post in a new D3 blog series looking at employment and commuting patterns in Detroit and the surrounding region.
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