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Data Driven Detroit (D3) provides accessible, high-quality information and analysis to drive decision-making that strengthens communities in Southeast Michigan.

City of Change – Occupancy Density in Detroit’s Residential Neighborhoods

City of Change is a Data Driven Detroit (D3) blog series analyzing changes in Detroit’s residential neighborhoods from 2009 through 2014. This series is a collaborative effort between Noah Urban at D3 and Gary Sands, professor emeritus of urban planning at Wayne State University. 

**Note:  This blog post will make several references to Detroit’s Master Plan Neighborhoods. If you would like to view a reference map of these neighborhoods to help orient yourself, please click here.


This edition of City of Change analyzes the density of occupied residential structures (the number of residential structures per square mile that are occupied) and uses this indicator as a proxy for population.  Many neighborhoods in Detroit have endured increasing emptiness since 1960, though some areas of considerable density do remain. If these neighborhoods suffer decline, the potential impact on the city in terms of lost population and revenue is far greater than decline in areas of lower density.

As in the previous installment of City of Change, the data have been summarized for 840 census block groups in the city of Detroit. To account for differences in the size of the block groups, the analysis measures the number of occupied residential structures per square mile. The occupancy rates are based on data collected by the Detroit Residential Parcel Survey (DRPS) in 2009 and Motor City Mapping in 2014.

Occupancy in 2009

Detroit had an average of just over 2,100 occupied residential structures per square mile in 2009. This figure represents a substantial decline (almost 43 percent) from a peak of 3,675 structures per square mile in 1960 (based on archival Census data). The declines over that half-century have been severe across the city — just 18 block groups in 2009, out of 840 total, had occupancy densities greater than the 1960 citywide average.

There was considerable variation evident across block groups, with densities ranging from 51 to more than 4,500 occupied residential structures per square mile. It is important to note that some block groups with low residential densities contain large parcels with industrial, institutional or commercial land uses (parks, cemeteries, factories, etc.). In these areas, residential density is below average, regardless of the vacancy rate. In block groups that are predominantly residential, however, a low occupancy rate can reflect both a predominance of vacant lots and a high number of vacant residential structures.

The highest occupancy densities were generally found on the Far East Side (particularly in the Finney and Denby neighborhoods), the Northwest Side, and in neighborhoods close to Dearborn (particularly the Cody Rouge area). The relatively high density in these latter block groups was likely affected by the expansion of the growing immigrant population in Dearborn. Although many of the areas closest to downtown were not included in the DRPS project boundaries, areas closest to the city center possessed some of the lowest densities. Even along the east riverfront, occupancy levels were primarily in the bottom two ranges.

Figure 1:


This map shows the density of occupied residential structures per square mile in Detroit based on data from the 2009 Detroit Residential Parcel Survey. Note that the Detroit Residential Parcel Survey only collected information for 1-4 unit residential structures.

Occupancy in 2014

Continued population decline over the past five years, along with increased demolition activity, brought the average density of occupied residential structures down to 1,860 in 2014, almost 12 percent below the 2009 figure and less than half the peak density of 1960. As indicated in Table 1, the number of block groups in the highest-density range decreased by 77, or more than 35 percent. The lowest-density block group had just 18 occupied residential structures per square mile.

Table 1: Change in Density of Occupied Structures

Number of Block Groups
Occupied Structures per Square Mile20092014Change
2,850 or Higher210133-77
2,166 to 2,849210197-13
1,411 to 2,165210216+6
1,410 or Lower210294+84

The decline in occupancy occurred across the city. While the same general areas continued to have the highest occupancy densities, many of the block groups shifted from the highest to the second-highest category, indicating that even denser neighborhoods are experiencing depopulation. Declines are particularly noticeable on the East Side, including much of the Osborn area (identified as a portion of the Mt. Olivet Master Planning neighborhood). Densities remained high in areas with large immigrant populations, including the area just north of Hamtramck. There are even fewer block groups than in 2009 that are close to the city center and have residential occupancy densities higher than the lowest range.

Figure 2:


This map shows occupied structures per square mile in 2014, using the same ranges as the map from 2009. Note the considerable decrease in the number of block groups in top range. This decrease is even more notable when considering that the Motor City Mapping survey expanded upon the DRPS to encompass all residential structures.

Absolute Changes in Residential Density Between 2009 and 2014

Between 2009 and 2014, only 90 block groups recorded an increase in residential structures per square mile; 10 recorded no change, and 740 recorded a decline. Thirteen percent of all block groups reported declines of more than 600 units per square mile in only five years. Areas with increasing occupancy densities are found along the Woodward Corridor and the Near West Side (Woodbridge and Corktown areas), which have been the focal points of several residential investment initiatives. Elsewhere in the city, some of the higher-density block groups in 2009 did observe an increase in residential occupancy density, but these areas are somewhat randomly distributed.

Figure 3:


This map shows block groups that saw either an increase or a decrease/no change in occupied structures per square mile from 2009 to 2014. The block groups in the latter category accounted for nearly 90% of all block groups studied in this analysis.

To a large extent, the decline in occupancy density is a result of a rise in vacancies, rather than a decline in the number of residential structures. In 2009, the average residential occupancy density was 82 percent of its maximum potential (that is, if every residential structure in the block group were occupied). In 2014, the average was just 73 percent of the maximum potential. The number of block groups where the occupancy density was less than half of the potential increased from just five in 2009 to 44 in 2014.


The past five-year period has seen a substantial decrease in the density of occupied residential structures in Detroit. Since the number of new homes built during this period was relatively low, the lower densities are the result of higher vacancy rates in the existing housing stock. A net increase in the number of households occurred in just 10.6 percent of the block groups. These trends seem to suggest that, aside from scattered pockets throughout the city, the population declines that have characterized the past fifty years in Detroit – culminating in the 25 percent decline in population from 2000 to 2010 – are continuing. Future policies should recognize this trend and, in addition to aiming to reverse the decline, should address the potential that it may continue in the future.

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City of Change will return in two weeks with an in-depth examination of mortgage deeds and residential market health in Detroit’s neighborhoods over the past five years.

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The Motor City Mapping data referenced in this article are available in their raw format on the Data Driven Detroit (D3) Open Data Portal – http://portal.datadrivendetroit.org.

City of Change – Evolution in the Condition of Detroit’s Housing Stock

City of Change is a Data Driven Detroit blog series analyzing changes in Detroit’s residential neighborhoods from 2009 through 2014.  This series is a collaborative effort between Noah Urban at D3 and Gary Sands, Professor Emeritus of Urban Planning at Wayne State University.  This week, the series examines variation and change in Detroit’s residential structure condition.

**Note:  This blog post will make several references to Detroit’s Master Plan Neighborhoods.  If you would like to view a reference map of these neighborhoods, please click here.

What is Average Residential Structure Condition?

The Motor City Mapping project is a survey of Detroit properties that was completed in early 2014, providing information on some 380,000 individual parcels in the city.  This survey recorded whether each parcel had a structure on it, the condition of the structure, and whether or not the building was occupied. This information has also been summarized for the roughly 880 census block groups in Detroit (each block group includes several contiguous blocks).

Much of the data from Motor City Mapping can be compared to the 2009 Detroit Residential Parcel Survey (DRPS) conducted by Data Driven Detroit and its partners.  Though the earlier survey was limited to 1-4 unit residential properties, it is possible to compare the structural condition data for about 840 block groups at two points in time, from 2009 to 2014.  For every structure surveyed, the property’s condition was assigned a value from 1 to 4 with 1 indicating the best possible condition.  To account for differences in size of block groups, we created a weighted average of block group condition by summing condition values for all residential structures in the block group and then dividing the total by the total number of residential structures in the block group.  Values closer to 1 indicate stronger average condition, while a value closer to 2 (or even below 2) indicates an area with much poorer structural condition.

Average Residential Structure Condition in 2009

In general, the condition of residential structures is related to their age.  Many block groups with the strongest average condition were found at the outer edges of the city, while many of those with the poorest-condition housing stock were located in older neighborhoods just outside of the core of the city (Figure 1). An exception is evident in northwest Detroit, where the Brightmoor neighborhood stands out as a pocket of blight located between more stable areas.  This may be due to the wood-frame, 1950’s housing construction that once abandoned, has decayed at a faster rate than in other areas of the city where the construction typically incorporates greater amounts of stone or brick.

Figure 1:


This map shows Detroit’s residential average housing condition based on data from the 2009 Detroit Residential Parcel Survey. Note the clustering of stronger-condition neighborhoods on the edges of the city.

With limited exceptions, the area south of the Ford Freeway (Interstate 94) included very few block groups in the highest two ranges.  The few such neighborhoods with better average condition ratings included the Far East Side (particular the Finney Master Plan Neighborhood), some areas of the East Riverside and Indian Village neighborhoods, and portions of Corktown, Hubbard Richard, Vernor/Junction, and Springwells in Southwest Detroit.

There was typically a gradual transition between the average condition ratings in adjacent neighborhoods.  That is, neighborhoods in the top range were typically bordered by neighborhoods in the first or second categories.  There were, however, a few instances where block groups with the strongest average condition were adjacent to those with the weakest average condition.  Particularly prominent examples existed on either side of Woodward north of 7 Mile, as well as some of the block groups on the borders of the Brightmoor and Rosedale neighborhoods.

Average Residential Structure Condition in 2014, Compared to 2009

By the time that the Motor City Mapping Survey took place, conditions had changed considerably in many portions of the city.  The average condition rating for each of the block groups in 2014 is shown in Figure 2.   While the overall geographic concentrations of good and poor quality housing are similar to 2009, there are some important differences.  Southwest Detroit has seen a decline in housing condition, and the blight that was evident around Brightmoor in northwest Detroit and the Gratiot-McNichols area in northeast Detroit seems to be spreading.  The Rosedale and Cerveny/Grandmont Master Plan Neighborhoods (which contain Grandmont-Rosedale) are no longer solidly in the top range of block groups, and similar declines can be seen in the Finney Master Plan Neighborhood, which includes East English Village.  Both of these areas have resisted the encroachment of nearby blight, but these data indicate that even traditionally-stable neighborhoods are experiencing some degree of erosion.

Figure 2:


This map shows average residential structure condition in 2014, using the same categories as the map from 2009. Note the significant decline in average condition that is visible on the far western side of the city, particularly in the Cody Rouge area (west of M-39, south of I-96).

There was substantial movement between the categories defined in 2009 and 2014, as shown in Table 1.  The top two ranges contained a net total of 79 fewer areas, a decrease of nearly 19%.  The growth in the range with the weakest average condition – an increase of 70 block groups – is particularly concerning, and indicates that an increasing number of neighborhoods across the city may be entering steeper spirals of structural decline.

Table 1:  Change in Residential Structure Condition

Number of Block Groups
Average Condition Rating20092014Change
1.05 or lower (strongest)210150-60
1.36 or higher (weakest)210280+70

Absolute Changes in Residential Condition from 2009 to 2014

The two surveys recorded a small overall decline in the average residential condition rating for the city of Detroit, from an average rating of 1.23 to 1.27 in 2014.  As shown in Figure 3, more than 500 block groups observed declines in average condition, while 315 saw an improvement or no change. Much of the improvement occurred on the East Side, including some of the neighborhoods with the poorest structural conditions in 2009.  Considering that many of these areas have been identified by city planning processes as high-vacancy, it is likely that much of the observed increase is due to demolition of blighted structures, rather than as a result of new construction or rehabilitation.

Figure 3:


This map shows block groups that had an increase/no change or a decrease in average residential structure condition from 2009 to 2014. Note that many of the improvements were in the areas that had the weakest average condition in 2009 and 2014, indicating that these trends may be due more to demolition activity than new construction or improving physical condition.

Studying the data based on the ranges defined in 2009 reveals additional insights.  Most of the block groups in that were in the top category in 2009 experienced a decline in average condition rating; only one in eight showed improvement.  In contrast, over 60% of the neighborhoods in the weakest category in 2009 observed an improvement in average condition.  In 2009, 25% of block groups had 95%+ of structures rated in good condition.  By 2014, this number had declined by 20%, and the number of block groups where less than two-thirds of all structures received a good rating increased from 202 to 269.  The average score in the top quarter of block groups declined by 0.042 while the average in the bottom quarter showed an improvement of 0.036.


Although five years is a relatively short time in the life of a city, there has been a noticeable decline in the average condition of residential structures since 2009.  The areas where the best-quality housing predominates are shrinking, while the pockets of blight are growing. While there are large areas where the average structural condition rating has improved, this appears to have most often been the result of the demolition of the poorest-condition homes.   In general, the changing landscape of average residential structure condition illustrates a concerning trend in the city that must be reversed if Detroit’s neighborhoods are to have any chance at recovery.

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Next week, City of Change will examine occupancy in Detroit’s neighborhoods, and the changing patterns of where the city’s residents call home.

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The Motor City Mapping data are available in their raw format on the Data Driven Detroit (D3) Open Data Portal – http://portal.datadrivendetroit.org.

Introducing City of Change – A window into Detroit’s residential neighborhoods from 2009 to 2014

CityofChange_2009               CityofChange_2013

2009                                                                2013

In 2009, Data Driven Detroit (D3) participated in the Detroit Residential Parcel Survey (DRPS), collecting data on roughly 350,000 structures and vacant lots in the city of Detroit.  The survey captured information on the physical condition of Detroit’s residential neighborhoods and empty lots.  The DRPS provided a snapshot of Detroit’s urban fabric that has formed the backbone of policy efforts across Detroit, ranging from the high-profile Detroit Future City project to neighborhood-oriented mortgage market studies and local community agriculture endeavors.

Eventually, passage of time rendered the DRPS dataset less representative of current conditions in the city and thus less useful for decision-makers.  In the winter of 2013, the Motor City Mapping project once again undertook the collection of parcel-level data in Detroit.  On this project, D3 worked with multiple partners, including the Michigan Nonprofit Association, LOVELAND Technologies, and Rock Ventures, to survey every property in the city, regardless of use.  Using teams of resident surveyors and volunteer drivers, Motor City Mapping covered nearly 380,000 parcels in only six weeks of field work, providing information on structural condition and occupancy.  Many of the definitions used in Motor City Mapping were adopted from the DRPS.  In addition, D3 incorporated more than twenty other datasets into Motor City Mapping, creating the most comprehensive property database ever for Detroit.

The Motor City Mapping project provides a new benchmark dataset for policymakers and analysts.  With Motor City Mapping and DRPS combined, it is now possible to compare data across time with an unprecedented level of granularity, illuminating how Detroit’s neighborhoods have changed from 2009 to 2014.  The observed changes, based on two data points just five years apart, illustrate a small slice of a constantly-evolving environment.

City of Change is a new weekly D3 blog series dedicated to using these newly available data to explore how Detroit has changed over the past five years.  We assembled indicators that tell the city’s story from a number of different perspectives.  We then mapped these indicators at the census block group level, comparing 840 separate geographies between 2009 and 2014.  The insights offered by these comparisons are striking, frequently shocking, and occasionally hopeful.  They reinforce some of the trends that have been well-documented over the past five years, and shed new light on others.  They paint a picture of both tremendous decline and overwhelming potential.  They highlight neighborhoods that have faced tremendous stresses over the past five years, as well as areas that have endured the city’s continued crises, and even some areas where nascent turnarounds may be starting to become more entrenched.

This series is organized around two main themes.  The first part of the series will evaluate the various changes that have taken place over the preceding half-decade at a city-wide level, examining trends in Detroit’s population, housing, and markets.  The second half of the series will examine several high-profile, geographically-concentrated investment initiatives, with a particular focus on the changes within these various areas compared to the rest of the city over this five-year period.

Next week, we’ll take a deeper dive into Detroit’s structural climate – where buildings in the best condition in 2009 were located, which areas appear to have improved given the updated data from Motor City Mapping, and which areas appear to be facing the greatest threat from declining structural condition.

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For more information about the Motor City Mapping project, please visit www.motorcitymapping.org.  You can download the full, parcel-level survey results from D3’s Open Data Portal, http://portal.datadrivendetroit.org.  We’ll be posting many additional datasets from the Motor City Mapping comprehensive property database, so be sure to check the Open Data Portal regularly in the coming weeks!

National Network Spotlight


My favorite time of the season!  Halloween?  Sure, it’s fun, and I’m looking forward to Friday, but how much data can we squeeze out of that day?  I’m just returning from my hands down favorite event in the fall – the National Neighborhood Indicators Partnership (NNIP) meeting.

For those who haven’t heard me rave before, NNIP is a national network of organizations that share a commitment to creating and maintaining neighborhood-level data systems and helping their communities use the information to make better decisions.  We meet up twice a year to talk about our data shops – new discoveries, advances in analysis and online tools, challenges we’ve encountered, and much, much more.  D3 has been a proud partner organization since 2009.  Partners take turns hosting the meeting, and this time we went to Denver, Colorado, where the Piton Foundation makes data come alive through their Data Initiative.

As always, I learned so much last week from all of our partners in the network.  Here are some of the highlights I took away…

Nonprofit does not mean we can keep going without funds!

(A quick aside: I’m using the term nonprofit here, but I agree with Doug Bitonti Stewart of the Max M. and Marjorie S. Fisher Foundation that we need find a new term for our sector such as the “for impact sector”.  Why should we define ourselves by what we are not, versus what we are for?)

As D3 approaches its sixth anniversary, we’ve been doing a lot of thinking here about how we fit into the local environment, how we can best serve our community through the products and services we provide, and how to ensure D3 can continue to serve our community over the long haul.  As we grow up, we are also turning our data-driven philosophy inward and discovering some really important information about ourselves.

Taking a data-driven approach on the inside as well as out is certainly not new among the national partners, but it seemed to be a common theme of our meeting this fall.  Many of us are working through formulating business models, diversifying funding streams, monitoring internal performance, and understanding external impact.  Check back in the near future to read more about what D3 is learning through our investigation and analysis.

Our partners are building awesome online tools

Rhode Island Community Profiles from the Providence Plan show us a creative and expertly crafted use of the new Census Bureau API.  And the code is on Github!

Greater Portland Pulse is demonstrating incredible potential for regional collaboration and metric tracking for a region comprised of seven counties in two states.

My hands down favorite illustration of the uncertainty we should feel when using American Community Survey (ACS) data: The Randomizer.  The tool generates random values for households in poverty by census tract between the range of possible values given by ACS, and a resulting total value of the households in poverty based on the tract numbers generated.  Thanks to John Garvey for sharing his creation with me, and to Urban Strategies Council in Oakland for always pushing the cutting edge.

We can all be powerful data advocates

As John’s tool depicts, American Community Survey (ACS) data can present some challenges when presenting data for small areas.  It seems I find myself engaged in multiple discussions at every NNIP meeting about ACS data: to use or not to use? Are the data reliable enough or just garbage?  Should we replace ACS with locally sourced data, data which may be more accurate, but less comparable to other places and much more time consuming to produce for more than one jurisdiction?

I didn’t walk away with any clear answers, but I do know one thing: if we appropriately fund the ACS as a nation the data would be more reliable than they are now, which is important not only for the annual $400 billion in federal and state fund disbursement which relies on the data, but also for all the community development and planning work that goes on every day at a local level across the country.  Congress appropriates ACS funding – let your congressperson know you care!  And I beg you – if you are lucky enough to receive an ACS questionnaire, please fill it out!  I’m still waiting for mine…

NNIP data advocacy work extends beyond government or administrative data use, it also includes maintaining open data sites and convening data user groups.  D3 only recently launched our Open Data Site, but some partners have been publishing open data in their respective cities for years.  Some have even developed sophisticated user groups, trainings, and conferences to more broadly engage their communities in the practical use and application of data.  Why is this so important?  William Gibson put it best: “The future has already arrived.  It’s just not evenly distributed yet.”

Baltimore, Columbus, Indianapolis, and the MacArthur Foundation (on behalf of the Chicago School of Data) shared with us their experiences with local user conferences during the last day of the meeting.  I still have much to digest and apply to D3’s trainings and advocacy for improving data literacy, but in the meantime, I’ll suggest readers refer to NNIP’s putting open data to work in communities.

Thanks NNIP for gathering us all together once again!

New Student Dispersion Tools

Just as longer commutes can have detrimental effects on adults, it reasonably follows that longer school commutes may have such effects as an increase in stress, tardiness and obesity rates on our youth. Conversely, in areas where housing patterns concentrate poverty and race in a neighborhood, longer commutes outside those neighborhoods might improve student outcomes. Though it is not yet clear just how school commute distance effects student performance, what is clear is that Detroit students and families are exercising their choice. Given the potential problems and benefits, where do these patterns exist and how might schools and families adapt?

For the first time, in partnership with The Skillman Foundation, Excellent Schools Detroit and Great Gains, Data Driven Detroit was able to perform our student dispersion analysis with data covering all publicly funded schools in Detroit*. The Michigan Center for Educational Performance and Information (CEPI), working with the Michigan Center for Shared Solutions (CSS) provided Data Driven Detroit unidentifiable enrollment data and census block codes approximating student residence location from the fall of 2013. CEPI also provided information for students attending schools in the Cities of Warren, River Rouge, Southfield, Hamtramck and Highland Park. This summary analysis is the most comprehensive student dispersion analysis D3 has done to this point in terms of breadth of schools, and includes data from more than 141,000 students and 300 schools!

Student Dispersion map of Chrysler Elementary School Student Dispersion 13-14_Western International High School_04477

Student dispersion map examples: Chrysler Elementary School and Western International High School

The Detroit Context

Educational reforms in Michigan have broadly opened up public grade school options and resulted in a complicated school environment where families have many choices, near and far, in where to send their kids to school.

Detroit’s school context is dynamic, if not chaotic, where several schools may open and close year to year, and where there are a number of different public school systems serving students, including suburban schools of choice, charter schools (Public School Academies), the Detroit Public Schools (DPS) system, and the state-run Educational Achievement Authority (EAA). With more options, inconsistent transportation, school openings and closings, and other reasons, some students may find themselves, either by choice or necessity, with a more difficult commute. Because of this complex environment, it is not well understood just where youth from different parts of the city choose to attend school and how far they go.

The way students in Detroit are dispersed may have many important implications of interest for educators and planners.

  • Which schools have the longest commutes for students? Which schools fail to draw students outside their neighborhood, and why?
  • Are there parts of the city that have a stronger local draw?
  • Are there parts of the city whose students are more mobile?
  • Are certain students drawn to DPS schools vs. charter schools vs. the EAA schools?
  • How can future transit decisions be made that help students get to the schools that they attend?
  • If schools create neighborhood identity and community by causing local youth to attend classes together, are there neighborhoods that might be disproportionately affected because students aren’t attending local schools?
  • In schools that have further average commutes, how can educators help mitigate those students’ further commutes?

Data Driven Detroit is now making available a couple of new resources to help explore this data. First, our new interactive map allows users to view the locations of schools by type or level and then download digital maps showing the dispersion patterns of each individual school. In addition, we have calculated the average student commute distance for every public school in Detroit and summarized our results in a brief report.

While these data and map resources will not answer these questions on their own, we hope that these tools are useful to researchers, policy makers and educators as they make plans and formulate policy to improve the educational environment in the city.

New Resources

Student Dispersion Maps
D3′s student dispersion maps show the patterns of where students who attend certain schools live. Our tool allows users view maps of any publicly funded school from across the city by level, type, or by searching by name. These maps reveal spatial patterns that would be impossible to understand without this type of visualization.


Commute Distance Summary
The commute distance summary report helps to quantify just how far students from different schools are traveling, and how different types of schools compare against each other. This summary is meant to help educators understand the commute burden placed on their students and assist administrators in planning for school location. The summary lists the average distance for each public school. In addition, the school locations from our study, complete with average distance information, are available as a GIS shape file or table from Data Driven Detroit’s Open Data Portal  (Search for “commute”).

Next Up
D3 will be following up this blog post in the near future with two more blogs on this topic. The first will take a deeper look at a few interesting dispersion pattern maps.  The second will take a closer look at the average commute distance analysis.

Since this analysis elicits more questions than answers, D3 is hoping to continue its analysis of student location data. Through our partnership with ESD and CEPI, we are expecting to get a more complete data set including not just students that attend Detroit publicly funded schools, but also Detroit residents who attend schools outside the city. As a large percentage of Detroit youth do attend school in the suburbs, this next data set should draw a much more complete picture.


 * Ann Arbor Trail Magnet School was mistakenly excluded from the data set delivered to Data Driven Detroit.

Celebrating the Data Guru

Today marks a bittersweet day in Data Driven Detroit’s history as we celebrate the achievements of Detroit’s own data guru and D3’s founding director, Kurt Metzger.

This morning, Kurt was honored at a breakfast at Detroit’s Rattlesnake Club. Friends and colleagues praised his accomplishments and thanked him for his work using data to make [Read on...]

Building the 2014 One D Scorecard with NiJeL: D3.js for D3

Data Driven Detroit (D3) launched the 2014 One D Scorecard in May (read more about that here). Today, we’re writing to share more about our process for making this interactive data tool through a Q & A with NiJeL, a team of data scientists and developers that D3 collaborated with to build out the Scorecard. [Read on...]

The Historic Resource Survey: Community Data Collection in Action

In November 2013, the Motor City Mapping project moved forward at a dizzying pace, with the goal of identifying every blighted structure and empty lot in the city of Detroit. Loveland Technologies was in the process of finalizing the Blexting application for the city-wide parcel survey, and the Michigan Nonprofit Association [Read on...]

2014 National Day of Civic Hacking

The nation-wide movement toward public data transparency and democratization is continuing to gain support. As cities including Portland, Chicago, New York, Louisville, Ann Arbor and others are embracing Open Data in government by creating web sites for citizens to easily view and download data, the potential for developing useful applications driven by these data [Read on...]

Detroit Blight Removal Task Force Releases Blight Plan and Recommendations Today

Data Driven Detroit (D3), an affiliate of Michigan Nonprofit Association (MNA), is excited to participate in the release of the Detroit Blight Removal Task Force’s final report, “Every Neighborhood Has a Future…And It Doesn’t Include Blight.”

Detroit Blight Removal Task Force Co-Chairs

Glenda Price, President of the Detroit [Read on...]