Blog

• NNIP Milwaukee Recap
  by Bob Gradeck on 6/17/2019
  Since 2008, the Center for Social and Urban Research has been the Pittsburgh partner of the National Neighborhood Indicators Partnership (NNIP), a peer learning network of community data intermediaries. The work of NNIP is supported and coordinated by the Urban Institute, and there are partners in over 30 cities. It’s critical to our work to be connected to peers that do similar work in other communities, and NNIP enables us to make connections with similar organizations. These interactions give us new ideas, provide opportunities to collaborate with people in  and also enable us to tell the story about what we’ve done here in our community. The annual partner meeting took place this year in Milwaukee Wisconsin, and the organizers at the Urban Institute and our gracious hosts at our NNIP partner Data You Can Use made it a great event. Here are a few takeaways from the partner meeting and my experience in Milwaukee.

  
  
  • We learned a lot about the new Federal Opportunity Zone program thanks to Brett Theodos of the Urban Institute. Included as part of the Tax Cuts and Jobs Act of 2017, the Opportunity Zone program is designed to allow people to defer capital gains taxes if they make contributions to funds that make equity investments in specially designated communities across the country. While the program is not limited to real estate, there is fear that the incentive will spur speculation in communities where residents are already worried about rapidly appreciating real estate. Our presenter also acknowledged that the program may not spur development in places with few investment opportunities. When it comes to the data, we learned that it would take an act of Congress for the Opportunity Zone Census Tract designations to change following the next Census. There are also  few requirements for opportunity funds to report their investments in a publicly-accessible fashion, so it will be important to track  property sales, building permit, and lending data to understand the impact of the program in local communities.
  
  

  
  
  • Partners in Minneapolis-St. Paul, Baltimore, and Durham discussed how they are approaching their relationships with advocacy organizations. Data intermediaries are in an important position to leverage data to benefit historically marginalized and oppressed people, and can expand their impact by working with advocates. At the same time, intermediaries are also challenged to maintain their reputation as a trusted place for community information when they partner with people that have a strong point of view on an issue. Some of the ways that our partners are able to maintain their brand as a trusted intermediary include being open and transparent about their relationship with advocates, asking that advocacy partners allow them to review any data being released or analyzed for accuracy prior to publishing, and to share their data and recommendations with people on all sides of an issue. To work in a trusted way with advocates, our partners encouraged us to build relationships with organizations by working at the same table with them, and doing what we can do to support them. The presenters finally  encouraged us to be comfortable going outside of “our lane” in order to have a larger impact in the community.
  
  

  
  
  • I also am thinking about implementing some of the ideas shared in a session about how to get influence through partnerships with local media from my co-panelists in Los Angeles and New Orleans. The number of staff and revenues at local newspapers have been rapidly shrinking, and turnover is the norm among journalists and publications. Trying to gain influence through media in the current climate has become much harder as a result. Intermediaries that choose to work with the media will need to invest time in building and maintaining relationships with journalists. One of the panelists suggested scheduling a standing monthly meeting with local outlets, which is something that we’d like to adopt. Framing stories in addition to releasing data will also make it more-likely that journalists will turn data into stories for their audience. Even highly-skilled journalists lack the time to pore through a dataset to find a narrative. One of our partners that used to work in a local newspaper also shared that some journalists may be given a quota of website hits they’re responsible for, so those operating under this sort of performance structure may be most-receptive to clickbait-friendly content. We were also encouraged to invite journalists to participate in workshops we may offer that can help them build their data skills, and we should do what we can to be available for interviews with print, online, and television journalists.
  
  

  
  
  • I was humbled to receive the Network Steward of the Year Award from my peers. It is important for everyone involved in a peer learning network like NNIP to make a  contribution to the health of the community, whether it’s by attending the partner meetings, documenting work, or sharing advice.
  
  

  If your version of heaven consists of beer, cheese, pretzels, and sausage, then it would look a lot like Milwaukee. I had a great time exploring the City for the first time. The most-interesting thing I saw on my trip was the diorama of the Usinger’s Famous Sausage shop, which included marshmallow peeps. This diorama can be found on the counter of the real-life store and factory in downtown, Milwaukee.
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• 911 Dispatch Data Cleaning
  by Ellie Newman on 2/4/2019
  Ever since WPRDC was launched as our Pittsburgh-area open data portal, 911 call and dispatch data has been one of the most requested datasets. And no wonder: by looking at the time and location of different types of emergencies, community members, researchers, nonprofits and others can answer important questions about their community. What areas have the most vacant building fires? Has the overdose crisis eased off over the past few years?Allegheny County's 911 center handles emergency dispatch services for 111 of the 130 municipalities in the county. Trained operators take information about the call, determine the location of the emergency, and dispatch emergency responders according to the priority level of the call. The value of the dataset is obvious, and we initially thought it would be a quick release. But a closer look revealed some thorny questions about privacy and re-identification.The call description field was extremely detailed: so much so that even the age and medical information about the victim was sometimes revealed. Combined with precise location information, someone could potentially re-identify people using their own knowledge about ambulance calls to an area. For example, here's a snapshot of some actual call types found in the CAD:Sample call descriptions in CADOur goal was to find a method of aggregating the data that kept useful information that people cared about, but not release enough detail to compromise anonymity. We knew that was wasn't so much the detail in any one field, but rather the combination of fields in a record, that threatened privacy. The three most potentially identifying pieces of data in each record was the date/time, location, and nature of the emergency. Of course, those were also the most interesting and useful fields--so we needed a way to aggregate each field just enough to de-identify, but not so much to whitewash the data.We tested various combinations of data aggregation, and ultimately settled on the following:

  
  
  • Geocode to census block groups. Remove address and lat/long.
  
  

  Downtown Pittsburgh-area census block groups. Block groups generally have about 600-3,000 residents.

  
  
  • Keep only quarter and year. Remove exact date and time.
  
  
  • Shorten call types, in most cases taking the first couple words before the dash in the above sample.
  
  
  • Sexual assaults were grouped with assaults.
  
  
  • Resulting "short" call types were reviewed by our data privacy team, and some were marked as relating to protected health conditions, such as pregnancy, diabetes, and mental health issues
  
  

  Finally, records were tested for unique combinations of census block group-quarter-year. If the number of calls in a block group for one quarter was less than five, and the short call types could be considered sensitive (e.g. not fires or traffic-related), then the call type was removed.You can still make a detailed map with this data, or analyze trends over time. But if you're looking up ambulance calls to your street to find out if your neighbor had a heart attack, you'll be foiled--there will be too many records, blurred just enough, to tell what exactly that call was for.
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• Los Angeles NNIP Partner Meeting Recap
  by Bob Gradeck on 11/15/2018
  It’s critical to our work to be connected to peers that do similar work in other communities. These interactions both give us new ideas and also enable us to share more about what we’ve learned here in our community. The National Neighborhood Indicators Partnership (NNIP) is the network that is most important to our work at the Regional Data Center. The work of NNIP is supported and coordinated by the Urban Institute, and there are partners in over 30 cities. Each of these partners works in their own community to strengthen capacity for data-driven decisionmaking, inform local and national policy, and build support for community indicators systems.We’ve been a member of NNIP since 2008, and have benefitted from the many peer-learning opportunities provided through the network, including meetings, webinars, technical assistance,  tools and guides. We try to never miss a partner meeting, as they provide an outstanding opportunity to connect with our peers. This fall’s meeting took place in Los Angeles in October, and it was both a great learning opportunity, and provided me with a chance to visit the City for the first time. Special thanks to our Los Angeles partner at the Sol Price Center for Social Innovation at the University of Southern California for helping to make it a great experience for all of us. Here are a few takeaways about the NNIP Partner Meeting and my Los Angeles experience.

  
  
  • An interactive session gave attendees a better-sense of how to incorporate diversity into their organizations. The workshop stressed techniques for writing inclusive job descriptions in a way that does not discourage people who may not think of themselves as a good fit from applying. The workshop leader also advised us to promote opportunities through unfamiliar networks when recruiting employment and board candidates. We were also encouraged to look for candidates that will add a new and different perspective to our team rather than finding someone who will “fit” the existing culture. Finally, everyone was encouraged to collect and share data on the diversity of their staff and advisory boards as a way to stress and communicate the importance of diversity to our work. We expect to add to our team next year, so this session was very timely.
  
  

  
  
  • At NNIP, a portion of the agenda is devoted to “unconference” sessions that are proposed and led by the members themselves. One of these sessions really helped me learn more about how to price our services, and I’ve already incorporated this into our pricing model. Some of our partners have a sliding-scale price structure, where rates for services are based on the client’s mission, ability to pay, or the immediacy of the deadline. Partners also shared ideas in how to build evaluation, technical assistance, marketing, and project management into their project budgets. The importance of having long-term contracts or retainers was also something partners discussed as helpful for their financial stability. I appreciate that our partners are so willing to share not only what works but also what doesn’t. I’m also glad that that details from the conversations like this one are incorporated into the guide “Business of a Local Data Intermediary” by the Urban Institute.
  
  

  
  
  • A panel of funders also provided me with a better understanding of how to approach corporations for financial support. Corporate motives for supporting the work of data intermediaries includes alignment with the personal interest of a key company stakeholder, the degree to which the products and services are important to communities where the company does business, and the opportunity that a particular project will give the company a good story to tell. Companies also like to have a partner with a good track record, and so this session reinforced the importance of developing and maintaining a reputation for delivering quality work.
  
  

  
  
  • NNIP also provides an opportunity to learn about interesting data projects in other communities. Our peers in Boston have been trying to get a handle on their local ride-sharing industry. After unsuccessful attempts to acquire data from the ride-sharing firms, our partner recruited drivers to collect data by distributing surveys to their riders. Through this research, they learned that forty percent of ride share customers would have otherwise taken transit, and our NNIP partner estimated that each ride-sharing trip diverts about 35 cents of potential revenue from the Massachusetts Bay Transportation Authority. Policymakers are now using data from the survey to develop a strategy for managing the ride sharing industry and its effect on other transportation systems.
  
  

  
  
  • More communities are starting to think about their role with Census 2020 outreach efforts. Thanks to the leadership of our local elected officials and our nonprofit and foundation community, we seem to be starting this work much earlier than many of our peers around the U.S. We’re a member of our local Complete Count Committee, and shared our thoughts with our peers about the kinds of roles they can play in supporting Census 2020 outreach efforts.
  
  

  This was my first-ever trip to Los Angeles. In a place this big, three days is only enough time to scratch the surface of what the city is like. It was nice to be able to visit the beach in October and not be cold, but by far the best thing I did was visit the Pinata District on the edge of Downtown LA. Just imagine a slightly smaller version of Pittsburgh’s Strip District market. Instead of wholesalers selling cheese, meat, coffee, baked goods, produce, and yinzer apparel, the Pinata District only seemed to sell colorful pinatas, candy, and party supplies - some stores even advertised custom pinata manufacturing. It was fantastic to see so many different types of pinatas, including a pig, turkey, dinosaur, skeletons, ghost, president, cactus, shark, donkeys, and a duck. If anyone’s going to Los Angeles, I’d highly recommend the Pinata District experience - FYI you can take a pinata through airport security.
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• Data 101 Toolkit
  by Bob Gradeck on 10/2/2018
  Data 101 is an introductory data workshop series designed for people looking to get started on their journey toward data literacy. The Carnegie Library of Pittsburgh and the Western Pennsylvania Regional Data Center developed the set of workshops  after testing a variety of formats with different audiences and in different locations throughout the Pittsburgh region.The goal of the Data 101 workshops is to build participants’ confidence and literacy in using data. After completing all four workshops, we expect that participants will feel prepared and confident to take a computer oriented class knowing that they are already familiar with the concepts behind the technology.We're now excited to share everything others will need to replicate and expand on the Data 101 workshops in our Data 101 Toolkit (in Google Drive). Each workshop has a theme and consists of a set of activities built around paper-based or low-tech activities, which workshop attendees have found to be inviting and inclusive.

  Included in the Data 101 Toolkit, you will find materials for the following workshops:

  What is Data - a general introduction to data concepts. Through presentation, activities and discussion we explore data types, uses, metadata, privacy, ethics and critical thinking around dataCharts - a general introduction to charting data. Through presentation, activities and discussion we explore language, design principles, clutter, basic guidelines around what chart to use when.  Mapping - a general introduction to spatial analysis concepts.  Through presentation, activities, and discussion we explore the fundamental concepts behind Geographic Information Systems (GIS) applications.  Finding Stories in Data - a general introduction to telling stories using data. Through presentation, activities and discussion we explore different types of stories found in data and practice applying that knowledge by working in groups to explore printed datasets.Special thanks to Neighborhood Allies for supporting the development of Data 101, along with the supporters of the Regional Data Center and the Carnegie Library of Pittsburgh. 
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• A Summer of Num(b)ers
  by Claire Suh on 7/23/2018
  Coming from a decidedly non-quant, non-technical background, choosing a masters program that emphasizes its data-heavy curriculum was my way of learning to swim by getting thrown in the deep-end. I’m a student at University of Pennsylvania, pursuing a masters degree in public administration with an eye towards technology policy. I’m in Pittsburgh this summer to learn from the best, the Civic Engagement and Technology Director for Microsoft in their Cities program. My interest in public-private partnerships, civic uses of innovative (and lean!) tech, and the impact of “big tech” on regulations, ethics, and the other questions that come along with any industry scaling this quickly, led me to a fellowship interrogating these things in the lovely city of Pittsburgh this summer.To keep to the masochistic theme of forcing myself to interact with numbers and code and all the rest, I requested to work on projects that would give me an opportunity to keep practicing my data analysis skills I picked up academically and in conducting research. I can be dangerous in R, but this is The Summer of Python (and a little spritz of JavaScript). I’ve joined the team at Western Pennsylvania Regional Data Center to see if there’s anything Pittsburgh housing data can tell us that we don’t know already by way of socio-economics, demographic trends, and other insights. I’m excited for this opportunity and driven by my conviction for resource equity and urban cities. I’ll be chronicling my journey of self-teaching, data wrangling, cleaning, analysis, and visualization on this very medium for the weeks to come.* I’m always looking for new resources and reads, so forward over anything that might be a helpful addition in this process. You can reach me at cdsuh17@gmail.com.
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• Open Data - Everyone's (Housing) Property (Values)
  by Claire Suh on 7/23/2018
  If you’ve made it here, I don’t have to tell you about the great resources Western Pennsylvania Regional Data Center (WPRDC) has made available (with excellent documentation and tutorials to boot!). I was charged with using the Market Value Analysis (MVA) data from the Urban Redevelopment Authority (URA) to analyze at sale structures and market strength. This dataset is a useful tool to understand real estate markets in municipalities across Pittsburgh, and is particularly helpful in identifying types of distress or conversely, value, that characterizes an area. It also provides insight into market strength and weakness using Census block groups. These insights can be applied when considering investments or interventions. Below, I outline the steps I took to do some initial analysis, followed by my findings.I downloaded the MVA data from WPRDC’s website and opened it up in R. Like any success-seeking data analyst, I began by inspecting the data. What am I looking at? What’s being measured? What is the unit of measure? This is something you can choose your adventure for, and I chose to look at summary statistics, basic plots to help me visualize, and some quick cross-tabs to see if there were any trends.This first order analysis yielded nothing too complex – I simply wanted to visualize what the median residential sale price was per 2016 MVA cluster. You can see a few things from this graph, but most clearly, property clusters in the beginning of the alphabet have higher value than those towards that follow.Next, I was curious whether there were any trends, correlations, variations, or outliers that I might find in this initial analysis. I wanted to look at the clusters visually, and thought it would be interesting to see if there were any trends in housing prices. This graph was meant to support the trend towards clusters earlier in the alphabet having higher median value, but also shows that variance in median sale price is pretty similar across all clusters, with a trend towards higher variance for lower median value sales.At this point, I felt like this dataset alone was a good starting point, but could benefit from additional information. This was the impetus for making me wonder what else could be useful to supplement MVA to put together a robust picture of Pittsburgh housing. In the next post, I will get into some other sources of data available publicly, and how to use them.  
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• Carnegie Library Wi-Fi Usage Data Analysis
  by Alexa Marzina on 7/11/2018
  When your boss gives you a project and says to do whatever you want with it, that can give you the impression to set the bar really high. I did just that for trying to analyze the Carnegie Library Wi-Fi data. I wanted to do a super cool year-by-year visualization or a stacked bar graph that compared each library month-to-month over different years. Though I am a writing major and not a computer science person, I like to consider myself "knowledged" in Python (if that is even a word) such that I can make cool graphs. And I can — just not as cool as I'd like. Taking one class about coding that only spends half the time teaching Python only gets you so far, but here I am!Above, you can see my first attempt at a cool graph, the very first thing  I did with the CLP Wi-Fi data and what I'm most proud of. The boss tells me that data should tell a story, and at first, I struggled to figure out what story this graph told. One thing that I assumed of this data is that some type of trend — be it positive or negative — would appear in the summer. Students leave school for the summer and all the work-o-holics start taking "sick days" to take their kids to the pool, so I thought there might be a decline in library patrons. Conversely, I thought there could be a spike in library patrons in the summer as parents bring in their children for activities or students decide to visit for non-academic reasons. But this data doesn't support either of those conclusions, to my surprise.So another, more obvious visual story lies in the simple fact that the main library in Oakland falls so far ahead of its sister libraries in Wi-Fi minutes. Just look at how much higher its line is on the graph. This can be explained by a few reasons, the first one being that the Oakland library is the biggest library in the area with the most resources in it. Not to mention that students in the Oakland area — including Pitt, CMU and Carlow  — use the Oakland library as a study space or just as somewhere to escape the hustle and bustle of normal campus. This library is used year-round by students, families and casual readers, unlike other libraries that aren't as centrally located.To fairly show the data from the other years represented in the data, I ended up making a pivot table. While it's not a super awesome graph, I will chalk this creation up as a victory anyway because before this project I didn't even know what a pivot table was. So yay, I learned something! This table shows each library's average wifi minutes per month, separated by year.Reflecting back on this endeavor, I've re-learned something that I already knew about myself: I'm stubborn. I looked at this data once, said to myself, "This is what I am going to do with it." And once I couldn't do exactly that, I got stuck. My brain, like, froze, resulting in me just staring at this data for the past two weeks. When I do a visualization project like this again, my strategy is definitely going to be to brainstorm more than one coherent goal so that way if I get stuck again, I have alternate options.
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• Crash Course in APIs Workshop Reflection
  by Alexa Marzina on 7/5/2018
  I like using acronyms because it reminds me of texting (tbh). More than once, I've told professors that my essays would be more entertaining and persuasive if I was able to use emojis and the occasional "lol" in my academic writing. So when I was told we were hosting an API workshop, of course I had to come up with some alternate meanings for API:

  
  
  • A Penguin Investigator
  
  
  • All Pasta is Important
  
  
  • Advanced Pigeon Intelligence
  
  
  • Anchovy Pizza: Impure
  
  
  • Awkward Panda Incident
  
  

  At our Crash Course in APIs workshop, David and Steve from the Regional Data Center taught an enthralled crowd of 40 hobbyists, students, start-up wizards and public-sector workers about what the heck an API is and some cool things to do with them. The actual meaning of API is Application Programming Interface, and they are sets of rules and methods for communication between software components. They’re kind of like LEGO bricks because each step of using an API builds on the previous ones, but David and Steve think it’s because they both hurt when you step on them.As a computer user, you utilize APIs more than you think. Any time you request information from a website, like searching for an open room in the hotel nearest you, you are asking that service to use the hotel's API to deliver that information. Take a look at this example of an API "get" request from the workshop.Does the top line look familiar? As a member of an internet-obsessed society, it sure should. If you ever thought that internet URLs just looked like a bunch of jibberish (like I did), then it can be a big revelation to learn that it all means something.APIs use Hypertext Transport Protocol requests to transfer information back and forth between clients (like your cell phone and computer) and web servers. Think of it this way: The clients are restaurant customers, the server is the restaurant kitchen and the HTTP request (carried by the waiter) could be an order for food (carried from the client to the server) or the food itself (carried from the server back to the client).Services like Spotify, Twitter and Reddit all have their own APIs in play that do things through the HTTP "get" and "post" methods. In Spotify's, for example, you can "get" information about songs, playlists, artists, users, etc., and you can "post" songs to build and modify playlists.[caption id="" align="alignnone" width="1000"] Image via Spotify[/caption]WPRDC also uses APIs; our Property Dashboard and Burgh's Eye View tools both use APIs to update the interactive maps with current information. We also use APIs extensively to push data to our open data portal. When we receive new data, it gets pushed to where it needs to go — under the correct publisher, dataset and topic — so that when you search for it, it's already there.Turns out, APIs can do a lot of fun things — like make a bus tracker or order a pizza right to your house, which is a good example of how while APIs can do virtual things, they can also effect change in the real world. You can see all that David and Steve taught about APIs (and get a good starting point to use them yourself) on our GitHub repository.We plan on giving this workshop again in the future, so make sure you subscribe to our newsletter and keep up to date with our events calendar to be able to save the date!
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• Data 101: Mapping and Internship 101: Following Directions
  by Alexa Marzina on 6/18/2018
  Starting a new job is always scary, especially when you’re thrown into helping with a data mapping class first thing in the morning and set off the emergency exit alarm in the building not once, but twice. The goal: Take the class and write about my personal experience. While I had plenty of anxiety about it, I was ready for the challenge.The class was the Western Pennsylvania Regional Data Center’s Data 101: Mapping at the Carnegie Library of Pittsburgh in Beechview. Data 101 is a series of classes designed to build people’s skills and confidence in data literacy. The focus was learning about and visualizing different types of data mapping through four hands-on activities, which could later be replicated or expanded upon by using computer mapping software.I was visibly excited when I found out that this workshop included not only colorful pencils and markers, but fun stickers as well. Obviously, I opted for the sparkly pink unicorn ones, and suddenly, data didn’t seem so scary.[caption id="attachment_3526" align="aligncenter" width="300"] While mapping data doesn't require magic, having unicorns around always helps.[/caption]During the first activity, we learned about choropleth maps — maps that are colored, shaded or patterned based on a variable. In this case, the visualized example was the number of licensed dogs by county. Equipped with a map of eight made-up counties and the number of their licensed dogs, it was up to us to decide the color-coded scale to represent those data on a map.Mapped data can tell different stories depending on how you represent them. I categorized my dog data into four bins, with no counties falling into the third bin. So, the story of my map was out of those eight counties, none of them had between 15 thousand and 19,999 licensed dogs. It’s not a very long story, but it doesn’t need to be![caption id="attachment_3527" align="aligncenter" width="300"] I grouped my data using the arbitrary grouping method, as opposed to other more organized methods such as continuous, equal interval or data distribution groupings.[/caption]The second activity, though eight steps long and including serious orders from the city’s department of business planning, allowed me to create a beautifully vibrant birds-eye-view street map. The scenario for the workshop was a popular parking garage was closing and the department of business planning needed us to ensure that all the appropriate establishments got notified of the closure, specifically within 250 feet of the garage.To do so, we used latitude and longitude coordinates — yes, like you learned in middle school social studies — to locate the garage, then used a pushpin and a string as a makeshift compass to draw a scaled 250-foot radius, or buffer, around it. Buffer technology is useful for things like finding the nearest coffee shop near you on your smartphone maps app. Turns out, we needed to notify six business about the garage closure.Activity three taught us to be careful when using data, and that you can’t only use one type to fully visualize a problem. Given a list of properties and their location on the street, we had to determine which properties were vacant city property and who was eligible to buy them. After looking at the data carefully, we saw that there was one vacant property in the set that was actually owned by a homeowner already, so it was not eligible for sale. We realized that paying close attention to the data is always of the utmost importance to prevent mistakes.[caption id="attachment_3535" align="aligncenter" width="225"] While these don't look like variables you'd typically need a map for, having a visual can be helpful to solve problems.[/caption]The final activity, which required us to fold a paper into a four-by-four grid, or a 16mo if you’ve ever studied bookmaking, to see which areas of a fictional town were at high-risk for spreading cooties. An important lesson about data is that while a lot of it is public knowledge, you never want to release data that could put a person, represented here as a point on a map, at risk for violence or other negative circumstances. In this case, if one of the grid squares had less than three cases of cooties, it couldn’t be represented in the final map to protect the individuals’ privacy.Each remaining square with three or more cases was color coded based on the number of cases they had, creating a point density map. Though we as the mapmakers had access to each point, or case of cooties, in all areas, users of the final map would only be able to see the color-coded boxes and not each individual case.[caption id="attachment_3536" align="aligncenter" width="300"] Sometimes, grouping your data into different chunks leads to a wonky looking end result, but you can always try to break it up a different way.[/caption]While that all sounds like a lot, above all we learned that map making and data mapping is allowed to be fun and doesn’t have to be super serious or boring. Though there are some guidelines with using data, like prioritizing privacy protection and accessibility to those who may be colorblind or have other disabilities, there isn’t only one “right” way to map out a data set.The next Data 101 is Finding Stories in Data Wednesday, July 25 at 10 a.m. at the Carnegie Library of Pittsburgh in Beechview.
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• NNIP Recap and Census 2020 Thoughts
  by Bob Gradeck on 6/6/2018
  Last month, I attended the Spring, 2018 partner meeting of the National Neighborhood Indicators Partnership (NNIP) in Atlanta, Georgia. NNIP is a network of practice for community data intermediaries managed by the Urban Institute. NNIP partners in over 30 cities collect and transform neighborhood data, promote the use of data, and use information to improve the lives of people in marginalized communities. As always, the team at the Urban Institute assembled a great agenda for the meeting, and our good friends in Atlanta at Neighborhood Nexus and the Atlanta Regional Commission truly raised the bar as a local host. We’re also grateful that our Atlanta friends also hosted the first workshop of the Civic Switchboard project that brought together librarians and civic data intermediaries from 11 communities the day before the partner meeting.  At the partner meeting, the overall theme was equity and data. There was a mix of presentations covering topics like monitoring residential instability, communicating with data to advance racial equity, and managing civic technology projects in local ecosystems. There was also a panel covering equity issues in Atlanta organized by our local hosts, along with a series of Atlanta tours touching on the broader theme of equity. The reception featuring barbecue and generous drinks was just about the only thing on the agenda that didn’t come with an equity lens. Our partners in Philadelphia and Houston wrote about their experiences at the meeting, so be sure to check out their blogs.I found one of the participant-led “unconference” sessions on the 2020 Census particularly relevant to recent local conversations here in Pittsburgh, so my thoughts will be focused on that topic in the rest of this post. Readers may be aware that the Census is essential in determining political representation, allocating hundreds of billions of dollars in Federal funding, and providing a detailed understanding of our nation.

  Why people are worried about the Census

  While community mobilizations to encourage participation in the decennial Census are nothing new, there is particular worry that the count will fall short in 2020. People most-likely to be missed in the count include families with children under age 5, foreign born people, minorities, and the poor. These participation concerns are magnified by the following three major changes to the 2020 Census:

  
  
  • A citizenship question was recently added to the Census for the first time since 1950, and raised fears that the Census may become politicized. The impact of adding this question in our current political climate could well mean that people not born in the U.S. or those living with someone in a vulnerable situation will be reluctant to participate. Since this question was added very late in the process, the standard processes for testing the impact of this question on participation were not conducted, so we really won’t know how much its inclusion will affect response rates.
    
    
    
  
  
  • For the first time, many of the responses to the Census in 2020 will be collected over the internet. There is worry that people with low digital literacy, people without access to digital devices, and people living in communities without reliable high-speed internet service may not be counted.
    
    
    
  
  
  • The budget for the Census has also been drastically reduced in comparison to previous efforts. According to meeting attendees, the number of visits enumerators make to non-respondents will fall by at least two-thirds compared to 2010. In 2010, enumerators made as many as six in-person visits to people that did not respond.  
  
  

  What's a data intermediary to do?

  Local conversations around how to mobilize for the 2020 Census have started to ramp-up, and a Complete Count Committee is being formed to help develop an overall outreach strategy. Local conversations and the discussion at NNIP enabled me to organize my thoughts on what the Regional Data Center can do to support the Census as a data intermediary. I found it helpful to frame possible roles in terms of things to do before and during the count. We would need to raise funding to develop and scale these ideas.

  Before the count

  
  
  • Our staff is equipped to provide data to inform planning efforts, and are very interested in creating a local version of the New York Planning Labs’ Population Fact Finder. If we were to adapt this open-source tool for local use, people in our community would be able to explore Census and other place-based data to identify communities that can be the focus of outreach efforts. We envision this code base would serve as the next iteration of our Southwestern Pennsylvania Community Profiles tool.  
    
    
    
  
  
  • We can also begin to prioritize the collection and publication of data important to outreach efforts in 2020. Data on community assets like public wi-fi hotspots, libraries, ESL Class locations, and computer labs can be very important to outreach efforts. It makes sense to assemble a comprehensive list of target administrative datasets, developing processes and relationships needed to collect asset data from communities, and begin collecting and sharing this data on the open data portal before 2020.
    
    
    
  
  
  • Inspired by the Data Refuge Storytelling project run by colleagues in Philadelphia, now is the time to start telling the story of all of the different ways people in our community use data from the Census. These stories can both provide inspiration for data lovers, and raise awareness of how the Census impacts everyone’s lives. We’ll soon have a student joining us to begin to capture stories from several Regional Data Center users this summer, and would like to broaden this effort to tell stories about Census data users.
    
    
    
  
  
  • Last year at the Greater Pittsburgh Nonprofit Partnership Summit, we worked with staff from the Census Bureau to pilot a workshop that could be used to generate ideas in how to reach potentially hard to count people. We would be interested in showing others how to replicate this workshop and use other engagement tools in order to capture outreach ideas directly from community members and local stakeholders. These workshops can also help to prioritize the collection and publication of datasets to support outreach efforts.
  
  

  During the count

  
  
  • The Census plans to provide updates on Census completions in local communities through an API, and community mapping tools like the one developed in New York City can be modified to provide daily updates on where people are completing the Census. This real-time information will help to identify successful outreach techniques, and allocate resources to areas with the greatest opportunity.
    
    
    
  
  
  • The Regional Data Center can set-up a rapid response team of student fellows in the spring of 2020 to provide on-demand technical assistance to people and organizations working on direct outreach efforts during crunch-time.
    
    
    
  
  

  Now that I’m through with this much-needed brain dump, I’d be happy to talk with anyone (including our NNIP partners) about any of the ideas I laid out in this blog post. Please let me know if you’d like to connect. The Census Project is a great resource if you’d like to stay on top of the latest Census 2020 news. Many more links are also available on the NNIP Census 2020 webpage.
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