![nasa-1lfi7wkgwz4-unsplash.jpg](/sites/g/files/omnuum10826/files/styles/hwp_21_9__1920x825/public/datasmart/files/nasa-1lfi7wkgwz4-unsplash.jpg?itok=uvH4ybup) # Maps, Stats, and Equity Lessons from the Civic Analytics Network December 09, 2022 [ Betsy Gardner ](/betsy-gardner) - December 9, 2022 - [Data Visualization](/data-visualization) Cities across the country are trying to address systemic and long-standing inequities, a job that many Chief Data Officers (CDOs) are asked to assist with. Collecting and visualizing data is an important factor for many city leaders who want to show exactly where challenges exist and how they’re being tackled. Yet for all the power and potential of data, how can CDOs best assist this work? During a recent meeting of [Harvard’s Civic Analytics Network](https://datasmart.ash.harvard.edu/civic-analytics-network), CDOs discussed the equity maps that they’re often asked to make, showing everything from air pollution to educational attainment to income inequality across the city. As part of this discussion, one CDO asked the group if they ever felt frustrated by the fact that these maps are all essentially the same; each socio-economic metric shows the exact same pattern of inequities in areas that would already be identified as lacking resources on nearly any other map. And, all too often, these correlated with communities of color in the city. So, is there any value in mapping these factors at all? Two other CDOs, Eva Pereira in Los Angeles and Dr. Brita Andercheck in Dallas offered their perspectives, and Data-Smart followed up with them to learn more about how to be a “data surgeon,” who benefits from nuanced data, and why mapping is a form of descriptive statistics. ## Think Small “The more you dig down into the geography, the more you’re going to see different things pop,” said Andercheck, who is both the chief data officer and the director of the [Office of Data Analytics and Business Intelligence](https://dallascityhall.com/departments/Data-Analytics-Business-Intelligence/Pages/About-Us.aspx) for Dallas. CDO Pereira, who leads the [mayor’s data team](https://data.lacity.org/) in Los Angeles, agreed with this assessment; LA is a city with vast wealth and income inequality, and she has seen how digging into granular geographic data can reveal small pockets of need even in typically privileged areas. One example of this came to light this summer when Pereira’s team [worked with the city’s Chief Heat Officer](https://www.esri.com/about/newsroom/blog/los-angeles-chief-heat-officer-expands-cooling-centers/) to understand extreme heat vulnerability in the City of Los Angeles and develop strategies to keep residents safe. Public health researchers from UCLA shared data on [excess ER visits by zip code](https://sites.google.com/g.ucla.edu/uclaheatmaps/map) which uncovered surprising areas of need. These maps showed that a few small communities in wealthier areas, and even neighboring zip codes experiencing the exact same temperatures, had vastly different health outcomes from the heat due to underlying social vulnerabilities, something that wouldn’t have otherwise shown up on a simple temperature map. Granular data is one of the most important aspects of equity maps; there can be crucially important insights and nuance that gets lost when data is aggregated over larger geographies. Andercheck recommends “measuring all the things you care about” as a way of sourcing granular data, and then utilizing multiple variables when creating maps. For example, District 13 in Dallas is home to multiple billionaires — and has the highest concentration of refugees in the city. The concentrated wealth could mask the small but high-need refugee communities. “In instances like these you need to get creative in order to precisely identify acute issues,” said Andercheck “There were areas in Dallas where the age of the infrastructure was a concern, so we overlaid the equity layers with our age of infrastructure data to hone in on the issue.” Acting as “data surgeons,” the Dallas team was able to slice general measures of inequality like income with more precise data on infrastructure to carve out the exact spots in need of additional assistance. Since the city already had this data, thanks to the fact that it had already established an interest in infrastructure equity, it was easy to pull and layer the district maps. She recommended that data teams dig in and make the geographies smaller with at least one additional need layered over any kind of equity or risk map. “We can’t solve ‘equity’ as a concept, we can only solve an actual issue,” said Andercheck, “Work with other departments, utilities, agencies and have an equity overlay on each of the different things you’re doing.” Not only will this reveal more detailed insights, it will also help verify data by bringing together people across the organization to compare and check their work. ## Engage and Verify Of course, this high-quality granular data might not always be readily available. The team in Dallas is much larger than in LA, and Andercheck has a specific employee who is full-time focused on equity and data work. However, both CDOs agreed that maintaining positive, trusting relationships with city departments, as well as community-based organizations is critical to gathering good data. That can look like partnering with local community organizations, finding the frontline government service providers, and even going out to the neighborhoods themselves to connect with residents. For example, other government employees normally come to Andercheck with a problem they found by being out in the community. From there she can map out the government’s existing data on the issue, then her team and the requesting department can go out and verify the data coming from those maps. Triangulating and comparing or contrasting multiple granular data sets also opens up new insights. Pereira has done this to identify gaps like what data is missing, and verify who isn’t represented in the data or engaged with the city. During the pandemic she had data fellows [analyze](https://storymaps.arcgis.com/stories/0c59272161634bc69f3e0dd077fcf41b) the city’s emergency rental assistance program that had eligibility requirements. They started by looking at the [Urban Institute’s housing priority index](https://www.urban.org/sites/default/files/2021/04/02/where_to_prioritize_emergency_rental_assistance_to_keep_renters_in_their_homes_technical_appendix.pdf) and matched it with data on where residents met the requirements but were not applying to the program. They found a big gap by layering all the eligibility, priority index, and program application data with a fine tooth comb; the team highlighted this issue and presented it to LA’s Housing Department so they could target residents in need who were missing out on rental assistance. ## Evaluate Results While the two data teams are different sizes, and therefore have different abilities to support other departments, both CDOs emphasize the importance of evaluation. Pereira makes sure to continuously monitor and evaluate the success of programs after they launch. “Just making a program/service/benefit available doesn’t mean the problem is solved,” she said. Once an equity-focused solution has been deployed, it’s important to map the uptake of that program or service. In the rental assistance example, it was important for Pereira’s team to make sure that the eligible yet unenrolled folks were not just offered the emergency assistance, but also that they took up the offer. “We need to keep tabs on the uptake and adjust as needed,” she said, so following up with changes in community outreach strategy and remapping the updated data are crucial to make sure that solutions are working. In Dallas, Andercheck recommends iterating in the development process. She encourages her team to “let the data speak to you; let the data group naturally, then go in and see what the data is saying.” Looking at the same data set with a different lens can reveal discrepancies or inequities that would otherwise remain hidden. For instance, she may ask the team “What does it look like if someone can access this city service in under a day — who are the people who can get this easily?” Iterating in the initial data evaluation and analysis means that the data is interrogated more thoroughly and shows that there may be multiple solutions or responses to the problem. ## Trust and Preserve Data As discussed in a recent [Data-Smart article about racial categorization and Indigenous populations](https://datasmart.ash.harvard.edu/racial-categories-and-public-health-data), it is incredibly important to preserve data granularity for the future, particularly in regards to race/ethnicity. Pereira sees this play out frequently in LA, one of the biggest and most diverse cities in the country. Without researching and collecting granular data, her team is reliant on census categorizations which are so broad that it’s difficult to surface deeper insights. For example, Pereira referenced the [Color of Wealth report](https://www.aasc.ucla.edu/besol/Color_of_Wealth_Report.pdf), a joint university research project on wealth disparity in LA which broke down racial categories into more granular data. The research revealed extremely valuable information about populations by ethnicity, country of origin, and immigration history to show that such communities are not monoliths (i.e., “Asian” is a broad category that doesn’t account for nuances in a huge, diverse population) which is incredibly useful for her work. “Once you lose granularity, it’s lost,” cautioned Andercheck, “Then you lose data for future research and assumptions are made on that broad data.” Again, collecting this granular data requires working with the affected communities, which can be difficult if there isn’t a trusted, established relationship between the local government and the people being served. Andercheck recommends that in addition to using trusted community messengers, cities should be transparent about why they want specific, detailed information from residents. She also has a standard for her team that the “end result of any data project is a policy lever that can be pulled, that can make an actual change.” Mapping and sharing the data, reporting back to the communities, and implementing or updating policies show the residents that their participation in this granular data collection is purposeful and helps the city serve their neighborhood and community better. CDOs have a unique and critical role to play in serving their city. Combatting existing and systemic inequities requires the use of data to find, show, and track where issues exist and how they’re being addressed. While it is understandable that visualizing the same broad, frequently underserved communities can be frustrating and disappointing, it is a first step to digging deeper into granular data, segmenting out areas of specific need, and accurately addressing equity gaps. ## About the Author ### Betsy Gardner ![Headshot of Betsy Gardner](/sites/g/files/omnuum10826/files/styles/hwp_1_1__100x100_scale/public/2025-05/Betsy%20Headshot%20resize.jpg?itok=k2OsSp1g) Betsy Gardner is the editor of Data-Smart City Solutions and the producer of the Data-Smart City Pod. Prior to this, Betsy worked in a variety of roles in higher education, focusing on deconstructing racial and gender inequality through research, writing, and facilitation. She also researched government spending and transparency at the Lincoln Institute of Land Policy. Betsy holds a master’s degree in Urban and Regional Policy from Northeastern University, a bachelor’s degree in Art History from Boston University, and a graduate certificate in Digital Storytelling from the Harvard Extension School. See also:- [ Civic Analytics Network ](/topics/civic-analytics-network) - [ Civic Data ](/topics/civic-data) - [ Data Visualization ](/topics/data-visualization) Share on:- [ Facebook ](#) - [ Twitter ](#) - [ Linkedin ](#)