Tools and Techniques to Evidence with Data

Introduction: Effective use of data for health justice involves being creative in how you approach data as a tool and language. While there is no absolute best practice, it is a practice that we each can develop and learn from each other.

Why this lesson is important: With the analogy of data being a language, people have different purposes for using data for their advocacy. Data should be a tool to accentuate the goals, context, and discourse that you already hope to achieve. The tips outlined in this lesson are not the only approaches but reflect our knowledge and experiences in practice as well as those within our community. You can then determine which will complement the work you are currently trying to achieve.

Story: The first step is to clarify whether you're trying to create understanding or inform decisions. Trying to do both at once risks overextending yourself and doing neither effectively.

If awareness about how your community’s health is affected is lacking, your first step might be advocacy to paint a picture of community health dysregulation. Informing decisions usually involves larger authorities where you can start by using existing data to generate new questions.

It’s also essential to understand the language of your intended audience, especially if you're aiming to influence decision-making. When analyzing data, consider causation (e.g. “the smoke causes me to become sick”), correlation (e.g. “I get more light headed when construction activities increase near my house”), and hotspots (e.g. children walking a red route to school also live in multigenerational homes with preexisting illnesses). Those factors may not be directly related but together increase susceptibility.

Set principles and ethics around data use. Labeling something or making it countable can expose it to limits—saying “some” pollution is allowed is different from saying pollution isn’t acceptable. The difference between clean air, cleaner air, and Air, is that by the time you get to Air, you're not trying to give a caveat of saying it should be air enough—you're saying it should actively have properties that heal.

Ethically, think about working backward: establish the need before collecting data. Consider verifying or “ground-truthing” your findings before sharing them publicly. Ethics also evolve as your experiences change. What you were comfortable sharing a year ago may not feel appropriate now—like names, addresses, or personal identifiers—and that’s okay.

When it comes to communicating and visualizing data, treat it as a language. Tailor it to your audience. A policymaker might want long-term trends, whereas a local councillor needs data that reflects daily life. We’ve used an exercise that identifies three types of audience:

Influencers: e.g. councillors, GPs, faith community leaders, educators

Decision-makers: e.g. local authority or regional staff

Mobilisers: e.g. local advocates, students, young people

Understand their roles, responsibilities, motivators, and spheres of influence. Consider how they process and share information. They may care about the overall topic, but in order to do their job, they may not actually have a metric that's tied to what you care about. For example, someone might prefer documents to videos if they need to pass materials to managers. Think about how your data can support their work and decision-making.

Use key questions to guide your strategy: What motivates them? What’s in their control? What story do you want to tell with your data? What other datasets complement yours?

Quantitative data is good for tracking frequency or duration, but be cautious with averages—they may obscure those on the margins. If you're advocating for people most affected, average figures won’t capture their experience. Susceptibility may be difficult to quantify, which is where qualitative data becomes powerful.

Qualitative data lets communities identify important factors—relationships with the environment, physical or emotional health, priorities, or mental load. These are insights best gathered in conversation or participatory settings. 

We previously published a framework, Microenvironmental Data for Health Justice, which outlines three stages in data for health justice that also apply generally to science and research:

1. Context – Understand factors around the context in which data is being collected such as terminology, race, air pollution activity and experiences, and commercial activity that influence how the data would be framed.

2. Collection – Identify gaps or conversations that need more support to determine specific questions and methods, such as going to the community centre and asking people who enter and use the centre a specific question every week.

3. Communication – Don’t let analysis sit unused or with visibility only to researchers and practitioners in institutions. It might be the case that the people who picked it up and made the article have a different spin than the people who actually produced the research.

If you're producing data as a community, you may be worried that a politician finds your work who does not have the ability to communicate exactly what the phenomena is using the data that you've provided. You might say, ‘I'm good at setting the scene for research but I want cultural workers to really unearth what is the context in which we're collecting data so that we're collecting ethically.’

Building a repository of data that's accessible to communities can help equip these communities for climate resilience. Data strategies like the ones proposed can help communities understand local and proposed health infrastructures—materials, climate risks, construction changes. They can use data to evidence needs, engage demographics, and support campaigns with consensual pictures of the current environment.

Visualisation is a key tool. You shouldn't overcomplicate visualising information if it's not going to help you clarify or get the point across. Align your visuals with justice goals, not just to showcase technical achievement. In this process, don’t let quantitative data erase qualitative data and lived experience. Don’t be afraid to mix formats: use charts, lists, images, or videos to appeal to different audiences.

We use both our own research tools and public resources. One of our key resources is Right to Know, which helps users explore environmental hazards in their area by entering their postcode. It’s based on a snapshot in time, so remember it’s not real-time, but it’s a strong point of reference.

Another resource is the Environmental Data for Health Justice Working Board, which includes interviews, terminology, reports, and strategies. You can use it to build your vocabulary, understand key players, and position your work—whether you're an academic, journalist, or advocate.

Public tools like Find Open Data (data.gov.uk) and the Office of National Statistics (ONS) provide datasets on population, economy, and society. These are the kind of sources that you can use just to start painting the picture before you get to the specific data collection that you want to do. You might even use these sources to make your case from data that already exists when applying for funding to do the research you want to do.

Hopefully, these strategies and tools help build your confidence using data as a language for health justice and advocacy.

Learning Points: 

• Decide if you're trying to create understanding or inform a decision in your more immediate plans. You can then determine the role data can play in achieving that goal.

• The Macro Environmental Data for Health Justice report gives an additional framework on where to focus your efforts between: Setting the context for data, supporting data collection, or communicating the data to the right audience. You can partner with others to perform different parts of this framework.

• Use already available data to generate new questions.

• Understand the "language" of the intended audience. Communicating and visualising data is dependent on who you are speaking to.

• Ethics are not static but Set some principles and ethics about what should be used as data

• Never let quantitative data erase qualitative data or lived experience. Don’t be afraid to experiment mixing types of data visualisations under the same campaign.

• The Office for National Statistics (ONS) can be a great place to start in secondary data.

Urban Sacrifice Zone and Red Route Case Studies

Introduction: Urban sacrifice zones and red routes are two concepts that demonstrate the harm to communities when pollution is not just a cause of harm but enabled by other structural inequities from policy and planning.

Why this lesson is important: Air pollution does not occur in isolation. To create robust solutions that address the root political causes that lead to polluting infrastructure, we must encourage methods such as the ones showcased in these reports. Robust strategies also allow collaboration and unity between communities and those in different advocacies or sections of society.

Story: We begin with the Right to Pollute case study. The purpose of this data-led work was to bring attention to the idea that some people, institutions, or commercial operations are effectively given the right to pollute in certain neighborhoods. This lets people make more informed decisions when it comes to voting priorities for our shared health and climate change action points.

This ties into the concept of urban sacrifice zones—places that, through policy or commercial activity, become areas where pollution is considered more acceptable. These zones are formed through interactions between stakeholders, driven by national or commercial objectives that support this idea that some polluters are still part of the greater good. So then this community, this neighbourhood, this piece of land is going to be sacrificed to enable the wider community, city, or country to be able to produce what it needs to produce. We look at these right to pollute policies as they are environmentally damaging and they do end up having repercussions— even to those that feel like they have the right mitigations to the pollution.

But Right to Pollute policies are not neutral. They are conscious choices—legal, social, and cognitive frameworks that justify environmental harm. If we socially accept river pollution, the law only creates a boundary to dictate how much and in what context it is acceptable to pollute. But if we reject pollution socially, the law must adapt accordingly. Social acceptability sets the boundaries.

For this analysis, we used two datasets: the Index of Multiple Deprivation (IMD), which ranks areas by relative deprivation across the UK, and the UK National Atmospheric Emissions Inventory (NAEI), which lists permitted polluters and what they emit. By overlapping these datasets across the UK, we identified hotspots—areas where deprivation and pollution coincide. It’s not about proving one causes the other; it’s about seeing where two things happen together and asking why.

For example, in a place with significant deprivation and a high variety of polluters and pollutants, we can ask: what enables seven different types of polluters and 40 different pollutants to be allowed to be emitted in that specific place? Reflections on this kind of work can lead to better questions rather than jumping to conclusions declaring a place the “worst.”

We then move to the Red Routes case study, which focuses on arterial roads in London designated for commercial traffic in London that prioritize through-traffic over local journeys. In theory, this keeps lorries and delivery vehicles off smaller residential streets in neighbourhoods.

While seemingly efficient, Red Routes were not designed with future contexts in mind—particularly the rise of e-commerce and the sharp increase in vehicle usage. It's not that the residents living on these routes have chosen to be polluted and most likely they're not going to be able to just move—nor should they have to. Worse, we continue to build housing along these polluted routes. This is another way of enacting biological inequity. It’s not just that people are living next to pollution—they're systematically exposed to it through planning decisions.

For this analysis, we combined datasets including IMD, our own Stress Risk Score (covering air, noise, heat, and light pollution), census data on the elderly and youth, public transport accessibility, population density, proportion of Black, Asian, and minority ethnic (BAME) residents (a term we usually would prefer not to use as a blanket data set but was the category in the available data set), and car ownership.

This allowed us to break things down into health impact scenarios to make the data more relevant and understandable. For example, what does it mean for someone who’s neurodivergent to navigate a street in this context? We looked at susceptibility, local equity, race-based inequity, and transport patterns. This kind of analysis brings clarity to the lived experience of pollution.

We also looked at local equity, susceptibility, race-based inequity and car ownership to create matches between different data within the wider set that we used. Reflecting on this analysis leaves opportunity for more lived experience data to be acknowledged in understandings and the decisions made about place when we discuss pollution. 

Even for your own descriptions and advocacy, it is worth experimenting with looking at where things happen at the same time. Or if you think that, ‘when something is enacted in our neighbourhood it's going to reduce our ability to get here, we're going to have less access to this,’ finding the data that actually supports that concern on a structural level can be the starting point for wider advocacy or narrowing down and saying, ‘this is where I want to tackle the problem. I care about the wider pollution but I'm not going to start just by using monitors, I'm going to start by questioning what methods of transport we actually use and why there are not as many restrictions on the red route that has been built into our neighbourhood.’

The wider takeaway from both studies is the value of experimenting with different datasets. Hopefully by now, it's clearer that air pollution alone—framed as just air quality—is too narrow and doesn't do justice to just how thorough and complicated our lives and environments are and why we're talking about health and justice.

Learning Points: 

• The purpose of this data led study is to bring attention to everyday people about those who have the right to pollute in their neighbourhoods, so that people can make more informed decisions when it comes to voting and priorities for our shared health and climate change action points.

• We used the urban sacrifice zone data to engage people about the intentions and structures affecting their health in their environment. 

• This right to pollute work does not focus on causality, it takes the assumptions that pollution and deprivation both leave communities vulnerable to tie into to other ways susceptibility is enacted that may be more relatable.

• This red route hot spot analysis that looks at the intersections of data such as deprivation, population density, pollution, transport accessibility and ethnicity leaves opportunity for more lived experience data to be acknowledged in the understandings and the decisions made about place when we discuss pollution.

• Experimenting with different data sets at a macro level can support education and awareness to strategy at levels you wouldn’t intuit as individuals.

Segueing into the next Lesson theme: The final lesson in this module puts some of these learnings together to give guidance to how strategy and advocacy can be supported meaningfully by data. We'll review how to use data to tell a story or support your advocacy around community health.

Applying Air Pollution Research to Community: Imperial LTN Case Study

Introduction: In health policy and urban planning, select scientific reporting is sometimes used to justify political decisions on the notion that science presents strong or irrefutable evidence. However, as scientific research operates on a different set of principles than how knowledge and evidence may be developed in these public realms, there are risks of studies being used to justify high impact political decisions outside of the scope of the research.

Why this lesson is important: Researchers focus on isolating specific inquiries or relationships to test hypotheses and suggest links, such as how one set of variables correlates, causes, or impacts another. When study results get repurposed within more applied practices, such as local governance and urban planning, individual study conclusions often have the potential to have much higher impacts than is appropriate, with impacts on communities even outside of the realm of what the researchers themselves might have pictured. Advocates understanding how to identify these situations, critique them, and work with the institutional researchers involved is important for mitigating these effects.

Story: This story paraphrases the discussion between Daniel Akinola-Odusola (Centric Lab), Prof Ilan Kelman (UCL), Dr Julia Pescarini (LSHTM), and Araceli Camargo (Centric Lab). 

Daniel: In this lesson, we’re looking at how science and scientific reporting are translated for community health and urban planning. We’ll explore a 2022 Imperial College LTN study by Yang et al. on whether LTNs displace traffic and pollution, plus an article reflecting on the study. We’ll use our Macro Environmental Data for Health Justice framework: 

(1) Context of data creation,

(2) Reasons and mechanisms for collecting it,

(3) Ethical communication of results

Daniel: 

Ilan, what do you think of how they’ve presented the study’s context?

Ilan: 

Both the paper and the news report give context realistically. The difficulty is communicating scientific nuance without losing people’s attention but still ensuring that everything is there so that people do understand the limitations of it. I like that the paper includes “highlights” as three bullet points for quick takeaway. I think that's a really positive development because it means that people can read the full abstract if they want or just read the three bullet points. 

The journalists did an excellent job trying to distil very complicated science for a wide audience.The news article’s headline and first paragraph match the study’s conclusions exactly. These summaries are useful if you lack time to read everything, but they inevitably generalise. In that sense, I think both the journalists and the scientists have done exceptionally well in giving you the very quick, rapid takeaway that you can use. Just be aware that, as always, there is an hour's worth of nuances, subtleties and provisos to put within the specific context of where the study was conducted, why that was chosen, how it was conducted and especially what was not covered.

Daniel: 

In our circles, the LTN debate is layered. Reducing traffic is widely supported, but traffic and pollution displacement concerns are important. Advocates like David Smith (“Little Ninja”) have shown other reasons why displacement does not get included in overarching campaigns for LTNs. This paper isn’t meant to solve the whole argument but make something propositional towards that conversation that people can use.

Ilan: 

Exactly—it’s about balance. Scientists have certain skills, certain worldviews and certain approaches. It's not that they're better or worse than the approaches, skills and worldviews of community activists or politicians or civil servants. The science looks technically correct to me. That does not mean it matches people's experience in living in the location. Intriguingly, some of the comments on the news article actually indicate that. They're not trying to undermine the science, nor should they. Comments on the article show local residents interpreting it differently. We must work together, valuing both measurable data and lived experience, without dismissing either.

Daniel: 

Let’s talk about data collection. They chose to look at traffic volume and NO₂ as metrics, based on monthly averages. When we work with chronic stress, we do PM2.5, but they would have their reasons why they've chosen NO2. How do you feel about the utility of using these two factors or variables?

Ilan: 

Their choices aren’t right or wrong—just scoped. Any study has boundaries, which can be critiqued. In the paper, the scientists are completely open, detailed and appropriate regarding the limitations of their work. That drops out to a large extent in the news article, as it has to be. The news article is not a scientific paper. We should ask: are the data appropriate for the problem? For some, the answer may be no. Science is valuable but partial—it opens more questions. We need to identify what data actually serve a community's needs. Maybe PM2.5 would be more relevant. Frequency, scale, and location of measurement also matter. The starting point has to be collaborating to come up with the appropriate questions.

Daniel: 

Right. This is why we looked at both the paper and the article. The public often encounters the media version, not the study. The policy question—should we close this road?—isn’t scientific. The study’s authors acknowledge other benefits, like social interaction, beyond pollution reduction. That connects with broader community priorities.

Tying into some of the questions that we're going to ask about the communication [of results and conclusions] is this wider question of what role and responsibilities scientists have when the study is now being handed off to the commons. Should they remain involved when studies are used in policy or media? I also noticed shifts in how co-authors presented their findings and conclusions between the study and the article.

Ilan: 

This is a long-standing question that we've struggled with since the beginning of science and the beginning of knowledge. To me [as a scientist], this is what society has to tell me. Should I be advocating for policy or should I be giving options and consequences of taking certain options? How much responsibility, ethical and other practical aspects should I have for the science I produce and provide open access to the public? The Royal Society of the UK had a brilliant report on genetically modified organisms. What they said is science has to be a major input into the policy decision.

Science should inform policy but not be its only input. We must separate science (a process) from scientists (flawed humans). We can see in climate change where many climate change scientists who are absolutely brilliant within their speciality have then become public figures advocating far beyond what they've ever researched, far beyond what they actually know, far beyond what they understand, which is highly damaging and dangerous. I see some climate change scientists deliberately getting arrested by breaking the law because they're opposing policy decisions.

I'm definitely accountable in some ways to the public, but not in a democratic manner. I've not set out there to get my job on a policy platform. I've not set out there to get my job by being elected by the people who are affected by my policies. Personally, I won’t dictate policy; I see my role as presenting options and consequences—but misinterpretation is a risk. For example, the article quotes a co-author saying the research “effectively disproves” that LTNs necessarily cause an increase in traffic and air pollution in neighbouring streets.. The way that's phrased is perfectly technically correct. The challenge is that in a political environment, it would be so easy to misinterpret or deliberately pivot to reach the conclusion that all low traffic neighbourhoods in all circumstances should be implemented because they always lower pollution. Scientists must be careful in public communication, but also accept that we can’t control all uses of our work.

Circling right back to the nuclear bomb, the physics was absolutely brilliant. We have gained so much from society through the physics and the chemistry that were done to produce a nuclear bomb. But yet, we still have that nuclear bomb and almost all the scientists involved were appalled at what they produced, and spent their life campaigning against its use. We don't have straight answers. We still struggle with it.

*Julia joins the conversation*

Daniel: 

Julia, thoughts on scientists’ roles and the report/article?

Julia: 

I think sometimes we draw too many conclusions from studies that are quite unique and quite related to one specific setting. Then policies are made or identified based on one specific study. Even once this policy is made, not enough effort is done to evaluate the policy and the impacts on people who were imagined to be affected by the policy and the general population. The media can misinterpret findings and scientists aren’t always consulted on coverage. 

For instance, alcohol use. Many years ago, we had specific studies coming out saying that alcohol, or having wine, was beneficial for some specific populations. Then the media took it to be widespread that having a few glasses of wine was beneficial. But many systematic reviews, which are studies that combine multiple studies, have shown that that's not the case. Alcohol is majorly bad for almost everyone, depending on alcohol. But now people still think that having a few glasses of wine a week is good for your health. 

So in that sense, I think we need to be careful how we draw conclusions from some specific studies and how we translate that to the public. Sometimes what a scientist says in one study is also misinterpreted by others and by the media when there is not very good communication between the media and the scientists who made the study. We are often, as scientists, approached by the media to talk about our studies. When we see the media article that comes out in the newspapers, it's quite different. 

Many times we're not consulted about what is being written and published in the newspapers. We are not sure how policy makers are actually looking at the study or if they're just taking conclusions from newspaper articles. We must monitor policies over time, assess both intended and unintended effects, short term and long term, including mental health and social impacts.

Daniel: 

That connects to our experience—smaller, longitudinal, trust-based engagement keeps work grounded. It prevents policy from feeling like a top-down surprise. This also relates to the “intellectual gap” between science, policy, and daily life. Araceli, you had a framing for that?

Araceli: 

Saying it’s 13°C outside is mathematical, scientific knowledge. For a person with arthritis that temperature might mean it's very cold, but for another person, 13°C might mean it's quite mild. Assigning a number or a scientific intellectualisation to something doesn't necessarily mean it will have a direct, literal application at a policy level or to day-to-day life. Communities are told “science says” and “policy says,” but impacts vary. Any comments?

Julia: 

What I sometimes see missing from the policy perspective is having a deep understanding on how this will translate into different levels and will actually affect the individual.

Ilan: 

All knowledge is subjective, including numbers. The standard mantra is if you turn on the oven and stick your feet in it and put your head in the freezer your average body temperature is excellent. It doesn't mean you're healthy, so NO₂ may be reduced in LTNs and all around the areas. That might or might not mean that air pollution has been reduced in all these areas. It might or might not mean that traffic is safer in all these areas. It might or might not mean that pedestrians, cyclists, scooter drivers and vehicle drivers are actually behaving in a healthier and safer manner. So it really is a question of do we want a good average body temperature or do we want to avoid being frozen and on fire. Do we simply want to reduce the measurements of air pollution or are we seeking healthier safer healthier safe places to live? With different types of traffic, sources of what's happening to our air, our light, our water, our soil, our noise—it's always contextual.

NO₂ reduction may not equal healthier streets. It’s like having an “average” good body temperature while your head’s in the freezer and feet in the oven. Context matters—do we want lower NO₂ or healthier, safer places? One study’s findings may be correct but still not answer the real question.

Daniel: 

Hopefully this conversation shows advocates that scientists can be allies. How often are you invited into policy or implementation processes?

Ilan: 

Scientists can be friendly, but we’re human and can’t know everything. I live in the borough studied, so I share its concerns. Collaboration means recognising everyone’s expertise. Not everyone experiences the same environment the same way. What’s good for me today might not be for my neighbour tomorrow.

Julia: 

Some scientists are involved in policymaking, especially in the UK, but not always. Ideally, policies are co-created by scientists, policymakers, and communities, with feedback loops. That’s not always the case—but it should be. Scientists can and do have personal political views. Outside formal research, I’m politically active. It’s hard to fully separate personal beliefs from science, but we aim for neutrality in testing hypotheses. Dialogue, critique, and combining multiple studies can lead to policies supported by diverse perspectives.

Learning Points: 

• Scientific studies are smaller concise investigations that draw from a more complex set of conditions and contribute back to the wider scientific discourse around a topic.

• The ability to translate conclusions of studies to community application through planning and policy is not guaranteed to be in the skill set or job responsibilities of a researcher.

• The LTN study looked at traffic and nitrous dioxide as variables in the study which are much more specific than the wide range of factors people use to determine health in practice. This argument does not negate the quality of work produced with their chosen variables.

• It is entirely possible that research done within a field relevant to your advocacy (e.g. air pollution, heatwaves, food scarcity) doesn’t directly affect decisions that are related to you.

• How the results and conclusion from the study were represented, including by one of the coauthors, highlights the difference in communicating the same outcomes to a different audience with a different purpose.

• More collaboration between community and formal researchers could help outcomes be better understood.

Segueing into the next Lesson theme: The next lesson centres two Centric Lab reports covering approaches to two important structural injustices where air pollution is a factor, amongst others. These reports showcase using other sources of data to support understanding the impacts and identifying solutions.

The Relationality Between Air Pollution and Health

Introduction: 

Air pollution data, in the form of air quality monitor readings and models, is often used as a convenient proxy when institutions are talking about community health. However, air pollution and health are separate yet interacting phenomena which makes pollution data less representative of the health concerns that people have around pollution.

Why this lesson is important: 

There are issues from violence to erasure that come with using data that is not as relational to the [health] phenomena in question as it should be. Often, people seeking health justice for their communities and environments are directed towards relying on data that does not represent the impacts of pollution or hold polluters accountable for these impacts. By making this clear distinction, we can then look at how communities form a culture and practice of using data to evidence and advocate for their own health justice. 

Story: Most air pollution data comes from air quality monitors measuring substances like particulate matter (PM2.5 and PM10) and nitrous oxide. These numbers tell us the amount of pollution, but not necessarily its impact. For example, PM2.5 and PM10 refer to particle size, not the composition. So even if you see PM levels go up or down, that doesn't tell you what the particles are made of or how dangerous they are to a given community or individual. If you're not describing the contents of this pollution you can barely make the relation to health.

Basically, air pollution readings aren't health indicators—they're environmental indicators. They don’t account for the human body, individual susceptibility, or ethical concerns. In a conversation on the impacts of pollution, quantified air pollution calculations alone are devoid of accountability by assuming impact on the body is inevitable. It’s a quantified process, which does not leave room for the nuance of ethics, such as whether the right factor is being counted or if a count is the priority.

Even with advocacy tools like community air quality monitors, relying only on pollution data limits how far we can go in demanding justice. It’s important, but not enough. We often talk about the HPA axis and chronic stress link physical and mental health—concepts that also apply here.

PM10 often settles in the lungs, but PM2.5 is small enough to enter the bloodstream and even cross the blood-brain barrier which can cause systemic inflammation. Knowing a pollutant exists isn’t enough when we’re looking at health and how to heal communities.

There are a few concepts that are also worth grasping to understand the difference between focusing on air quality data and focusing on health data and lived experience as impacts of air pollution.

Assimilative capacity is the idea that we can push bodies, whether it's human bodies, a river, or the air, to safe levels of pollution. But this idea ignores consent and dignity. For those you care about, you wouldn't say that if their capacity is 8, they should go to 7.5 just because you feel like you have the right to push them that far. If you do, then you have an unsafe relationship with the people and things around you. Most people would not say they want to be polluted to 90% of their potential limit. They’d rather keep their body cleaner, if at all possible. We (as a lab) don’t conform to the idea of working to assimilative capacity, because justice means not pushing people or ecosystems to their breaking point.

Biological Inequity describes how systemic factors like poverty and racism make people more vulnerable to pollution. It’s not just about behavior—it’s about embedded disadvantages. Someone's already at a disadvantage just by existing in these places, even without considering their behavior. Industrial sites are often placed in poorer neighborhoods. That’s not accidental and leads to real biological outcomes, such as asthma, eczema, blood and weight issues, which are rooted in environmental injustice.

In community gaslighting, institutions often deny the harm communities experience because their monitors show “safe” levels. You come and say, ‘We are sick,’ and they say, ‘You can’t be sick,’ or at least that they don't play a part in your sickness. If you’re polluting an already vulnerable community, even if you're not the original cause of their deprivation and poor health, you’re part of the harm. Using average pollution levels to justify polluting the community shows a lack of care and consent for those who live and navigate the area.

Justice starts by centering community health, knowledge, and advocacy—not just pollutant counts. That means shifting our focus to metrics that matter to people: developing rashes, shortness of breath, avoiding certain streets, children needing care after being outside.

Learning Points: 

• Particulate matter is used to identify air quality and the condition of the environment but does not directly identify health as it does not account for the variance in susceptibility.

• Air quality metrics, by being quantified, give polluters limited accountability on the nuanced health outcomes of the communities that they impact as lived experience is usually also qualitative.

• By focusing on environmental factors as a representation of community health, there’s a risk of maintaining an assimilative capacity approach to health and planning where authorities feel they can pollute if their emissions hit certain metrics.

• Justice starts with making community health knowledge and advocacy determine goals based on direct health metrics then the environmental proxies that affect health.

Segueing into the next Lesson theme: Our Air is Kin scientific advisors, Prof Ilan Kelman and Dr Julia Pescarini, joined us to look at how science and scientific reporting is used in translation for wider community health and urban planning. As Low Traffic Neighbourhoods (LTNs) are a familiar and current issue for clean air advocates in London, we use a study by Imperial College and the article discussing the impact as a point of reference and analysis.

Defining Data for Health Justice

Introduction: Data is a powerful tool for justice and language for accountability. However, data can easily be manipulated and used to entrench inequities. In this lesson, I'll cover some of the fundamentals around how we define data and what this means for health justice. 

Why this lesson is important: Organisations seek data to fill knowledge gaps they have when making decisions, but often these data practices do not account for the more human and complicated factors, such as community lived experience and health impacts, that may be at odds with commercial and political agendas that factor ownership, capital, and the benefit of a few over the health of communities. To support how communities can evidence the impacts of structural injustices and inform how community-favoured health justice solutions are developed, we need to develop ways for communities to “speak this language” in their advocacy while still maintaining their priorities and values.

Story: Let’s begin with the definition of data. We can look at data as factual information such as measurements, statistics, documented responses, used as a basis for reasoning, discussion or calculation. One useful definition is from the UN, which calls data “the lifeblood of decision-making and the raw material of accountability.” This definition shows why it's important for communities experiencing injustice to feel empowered to create data that serves their advocacy and justice needs.

We can break data down into two primary uses:

• Creating understanding of a phenomenon, such as your health, your community’s needs, or your relationship with your environment in the case of health justice.

• Informing decisions that individuals or institutions make based on their knowledge of people, places, or systems.

Two fundamental ways of looking at types of data are quantitative versus qualitative and primary versus secondary with each of them having their pros, cons, and reasons why we pick them. 

Quantitative data can be expressed as numbers or measured, like air quality readings or number of residents. Qualitative data includes responses, observations, or descriptions—like how a space feels, or naming community professions.

Quantitative data is easier to calculate but often lacks context. Qualitative data offers depth and humanity, but is harder to compare or quantify. There's no superiority between them—we often need both.

Primary data is what you collect. Primary data collection allows control over the narrative and framing of the data and can utilise your proximity to the community for relevance and accuracy. But often primary data takes more effort to produce after a certain scale and may be questioned by institutions if not seen as “scientific,” even if the data produced is more representative of the phenomenon.

Secondary data comes from others, often with wider coverage (e.g., city or national data). Secondary data can be easier to access, but you rely on how ethically and accurately others collected and labeled the data. Again, neither is better—they serve different roles and people often use a mix of these types.

You’ve likely heard of lived experience which refers to insights from people’s actual lives—like self-reporting symptoms, or a shop owner noting common customer questions. These everyday observations are data, too.

Lived experience matters. Data doesn’t need to be technical or algorithmic. A note in your phone tracking when you cough is valid data for that experience. This leads to the idea of data culture—your and your community's comfort and confidence in using data to make informed decisions. It’s about the agency to gather, analyze, and communicate data to support your goals. Data is not neutral and serves a purpose—whether that’s understanding, aligning people, or expressing lived realities.

The Ada Lovelace Institute coined the term data divide during the pandemic, referring to the inequality created as more data-driven systems are adopted and many communities are left behind due to complexity or lack of access. This divide often arises when the purpose of data collection becomes disconnected from the people it’s meant to serve. To close this divide, communities need strategies for deciding what data matters to them, what’s ethical to collect, and what language or format best supports their needs. No matter how advanced the data system, data is not the phenomenon. If you’re sick, no data perfectly reflects how sick you feel—it’s just a reference point to help inform understanding or decisions.

Communities often know the phenomena they’re experiencing, but existing mechanisms may not capture it—or their data may be dismissed as unscientific. A useful example is from Clean Air for Southall and Hayes (CASH). In Southall, residents reported worsening respiratory health after nearby construction began. Public Health England reviewed specific pollutant readings, concluding there wasn’t significant harm. But if they had used a holistic approach including pollution levels, NHS visits, and construction timing, they might have reached a different conclusion.The question becomes: does adding pollution help people who are already sick, even if they didn't believe that the pollution was causing their sickness?

This shows no single dataset tells the whole story. That’s why in other case studies, we emphasize using a mix of data, not just air monitors quantifying air quality. An ideal data culture for health justice includes:

• Trusted, transparent resources for understanding health and environment.

• Clear instructions for self-reporting strategies.

• Community repositories, like local libraries, to store and reflect shared experiences.

• Responsive stakeholders who use updated community data to support health.

• A sense of agency—that anyone can contribute to and interpret data.

Ultimately, data is not just digits and monitor readings—it’s a language that everyone can generate. Defining your intentions with data starts with questions like: What are we trying to understand? What decisions are being made that need our voices? What kind of data supports those discussions?

Learning Points: 

• Data can be seen as a language to create understanding and inform decisions. Different types of data (such as quantitative vs qualitative, primary vs secondary) can contribute towards these two goals in different ways. No type of data is superior to the other.

• Developing your data culture as a community is important for being able to understand and affect macro decisions that can impact your health and environment.

• Lived experience data is a crucial and valid source of data that does not need hi-tech methods to create.

• Data is a proxy for a phenomenon. The data about your health, for instance, is just a metric to create understanding and support decisions but cannot fully represent the experience.

• An ideal data culture for health justice involves a mix of transparent and accessible sources of current knowledge, intuitive and achievable strategies for activities such as self-reporting, trusted sources to collect and store this information, and a greater involvement in using these processes to embed civic participation in decisions that affect the health and environments of communities.

Segueing into the next Lesson theme: In the next lesson we're going to build on this understanding of data and understand its relationality to health. We'll discuss how air pollution and health are separate yet interacting phenomena and what that means for data used to frame the health of communities who are being polluted.