Open Sourcing HabitatDAO

Cormac

October 1, 2024

What is HabitatDAO?

HabitatDAO’s mission is to turn environmental data into a public good owned by the global community, accessible to and actionable by all. Our belief is that by empowering people to contribute to hyper-local environmental initiatives where they live, we give people a tangible way to be part of the climate solution.

Our work is inspired by the following beliefs:

1. Communities don’t have enough access to the environmental data that affects their lives.
2. Emerging technologies make it possible to know so much more about the environment
3. Society will make most progress if environmental data is owned and shared collectively.
4. People want to be part of addressing climate change, and they care most about where they live.
5. Restructuring how we communicate about the state of the environment is a huge opportunity to improve the world we live in.

In the remainder of this article we’ll walk you through how you can start up a similar DAO in your location. We’ll start with some background on why we think DAOs like this are important, before diving into all of the details you’ll need to get started.

What are Environmental Public Goods?

Environmental public goods are critical things like access to clean air, drinking water, low flood risk, and ample shade. Unfortunately, while access is not evenly distributed, understanding the problem via data is the first step towards finding a solution.

Through the use of emerging environmental technology and decentralized, community-driven organizing, HabitatDAO aims to deliver a new type of public good in the form of open and accessible environmental insights. We collect sensor data in the course of our work (think air, soil, and water quality), and make the findings available to the Habitat community and beyond with a view to helping people take action. By lowering the barrier to getting involved in stewarding environmental initiatives, we give people a tangible way to make an impact at the hyper-local level and to play their part in addressing the climate crisis.

HabitatDAO’s goal of turning environmental data into a public good is extremely well aligned with web3’s mission to give ownership back to individuals as opposed to corporations. HabitatDAO intends to give ownership back to the people, in the form of powerful, actionable knowledge about the world we live in.

How does HabitatDAO work?

With funding from the Solana Foundation, we launched a set of environmental monitoring projects in New York City, selected and stewarded by the HabitatDAO community. We gather, store, analyze, and share air quality at three locations in Brooklyn, NY.

It works as follows:

1. HabitatDAO members select which environmental monitoring initiatives to pursue.
2. Once projects and locations are chosen, DAO members design environmental monitoring systems to gain new information about existing community needs. Specifically, we focused on gaining insights into poor local air quality.
3. Members obtain and install sensors, monitor data collection, and conduct routine sensor maintenance.
4. Members work together to understand the practical value in the collected data, and collaborate to share this with their local community in an actionable manner.

Why start a similar DAO?

Our goal is that everyone should have free and easy access to important environmental information from the places they care about. This usually translates to where they live. In reality, that means that to cover everywhere that people live, there needs to be similar DAOs all across the world doing the work of organizing people around the idea of taking ownership of knowing what is in the air they breathe, the water they drink, the buildings they live in, and a lot more. Along similarly practical lines, environmental sensors need to be placed in the location they are monitoring. You can’t sense the air quality in New Delhi from New York—you need to place sensors in the place you want to measure. In reality, it is only the people who care about the location in question that are motivated enough to measure it.

By starting something similar to HabitatDAO in your area, you will be contributing to an important movement that gives people power through knowledge and provides them a tangible way to play a role in combating the effects of the climate crisis close to home. Rather than have people grow increasingly concerned and feeling powerless, you will be able to equip them with the tools they need to inform themselves, their loved ones, and to take action towards building healthier, more sustainable communities.

How to create your own version of HabitatDAO

HabitatDAO is very much an experiment in collective ownership of environmental data and insights. We don’t claim to have immediately landed on the absolute best way to run this type of initiative, much less replicate it in communities we are not familiar with. So please take our advice and guidelines as a starting point and use your judgment to adapt the advice so that it works best for you and your community.

Recruiting members

With a primary focus on the environment as opposed to some of the more financially-oriented concerns typically associated with DAOs, the goal should be to recruit people who first and foremost care about the environment. An interest in blockchain and web3 technologies is potentially a plus, but it is certainly not a requirement. Sense of place (physical location) is another important aspect that sets HabitatDAO apart from a typical DAO. It is most compelling to join if you care about the geography in which the DAO is active. This means that you’ll be recruiting members based on location, as opposed to their interest in crypto-native concerns. Finally, we felt it was important to bring people on board who care about how people organize, regardless of the specific reasons why they are organizing.

When we had the idea to start HabitatDAO we recruited early core members through our own networks. We focused on people with the following characteristics:

1. Significantly experienced in their field (either environmental or organizing).
2. Open mindset/curious about new ways of organizing people around shared goals.
3. Willing to put in the effort to help flesh out HabitatDAO operations and assist with defining a path to growth.

We were fortunate enough to have access to some people who were a great match to the profile we were looking for. If you don’t have such people in your network already, there’s no need to be shy about reaching out cold to people who you think would be ideal DAO members. What you’re attempting to do is both genuinely virtuous and really interesting—you’d be surprised at who will be interested in joining you.

Once we had onboarded a handful of early members, we turned our attention to building the DAO membership through our pilot projects (generously funded by The Solana Foundation). Given that we knew we wanted people who cared about the environment, a specific location, and were generally curious and community-centric, we thought our best bet would be to tap into existing organizations that meet these criteria, as opposed to trying to recruit individuals one by one. Additionally, by bringing existing organizations into the DAO en masse, and enabling them to work together on a shared goal that is consistent with their existing mission, you give yourself a great chance of success.

We knew that we wanted to work in the New York City area, so we started looking for groups there. After considering everyone from non-profits to schools, libraries to local government, we decided to focus on community gardens for the following reasons:

1. They attract a diverse mix of people.
2. They tend to have a strong interest in environmental matters.
3. They have a well-defined place that they care about (their garden) and a wider space beyond that (their neighborhood).
4. They are accustomed to meeting new people and gathering in person.
5. They are typically an established, cohesive, and productive group of people.

Once we’ve identified community gardens as ready-made communities to start with, we did some research on the gardens in the city, got in touch, and set up some calls. We explained the HabitatDAO concept, which people generally found intriguing and compelling, and several gardens were excited to join us on our collective ownership experiment.

To recap, while there’s rarely any strict rules to starting a DAO, we had good experience onboarding established groups who care about the environment. This expedited our data collection initiatives, and a much better chance of getting good input and feedback from DAO members since they had already been putting energy into caring for the environment where they live before HabitatDAO was established.

Choosing focus areas

Once we had onboarded a sufficient number of members (we started with around 50), it was time to kick off our first batch of projects. When it comes to environmental monitoring, you can ultimately monitor almost anything, from air and water quality, to soil moisture, or even how much trash volunteers are collecting.

When it comes to choosing a set of environmental factors to focus on, we recommend considering the following:

1. What do the DAO members care about?
2. What is technically possible?
3. What will be of most value to community members?
4. What will be easy to compare across projects?

Given that we were dealing with a city environment with gardens in different locations, we wanted to focus on an environmental factor that was shared by all DAO members and we were trying to avoid dealing with different factors at different sites. For this reason we chose to focus on air quality.

Air quality is vitally important to everyone because it directly impacts human health, causing respiratory and cardiovascular issues, and can lead to premature death, particularly among vulnerable groups like children and the elderly. It also affects the environment itself, damaging ecosystems and negatively contributing to climate change. The combined human and environmental impacts add up to significant economic costs. Additionally, it affects quality of life by reducing visibility and causing physical discomfort.

You could write a similar paragraph for almost any environmental factor, so it’s not that air quality is the most important factor to focus on, but in our case it was something that all of our DAO members cared about. Additionally, it also happens to be one of the easier air quality factors to monitor (more on this below), and something that is easily comparable across project sites (community gardens in our case).

Finally, some DAO members had a special interest in air quality because they were eager to advocate for air quality improvements in their neighborhoods based on some pre-existing issues that they were passionate about resolving, so we were able to channel that energy into kicking off HabitatDAO.

Tools & Technology

One big thing that makes HabitatDAO possible is continued improvements in environmental monitoring technology. From low cost sensors to environmental data platforms, it is getting cheaper and easier for the average person to discover what is happening in the world around them. This is a refreshing change from the previous state of affairs, in which large centralized entities (usually governments) would collect data from very few places and perhaps not share the data at all, or not in a way that made sense to the average citizen.

When it comes to technology, your major choices will be around the hardware (sensors) and the software you’ll use to manage the data collected by your sensors.

Sensor Hardware

To get started collecting data about your chosen sites, irrespective of which environmental factor(s) you care about, you’ll need to consider the following:

1. Type of pollutants detected. What does the sensor actually sense? In the case of air quality, the overall air quality score is made up of several factors, like particulate matter, volatile organic compounds, carbon monoxide, carbon dioxide, ozone, and more. Make sure that you understand exactly what you want to measure and make sure that your chosen sensor actually measures it and not just a bunch of environmental factors that you don’t currently care about.
2. Accuracy & sensitivity. In general, sensor cost will determine how closely a given sensor's readings match the actual state of the environment. Sensitivity is particularly important when dealing with low levels of pollutants, and accuracy is important because you want your data to be taken seriously. It is often a good idea to pair higher cost sensors that are more accurate with lower cost sensors that are less accurate but directionally correct and easily calibrated based on the higher cost sensors.
3. Range & resolution. It is also important to ensure that the sensor is capable of picking up the expected concentration of pollutants in your environment i.e., that it covers the full range of possible pollution levels so that you’re not missing data at the top and bottom of the range. Separately, resolution refers to the smallest change that the sensor can detect, and higher resolution is preferable so that you don’t miss small changes.
4. Calibration & maintenance. Sensors often require regular calibration to maintain accuracy, so it's important to understand both what is required and how easy it is to perform. Since our goal is to have DAO members take ownership of the data collection process, we recommend selecting sensors that come pre-calibrated or provide self-calibration features where possible.
5. Response time. A sensor’s response time represents how quickly it responds to changes in pollutant levels. Most environmental factors tend not to change rapidly e.g., the air quality tends not to fluctuate wildly by the second, but a fast response time is essential in cases where taking immediate action on high pollution levels is a priority.
6. Durability. Environmental sensors tend to get a lot of abuse from the elements given that they are typically deployed outdoors. They need to be able to withstand extremes when it comes to temperature, humidity, wind, rain, and everything else that will be thrown at them. Outdoor sensors should be made as weather-resistant and robust as possible, while taking care not to alter the environment around sensing elements such that you artificially affect the results.
7. Power Requirements. Sensors can usually be powered by batteries, direct power, or solar. Low power consumption is critical for long-term unattended monitoring, which is the type of monitoring we’re doing on our HabitatDAO projects.
8. Internet connectivity. Environmental sensors need some way to get their information off the device and onto your screen. They typically do this by sending data to a cloud platform first (alternatively you could send data into a local network). This means that they need a way to connect to the internet (WiFi, cellular, wired). 
9. Cost vs Quality. This is perhaps the biggest concern. Higher cost typically means higher quality, but managing costs is key to actually getting projects off the ground. When considering costs make sure to factor in the cost of hardware purchase, installation, calibration, maintenance, and data management over the lifetime of the project.
10. Certification & Compliance. If you want or need your data to comply with regulatory requirements in your area, you’ll need to make sure that your sensor is recognized by your local regulatory body. This is important if you want to use your data to advocate for policy change, and you don’t want to risk being dismissed for using an untrustworthy sensor. Certifications like EPA or EU standards can be indicators of reliability and quality, and often come with increased cost. If your goal, as ours was, is primarily to raise awareness and get people accustomed to caring about local environmental data, then lower cost, easier to use sensors make more sense.
11. Size & Portability. Environmental sensors can be mobile, fixed, or capable of working in both scenarios. Portable sensors are good for fieldwork where connectivity and power are not easily available, while fixed installations are generally best for long-term projects that require continuous monitoring.
12. Usability. The ease with which a given sensor can be used is of the highest importance on a citizen-led initiative. Our goal with HabitatDAO is both to understand if people care enough to engage in local monitoring initiatives, and also to understand if they are capable of managing the equipment themselves. In general, where you have a choice, we recommend favoring ease of use above many other factors.

In our case, we went with hardware from National Control Devices, who have a wide range of environmental sensors. We’ve found that their products achieved a good balance between functionality, usability, and cost.

Data Management

While choosing your sensors, we recommend also deciding what you will do with the data that the sensors are collecting i.e., how will you store, access, interpret, and share that data. It is best to do these two steps in tandem, so that you don’t accidentally choose sensors that are incompatible with your chosen data management approach. For example, you could pick a sensor and a cloud data platform separately, only to later find out that the two are incompatible.

Here’s the most important factors to consider when choosing a data management approach for environmental data.

1. Storage Capacity. While environmental monitoring projects tend to generate a lot of data in terms of readings, that data is usually in plaintext format and therefore very small. That said, some projects will be collecting video and image data, and thereby creating massive datasets. Either way, the main thing is to choose a storage solution that meets your initial needs, and can scale with you as you grow your monitoring activities. It is also important to make sure that your chosen storage solution can handle the type of data you are collecting e.g., time-series, spatial data, images, and so on.
2. Data Quality & Integrity. Sensor data should be validated to ensure that it is collected accurately. There are many ways of doing this, but none of them are perfect. In reality, ensuring accuracy involves a combination of automated and manual checks, both on the sensor itself (for calibration purposes) and on the data generated by the sensor. It is ideal if your chosen data management approach already has features built in it to help with this.
3. Usability. As with choosing sensor hardware, accessibility and usability are crucial factors to consider when choosing how you are going to manage your environmental data. You should assume that everyone in the DAO needs full access to the data, at least at read-only level, and you should also assume that not everyone in the DAO has a background in, or even a big interest in, environmental science. This means that your chosen solution should be capable of surfacing data at various levels and in various formats to meet the needs of all stakeholders. Additionally, at a more practical level, make sure to work with data formats that are most commonly supported by analysis tools e.g., CSV files.
4. Data Security & Privacy. While all of the data collected by HabitatDAO is intended to be owned by the members and generally public, it is still important to be clear about your data security and practices. That is, you want to consciously choose to make your data public, you don’t want your sensors or data management systems making this decision for you. Your data should be stored and transmitted securely, and should use appropriate encryption and access protocols so as to protect against unauthorized access. There may also be regulatory requirements depending on the type of data you are collecting, where you are collecting it, and why you are collecting it.
5. Data Backup & Recovery. Preventing data loss is extremely important in general, but especially on environmental monitoring projects which tend to move at real-world speed i.e., you’ll want to monitor for at least a year to get an accurate read on how seasonality affects the data that you are collecting. You’ll want to make sure you have a good backup strategy in place to prevent data loss due to accidental deletion or other unforeseen events. Make sure that you are aware of your recovery procedures, and test the recovery process as regularly as necessary. If you’re outsourcing your data management to a cloud platform, then you won’t need to worry about this (as long as the service provider is!).
6. Data Processing & Analysis. Beyond just storing your data securely and making it available to your DAO members, you’ll want to make sure that your chosen approach can process the data in a way that supports your goals. Do you need real-time data? Do you need long-term storage and retrieval of historical data for trend analysis? We’ll talk more about doing analysis and generating insights later.
7. Scalability & Flexibility. We touched on scale a little earlier (you want to make sure that there’s not a cap on how much data you can store), but we didn’t touch on flexibility. While you’ll likely start your initiative with a fixed view on the data that you want to collect e.g., air quality, it may transpire that you want to start collecting additional types of data, or doing different types of analysis. If you think this is likely, or even if you don’t, make sure to factor it into your choice of data management solution.
8. Cost & Budget. Sensors are usually a fixed cost, and while they can sometimes feel like a big upfront expense, it is likely that the biggest expense over the long term will be data management. This stands to reason because data management is an active ongoing process, and there is an effectively endless amount of work that can be done with the data you’ve collected. The most important exercise to do is to figure out which features you’ll definitely need, which features you’ll likely need, and to only pay for what you are likely to use, so that you avoid unnecessary expenditure.
9. Training & Support. You should choose a data management approach that meets the current and future technical expertise of your team. You need a system that is both powerful enough to safely store masses of data and facilitate sophisticated data analysis, and also make data available in a user-friendly manner so that all DAO members can benefit from it. It is most likely that not all DAO members will engage equally with the system, so ensure that appropriate documentation exists to help people engage at the level they are most comfortable with.
10. Sustainability & Longevity. How long will your DAO be running for? Ideally it will be running forever, and expanding all the time. For that reason it makes sense to consider the long-term viability of your data management approach. Additionally, make sure that your data is fully exportable at any time in case you need to migrate to a different system in the future.
11. Collaboration & Sharing. Since your project will ideally involve as many people as possible accessing the data, and potentially collaboration with those outside your DAO e.g., local government or environmental non-profits, it is important that your system supports the types of collaboration your need e.g., allowing multiple people to work on the data simultaneously, or to easily share insights. Also, make sure that you establish clear data usage policies if sharing with external third-parties.
12. Decentralization. Given that we’re exploring a DAO structure for organizing people around environmental data collection, it was only natural that we would explore decentralized data storage. DePIN (Decentralized Physical Infrastructure Networks) involve the use of blockchains and tokens to motivate communities to develop and maintain physical infrastructure networks, including IoT networks like those used for environmental monitoring. Depending on the level of interest among your DAO members, and also their level of technical expertise around web3 topics, this could be a very interesting aspect to explore. You can learn more about DePIN on Solana here.

Analysis & Insights

Once you have your environmental monitoring program up and running, and you’re collecting data in a way that is both technologically sound and accessible to your DAO members, it is finally time to start getting value from that data.

What you’ll do next depends on what matters to you and your community, and that ties back to the type of data you’re collecting. In our case, we were monitoring air quality, and had several goals:

1. Habituating people to regularly checking their local air quality.
2. Making people aware of how air quality affects quality of life, and educating them how they can protect themselves on poor air quality days.
3. Comparing air quality across locations.
4. Combining quantitative and qualitative data, such as descriptions and photos from community members, to interpret notable air quality events.
5. Quantifying how green infrastructure like trees can help improve air quality and lower temperature, in order to advocate for more green infrastructure in urban environments.

Typically, environmental data is time series numeric data, presented on line graphs like the one below.

As you can see, this type of data visualization is not necessarily the best way to achieve any of our stated goals. This is where your choice of data management system really starts to matter, because ideally your chosen platform will do more than just show you graphs. In our case we achieved our goals in the following ways:

1. We had an automated system that sent daily air quality updates to DAO members each morning. These emails were color coded based on the prevailing air quality, so that people could see the current air quality at a glance. These emails were a really simple way of communicating the current air quality, didn’t require looking at and interpreting graphs, showed up automatically so people didn’t have to go looking for them, and were a good way of helping people form a habit around air quality.
2. The automated emails contained EPA-approved health advice on poor air quality days. Adding the health information to the emails makes good sense because people only see it when they need it, and they see it in a place they are already used to looking at each day.
3. Our chosen environmental data platform, Temboo, made it easy to generate comparison graphs, enabling us to easily compare not only different air quality sensors at our monitoring locations, but also to compare against publicly available air quality data collected by the EPA. This enables DAO members to get a better understanding of how variable air quality can be over short distances.
4. In order to capture human interpretation of air quality events we made it possible for DAO members to nominate themselves as “Air Quality Ambassadors”. These ambassadors were then granted permission to add text notes and photos on top of the line graphs, so that whenever a notable spike occurred, or even if there was just a gap in the data, there would be an explanation from a human that made the numbers (or lack thereof) make perfect sense. This added a lot more meaning to the data and played to the strengths of both the sensors and the DAO members.
5. Finally, in order to quantify the impact that trees can have on air quality, we installed sensors not only at community gardens but also at less hospitable, non-shaded places with lots of hard surfaces (in our case, parking lots). These urban heat islands not only get direct sunlight due to their lack of natural shade, but also trap heat, making them even harder to exist in. By capturing environmental data in both locations it made it possible to compare and contrast and to make our point via comparison graphs like the one below.

The analysis that will make sense for you will depend on the type of data that you’re collecting, and your overall goals in collecting that data in the first place. Regardless of what type of environmental data you’re gathering, and the reasons why, here’s a few ways to keep data analysis that will help you get maximum value from the effort you’ve put into setting up the sensors in the first place.

1. Trend analysis. Long-term monitoring is usually the most insightful because you get to observe how environmental variables like temperature, air quality, or biodiversity change over time. Putting numbers on these phenomena is the first step in taking action to improve them. Another interesting aspect of long-term monitoring is the opportunity to examine how environmental conditions fluctuate across seasons.
2. Hotspot identification. A common use of environmental monitoring is to figure out which geographic areas have high concentrations of pollutants. This is crucial in targeting conservation efforts or regulatory interventions.
3. Correlation & Causation. Armed with your sensor data, you can start to figure out the relationship between air or water pollution levels, and public health outcomes like respiratory diseases. This type of analysis can be very useful in figuring out if there is a genuine relationship between pollution sources and public health, and can be invaluable in shaping public health policies. Equally, you can study impact not directly on humans but on ecosystems e.g., how do rising temperatures affect insect populations in a given area?
4. Risk assessment. By collecting data on environmental patterns you’re in a better position to understand the factors that lead to natural hazards like floods. This can be useful in both developing mitigation strategies and understanding how proposed new developments or industrial activity might affect the existing ecosystem.
5. Event Detection. This is ultimately the opposite of trend analysis i.e., instead of looking at the long-term you’re looking out for specific events of note. This is particularly suited to time-series sensor data sets where you can clearly see spikes. Your job then is to link these spikes to specific events like industrial activity or other forms of human activity (since these are most easily remedied).
6. Machine Learning & AI. Environmental data is particularly well suited to use in machine learning models. Applications include anomaly detection e.g., unusual temperature patterns or abnormal pollution levels, and prediction e.g., forecasting environmental conditions as opposed to simply collecting historical data via your sensors.
7. Human-Environment Interaction. This has been somewhat covered by the categories above, but it’s so important that it's worth calling out separately. Collecting environmental sensor data enables you to understand how urban development affects key variables like air quality, temperature, and water runoff. This can help steer urban planning toward sustainable practices. This is the category our work on HabitatDAO falls into. Land use change analysis is a related area that examines how land change affects environmental variables e.g., the effect of deforestation on water quality or soil erosion. Finally, this all ties into the analysis of environmental justice issues i.e., how environmental hazards disproportionately affect marginalized communities, or how access to green spaces is more or less available depending on your economic status.

These types of analysis can provide your DAO with valuable information to advocate for improvements to the locations you care about, and can help inform decision-making around public health and urban planning.

Collective decision making

Given the nature of organizing as a DAO it is likely that you will want to put a decision making structure in place so that your members can collectively decide on how best to collect, store, share, and take action on your environmental data sharing. We recommend exploring a Solana-based DAO. The Solana network's high-speed and low-cost transactions enable efficient, real-time voting and consensus-building, making it a good choice for managing decentralized communities. The transparency of the blockchain ensures that all decisions and transactions are recorded immutably for all to see, helping foster trust and a sense of fairness among participants. Additionally, the smart contracts governing the DAO automatically enforce rules and decisions, reducing the need for intermediaries and minimizing the risk of human error or corruption. The introduction of a DAO-native token for managing DAO governance raises new possibilities for recognizing member contribution and granting them a louder voice in the decision making process, thereby giving the most involved members the most control over the future of the DAO.

HabitatDAO Workflow Recap

OK, we’ve covered a lot of important topics so far, so before we wrap up, let’s do a quick summary of the process of setting up your own version of HabitatDAO.

The workflow for recreating HabitatDAO

Naturally, the starting point is recruiting members. The main thing to optimize for is a strong interest in environmental matters. Beyond that, an interest in environmental technology, advocacy, organizing, and web3 are all big pluses. Consider tapping into pre-existing networks in your local area.

Once you’ve onboarded your initial DAO members, the next step is to agree on what you’re all going to work on. The biggest consideration here is to a) choose something relevant to the environment in your location and b) choose something meaningful to your initial DAO membership.

With your DAO membership taking shape, and a solid idea, the next step is to select the best sensors and data management platform to achieve your goals. Remember to prioritize usability and accessibility so that as many DAO members as possible can play an active role in the process.

Last, but certainly not least, it will be time to start making sense of the data you’ve been collecting, turning sensor data into meaningful insights, and using those findings to help drive positive change in your community.

By this point, it’ll be time to use the publicity that you’ve hopefully been able to generate based on your good work to recruit new members and begin the cycle again as you expand your efforts. Good luck!