Please note, VanEck has made an investment in Hivemapper's HONEY token. This investment was made through a transaction directly with Hivemapper, Inc. Post purchase, VanEck owns less than 75 bps of the fully diluted token supply. Our investment comes with a 1 year lock up.
Hivemapper presents a unique opportunity utilizing cryptocurrency to crowdsource contribution toward building a decentralized mapping network. In the short term, Hivemapper has proven crypto is a transformative means of incentivizing a global network of actors to work toward a common goal. In the long term, Hivemapper may be able to take market share away from incumbent mapping players by offering a better, cheaper product to its clients.
Hivemapper’s goal is to create a decentralized and permissionless global map that anyone can contribute to and utilize however they would like. They aim to have more global, street-level coverage than existing players while providing higher quality data all for a lower cost.
How it Works
Drivers around the world can purchase and install a 4k dashcam into their vehicle to record their daily routes. In return for providing constant real-time and high-quality global map data, drivers are rewarded in $HONEY token. Additionally, if you don’t have a vehicle or the cost of the dashcam is prohibitively expensive, you can still contribute to the network as an Annotator. Annotators play a machine learning model training game, labeling traffic lights, speed limits, and other street signs. This is similar to how Google uses Captcha for ML training, except these participants get paid. They too earn $HONEY for bettering the mapping data and training the machine learning model, which will eventually be able to annotate map data itself. These contributors are crucial to the supply side of the Hivemapper ecosystem.
The demand side network participants consist of consumers, such as enterprises or developers, who want to purchase the street level data the Hivemapper suppliers have aggregated. Consumers who may be particularly interested in digital mapping data could include location-based service applications, logistics companies, and automotive manufacturers who require accurate navigation systems.
Interested consumers can purchase Hivemapper Map API services via Map Credits. Credits are purchased in fiat, priced at $0.02/map credit. 50 map credits provide access to 1 kilometer worth of map data. For example, if a customer wanted all of NYC’s map data, it would be 7,837,650 map credits or $156,753. After the consumer purchases map credits, $HONEY tokens are burned equal to the fiat amount of the contract. The burned tokens are then re-allocated toward a new rewards pool for contributors. Under this burn and mint token structure, the supply of $HONEY stays fixed at 10 billion tokens.
Key Performance Indicators
Global map coverage is the foundation upon which Hivemapper needs to build for long term success. To compete with the centralized tech offerings, Hivemapper must have just as much if not more coverage. Since launching in November ’22, Hivemapper’s network has grown dramatically with coverage across 3.6 million unique road kilometers. For reference, it took Google Maps 12 years to collect 16 million km. If unique road coverage continues to grow at its current pace, Hivemapper could reach 10 million km by 2024.
Another KPI we can observe is the total number of contributors to the network. As more contributors come aboard, they further map coverage as well as data quality and freshness, increasing value and thus demand from end clients.
Lastly, we can track Token Burn, which provides insight into demand for Map Credits and thereby a measure of Revenue generated by the network. Currently, there are significantly more tokens being newly emitted as rewards for contributing than tokens being burned for map credits. We expect that to change over time as fresher, cheaper, and broader coverage will increase demand for map credits.
Key Issues Being Solved
A host of issues stem from centralized tech giants’ approach to building digital mapping networks. Namely:
Price Gouging: Existing offerings upcharge their customers because comparable alternatives largely do not exist. Many customers, especially SME’s, can no longer afford their services with rampant price hikes.
Maps are not up to date: Company street-view vehicles can only reach most locations on an infrequent basis. Data collection is especially sparce and outdated in emerging market areas where firms believe they will not receive as much economic benefit for providing up-to-date coverage.
OpEx and CapEx Heavy: Mapping the world is capital and human resource intensive. Gathering data through centralized company vehicles can cost ~$500k each to build and deploy. It is incredibly expensive to gather mass coverage without deep pockets.
Freely uses our data: Users are not compensated for data collection.
Source: Messari Hivemapper aims to solve these issues. The network’s costs to the end consumer are significantly cheaper because they do not have the high operational or capital expenditures to collect the mapping data. Hivemapper’s data is fresh and up to date as the global network of vehicle dashcams are recording constantly while drivers go about their normal routes. Furthermore, the $HONEY token can incentivize coverage of areas less frequently traveled. $HONEY not only incentivizes and manages behavior on the network, but it also allows for contributors to be compensated for their data as well as be financially aligned in the success of the network.
Interesting Use Cases
Currently Hivemapper’s focus is on navigational use cases, of which there are many. Advanced Driver Assistance Systems (ADAS) are being implemented into new vehicles for high level autonomous driving. Up to date mapping data is critical to ADAS system as knowing detailed road object information such as lane definitions, debris, traffic lights, and signage is paramount.
Logistics companies may want to have visuals of loading docks and parking restrictions prior to delivery, as knowing can save them time and money. Governments want to know where road maintenance is necessary, have traffic intelligence, or the fastest routes available for emergency response times.
$HONEY Valuation Framework
Based on Hivemapper’s Burn and Mint tokenomic model, whereby $HONEY is emitted to contributors for participating in the network and $HONEY is burned as a receipt for map credit purchases by consumers, we can use an Equation of Exchange framework to derive its value. The equation used is MV=PQ. M, the size of the asset base, V, the velocity of the token, and PQ, the yearly transaction value. We start by estimating the Total Addressable Market Size for the Digital Mapping Market. Then we need to estimate Hivemapper’s take rate of that market to get an implied revenue (PQ). In Hivemapper’s case, revenue is a product of map credits sold multiplied by the fiat cost of map credits. We envision Hivemapper’s data being integrated into multiple incumbent map players’ offerings as it could lead to 50%+ in cost savings for them, as well as offer fresher, higher quality data to their customers. Due to the cost savings they could provide to their competitors as well as a superior product offering, we believe Hivemapper will have a considerable market share. Next, we need to estimate $HONEY’s velocity, or how often the token exchanges hands each year. As a utility token in the Hivemapper ecosystem, its velocity will be greater than store of value assets like Bitcoin, but less than a stablecoin which has no speculation over its future value. We can use an average velocity of a basket of utility tokens for this (V). Estimating $HONEY’s Revenue (PQ) and Velocity (V), we can back into its implied market cap (M). Lastly, we must estimate the number of $HONEY tokens circulating which is a product of emissions to contributors for global map coverage progress as well as token unlocks. Dividing the market cap by the number of tokens circulating, we can come up with an implied value for $HONEY.
Key Risks
Hivemapper certainly doesn’t come without its risks and concerns. The cost of the dashcams, $299, may be prohibitively expensive for many to join at a global scale. Though this could be alleviated as third party dashcam providers come to market offering cheaper solutions. Our other concern lies in the fact that map data collection is only half the battle. The other half is marketing and sourcing clients to sell this data to. While we believe Hivemapper is on the path towards building a superior product, they must compete with the tech giants’ sales teams who have robust pipelines and headcount. However, we envision a world in which Hivemapper’s data could be integrated into the backend of existing map players’ offerings as it can provide more real-time, street-level data and cut their costs significantly.
Closing Thoughts
Hivemapper takes an interesting bottom-up approach to building a mapping infrastructure network traditionally built top-down from centralized tech players with deep pockets. By using a token incentive model, contributors are compensated for their data and are willing to continue to provide coverage to the network as they have economic motivation sharing in its success. With the potential for broader, fresher, and higher quality coverage than existing offerings, coupled with lower costs to the end clients, we believe Hivemapper will gain traction and market share over the coming years.
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Thank you. I really enjoyed this essay. It seems like the DePin thesis is getting more and more relevant.