## People want a clear sense of “here” that isn’t owned by any one app tyred of statistics that feel abstract or weaponised like simple maps that show what’s happening nearby, and what might actually help everyday participants give the map its human meaning — “this is where I am” active citizens turn indicators into lived projects and local improvements institutions and non-government organisations bring continuity and deeper datasets data people keep the modelling transparent and improvable peaceful foundation volunteers tie the projects and local realities together together they make hexagons.world less of a “data project” and more of a quiet backdrop: a shared, neutral way to say “here”, “this needs care”, and “this is improving” ### People using Peaceful projects people using calm.college, reasonable.diet, learnstuff.today, quiteasily.org, peaceful.network, and others see hexagon words and local statistics in passing, without needing to think about them would like to understand their own area a little better, then get back to their day #### What They Need a calm, recognisable map that shows: - “this is my rough area” - “these are the projects active around here” a way to notice how many people nearby are taking part where they are: project maps, QR codes, posters, small local dashboards #### How We Can Be Useful change open to: checking the hexagon of their area now and then barriers: assume maps are “for experts”, or that nothing they do matters first actions: - open the map once from a Peaceful project and see their neighbourhood - notice where others are running posters, meetups, meals, or guides - link one small action (meal, meetup, cleanup) to their hexagon if relevant ### Active citizens and local organisers students and neighbours who take small, everyday interest in their local area not activists, not organisers — just people who enjoy knowing how their place is doing for them, local statistics become something to talk about casually with friends #### What They Need indicators that feel human and easy to follow a map that naturally fits into conversation (“how’s our hexagon going?”) ways to notice change without needing technical knowledge where they are: campus lawns, study groups, calm.college meetups, neighbourhood chats #### How We Can Be Useful change open to: treating local indicators as a shared curiosity barriers: assuming statistics are too heavy or “not for me” first actions: - at a calm.college meetup, someone asks, “Which statistic are you passionate about?” - people open the map out of curiosity and see which indicators feel meaningful - friends begin casually referring to “our hexagon” in group chats - if someone takes a small action (cleanup, food sharing, posters), they simply note it for their hexagon without any ceremony This keeps active citizenship relaxed and unforced. People aren’t “joining a movement.” They’re just talking about their place, and taking small steps together when they feel like it. ### Institutions, non-government organisations, and universities universities, councils, research centres, charities, and service providers want consistent, region-wide views they can plug into their work value neutral framing that doesn’t feel like a movement or a brand #### What They Need clear documentation of indicators, uncertainty, and data sources exportable views at scales they already use: - campus, suburb, LGA, region ways to place their own programmes on top of the same grid where they are: planning meetings, grant applications, dashboards, academic projects #### How We Can Be Useful change open to: treating hexagons.world as a shared base layer barriers: concerns about accuracy, privacy, and reliability over time first actions: - overlay one region and compare with their own internal measures - use a single hexagon map in a report or grant submission - invite a student or volunteer group to run a small project tied to one hexagon ### People working in data, mapping, and statistics geospatial engineers, statisticians, modellers, data journalists, method people curiosity about how indicators are built and how far the system can be pushed want to run their own experiments instead of trusting a black box #### What They Need open methods and reproducible pipelines raw or lightly processed indicator values per hexagon clear statements of priors, weights, assumptions, and uncertainty space to question and refine the indicators themselves where they are: GitHub, academic labs, open-data circles, mapping forums #### How We Can Be Useful change open to: using hexagons.world as a reference grid and baseline model barriers: scepticism about modelling choices; dislike of hidden logic first actions: - replicate one indicator for a country or region - run a small “what if” scenario by changing priors or data sources - propose refinements or additional indicator layers - offer alternative interpretations of priors or uncertainty ranges They make sure the modelling remains transparent, debatable, and easy to improve over time. ### Ambassadors and volunteers people supporting peaceful foundation projects across different countries students, organisers, developers, editors, and creative contributors use hexagons.world as the quiet background layer for coordinating local work #### What They Need a stable, shared hexagon language across PF projects clear ways to see activity in their own area a structure that helps them tailor each project to their local context where they are: peaceful foundation Discord, local meetups, project workspaces, livestreams #### How We Can Be Useful change open to: using hexagon words to anchor their local work barriers: learning another tool; fear it adds bureaucracy first actions: - notice project activity in their area (posters, meals, meetups, guides) - collate local resources and contacts for each project by hexagon - create practical, localised materials that reference the grid - coordinate with other volunteers inside the same hexagon or adjacent ones Community connect people across nearby hexagon; surface local groups, clubs, and organisations who might benefit from open data; find local organisations who might have data about their local community. Programming build and maintain map tools; implement deterministic hexagon naming; wire indicators into tiles; keep exports and “what if” tools easy enough that others actually rely on them. Organising connect local organisers, students, non-government organisations, and councils; help groups adopt one or two indicators for their area; ensure real-world actions are reflected calmly on the map. Influencing show how a single hexagon relates to real issues—food, loneliness, heat, safety; turn numbers into simple, human stories; normalise checking your hexagon across PF projects. Research find and vet data; stress-test indicators; run region comparisons; translate technical ideas into clear language for community groups. Editing + Artistry keep the platform calm and humane; design visuals and printouts that are easy to share; keep explanations short, clear, and consistent across all PF projects. ### Specialist registered volunteers #### Systems consensus #### . ### Staff What is the smallest group that can keep this clear, accurate, and alive while everyone else uses it? we have a lot of volunteers, and they need direction #### Geospatial // the hexagons part of hexagons turning the entire physical world into a stable, readable, low-friction layer that everything else can sit on. define the hexagon system (hex0 → hex6) ensure names feel local and grounded in real place keep the map fast, consistent, and predictable everywhere if this feels wrong, everything on top feels wrong Angus MacAulay #### Statisticians // accurate data // the systems they use to debate and discuss things turning messy, incomplete reality into something that can be understood, questioned, and trusted. defining how truth can be represented, debated, and trusted at scale translate the social world into understandable data indicator design (loneliness, food, safety, etc.) data ingestion + cleaning uncertainty + transparency models “what if” logic and reproducibility define: indicator structure how uncertainty works what “valid” looks like keep numbers honest, interpretable, and human make disagreement visible, not hidden #### Systems architecture // keep infra simple, cheap and stable turning infrastructure into something that is invisible, reliable, and able to run anywhere without friction. keep infra simple, cheap, stable lightweight, offline-capable, cheap define constraints: no heavy dependencies no fragile systems no unnecessary compute if it can’t run anywhere, it doesn’t belong #### Software developers // turning ideas into simple, optional tools that anyone can use without creating dependency or lock-in. define architecture + constraints keep things simple and consistent volunteers: build: small tools overlays experiments integrations important constraint: no one builds something that becomes required everything optional, replaceable, ignorable #### User-experience designers turning complexity into something immediately clear, so anyone can understand what they are seeing without effort. protect the feel reject complexity maintain consistency place first, hexes second no clutter, no sudden redesigns if someone opens it once, they should understand it immediately #### Local coordination turning many independent communities into a map that can be seen and connected without being controlled. completing the interfacing with communities that already exist safety: protect participants in real-world settings avoid misuse respect autonomy: communities act on their own terms the system does not direct them if it feels like an external system is managing them, it breaks ### Creating market assumptions for maps Maps are habitual utilities. Most smartphone users already have a default navigation app, and usually only keep one or two maps in regular use. The category is dominated by a few very large defaults: - Google Maps: ~2+ billion monthly active users - Apple Maps: hundreds of millions of users through the iPhone ecosystem - everyone else: fragmented, niche, or region-specific This means two things are true at once: 1. the market is enormous 2. breaking into it is difficult. So our aim isn't that “everyone switches”: it's more so to show that even a small minority of global maps users is still a very large number of people. #### Which maps do people use? Everyone has a navigation application on their phone. Around ~3 billion people use a maps application with some regularity. ##### Google Maps Google Maps is widely used because it works reliable routing fast search strong coverage of places and data but most friction does not come from failure it comes from changes people did not choose top-down updates shift how the map looks or behaves users adapt, rather than opt in preferences are overwritten rather than respected these changes are often subtle but felt immediately in everyday use colour and theming changes roads, traffic, and features become harder to distinguish contrast is reduced or shifted night use becomes less comfortable for some people visual clarity information competes for attention important elements are not always prioritised clearly people take longer to read the map at a glance personal preference users cannot easily keep a version that works for them there is no stable “this is how I like my map” each update resets familiarity this affects different groups unevenly people with visual sensitivity people navigating at night people who rely on quick visual parsing (drivers, couriers) over time, this creates a quiet kind of friction not enough to stop using the map but enough to be noticed people adjust rather than switch they accept the change but trust in the tool softens slightly this pattern is structural large platforms optimise globally individual preference becomes secondary the map improves in some ways but moves away from being personally tuned so complaints about Google Maps are rarely about “this does not work” they are more often about “this used to feel better to use” a small shift from a tool that feels stable and familiar to one that changes without asking and because maps are habitual even small discomforts repeat every day quietly accumulating over time ##### Apple Maps Apple Maps is shaped by its ecosystem deeply integrated into Apple devices default on iPhone, iPad, and Mac works smoothly within that environment but it is also constrained by it only available on Apple devices not a cross-platform tool limited reach compared to global defaults this creates a different kind of adoption not chosen as much as inherited used because it is already there the product itself is steady reliable for everyday navigation clear directions simple interface it includes small forward-looking features phrasing like “at the next light” clean turn-by-turn guidance integration with the wider device experience these are thoughtful improvements but not defining shifts it does not attempt to radically change how maps work it refines rather than reimagines the launch shaped its perception initial release was widely seen as unstable missing data inaccurate locations visual and routing issues this came from building a full map stack from scratch a large and complex undertaking done at global scale over time, this stabilised data improved routing became reliable coverage expanded it became usable then dependable then good enough for many people today, it sits in a stable position a default for Apple users trusted within its ecosystem rarely sought out beyond it privacy is part of its appeal reduced tracking compared to competitors alignment with Apple’s broader positioning but this is a supporting factor not usually the primary reason people use it overall, Apple Maps is not defined by extremes not groundbreaking not poor it is simply there stable and good enough for many people that is sufficient ##### Waze monthly active users roughly ~100–150 million people smaller than defaults, but highly engaged helps you get better directions optimised for driving efficiency constantly recalculates based on live conditions often prioritises time over simplicity social maps (and better data) users actively contribute information accidents speed cameras road hazards this creates a feedback loop: more users → better data → better routes tooting to other drivers small social gestures build a sense of shared participation make the map feel alive rather than static now it is just Google acquired and integrated into the broader ecosystem continues as a distinct product, but no longer an independent trajectory Waze serves a smaller but highly engaged segment, at roughly ~100–150 million users. ##### HERE Maps, or other proprietary applications soft spot for HERE because it's on the Light Phone 2 represents a quieter category of tools built for specific devices or contexts often prioritises: offline capability simplicity reliability over features these maps often exist in the background used in cars, embedded systems, logistics platforms not always visible as consumer apps any maps app has to make huge jumps global coverage is difficult accurate routing requires constant updates points of interest require ongoing maintenance user expectations are extremely high this creates a high barrier to entry most alternatives remain: regional specialised or infrastructure-focussed rather than consumer-facing ##### OpenStreetMap OpenStreetMap is a shared, collaborative dataset it is not a single app it is a global map database that others build on top of applications sit on top of it different apps, tools, and services use the same underlying data each implements its own interface, routing, and features this creates a separation the map data is shared the experience is customised by each client the license shapes how it grows data can be used, modified, and redistributed but improvements must be contributed back this keeps the map as a commons not owned by any one company continuously improved by its users the map improves through direct participation when something is missing or incorrect people can fix it themselves a path not shown a road mislabelled a new development not added these can be updated directly by the people who know the area this leads to strong local accuracy especially in areas where contributors are active walking paths cycling routes trails small connectors between streets these are often more complete than in commercial maps because they are added by people on the ground not inferred or prioritised centrally in many places this produces a map that feels closer to reality especially for non-driving use where smaller paths matter the data is flexible it can be used beyond typical navigation urban planning research logistics maritime and niche mapping uses it acts as a general-purpose geographic layer that many systems rely on offline use is a core strength entire regions can be downloaded stored locally on a device used without connectivity this makes it reliable in: low-signal areas travel situations environments where constant internet is not assumed updates happen differently the map does not change all at once it evolves incrementally as people contribute changes in fast-changing areas updates can be frequent in stable areas the map remains accurate over long periods the system reflects the physical world changes only when places change or when someone records that change this creates a different relationship to the map not something delivered but something maintained OpenStreetMap is not defined by a single experience it is a shared foundation a map that anyone can use anyone can improve and many tools can grow from Most people use more than one map, and new maps enter as a second tool before becoming a default. So even a small fraction of global users represents millions to tens of millions of people. #### How do people adopt maps apps? People usually do not replace their default map instantly. They tend to: - try a second map for a specific reason - keep using it situationally - promote it to a default only if it proves reliable This means replacement usually begins through a wedge: - better privacy - offline reliability - safer or calmer routes - local layers or useful overlays - alignment with a trusted ecosystem There is already a visible ecosystem of people seeking this, but existing alternatives remain relatively niche. That suggests demand is real, but product quality and completeness are hard. So the grounded assumption is: - the market for maps is very large - alternatives can attract meaningful minorities - switching one utility is easier than replacing an entire tech stack - adoption depends on usefulness first, values second - a good replacement map can spread gradually through habit, trust, and specific wedges. So the core question is: “how might we build a map that people genuinely prefer for specific situations, and then keep using often enough that it becomes one of their defaults?” ### Uptake of Maps A reasonable assertion of uptake is *a slow, steady capture of ~0.5–1% of global maps users over time*, with wider outcomes depending on how adoption evolves socially and structurally. If global maps app adoption has ~3B MAU: * **0.5%** = 15 million users * **1.0%** = 30 million users * **1.1%** = 33 million users It is reasonable that **1 out of 100 Global maps users** might prefer a * free * private * open source * charity-run * customisable * calmer user experience * map with useful extra layers (shade, walkability, local stats). This represents a small but meaningful shift: a minority choosing a calmer, more trustworthy default, not a mass migration. However, uptake is not a single scenario — it unfolds across a couple of distinct layers: hexagons.world isn't competing on values though -- it also has to be genuinely useful as a map: routing must work, search and POIs must work, speed and battery use must be acceptable, and, the interface must feel calm, stable, and trustworthy. Though, we're not entering the market as a random standalone startup; Peaceful Foundation creates a softer distribution path through: - students - local organisers - people already adjacent to Peaceful Foundation projects - posters, QR codes, and offline presence - a tone that is not corporate. This doesn't remove the need for product quality, but it does mean adoption is not purely dependent on app-store marketing. So, the question becomes, which fractions are culturally and structurally plausible for Peaceful Foundation? **Minimal** — ***people choose it*** *(~0.25% → ~6–8M users)* Adoption is deliberate and values-driven. The map remains a niche tool for: - privacy-conscious users - open-source and power users - Peaceful Foundation–adjacent communities. It is used, but rarely becomes a default. **Reasonable —** ***people keep it*** *(~0.5–1% → ~15–30M users)* The map proves reliable enough for repeated, everyday use. - becomes a secondary default for many - a primary map for a smaller group - growth driven by usefulness, not ideology. At this level, retention matters more than discovery. **Ambitious —** ***communities normalise it*** *(~1–1.8% → ~30–54M users)* Adoption becomes social rather than individual. - used across campuses, cities, and specific groups - reinforced by repeated exposure (posters, events, local use) - in some environments, it becomes the expected map. The map starts to feel like part of local infrastructure. **Optimistic —** ***regions and networks default to it*** *(~2–4% → ~60–120M users)* The map becomes a “third default” alongside incumbents. - widely recognised, even by non-users - normal within multiple regions and demographics - driven by cultural spread and local network effects. People use it because others around them do. **Reasonable, visionary and optimistic —** ***an infrastructure layer*** *(billions of users over time)* The map is no longer primarily a consumer app. It becomes: - a global addressing system - a shared reference layer across systems - quiet infrastructure that other tools depend on. Growth shifts from “app adoption” to embedded presence: - used directly by hundreds of millions to billions - indirectly used by many more through other systems. At this level, the question is no longer: “Will people switch maps? but: “Where does this become normal infrastructure?” #### Minimal The map is useful, but does not break into mainstream use; it is used by a smaller group of people who already care about: - privacy - calm tools - open-source software - non-corporate alternatives - Peaceful Foundation itself This is closer to the scale of existing privacy-first and OSM-based tools (around 10M installs on average) than to a broad consumer breakout. 0.25% of global maps users ~6–8 million people who this is made of trust and privacy users (~2–4 million users) people already uncomfortable with tracking, data collection, or large platforms as defaults they actively seek out non-tracking alternatives peaceful foundation adjacent users (~1–3 million users) people who encounter the map through PF projects, posters, or existing communities they try it through exposure within the PF ecosystem students with light exposure (~1–2 million users) people who see it on campuses, through posters or friends, but without strong institutional or social embedding they try it situationally (e.g. events, local use) open-source and power users (~0.5–1 million users) people used to trying alternative tools, including OpenStreetMap users, developers, and others who deliberately choose their software they adopt it as part of their existing toolset Most users at this stage treat it as a secondary map rather than a replacement. However, it remains limited: - it is not yet part of everyday habit for large groups - most people keep their current default map - there are no major distribution or social drivers - Peaceful Foundation ecosystem effects are still limited At this level, the map is chosen deliberately, not adopted socially. This makes a user base in the high single-digit millions plausible, without assuming a mainstream breakout. #### Reasonable The map begins to move beyond niche use and into repeated, everyday usage for specific groups. It is no longer only chosen deliberately; it is kept because it proves useful in regular situations. Adoption still does not require mass switching. Instead, the map becomes a second default for many users, and a primary map for a smaller but meaningful share. 0.5–1.0% of global maps users ~15–30 million people This corresponds to early-stage adoption patterns seen in other tools: Waze (pre-acquisition) ~20–50 million MAU DuckDuckGo ~1–2% of Google search usage who this is made of (note: segments overlap; users may belong to more than one group) students and campus users (~5–15 million users) people who encounter the map through campuses, posters, and local events it becomes part of how they check what is happening nearby walk, cycle, and transit users (~5–10 million users) people in urban areas who do not rely primarily on cars they use it for routes that feel simpler, calmer, or more predictable trust and privacy users (~3–8 million users) people who prefer not to be tracked they begin using it regularly once it proves reliable offline and reliability-focussed users (~3–6 million users) people who need maps to work in low-connectivity environments they rely on it because it works consistently offline work and navigation-heavy users (~2–6 million users) drivers, couriers, and mobile workers they keep it if it is dependable and reduces friction in daily routes what changes at this level - the map is opened repeatedly, not just tested - it becomes useful in specific, recurring situations - some users begin to rely on it as their default for certain tasks - word-of-mouth and local visibility start to matter why it grows - the product is reliable enough for everyday use - specific advantages (e.g. calm routes, offline use) are noticeable - exposure through PF projects and local environments is steady - users recommend it without needing to justify it what still limits it - most people still keep an existing default map - it is not yet dominant in any large region or demographic - distribution is steady but not accelerated - switching friction remains real At this level, the map is no longer only chosen on principle, it's kept because it is reliable enough to use again. #### Ambitious Within an ambitious scenario, the map becomes a default in specific environments rather than just a secondary tool. Growth is no longer driven mainly by individual choice. Instead, it is reinforced by repeated exposure across multiple contexts, like campuses, local events, Peaceful Foundation projects, and everyday use. The map is encountered in several places, and begins to feel like a normal part of local infrastructure. 1.0–1.8% of global maps users ~30–54 million people This level is comparable to strong niche platforms with broad but not universal adoption: DuckDuckGo ~1–2% of search usage Strava ~100 million users through utility and network effects Signal ~50 million MAU driven by trust and utility what drives growth at this level - the product is consistently reliable across regions - the map is embedded in multiple Peaceful Foundation touchpoints (campuses, food, events, local activity) - users encounter it repeatedly across different contexts - in some communities, it becomes the expected or recommended map how adoption changes - the map is no longer just “kept”; it is assumed to be available - it becomes a default for specific groups (e.g. students, urban users, certain regions) - local network effects begin to form (people recommend it because others use it) - usage is reinforced socially, not just individually - in these environments, not having the map begins to feel unusual where it becomes strongest adoption is not evenly distributed and concentrates in: - campuses and student populations - dense urban areas with walking, cycling, and transit use - regions with weaker addressing or stronger offline needs - communities already exposed to Peaceful Foundation projects what still limits it - most global users still rely on existing default maps - dominance is local or regional, not global - growth depends on continued product quality and trust - without continued exposure, usage can plateau assumptions required - the product is clearly competitive for everyday navigation - Peaceful Foundation ecosystem exposure is steady and visible - at least one external or social accelerator occurs (e.g. trust event, regional adoption, or strong word-of-mouth) At this level, the map is not just chosen or kept — it is preferred. In some environments, it is expected. ----- #### Potential drivers favouring adoption of hexagons.world - Widespread fatigue with extractive digital platforms (ads, tracking, algorithmic manipulation, attention capture) - Loss of trust in big tech as neutral infrastructure (maps, identity, communication increasingly feel owned and conditional) - Growing desire for calm, stable tools instead of “feeds” (people want utilities, not endless engagement) - Rising loneliness and social fragmentation (especially among students and urban populations) - Cost-of-living pressure (food, transport, housing, energy → people seek practical local help) - Declining effectiveness of institutions at the local level (universities, councils, and NGOs struggle to sense reality on the ground) - Renewed interest in place and locality (neighbourhoods, regions, biomes, “where am I really?”) - Climate stress and environmental awareness (heat, shade, water, safety increasingly matter at street level) - Normalisation of mutual aid and informal coordination (especially post-COVID and during economic shocks) - Increasing scepticism toward ideology-heavy solutions (people prefer things that work without asking them to agree) - Growth of open-source literacy and legitimacy (people trust systems they can inspect, fork, or abandon) - Fragmentation of the internet into many small worlds (group chats, campuses, neighbourhood networks replacing mass platforms) - Attention scarcity (tools that demand less survive better) - Desire for systems that feel durable across decades (not startups chasing exits or platforms chasing growth) - Rising discomfort with permanent records and surveillance (privacy, graceful forgetting, reversible participation) - Planet-scale problems becoming locally felt (heat waves, floods, food access, mental health strain) - Shift from “global narratives” to “local signals” (people trust what they can see nearby) - Increasing value of coordination over persuasion (doing small things together beats arguing online) - Decline of central authority as organiser (people coordinate horizontally by necessity) - Need for shared, neutral reference layers (common maps, shared vocabularies, non-owned substrates) - Re-emergence of civic curiosity (“how is our area doing?” rather than “who’s winning?”) - Rising legitimacy of “boring but reliable” infrastructure (unsexy systems that quietly keep working) - The quiet return of optimism grounded in action (hope tied to visible effort, not promises) ----- #### Optimistic The map becomes a “third default” in multiple environments. It is not universal, but in many cities, campuses, and communities, it is treated as a normal choice alongside existing maps. Growth is driven less by discovery and more by cultural and social reinforcement: people use it because others around them use it. An optimistic scenario could be: 2–4% of global maps users ~60–120 million people This level is comparable to products that became widely adopted without replacing incumbents: DuckDuckGo ~1–2% of search usage Signal ~50+ million MAU, with spikes driven by trust events Strava ~100 million users in a specific behavioural niche what changes at this level - the map is widely recognised, even by non-users - it becomes a default in multiple regions and demographics - social norms reinforce usage (friends, families, campuses) - in some environments, it is assumed to be installed what drives growth - strong product quality across core use cases (routing, search, offline) - repeated exposure through Peaceful Foundation projects and local environments - cultural spread (word-of-mouth, gifting, visible use in daily life) - at least one external accelerator (e.g. trust shock, regional adoption, or institutional distribution) what it looks like in practice - students adopt it at scale across campuses - families and social groups share access through gifting or recommendation - safety and comfort features drive adoption in specific demographics - some regions reach local “default” status expected scale 12 months: 10–30 million MAU 36 months: 60–150 million MAU 60 months: 150–250 million MAU (note: upper ranges assume at least one strong accelerator) Growth at this stage is not driven by a single channel. It comes from several overlapping systems reinforcing each other. People often encounter the map indirectly, through something else that is immediately useful: - a student sees a poster for a cheap meal and opens the map to find it - someone checking a campus noticeboard uses the map to see what is happening nearby - a friend shares a location using a hex name instead of an address - a parent installs the map for safety features and shares it with their household In each case, the map is not introduced as a product to evaluate. It is used as part of doing something else. These small entry points accumulate. The same person might: - use it once for food - later check it for an event - then use it for directions - and eventually keep it installed As this repeats across many people in the same environment, usage becomes visible and normal. On a campus, this can look like: - posters and QR codes referencing the map - events and meetups tied to locations within it - students casually referring to places through it - new users installing it because others already have it In a city or neighbourhood, it can look like: - people using it for walking or transit routes - local recommendations and places becoming easier to find through it - small groups (friends, families, communities) standardising on it - visible, repeated use in everyday situations This creates local network effects. The map does not need to win globally at once. It becomes strong in specific environments, and those environments spread or replicate. Over time, these pockets of adoption connect. what still limits it - incumbents remain dominant globally - adoption is uneven across regions - growth depends on continued trust and product quality - without reinforcement, some segments plateau what is required - replacement-quality performance (not just niche utility) - visible and sustained trust - time (multi-year habit formation) - no major regressions in product or values At this level, the map is not just present or preferred: it's a normal choice; and, in many environments, it's expected. ----- #### Factors Driving Optimistic Adoption of Hexagons.World - is genuinely useful as a map (fast, readable, low-bandwidth, offline-capable, battery-light) - feels calm, human, and non-extractive (no ads, no tracking, no manipulation, no dark patterns) - reduces cognitive and emotional load (clear visuals, stable design, no urgency or noise) - makes places feel like places, not coordinates (named hexes, local texture, human-scale orientation) - solves everyday problems quietly (food, safety, calm routes, warmth, shade, nearby help) - spreads socially without demanding attention (posters, QR codes, campus walls, group chats, conversations) - fits student and youth rhythms (low-stakes, optional, works between classes and during holidays) - supports care without labelling people (loneliness, recovery, stress, poverty addressed without stigma) - rewards curiosity rather than outrage (calm statistics, visible uncertainty, transparent sources) - invites participation without obligation (small actions count, nothing is required, nothing is ranked) - gives people a sense of local stewardship (naming, noticing, improving “our hexagon”) - aligns with ethical and charitable instincts (non-profit, open-source, not owned by big tech) - feels politically and institutionally safe (neutral tone, no ideology, compatible with universities and NGOs) - is technically simple, cheap, and durable (deterministic, cache-heavy, forkable, long-lived) - benefits from visible adoption (seeing others use it increases confidence) - accumulates usefulness over time (each project, layer, and contribution adds value) - arrives early and then just stays (installed once, quietly useful forever) - feels like infrastructure, not a product (background layer, not a brand) - offers an alternative future without demanding belief (useful first, meaningful later) - makes local action legible at every scale (neighbourhood → campus → city → region → world) - allows optimism to compound (small wins visible, momentum without hype) ----- #### Reasonable, visionary and optimistic hexagons.world is intended to be a global addressing system and a shared backdrop across Peaceful Foundation projects, not just a consumer maps product. In this scenario, the maps app is no longer mainly competing as a replacement app. It functions as an addressing layer, a shared reference layer, and a neutral piece of civic infrastructure that other tools and institutions can quietly build on. Addiction support, cheap food, calm spaces, skills, and local coordination are all things people already need. When those show up in real places — campuses, communities, small groups — a map becomes naturally useful as the shared reference for where things are and what’s happening nearby. We assume that, given the scale of addiction, cost-of-living pressure, and loneliness worldwide, and the open, forkable design of the stack, Peaceful Foundation projects can reach most of the global population over time. Within that, hexagons.world stands on its own as a useful system. A reasonable long-term outcome is: ***~1.0–1.5 billion*** people using it as their main map another ***~1.0–2.0 billion*** using it as a secondary or situational map At this level, the question is no longer “will people switch maps?” but “where does this become normal infrastructure?”. As such, the model shifts from top-down percentages to bottom-up adoption through funnels. Growth comes from specific, grounded use cases that already exist at scale: addressing, food, safety, campuses, and local coordination. --- 1) Addressing & Naming Funnel (“Where are you?” becomes “I’m at river-lantern-market”) Hexagons.World is, at root, a global addressing system: the planet is wrapped in nested hexagons, with short, human names that feel like the actual place. A 4–6-word name points to a 10–1 metre patch of Earth in a way your aunt can say out loud. Think of where this is *actually* painful today: * Large parts of Africa and Latin America: informal addresses, dropped pins in WhatsApp, “I’m near the blue shop, opposite the pharmacy”. * Even in rich countries, big campuses, parks, beaches, festivals, and informal housing don’t really have precise, shareable addresses. Rough demographic picture: * About 4 billion people live in countries where addressing is often messy in daily life (big chunks of India, SE Asia, Africa, Latin America, Middle East). * India: chaotic addressing, huge messaging culture, multi-language support makes hexagon names extremely useful. * Oceanic Asia (Indonesia, Philippines, Vietnam, Thailand): informal addresses, lots of “I’m here, come find me”. * Africa (Nigeria, Kenya, Ethiopia, South Africa): mobile-first adoption, informal addressing, rapid urbanisation. * Arabia: mixed addressing quality + widespread smartphone penetration. * Americas: similar patterns, especially Brazil and Mexico. * Europe: formal addressing, but interest in pronounceable location handles and non–big-tech alternatives. Assume that by the time Hexagons is mature, ~2–2.5 billion of these people have smartphones. If hex names are: * pronounceable in local languages * visible on posters, receipts, and event materials * integrated into Peaceful tools and local apps then it’s plausible that **40–50% of these users** routinely use hex names. That’s **~800 million to 1.2 billion people** for whom addressing alone justifies having the map. These users are not “switching maps”. They are using the easiest way to describe where they are. --- 2) Campus & Youth Calm Funnel (Campuses as default home hexagons) Students want a low-stakes way to see what’s happening nearby. This meets that need without the pressure of social media or the chaos of chat groups. Global higher education enrolment is ~260 million. Including school-age teens, that’s **~500–800 million 15–24 year-olds with smartphones**. On campuses where Calm.College and Reasonable.Diet are present, the map sits underneath: * QuitEasily posters in bathrooms * Reasonable.Diet posters in cafeterias and dorms * calm meetups marked by hex * cheap food and staples mapped locally Students: * scan QR codes * join small meetups * start referring to “our hex” On a 20,000-student campus: * Year 1–2: 2,000–4,000 installs * Year 3–4: 8,000–12,000 regular users If Hexagons reaches **~20,000 campuses** with ~10,000 users each: → **~200 million users** With spillover through peer networks: → **~200–300 million users globally** For many, this becomes their default map after graduation. --- 3) Addiction Cessation Funnel (QuitEasily becomes a cultural force) Porn, alcohol, nicotine, and phone addiction do not care about borders. Prevalence is enormous in India, Pakistan, the Middle East, the US, Europe — everywhere. QuitEasily lives in bathroom posters, private tabs, and late-night scrolling. The on-ramp into Maps is simple and quiet: * See a poster in a school or university bathroom * Scan or visit, read privately * Notice “there’s a calm meeting in this hex next week” or “this is what’s happening near me” Most people who find QuitEasily helpful will share it quietly — one-on-one chats, DMs, or a photo of a poster. That alone can reach **tens of millions** without ever becoming a visible “movement”. If, globally: * 200–400 million people seriously attempt to change an addictive behaviour over a decade, and * 20–30% of them look at the map to see what’s nearby, and * around half of those keep it installed you arrive at **~20–40 million people** whose first meaningful contact with Hexagons is: > “this helped me get through something difficult.” Those users tend to stay. The map becomes a calm, non-shaming background tool rather than something they consciously “use”. This funnel is small relative to others, but unusually deep. --- 4) Cheap Food & Cost-of-Living Funnel (“This map literally helps me eat”) Cost-of-living pressure is nearly universal: students, migrants, low-income households, single parents, elderly people on fixed incomes. Reasonable.Diet plus Hexagons looks like: * “Here’s a $2/day plan for *this area*, using *this supermarket*.” * A hex shows real staples and real price ranges nearby, not just recipes * Community kitchens and shared meals pinned to local hexes Roughly: * 1–1.5 billion people live under sustained cost pressure * ~600–800 million have consistent smartphone access If Reasonable.Diet is visible on campuses, in welfare settings, faith communities, and migrant networks, it is reasonable that: → **~200–400 million people** use the map at some point as a “food map” This is not occasional usage. It is tied to daily life: * checking what’s affordable this week * seeing where food is available nearby * deciding what to cook based on local reality Once the map is associated with “I didn’t skip meals this week”, it becomes hard to replace. This funnel grows because it is quietly useful every day. --- 5) Safety, Calm Routes & Daily Navigation Funnel (The strongest “paying” reason) Maps is designed as a calm navigation and planning tool: * safe walking, cycling, driving, and transit * well-lit paths at night * shade-first routing in hot climates * offline capability * no ads or tracking The paid layer (~$1/month) deepens this: * personalised routing * safer night navigation * multi-stop planning that respects time and energy Who cares most: * women * migrants and tourists in unfamiliar areas * people in regions affected by extreme heat * anyone needing predictable, low-stress navigation * delivery drivers and mobile workers If we assume: * ~3 billion women with smartphones as the system matures * plus several hundred million others with high safety needs and estimate: * **20–30%** use it as a comfort-first or night-time map * **10–20%** of those pay for deeper features you reach: → **~600–900 million users**, with a meaningful paying base This is not driven by preference or ideology. It is driven by physical safety and comfort. --- 6) Mutual Aid, Local Life & Third Places Funnel (When a map feels like a gentle noticeboard) Once mutual aid, events, and third places are visible by hex, the map becomes: > “the map of what’s actually happening nearby” It surfaces: * cafés where you can sit without pressure * libraries, parks, community centres, faith spaces * small events, workshops, skill-sharing * clean-ups, share shelves, repair cafés It does not require mass participation: * In a district of 50,000 people, ~50–200 active contributors is enough * If **5–10%** of residents check weekly, the map feels alive This creates a visible, low-pressure layer of local life. Even if only a small percentage of people use it this way globally, they become the ones who: * screenshot hexes * share local activity * bring others in This funnel grows because people want something that is local, calm, and real — not another feed. --- 7) Civic Curiosity & Local Statistics Funnel (“How is our area doing?”) Hexagons also functions as a statistics map: * poverty, food access, heat, shade, loneliness * shown at a local level * with visible uncertainty and clear methods This attracts: * students * journalists * council staff * NGO workers * residents who want a grounded view of their area Even: → **~50–100 million regular users** can have disproportionate impact. These users bring the map into: * classrooms * articles * council discussions * planning processes Over time, institutions may adopt the same layer for their own data. This funnel is small in number, but high in leverage. --- 8) Developer, Anti–Big Tech & Open Infrastructure Funnel Underneath everything, Hexagons is: * a deterministic grid * a simple, low-cost API * an open, forkable system This matters for: * developers who want an ethical alternative * local apps (campus tools, co-ops, faith communities) * regions that prefer not to depend on a single corporate provider If, over time, Hexagons becomes the default layer for: * 5–10% of new location-based tools * much of the Peaceful Foundation ecosystem then hundreds of millions of people interact with it without installing anything new. The map simply appears: * inside a campus app * inside a local tool * inside a community system This funnel does not show up cleanly in “installs”, but it multiplies every other funnel. --- 9) Curiosity and Aesthetic Joy Hexagons maps are deliberately calm, readable, and visually soft — closer to a gentle game map than a satellite image or ad surface. This creates a subtle effect: * people screenshot their home hex because it looks nice * they feel a small sense of ownership over “our hex” * they share names because they are pleasant to say Feeling connected to your hexagon * a quiet sense of place * a small emotional anchor * something that feels personal without being private Perhaps: → **~50–150 million people** are drawn in initially by this But the real role of this funnel is retention. It is the reason people do not delete the app. It is also why institutions are more willing to use it — it feels like public infrastructure, not a commercial surface. Over time, this is what keeps the system stable. --- Rather than summing funnels, it's more accurate to think in terms of places where multiple funnels overlap. The system grows by layering these funnels in the same environments. Over time, the map stops being something people “try” and becomes something the environment assumes is present. So we don’t add the funnel numbers directly. Instead, we can think in terms of bands of places where different layers are active. a) **Full-stack zones** Cities and regions where three or more Peaceful projects are active and visible. Realistically this could be ~40% of smartphone owners, around 2.4 billion people. In these places, the map is woven into daily life: * youth posters * food systems * mutual-aid chats * local campaigns * campus spaces People don’t “find” the map — they keep encountering it. It is very reasonable to expect: * ~70% install * ~60% of those treat it as their default ##### ~1.0–1.1 billion “this is my main map” users b) **Partial-stack zones** Areas with one or two strong entry points, plus some developer or community uptake. This is roughly ~35% of smartphone owners, or around 2.1 billion people. Here, people arrive through a specific need: * cheap food * safe routes * hex names for messy addresses The map is useful, but not yet assumed. We could assume of the funnels: * ~40% install * ~40% of those prefer it for certain tasks ##### ~300–400 million primary users c) **Outer zones** Places with little formal Peaceful presence, but strong underlying need: * addressing problems * safety concerns * offline use * developer embedding For the remaining **~1.5 billion smartphone owners**. Here, adoption is individual rather than environmental. If: * ~10–20% install * ~20–30% of those keep it as a “comfort map” ##### ~30–90 million primary users Put together, in a mature, optimistic-but-realistic scenario: Installed at some point: **~2.5–3.0B people** Used regularly (weekly or more): **~1.5–2.0B people** Felt as “my main map for everyday life”: **~1.0–1.5B people** This is not driven by a single feature. It comes from the map sitting underneath multiple everyday activities. These are not abstract users — they’re people, like: - women checking safe routes at night - students checking local food and events - families sharing simple location names - workers using reliable navigation - neighbours watching their area improve This is a different growth pattern than a typical product funnel; it's many overlapping reasons, appearing in the same places, until the map starts to feel like part of the environment itself. It grows more like: - Wikipedia - email - maps apps themselves Widely relied on, unevenly distributed, and reinforced by everyday use rather than a single moment of adoption. ### What if? The “default” public What if where anyone can poke the map and try “what if loneliness/food security/whatever changes by X%” using the shared indicator graph. Public, map-based or embed-based scenario tools. Using shared indicators and shared compute. No private datasets; just hex-level or otherwise aggregated public data. Free or very low friction (sometimes embedded inside calm.college, hexagons.world, etc.). this asks a simple question: how many people might actually use the public scenario tool not private institutional sandboxes not enterprise backends the open layer anyone can poke #### Universe The global universe for a public scenario tool consists of three layers: Demographic universe Hundreds of millions of people who might plausibly encounter the tool. Power-users A few million organisers, journalists, teachers, and practitioners who actively use the tool to explain ideas. Regular users A smaller subset who repeatedly run scenarios in their own work or thinking. The aim is not to count unique individuals precisely, but to establish the rough scale of people who might plausibly encounter the tool over a decade. Rather than counting the entire global public, we narrow the population step by step: from the reachable digital public, to people who regularly engage with public issues, to the groups most likely to encounter a scenario tool. The final step adjusts for overlaps between those groups, producing a rough but realistic universe of people who might plausibly encounter the tool over a decade. So, which demographics might encounter it? Each group, • Has internet access and some comfort with interactive tools. • Are at least mildly curious about local conditions and “what if X changed?”. From there, we're looking for a slice actively reads about pollution, poverty, housing, mental health, and other public indicators. The exact percentage is fuzzy, but it is safe to say that hundreds of millions of people regularly consume news or content about social issues. We can make a rough estimate of this as around 500 million people who are civically engaged within the reachable digital public. *Power users* Across each of these demographic groups is a smaller cross-cutting layer of power users who actively explain ideas to others. These are not a separate demographic so much as a behavioural layer inside the broader universe. Within the civically engaged is a much smaller subset of people who actively explore data and share insights locally. Examples include: • civic tech volunteers • community researchers • open data advocates • urban planning enthusiasts These “local civic data enthusiasts” often operate informally but can generate significant exposure. Estimated scale: ~1–3 million globally. Within this universe, several demographic groups are especially likely to encounter a public scenario tool. The groups below are not separate populations so much as common pathways through which people discover and use tools like this. a) ***Students*** Secondary and tertiary students are one of the most important exposure channels. • ~264 million higher-education students globally. • Including senior high school and vocational programmes with project-based learning brings the plausible exposure pool closer to 400–600 million. Within this population there are two common usage paths: Assignment users students using What if because a teacher or professor assigns it in coursework. Self-directed users students discovering the tool through calm.college, social media, or campus discussions. For modelling purposes, assume roughly: ~400 million students globally who could plausibly be shown 'What if?' over a decade. *Students as power users* A small subset of students become organisers or communicators who actively use the tool to explain ideas. Examples include: • campus sustainability organisers • mutual aid organisers • student journalists • project leaders in courses Because universities contain dense networks of curious people, even a small number of organisers can create large exposure. Estimated scale: ~2–5 million potential student power users. b) ***Practitioners*** Another major segment consists of people working in roles where indicators and planning already matter. • local government staff • NGO and community organisation staff • teachers and researchers • journalists and analysts • public health, social services, and urban planning professionals Rough order-of-magnitude estimate: ~150 million people globally who are professionally adjacent to indicator-based planning. *Practitioners as power users* Within this group are practitioners who regularly communicate insights to others. • teachers introducing models in class • researchers presenting scenarios • planners explaining outcomes to communities • policy professionals discussing indicators publicly Estimated scale: ~1–2 million practitioners who regularly communicate outward. Related group: NGO and campaign staff People working in advocacy or community organisations often need ways to explain potential outcomes. Scenario tools can support: • campaign messaging • workshops • grant proposals • local planning discussions These users sometimes publish simplified public scenarios derived from internal work. Estimated scale: ~1–2 million globally. c) ***Organisers, journalists, and civic communicators*** Across the groups above is a smaller category of people who naturally explain ideas publicly. • student organisers • local community organisers • journalists writing explanatory pieces • NGO communications teams For them, What if becomes both: • a tool for thinking through a scenario • a visual object they can share, embed, or screenshot to explain the idea to others *Organisers, journalists and civic communicators as power-users* These users actively employ the tool to communicate ideas, organise projects, or illustrate outcomes. Typical activities include: • embedding scenarios in articles or reports • demonstrating models during workshops or classes • sharing screenshots on social media • using scenario outputs in planning discussions Estimated scale: ~3–6 million organisers and civic communicators globally. Additonally, journalists are one of the most powerful distribution channels for interactive tools. Typical uses include: • embedding scenario tools inside explainers • linking to models in articles • using screenshots to illustrate outcomes Modern data journalism regularly uses interactive maps, sliders, and calculators, making scenario tools a natural fit. Estimated scale: ~200k–300k journalists worldwide, with a smaller subset focussed on explanatory or data-driven reporting. ##### Overlap In practice, the largest overlap is between students and the civically engaged public, with smaller but important overlaps between practitioners and communicators, and between students and organisers about 55% of students also belong in the civic-interest pool about 70% of practitioners also belong in the civic-interest pool about 10% of students overlap with practitioners in some meaningful way over a decade That gives: Students ∩ Civic = 0.55 × 400M = 220M Practitioners ∩ Civic = 0.70 × 150M = 105M Students ∩ Practitioners = 0.10 × 150M = 15M Now we need one triple-overlap correction, because some people sit in all three groups simultaneously. The student–practitioner overlap represents people who move between study and professional environments over the decade horizon. This includes: • graduate students doing research • teaching assistants • students working in NGOs or policy internships • students in public-health or planning placements • early-career professionals returning for further study These individuals are disproportionately concentrated in policy, research, planning, and other socially oriented fields. Because of this, civic engagement rates within the student–practitioner overlap are likely higher than in either group individually. A conservative assumption is therefore that roughly two-thirds of the student–practitioner overlap also belong in the civic-interest pool. Students ∩ Practitioners ∩ Civic ≈ 0.67 × 15M ≈ 10M So your overlap-adjusted demographic universe is: ~720 million unique people globally The cleaner decomposition of that 720M is: 185M civic-interest public who are not mainly captured through student/practitioner channels 175M student-only or mostly-student users 40M practitioner-only or mostly-practitioner users 320M in overlaps between those worlds The really nice thing is that this also matches Peaceful Foundation more broadly — people are not fixed categories, they’re overlapping participants moving through shared contexts, projects, and local roles. #### Adoption Without including organisations, there are three main adoption layers: 1. Ever touched it • A person ran at least one scenario, ever. • Maybe they dragged a slider or chose an option once, then bounced. 2. Occasional / annual • They come back at least once or twice a year, usually when triggered: – new course assignment – local election / referendum – a campaign 3. Regular / monthly • They run scenarios at least monthly: – organisers planning events – students doing multi-week projects – people who just like this way of thinking You can then define “adopted” at the person level as: at least in the “occasional” bucket; “core users” as “regular/monthly”. The adoption estimates below use a simple structural model rather than a forecast. Adoption is modelled as a sequence of filters: exposure universe (U) ↓ awareness (A) ↓ trial (T) ↓ return usage (R) ↓ regular/monthly users (M) Each scenario varies these parameters to explore how different levels of awareness and engagement affect the eventual size of the active user base. Because the model is multiplicative, relatively small changes in awareness or return behaviour can produce large differences in the number of regular users. The goal is not to predict exact user numbers, but to test whether the system is plausible at global scale given the exposure universe defined earlier. what drives adoption awareness and entry where first encounter class assignment calm.college challenge embedded in article tiktok youtube clip first experience friction one tap one slider one obvious visual change //: or does it need accounts onboarding explanations cognitive load non-technical person understands sliders in 5–10 seconds defaults sane and safe what if local food security improves 10% surface qualitative narratives more people eat this many meals not just raw numbers level of abstraction choose actual place they care about city campus hex-word place-based more sticky see my hex my what if story what we're trying locally social loop natural way to show share scenario screenshot link mini story card say we're trying to push our hex from this to this situations triggering use time-based prompts calm.college: "see how your hex changed after finals week" reasonable.diet: "track food security through Ramadan" seasonal campaigns local budget consultations global climate conversations ##### [Awareness rate] This is the fraction of that universe who ever encounter the tool. Awareness comes from: • coursework • embeds in media articles • NGO campaigns • calm.college / hexagons.world • journalists and organisers sharing links Typical awareness levels for niche analytical tools fall around: A = awareness rate 0.5% – 20% ##### [Trial rate] Of people who encounter it, how many actually try running a scenario? • A person ran at least one scenario, ever. • Maybe they dragged a slider or chose an option once, then bounced. The friction is low if the interface is just sliders and maps. Typical range: T = trial rate 20% – 60% ##### [Return rate] Of people who try it once, how many return occasionally? These are people who come back: • for assignments • during campaigns • when a news story links to it R = return rate 20% – 50% ##### [People who become regular users] Of those occasional users, how many become regular users? These are: • organisers • journalists • practitioners • students doing extended projects M = monthly fraction 20% – 50% The relationship can be approximated as: Regular users ≈ U × A × T × R × M Where: U = exposure universe A = awareness rate T = trial rate R = return rate M = fraction of return users who become regular users This is not a precise forecasting model. It is a way to understand how small improvements in awareness or usability can dramatically affect the number of active users. The spread of scenario tools does not happen evenly across the global population. Instead, it tends to follow the structure of existing social and institutional networks. Ideas often spread between these clusters through relatively small numbers of highly connected individuals, such as: • students sharing tools across universities • journalists referencing work from other newsrooms • NGOs collaborating across countries • researchers publishing or presenting tools at conferences Because these networks are already global, a tool that becomes useful within one cluster can gradually appear in others without needing mass marketing. Adoption often follows a predictable pattern: one university or city experiments ↓ students and organisers share results ↓ journalists or NGOs reference the work ↓ similar communities in other cities try the tool ↓ local adaptations appear This pattern explains why many civic (and analytical) tools suddenly appear in multiple countries once they become useful to a small number of connected communities. 'What if' is particularly well suited to this type of spread because it already connects several of these network clusters: • universities through calm.college • civic communities through hexagons.world • campaigns and NGOs through scenario sharing • media and public discussion through embedded scenarios Each of these environments forms a node in the global civic knowledge network. As adoption grows within one cluster, the probability that the tool appears in others increases through existing professional and social connections. This network structure means that the system does not need to reach a large share of the global population directly. Instead, adoption can expand gradually across connected clusters of universities, cities, and civic organisations. As more clusters adopt the tool, the overall awareness variable in the adoption model increases naturally through these networks. Additionally, our ecosystem dramatically increases the awareness variable. Most people do not directly seek out scenario tools. Instead, they encounter them because someone else used the tool to explain an idea — in a class, an article, a presentation, or a campaign. This means adoption is often driven less by individual discovery and more by explanation networks. For example: If 100,000 power users each expose just 100 people per year through classes, articles, workshops, or social sharing: 100,000 × 100 = 10,000,000 exposures per year Over a decade, that alone could generate tens of millions of encounters with the tool. 'What if' only needs to become useful to people who explain things to others. #### Adoption estimates Let’s assume that “normal What if?” is: • integrated into campaigns, maps, and scenario pages • accessed through hexagons.world, calm.college, and related projects • usable without requiring private institutional data • visible across some campuses, NGO pages, and journalism embeds • but not a viral consumer product like scrolling social media platforms. So, how many people might actually use that? As a quick summary, the model produces the following orders of magnitude: conservative niche but real ~4 million ever users ~0.9 million occasional users ~0.25 million monthly users middle strong niche known in circles ~21 million ever users ~6 million occasional users ~2.3 million monthly users ambitious standard tool in subculture ~69 million ever users ~27 million occasional users ~12.7 million monthly users I’ll walk through the key pieces: total addressable people → who is even a candidate → behaviour types → a realistic adoption range. These are not forecasts so much as shaped scenarios showing that even small percentages of the exposure universe translate into very large numbers of people. #### Low Low adoption scenario — it works, but stays niche This scenario assumes that public What if becomes known in some student, civic, and practitioner circles, but remains invisible to most people. It spreads through classes, occasional media embeds, and some organiser use, without becoming a standard public tool. It appears occasionally across: • some university courses and student projects • a few civic technology or open-data communities • occasional NGO experiments or local campaigns • isolated journalism explainers Most people never encounter it. Even within the circles where it appears, many only see it once or twice. The tool is respected by the people who find it, but it never becomes broadly expected. Students encounter it sporadically during coursework. A lecturer might include a small scenario exercise in a planning class, or a sustainability tutorial might experiment with it for a project. Sometimes a student organiser discovers it while exploring a local issue. But this happens irregularly. Most students never see it, and most who do only use it once for a specific assignment. Student exposure therefore creates a pool of one-time users, but does not yet produce strong cultural normalisation. Practitioners who discover the tool often find it genuinely useful. People working in community-facing roles — local organisers, NGO staff, some researchers or planners — occasionally use it to explore or illustrate an idea. They might use it to: • sketch out a rough scenario • explain a local issue in simple terms • test a small assumption before writing a reportsocial • share a visual explanation with colleagues But discovery remains inconsistent. Many practitioners never encounter it, and institutions rarely adopt it as a standard tool. Journalists and communicators occasionally surface the tool in public explanations. An article might embed a simple scenario or reference a What if model when discussing housing, public health, or economic conditions. These appearances introduce the tool to small new audiences, but the chain rarely continues very far. Most readers treat the scenario as an interesting illustration rather than something they return to later. The civically engaged public therefore encounters the tool only occasionally. People may see it through: • a shared article • an NGO campaign page • a student project • a civic data conversation Some try adjusting a scenario once. Most do not return. A small fraction remember the tool and revisit it later. Using the model: U = exposure universe ≈ 720M A = awareness rate T = trial rate R = return rate M = monthly / regular usage fraction A conservative low scenario might assume: • Awareness: ~2% Only a small slice of the exposure universe ever meaningfully encounter the tool. This reflects a world where What if is present in some courses, some NGO pages, and some journalism, but still feels niche. • Trial: ~25% Of the people who encounter it, about a quarter actually run a scenario at least once. Many will see it, understand roughly what it does, but not click through. • Return: ~20% Of the people who try it, about one in five come back occasionally. Most use it once for a class, article, or local curiosity, then do not build a habit. • Monthly / regular usage: ~30% Of those occasional users, about a third become regular users. These are mainly organisers, some students doing longer projects, and a small number of practitioners. ##### Low-scenario assumptions by segment | Segment | U | A | T | R | M | |--------------------------------|-----:|-----:|----:|----:|----:| | Civic-only | 185M | 0.7% | 15% | 10% | 10% | | Student-only | 175M | 2.5% | 25% | 18% | 20% | | Practitioner-only | 40M | 2.5% | 30% | 30% | 35% | | Student ∩ Civic | 210M | 2.5% | 25% | 20% | 22% | | Practitioner ∩ Civic | 95M | 2.5% | 30% | 30% | 38% | | Student ∩ Practitioner | 5M | 4% | 35% | 35% | 40% | | Student ∩ Practitioner ∩ Civic | 10M | 5% | 40% | 40% | 45% | ##### Worked outputs | Segment | Ever users | Occasional users | Monthly users | |--------------------------------|-----------:|-----------------:|--------------:| | Civic-only | 194,250 | 19,425 | 1,943 | | Student-only | 1,093,750 | 196,875 | 39,375 | | Practitioner-only | 300,000 | 90,000 | 31,500 | | Student ∩ Civic | 1,312,500 | 262,500 | 57,750 | | Practitioner ∩ Civic | 712,500 | 213,750 | 81,225 | | Student ∩ Practitioner | 70,000 | 24,500 | 9,800 | | Student ∩ Practitioner ∩ Civic | 200,000 | 80,000 | 36,000 | ##### Totals • ~3.9M ever users • ~0.9M occasional users • ~250k monthly users Within the low scenario: • most exposure comes from isolated classroom encounters, occasional civic data discussions, or individual practitioners discovering the tool • students still contribute a meaningful share of first-time users, but coursework exposure remains irregular • practitioners account for a large share of repeat usage relative to their population size, because the tool solves a real explanatory problem for them • the civically engaged public creates scattered awareness, but only a very small fraction become regular users Because the pathways between these groups remain thin, adoption grows slowly and unevenly. A class project here, a campaign page there, a practitioner using it in a workshop — but without strong institutional reinforcement. So, in the low scenario after 10–15 years: • around 4 million people have used public What if at least once • around 800,000 return at least occasionally • around 200,000–250,000 are regular / monthly users worldwide This is the “serious niche tool” outcome: respected and genuinely useful within certain communities, but still largely invisible to the wider public. #### Middle Mid adoption scenario — it becomes normal in certain contexts This scenario assumes that public What if becomes widely known inside the ecosystems where it is naturally useful, while still remaining largely invisible to the wider public. It spreads steadily through classrooms, civic communities, campaigns, and journalism explainers, and begins to appear often enough that people in these environments recognise the tool and know roughly what it does. It appears regularly across: • university courses and student projects • civic technology and open-data communities • NGO campaigns and workshops • journalism explainers and policy discussions Most people outside these environments still never encounter it. However, inside them the tool begins to feel familiar. What if is no longer just an unusual experiment discovered by a few technically curious people. Instead, it becomes a recognised way to explore how indicators might change under different assumptions. People discussing housing, food security, loneliness, public health, or local economic conditions may occasionally expect that someone can open a scenario and test a simple “what if”. Students encounter it semi-routinely in coursework, especially in subjects that already touch public indicators: • public health • urban planning • sustainability • economics • social science methods • local policy or geography projects A student might first see it because the cool teacher asks the class to compare how a local indicator changes under different assumptions. Another might encounter it through a campus organiser exploring a small food-security or wellbeing idea. It is still not universal across education, but it is common enough that students in the relevant disciplines do not find it strange. Student adoption normalises scenario-thinking, and graduates carry it into institutions. Students can bootstrap the whole What if ecosystem because they refill each year, and are simultaneously the easiest serious users to reach, the best natural distributors, and the next class of practitioners. Practitioners begin using it in outward-facing ways. Not every practitioner adopts it, and many still rely mainly on internal spreadsheets, dashboards, or institutional tools. But among people working in community-facing, planning-adjacent, or indicator-heavy roles, What if becomes a recognised public layer they can use to: • illustrate a problem simply • discuss possible changes with non-specialists • run a lightweight scenario during workshops • support a campaign, briefing, or teaching session The civically engaged public sees more of it, but still usually through other people. They encounter it in: • a journalism explainer • an NGO campaign page • a university project page • a local organiser’s screenshot or embed • a civic-tech or open-data conversation Most still do not become reguThese ranges illustrate how small shifts in awareness and retention can produce very large differences in total usage over time, especially when applied to a large exposure universe.lar users. But What if begins to acquire public familiarity inside the circles that discuss housing, food security, loneliness, urban change, and local conditions. People may not remember the product name immediately, but the pattern of “open a scenario and see what changes” becomes recognisable. A reasonable 'middle-case' scenario might assume: • Awareness: ~8–12% Within the exposure universe, roughly one in ten people meaningfully encounter the tool through coursework, NGO campaigns, civic-tech communities, or journalism explainers. • Trial: ~30–35% Of those who encounter it, roughly a third actually open the interface and run a scenario. • Return: ~25–30% Among people who try it, around a quarter return at least occasionally — often when working on a project, discussion, or local issue. • Monthly / regular usage: ~35–40% Among those occasional users, a portion integrate it into recurring workflows such as coursework, teaching, campaign planning, or civic discussions. This produces a world where What if becomes a strong niche tool inside the ecosystems that regularly discuss public indicators. ##### Middle-scenario assumptions by segment | Segment | U | A | T | R | M | |--------------------------------|-----:|----:|----:|----:|----:| | Civic-only | 185M | 2% | 16% | 10% | 10% | | Student-only | 175M | 10% | 32% | 22% | 25% | | Practitioner-only | 40M | 8% | 40% | 35% | 45% | | Student ∩ Civic | 210M | 12% | 35% | 25% | 30% | | Practitioner ∩ Civic | 95M | 10% | 40% | 40% | 50% | | Student ∩ Practitioner | 5M | 15% | 45% | 45% | 55% | | Student ∩ Practitioner ∩ Civic | 10M | 20% | 50% | 50% | 60% | *Worked outputs* | Segment | Ever users | Occasional users | Monthly users | |--------------------------------|-----------:|-----------------:|--------------:| | Civic-only | 592,000 | 59,200 | 5,920 | | Student-only | 5,600,000 | 1,232,000 | 308,000 | | Practitioner-only | 1,280,000 | 448,000 | 201,600 | | Student ∩ Civic | 8,820,000 | 2,205,000 | 661,500 | | Practitioner ∩ Civic | 3,800,000 | 1,520,000 | 760,000 | | Student ∩ Practitioner | 337,500 | 151,875 | 83,531 | | Student ∩ Practitioner ∩ Civic | 1,000,000 | 500,000 | 300,000 | ##### Totals • ~21.4M ever users • ~6.1M occasional users • ~2.3M monthly users In the middle scenario: • students and student–civic overlap groups drive a large share of first-touch exposure • practitioners and practitioner–civic overlap groups contribute a disproportionate share of repeat and monthly usage • the civic-only public creates breadth and familiarity, but still contributes relatively little to regular use So, in the middle scenario after 10–15 years: • around 20–25 million people have used public What if at least once • around 6–7 million return occasionally • around 2–3 million become regular / monthly users This is the “strong niche” outcome: the tool becomes a standard reference point in many classrooms, civic-data communities, NGOs, and campaign environments, while still remaining outside everyday mass public use. #### Ambitious Ambitious adoption scenario — scenario thinking becomes culturally normal This scenario assumes that scenario thinking itself becomes culturally normal inside civic and educational environments, and that What if becomes a widely recognised tool within the ecosystems that regularly discuss public indicators. The tool spreads through universities, NGOs, civic technology communities, and parts of journalism and policy discussion. It appears widely across: • university courses and student projects • NGO campaigns and workshops • civic technology communities • journalism explainers and public discussions Most people still do not treat it like a consumer product or entertainment platform. However, within civic, educational, and policy-adjacent communities the interface becomes familiar. Opening a scenario and adjusting assumptions becomes a normal step when exploring a public question. For students, courses that already work with public indicators — public health, planning, sustainability, economics, geography, social policy — routinely include small scenario exercises. Students compare places, test simple assumptions, and use scenarios to support arguments in essays or presentations. Some encounter it through coursework. Others see it through student organising or campus projects exploring housing, wellbeing, food security, or local conditions. Over time this produces a cultural shift in how students approach public questions. Running a simple scenario becomes a normal step when exploring an issue. Plus, because student populations refresh every year, this creates a continuously renewing intake of users who already understand the tool. Graduates carry that familiarity into institutions. Student adoption therefore continues to act as the bootstrap layer for the ecosystem. Practitioners increasingly use What if as a lightweight public layer for explaining and exploring ideas. Many still rely on internal modelling tools and spreadsheets for detailed analysis. But What if becomes a common way to illustrate a question or explore a possible change with non-specialists. Practitioners use it to: • show how local indicators interact • illustrate potential impacts of policy changes • run small workshop discussions • support campaign or briefing materials • explain trade-offs to community audiences Because the tool is simple and public-facing, it becomes useful in contexts where technical models would be difficult to share. Journalists and communicators amplify the tool’s visibility. What if is used to explain articles, and public discussions increasingly include small scenario explorations. A journalist might show how different housing assumptions affect a local indicator. A campaign page might embed a simple scenario that lets readers explore trade-offs. The civically engaged public encounters the tool more often, though still mostly indirectly. People see scenarios through: • journalism explainers • NGO campaign pages • civic data discussions • university project pages • organisers sharing screenshots or links Many interact briefly. Some return when the same tool appears again in another context. Over time the interface becomes familiar enough that a portion of this audience learns how to explore scenarios themselves. These uses do not turn most readers into regular users. But they make the pattern of scenario exploration widely recognisable. The exposure universe defined earlier is concentrated within environments where analytical tools already circulate. These environments regularly introduce tools through: • assignments and class projects • workshops and presentations • articles and explainers • campaign materials • peer-to-peer sharing Because these networks are interconnected — students become practitioners, journalists reference NGO work, civic tech communities collaborate across cities — awareness spreads gradually across institutions. Adoption therefore grows through institutional and civic networks rather than consumer advertising. ##### Overlap-adjusted universe • Civic-only = 185M • Student-only = 175M • Practitioner-only = 40M • Student ∩ Civic = 210M • Practitioner ∩ Civic = 95M • Student ∩ Practitioner = 5M • Student ∩ Practitioner ∩ Civic = 10M Total universe = 720M An ambitious scenario might assume: • Awareness: ~20–30% Within the exposure universe, a large minority of people encounter the tool through coursework, NGO campaigns, journalism explainers, or civic discussions. • Trial: ~40–50% Among those who encounter it, nearly half experiment with a scenario at least once. • Return: ~35–45% A substantial share of those who try the tool return occasionally when exploring public questions. • Monthly / regular usage: ~45–55% Among returning users, a significant fraction become regular users — often students working on projects, practitioners explaining trade-offs, or organisers exploring local scenarios. This produces a world where 'What if' becomes a widely recognised civic reasoning tool. ###### Ambitious-scenario assumptions by segment | Segment | U | A | T | R | M | |--------------------------------|-----:|----:|----:|----:|----:| | Civic-only | 185M | 7% | 20% | 14% | 14% | | Student-only | 175M | 25% | 43% | 33% | 33% | | Practitioner-only | 40M | 20% | 52% | 48% | 55% | | Student ∩ Civic | 210M | 28% | 48% | 38% | 42% | | Practitioner ∩ Civic | 95M | 23% | 52% | 52% | 60% | | Student ∩ Practitioner | 5M | 32% | 58% | 58% | 65% | | Student ∩ Practitioner ∩ Civic | 10M | 40% | 62% | 62% | 70% | ###### Worked outputs | Segment | Ever users | Occasional users | Monthly users | |--------------------------------|-----------:|-----------------:|--------------:| | Civic-only | 2,590,000 | 362,600 | 50,764 | | Student-only | 18,812,500 | 6,208,125 | 2,048,681 | | Practitioner-only | 4,160,000 | 1,996,800 | 1,098,240 | | Student ∩ Civic | 28,224,000 | 10,725,120 | 4,504,550 | | Practitioner ∩ Civic | 11,362,000 | 5,908,240 | 3,544,944 | | Student ∩ Practitioner | 928,000 | 538,240 | 349,856 | | Student ∩ Practitioner ∩ Civic | 2,480,000 | 1,537,600 | 1,076,320 | ###### Totals • ~68.6M ever users • ~27.3M occasional users • ~12.7M monthly users In the ambitious scenario: • students and student–civic overlap groups drive a very large share of first-touch exposure • practitioners and practitioner–civic overlap groups contribute a disproportionate share of durable, repeated usage • the civic-only public contributes broad visibility and cultural familiarity, but still a relatively modest share of monthly use • the overlap groups become the real engine of cultural normalisation, because they move naturally between classrooms, institutions, campaigns, and public explanation So, in the ambitious scenario after 10–15 years: • around 70 million people have used public What if at least once • around 25–30 million return occasionally • around 10–15 million become regular / monthly users In this world, scenario exploration becomes a normal part of civic reasoning. People discussing housing, loneliness, public health, or local economic conditions often expect that someone can open a simple scenario and test a few assumptions. What if does not dominate the internet, but it becomes a widely recognised way to explore public questions in classrooms, campaigns, civic discussions, and policy conversations. ----- The three scenarios describe different ways the ecosystem could evolve rather than precise forecasts. | Scenario | Awareness | Trial (of aware) | Return (of trial) | Monthly / regular (of return) | |-----------|-----------|------------------|-------------------|-------------------------------| | Low | ~2% | ~25% | ~20% | ~30% | | Middle | ~8–12% | ~30–35% | ~25–30% | ~35–40% | | Ambitious | ~20–30% | ~40–50% | ~35–45% | ~45–55% | These ranges illustrate how small shifts in awareness and retention can produce very large differences in total usage over time, especially when applied to a large exposure universe. In the low scenario, What if exists and is used by thoughtful communities, but adoption remains scattered. It appears in some classrooms, civic data circles, and campaigns, yet most people never encounter it. In the middle scenario, the tool becomes normal inside the environments where public indicators are already discussed. Students encounter it in courses, practitioners begin using it in workshops and campaigns, and journalists occasionally reference it. Within those circles it becomes familiar, even though the wider public still rarely sees it. In the ambitious scenario, scenario thinking itself becomes culturally normal in civic and educational spaces. What if is widely recognised across universities, NGOs, civic technology communities, and parts of journalism and policy discussion. It is still not a mass consumer product, but for demographics who find it useful it's a standard way to explore how changes might affect real-world conditions. | Scenario | Ever users | Occasional users | Regular users | | --------- | ---------- | ---------------- | ------------- | | Low | ~3.9M | ~0.9M | ~250k | | Middle | ~21.4M | ~6.1M | ~2.3M | | Ambitious | ~68.6M | ~27.3M | ~12.7M | Also, another important consideration is that the regular version of What if doesn’t live in isolation: What if – Private gives organisations an incentive to get comfortable with the mechanics, then encourages them to share some simplified public scenarios back out as normal What if views. The middle and ambitious scenarios above could reasonably assume that thousands, or tens of thousands, of organisations are using the private version and occasionally publishing cleaned-up, non-sensitive scenarios to the public layer. In other words, many users are not arriving at hexagons.world directly. A large share of exposure may come through: * calm.college or university portals * hexagons.world or city portals * NGO campaign pages * media embeds (“Explore this scenario in What if”) As such, a realistic long-horizon picture ranges from a serious niche tool with hundreds of thousands of regular users to a widely recognised civic tool with millions of regular users. ### What if? - Private **“What if? – Private”** is intended to be an organisation-oriented product where a team runs scenarios using their own datasets *(internal spreadsheets, research outputs, survey data, etc.)* inside a sandboxed private workspace; the private data stays within that workspace (not written into the public indicator base), and private runs can be executed on their own machines, or in their own cloud account within a Peaceful Foundation adjacent data centre. Global adoption is not “either normal or Private” – they reinforce each other: • Normal What if: – grows the *culture* of thinking in scenarios and indicators. – gives individuals and small groups a way to play/learn. • Private What if: – gives organisations a safe, deeper sandbox. – often entered from someone who first met the idea via normal What if (a student, organiser, journalist). As a summary, assuming that and 'What if? - Private' is good, easy, loved, and privacy-sane, the realistic long-run envelope is something like: | Scenario | Rounded active users | | ----------------------- | -------------------: | | Very conservative | ~450k | | Middle | ~1.4M | | Ambitious | ~3.9M | | Global / infrastructure | ~10M | #### Universe How many small organisations have recurring decisions where private data + place + scenario modelling is useful enough to become habit? | Sector group | Example users | Estimated reachable audience | Likely active contributors per workspace | | ------------ | ------------- | --------------------------: | ---------------------------------------: | | Civic and public services | councils, health teams, schools, public agencies | ~2M | 5–40 | | Charities and NGOs | charities, mutual aid, faith services, food groups | ~5M | 2–20 | | Education | universities, schools, labs, student projects | ~4M | 3–25 | | Local services | plumbers, electricians, cleaners, repair, home care | ~35M | 1–6 | | Retail and hospitality | cafés, grocers, restaurants, salons, gyms | ~45M | 1–8 | | Transport | delivery, couriers, fleet operators, market distribution | ~10M | 3–30 | | Agriculture and environment | farms, co-ops, conservation, water/tree/land teams | ~15M | 2–20 | | Property and infrastructure | strata, landlords, solar installers, builders, maintenance | ~10M | 2–20 | | Consultants and scenario people | grant writers, GIS freelancers, analysts, local advisors | ~4M | 1–10 directly, but many client organisations indirectly | | **Total** | | **~130M** | | // consolidate calculations into the following groups instead of the wide bands we've created earlier We can consolidate these into the following: ##### Institutions Councils, NGOs, charities, community services, schools, universities, research groups, public-health teams, and local service providers. These are the cleanest fit because they already think in terms of regions, programmes, reporting, grants, outcomes, and public benefit. Your notes already say councils, community organisations, and researchers need ways to combine service data, surveys, case notes, maps, and before/after views without learning full GIS. ##### Operational Local Businesses Plumbers, electricians, cleaners, delivery businesses, couriers, mobile mechanics, home-care providers, gardening businesses, pest control, trades, food trucks, market stalls, mobile hairdressers, local logistics, tutoring agencies, repair services. Where are jobs clustering? Which suburbs generate repeat visits? Where should we open next? What happens if fuel prices rise? Which route pattern wastes the most time? What if we hired one more person in this zone? ###### Retail and Hospitality Cafés, restaurants, shops, small grocers, market sellers, gyms, clinics, salons, laundromats, childcare centres. Their questions are different: Where do customers come from? Which nearby events affect demand? What if we open later? What if there’s a heatwave, festival, exam week, or road closure? Where would a second location make sense? ##### Consultants and 'scenario People' A lot of small businesses won’t use What if – Private directly. But accountants, local consultants, grant writers, business coaches, GIS freelancers, NGO advisors, and planning consultants could use it on behalf of many small clients. Most organisations already make place-based decisions constantly. The question is not whether spatial planning exists; it is whether the tooling becomes calm, cheap, transparent, and flexible enough that many more people can participate in it directly. So the adoption universe is less like “enterprise software seats” and more like a broad layer of operational people spread across institutions, local businesses, service networks, and planning-heavy environments. #### Assumptions i) It is better. // better data // better estimates // better than nothing * For the people who already do scenario thinking in Excel/PowerPoint/BI: * Time to “first useful scenario” drops dramatically. * Existing workflows can be partially replaced (grant modelling, programme scenario notes) instead of duplicated. It is very intuitive and enabling something new.” ii) It is easier. “It is easier than their current workflow and people love it. * For institutions not currently doing structured scenario work: * It unlocks a new behaviour: small, local, indicator-aware scenario conversations. * So once it’s tried in an organisation, **within-org diffusion can be fast**: * One team uses it for grant writing → others see the outputs → internal word-of-mouth. Integration friction is genuinely “spreadsheet-level.” That aligns with the design text explicitly calling out internal spreadsheets and sandboxed organisational datasets. * You’re not asking them to replatform; you’re saying: * “Give us the CSVs / exports you already use.” * Or “connect to your existing DB / BI layer in a read-only, privacy-preserving way.” * So integration friction is more: * “Who is allowed to upload these numbers?” than * “We must rebuild our data stack.” Product-market fit: strong for the people already suffering through clumsy “what if” work. iii) It's simple to implement. Privacy and control are credible enough for institutions to trust it. That aligns with “sandboxed datasets,” “not written into the global indicator set,” and “private jobs run only in the chosen environment.” Interpretting this as: * Clear, open documentation about: * What is stored where * Who can see what * How scenarios are separated from public PF indicator layers * Strong defaults: * No raw personal data * Clear red lines about what cannot be uploaded at all * Obvious “this stays inside your org” boundaries Result: * For universities and NGOs, IT/security signoff is significantly easier. * For councils, the political risk is lower: * “We’re using a neutral, open tool that doesn’t expose our internals” is sellable to managers and councillors. #### Adoption Adoption is mainly driven by people who are involved in decisions and planning; employment share is a good starting point, but not the final user base. A place-based private decision-maker is someone who, at least monthly or quarterly, uses private or internal data to decide where to allocate people, services, stock, money, routes, facilities, outreach, risk management, or local activity. The adoption rate is the share of candidate organisations that uptake private workspaces, these rates are intentionally small because even tiny adoption rates become meaningful because the candidate universe is large. | Sector group | Very conservative | Middle | Ambitious | Global / infrastructure | | ------------ | ----------------: | -----: | --------: | ----------------------: | | Civic and public services | 0.25% | 1.00% | 3.00% | 6.00% | | Charities and NGOs | 0.30% | 1.20% | 3.50% | 7.00% | | Education | 0.25% | 1.00% | 2.50% | 5.00% | | Local services | 0.025% | 0.10% | 0.40% | 1.00% | | Retail and hospitality | 0.020% | 0.075% | 0.30% | 0.80% | | Transport | 0.075% | 0.30% | 1.00% | 2.40% | | Agriculture and environment | 0.060% | 0.20% | 0.75% | 1.60% | | Property and infrastructure | 0.050% | 0.20% | 0.75% | 1.60% | | Consultants and scenario people | 0.50% | 2.00% | 5.00% | 10.00% | These rates are intentionally small, as even tiny adoption rates become meaningful because the candidate universe is large, and the rates reflect how likely each sector is to turn place-based scenarios into regular work. Civic, NGOs, education: higher need for reporting, grants, public explanation, and programme planning. Local services, retail, hospitality: much larger universe, but lighter usage and lower direct adoption. Transport, agriculture, property: practical operational use cases, especially where routing, heat, demand, land, or maintenance matter. Consultants: smaller population, but highest adoption intensity because one consultant can use the tool across many clients. The rates are higher for sectors where scenario work is already closer to normal planning, reporting, grant writing, modelling, consulting, or public explanation. They are lower for local services, retail, and hospitality because many of those organisations are small, busy, and unlikely to adopt scenario tools directly unless the product becomes extremely simple or arrives through another channel. #### Calculations #### Workspaces Calculations Per Sector Using the sector universe and the adoption rates above, the rough number of paying private workspaces becomes: | Sector group | Very conservative | Middle | Ambitious | Global / infrastructure | | ------------ | ----------------: | -----: | --------: | ----------------------: | | Civic and public services | 5,000 | 20,000 | 60,000 | 120,000 | | Charities and NGOs | 15,000 | 60,000 | 175,000 | 350,000 | | Education | 10,000 | 40,000 | 100,000 | 200,000 | | Local services | 8,750 | 35,000 | 140,000 | 350,000 | | Retail and hospitality | 9,000 | 33,750 | 135,000 | 360,000 | | Transport | 7,500 | 30,000 | 100,000 | 240,000 | | Agriculture and environment | 9,000 | 30,000 | 112,500 | 240,000 | | Property and infrastructure | 5,000 | 20,000 | 75,000 | 160,000 | | Consultants and scenario people | 20,000 | 80,000 | 200,000 | 400,000 | | **Total private workspaces** | **89,250** | **348,750** | **1,097,500** | **2,420,000** | #### Users Within Workspaces Once private workspaces are calculated, we can estimate how many people actually sit inside those workspaces. There are two user types: ##### 1. Active Contributors People who edit, upload, run, schedule, comment, approve, or manage scenarios. Different sectors have different workspace shapes. A consultant may have one or two people actively using the model, but several client viewers. A council project may have a larger active team and many internal reviewers. A café, local service business, or small retailer may only have a few people involved. To estimate people, multiply workspaces by likely active contributors. | Sector group | Active contributors per workspace | | ------------ | --------------------------------: | | Civic and public services | 12 | | Charities and NGOs | 6 | | Education | 8 | | Local services | 2 | | Retail and hospitality | 2 | | Transport | 6 | | Agriculture and environment | 4 | | Property and infrastructure | 4 | | Consultants and scenario people | 3 | | Sector group | Very conservative | Middle | Ambitious | Global / infrastructure | | ------------ | ----------------: | -----: | --------: | ----------------------: | | Civic and public services | 60,000 | 240,000 | 720,000 | 1,440,000 | | Charities and NGOs | 90,000 | 360,000 | 1,050,000 | 2,100,000 | | Education | 80,000 | 320,000 | 800,000 | 1,600,000 | | Local services | 17,500 | 70,000 | 280,000 | 700,000 | | Retail and hospitality | 18,000 | 67,500 | 270,000 | 720,000 | | Transport | 45,000 | 180,000 | 600,000 | 1,440,000 | | Agriculture and environment | 36,000 | 120,000 | 450,000 | 960,000 | | Property and infrastructure | 20,000 | 80,000 | 300,000 | 640,000 | | Consultants and scenario people | 60,000 | 240,000 | 600,000 | 1,200,000 | | **Total active contributors** | **426,500** | **1,677,500** | **5,070,000** | **10,800,000** | Using conservative average contributor counts by sector, the rough active contributor numbers become: | Scenario | Private workspaces | Active contributors | | -------- | -----------------: | ------------------: | | Very conservative | ~90k | ~430k | | Middle | ~350k | ~1.7M | | Ambitious | ~1.1M | ~5.1M | | Global / infrastructure | ~2.4M | ~10.7M | ##### 2. Viewers People who can open shared results, review maps, read exports, attend internal discussions, or understand the work without shaping the model. Only active contributors are paid, with viewers being free, and this keeps the model clean. The viewer layer is much larger, since, because viewers are free, workspaces can invite people without rationing access. A normal workspace may have: - 2–10 active contributors - 5–50 viewers - 20–500 people who see outputs through reports, dashboards, workshops, screenshots, or public summaries The viewer multiplier differs by sector. | Sector group | Typical viewer multiplier | | ------------ | ------------------------: | | Civic and public services | 5–20× active contributors | | Charities and NGOs | 5–30× | | Education | 5–50× | | Local services | 1–5× | | Retail and hospitality | 1–8× | | Transport | 2–10× | | Agriculture and environment | 3–20× | | Property and infrastructure | 2–15× | | Consultants and scenario people | 5–50× through client work | // use the median instead Using a blended rough multiplier of 6–12 viewers or influenced users per active contributor, we can calculate: | Scenario | Active contributors | Viewers / influenced users | | -------- | ------------------: | -------------------------: | | Very conservative | ~430k | ~2.5M–5M | | Middle | ~1.7M | ~10M–20M | | Ambitious | ~5.1M | ~30M–60M | | Global / infrastructure | ~10.7M | ~65M–130M | This still excludes the wider public affected by decisions, and only counts people close enough to the workspace to see or use the outputs, such as: - grant writers needing a clearer scenario - council teams needing to compare two options - an organisation wanting to show before/after impact - a student group wanting to test a local idea - consultants wanting a better map for client work - transport operators wanting to understand routes and demand - a café owner wanting to understand events, foot traffic, and opening hours - a conservation group wants to compare planting, shade, water, and heat The adoption model aims to not replace existing organisational systems all at once, but instead enter through small, ordinary moments. This means the product can spread inside organisations without immediately becoming a procurement monster. It's more like that one person tries it, then a few people contribute, and then many people view the output. This avoids treating all small businesses as equal prospects and instead focuses on the places where private scenario modelling could become part of recurring work. - Institutions adopt more slowly because trust, privacy, procurement, and internal approval matter. Once adopted, the recurring need is strong. - Operational local businesses have clear practical use cases, but adoption depends on the product being simple enough to use without specialist training. - Retail and hospitality can benefit from demand, event, location, and opening-hour scenarios, but many will use it less frequently. - Consultants and scenario people are the smallest group, but likely the highest-intensity users because they can use the tool across many clients or projects. What if – Private does not need mass adoption. If it becomes useful to tens of thousands of organisations and consultancies that already make place-based decisions, it can support hundreds of thousands to millions of serious professional users, while also (optionally) feeding cleaner public scenarios back into the wider What if ecosystem. #### Estimates | Scenario | Private workspaces | Active contributors | Free viewers | Total workspace users | | -------- | -----------------: | ------------------: | -----------: | --------------------: | | Very conservative | 89,250 | 426,500 | 758,500 | 1,185,000 | | Middle | 348,750 | 1,677,500 | 2,991,250 | 4,668,750 | | Ambitious | 1,097,500 | 5,070,000 | 8,690,000 | 13,760,000 | | Global / infrastructure | 2,420,000 | 10,800,000 | 18,180,000 | 28,980,000 | ##### Very Conservative *89,250 private workspaces* This means What if – Private becomes a serious niche tool. It is used by organisations that already feel the pain: - consultants - NGOs - civic teams - university groups - public service units - some operational businesses It has not become normal. It is still mostly chosen by people who already understand why private scenario modelling matters. ##### Middle 348,750 private workspaces This means What if – Private becomes a recognised tool in civic, research, NGO, consulting, and operational circles. It is not mainstream business software. But it is common enough that people in certain fields have seen it before. At this level, adoption probably comes from: - public What if examples - consultants using it with clients - student and university projects - councils and NGOs using it for planning and reporting - local businesses using it where place-based decisions are obvious ##### Ambitious 1,097,500 private workspaces This means What if – Private becomes a normal way to run local scenarios across many sectors. It is still not used by most organisations. But it is common in the places where scenario thinking matters: - local planning - grants - public health - transport - heat and shade - food systems - agriculture - conservation - retail locations - local service operations At this level, the product has crossed from “interesting tool” into “useful infrastructure for people who work with place”. ##### Global / Infrastructure 2,420,000 private workspaces This means What if – Private becomes a common private scenario layer. It is not just software people buy. It becomes part of how organisations reason about place-based decisions. At this level, many workspaces may be created through: - institutions - universities - local governments - consultants - NGOs - regional programmes - donor-backed deployments - operational networks - public-private civic projects This is the version where What if – Private starts behaving less like a product category and more like a shared modelling layer. ----- Otherwise, another potential formula might be: ##### (previous calculation for 'What if?' public) × more Where, "previous calculation for 'What if?' public" refers to the concept of the previous 'What if?' calculation. This is spiritually correct but incorrect mathematically, so we'll use the model above.