WISE Quotient 2026 · patterns book

13 things we noticed that nobody asked about.

The survey was built to score localities. But 2,878 people also left fingerprints: how they write, what words they reach for, what they never say. These are the patterns in the margins, in plain language. Every chart is interactive: hover, tap, switch.

Pattern 01 · We Name The Problem, Not The Person

We name the problem. We never name who should fix it.

0.9%mentioned the BMC

What people name, and who they hold responsible

What we found

We asked 2,878 people across Mumbai what was wrong in their area. People named garbage (39%), bad roads (22%), water problems (16%). But almost no one named who should fix these things. BMC was mentioned by fewer than 1 in 100. Corporator: literally one person out of 2,878. Ward office: zero.

What this might mean

When we don't have words for who is responsible, we can't hold anyone responsible. We just keep describing the same problems to each other.

What we could try

Bring these words into normal speech, "corporator", "ward office", "BMC E&CC dept". Posters. School sessions. Ward walks. The vocabulary itself is the work. And it's easy to measure, survey again next year and see if the percentage goes up.

Pattern 02 · A Data Quality Flag

Some of our surveys say more about the surveyor than the citizen.

99%gave the exact same answer

One surveyor’s 147 responses: answers cluster unnaturally

What we found

In Khar Road East, one fellow collected all 147 surveys. 99% of those citizens marked "water/waste" as the worst possible. 93% marked "air" as the best possible. Real citizen answers don't look like that, when we look at fellows working similar areas like Mahim, the answers spread naturally across all options.

What this might mean

Three of our fellows show this pattern. It might be the way they ask the question, or how they tick boxes for citizens. It means the data from those areas is less trustworthy than from places where multiple fellows worked.

What we could try

Two simple fixes for the next round, (1) check fellows whose answers cluster at the extremes and retrain them, (2) make sure at least two fellows cover any important area, so we can compare.

Pattern 03 · The Trainer Leaves A Mark

Who trained you matters more than where you surveyed.

+0.4gap between coordinator teams

Average score by volunteer coordinator (hover for range and n)

What we found

Each fellow has a coordinator who trains them. We compared the average WISE scores from each coordinator's team. The strongest coordinator's team averaged 2.83; the lowest averaged 2.46. That gap (about half a point) is bigger than the gap between most Mumbai neighbourhoods.

What this might mean

How a coordinator briefs and supports their fellows leaves a mark on the answers. The training shapes the data more than the city does.

What we could try

Mix things up next round. Have a fellow from one coordinator's team also work in an area covered by another coordinator's team. If their scores match, the data is solid. If not, we've found where the training is shaping the answers.

Pattern 04 · WHAT WE LOVE vs WHAT WE'D CHANGE

We love our people. We hate our garbage.

35×more "garbage" than "clean"

What we love (left) vs what we would change (right)

What we found

When we asked "what do you love about your area?" and "what would you change?", the answers almost never overlapped. Cleanliness shows up 35 times more often as a complaint than as something loved. Community is the one thing that shows up in BOTH lists, people love their neighbours AND want the bond stronger.

What this might mean

Mumbai's strongest thing is its people. Our weakest thing is the physical city around them, the streets, the water, the waste, the air. The energy is in social ties; the rot is in shared infrastructure.

What we could try

Use what's strong (community) to repair what's broken (physical commons). Neighbourhood-led garbage sorting. Mohalla tree-planting drives. Ward-level audit groups. The data shows people will turn up for these, when the "why" is each other.

Pattern 05 · Every Area Has Its Thing

Don't pitch the same thing to every area.

5distinct "love" personalities

Pick a profile to see which localities lead on it

What we found

When people in Khar Road East talk about what they love, they say transport. In Vile Parle: their neighbours. In Naigaon: how safe and quiet it feels. In Kharghar: the trees and parks. In Lalbaug: that everything is right there. These aren't stereotypes, they come from what people actually wrote.

What this might mean

Each area has a different door to walk in through. A "more green" idea lands easy in Sion or Kharghar; in Lalbaug, it's not where people's heads are. A "community kitchen" works in Vile Parle; in Khar Road East, it's the wrong opening line.

What we could try

Before any pitch or poster or workshop in an area, start with what people already love there. The first 90 seconds matter. Below: the love-cards for the 10 areas where we have enough data.

Pattern 06 · A Generational Inversion

In some areas, the young feel better than the old.

+0.51youth score above elders in Raheja Vihar

Youth vs elder scores, four locality types

What we found

Usually in Mumbai, older citizens are more positive than youth, they remember worse times. But in Raheja Vihar Powai, youth scored their area HALF A POINT higher than elders. Same in Khetwadi (+0.39), Lower Parel (+0.15). In Dharavi, the normal Mumbai pattern holds.

What this might mean

These are gated or managed-amenity areas where youth have things older residents don't use much, gym, library, internet, residents' clubs. The generational gap depends on the type of place, not just age.

What we could try

In a gated-society locality, treat youth and elders as different citizens. Two listening sessions, two pitches, two action paths. In an old neighbourhood like Dharavi, do the opposite, bring youth and elders together; the elders carry the memory the youth need.

Pattern 07 · Old Residents Have The Most To Say

People who've lived here longest have the most to say. And they're the toughest.

79%of the most detailed writers have lived here 10+ years

Share of long answers, by years lived in the locality

What we found

When we look at people who wrote long, detailed answers (300+ characters), 79% had lived in their area for 10 years or more. People who wrote almost nothing were mostly newer arrivals. Long-tenured residents have the memory, the words, and the stake.

What this might mean

They also score their area HARSHER than newcomers. Not because they're bitter, because they have a baseline. They remember when it was better. That memory is data we can't get any other way.

What we could try

Long-tenured residents are our deepest natural partners. Use them as anchor-citizens for any locality work, not as gatekeepers, but as witnesses and co-designers. Pair every youth-led initiative with at least one 20-year resident.

Pattern 08 · Civic Literacy Is Not Spread Evenly

Some areas can talk about 4-5 issues at once. Others struggle with one.

35×gap between most and least talkative areas

Civic-vocabulary fluency: six most fluent vs six least

What we found

We counted how many different civic issues an average citizen mentioned in one response. In Chembur: 2.11 issues per person. In Bandra Colony: 0.06, almost no one mentioned anything. Khetwadi, Vikhroli West, Lower Parel are also high. Dharavi and Sion are low.

What this might mean

This isn't about who's smarter. It's about which areas already have the words for civic life. Some communities have built a vocabulary over years; others haven't yet.

What we could try

For multi-issue work (like a "cool commons" pilot that mixes heat + waste + voice), start where the vocabulary already exists, Chembur, Khetwadi, Vikhroli West. In areas with lower civic-vocabulary, do single-issue work first, build the language, then layer.

Pattern 09 · A Paragraph Means Okay, An Essay Means Upset

The most expressive citizens are the most frustrated.

3.00hope score of essay writers (3.22 for short writers)

Hope by how much people wrote

What we found

WISE scores actually go UP as people write more, from silent (2.64) to vocal (2.75). But the tiny group who wrote essays (more than 500 characters, only 70 people) drops back to 2.62, with hope falling to 3.00 and voice to 2.49. The most expressive people are the most frustrated.

What this might mean

There's a sweet spot, people who write a few sentences are usually pretty satisfied. Above that, you find the people writing essays because they're upset. They're the most worth listening to first, but they aren't the average voice of the locality.

What we could try

For listening sessions and surveys: treat the top 2-3% most expressive responses as warning bells, not averages. They tell you what matters most to people who care most. Surface them, but always ask the median citizen too, who writes briefly because they're busy, not indifferent.

Pattern 10 · MUMBAI'S BUSY 30s

Mumbai's 35-year-olds are too busy to imagine change.

10.6%use action words at 31-40 (~16% at every other age)

Share willing to imagine change, by age

What we found

When we asked "if you had a superpower, what would you do?", teens, young adults, mid-lifers, seniors all answered with action words, build, plant, stop, clean, teach. But the 31-40 cohort drops to 10.6%. Almost half the rate of every other age group.

What this might mean

These are the busiest years, young kids, peak job, aging parents. Civic agency is the first thing that gets cut. Civic muscle atrophies if not held.

What we could try

Don't assume civic agency from the 20s carries into the 30s. Design a specific bridge for the 31-40 cohort, micro-actions that take 15 minutes, async engagement (Whatsapp not meetings), parent-friendly formats. Anchor it around their kids if you can, that's the door.

Pattern 11 · REMOVAL vs CREATION

We dream of cleaning up. We rarely dream of building.

more "remove" words than "create" words

The verbs in citizens’ dreams: removal vs creation

What we found

When citizens described what they'd change, they said clean (6.0%), stop, change. They almost never said build (2.4%), plant (1.7%), give (1.5%), unite (0.4%), teach (0.1%). The removal vocabulary outweighs the creation vocabulary roughly 4 to 1.

What this might mean

We've learned to ask for what's wrong to go away. We haven't learned to ask for what could be built. The collective-creative words, "teach", "unite", "host", "plant", are essentially missing from how we talk about our city.

What we could try

BRM's messaging can introduce these verbs into everyday civic speech. "Plant a tree corner." "Build a library shelf." "Host a Sunday meet." "Teach a free class." The verb itself is the offer, and the data shows the verb-shaped offers aren't currently in people's mental palette.

Pattern 12 · A Signal Of Absence

What we ask for says what we don't have.

-0.13inclusion score of inclusion-prioritisers (vs everyone else)

Scores where a lens was chosen as priority vs not

What we found

When citizens picked their top WISE priority (Well-Being, Inclusion, Sustainability, Enterprise), an interesting thing happened. People who picked "Inclusion" actually scored Inclusion 0.13 LOWER in their own area than people who didn't pick it. Well-Being: same, -0.08 lower.

What this might mean

We don't pick priorities based on what we care about in general. We pick them based on what's missing in our locality. Priority is a signal of absence, not importance.

What we could try

When we present the WISE priority data in donor or policy decks, change the framing. Not "33% of Mumbaikars prioritise Well-Being" (sounds like preference). Say "33% of citizens experience Well-Being as the most-missing thing in their area" (sounds like demand). Same number. Sharper political meaning.

Pattern 13 · A Silence We Need To Ask About

Zero mentions of harassment in 2,878 responses. Something is wrong with our question.

0out of 2,878

What we found

We searched every open-ended response, in English and Hindi, for harassment, eve teasing, and molestation. The word family appears twice in 2,878 responses: once by a woman saying she is not worried about being eve-teased in her area, once about a society hassling PG students. Not a single citizen reported gender-based street harassment as a civic issue.

What this might mean

Given what we know about gender-violence in Indian cities, this silence is not the absence of the experience. It's the absence of an invitation to name it. Our questions are too generic. People don't feel the prompt is asking them.

What we could try

The next survey must include an explicit prompt on public-space safety, "has anyone in your family experienced harassment in your area?" If the naming rate jumps, the question was the bottleneck. If it doesn't, we've uncovered a deeper research question about why citizens don't name this even when asked directly.

What this book is for

None of these patterns were hypotheses. They surfaced when we stopped asking “what is the score” and started asking “what else did 2,878 people just tell us”. Each one is a separate, testable question for the next round of listening.

WISE patterns book · 2,878 responses with open answers · rebuilt on the 5 July 2026 dataset