WISE Quotient 2026 · Patterns Book · a Blue Ribbon Movement project 3,447 citizens surveyed · 17 May – 4 June 2026
13 PATTERNS · IN PLAIN LANGUAGE

13 things we noticed
in what 2,712 Mumbaikars
told us about their area.

We left Govandi and Mankhurd out of this round — those findings have their own report. What's here is what the rest of Mumbai is quietly saying about civic life: who speaks, what they love, what they want, what words they're missing, and one big silence.

Each pattern has three parts: what we found, what it might mean, and what we could try. Read in order or skip around — pick the ones that match your work.

13patterns
2,712responses
17indicators
4open-ended questions
1question we didn't ask

Skip to a pattern →

01We name the problem. We never name who should fix it. 02Some surveys say more about the surveyor than the citizen. 03Who trained you matters more than which area you surveyed. 04We love our people. We hate our garbage. 05Every area has its thing. Don't pitch the same thing everywhere. 06In some areas, the young feel better than the old. 07Long-time residents have the most to say. And they're the toughest. 08Some areas talk about 4-5 issues at once. Others, almost none. 09A paragraph means okay. An essay means upset. 10Mumbai's 35-year-olds are too busy to imagine change. 11We dream of cleaning up. We rarely dream of building. 12What we ask for says what we don't have. 130 out of 2,712. Something is wrong with our question.
01
WE NAME THE PROBLEM, NOT THE PERSON
0.9%
mentioned the BMC

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

PROBLEMS people named
Cleanliness
39%
Roads
23%
Water
16%
Air
7%
Safety
2%
WHO should fix them — almost no one named
BMC
0.9%
Corporator / Nagarsevak
0.04%
Ward office
0.0%
Police
0.5%
NGO
0.1%

What we found

We asked 2,712 people across Mumbai what was wrong in their area. People named garbage (39%), bad roads (23%), 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 2,712. 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.

02
A DATA QUALITY FLAG
99%
gave the exact same answer

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

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.

03
THE TRAINER LEAVES A MARK
+0.4
gap between coordinator teams

Who trained you matters more than where you surveyed.

What we found

Each fellow has a coordinator who trains them. We compared the average WISE scores from each coordinator's team. Mohini's team averaged 2.83. Aniika's team 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.

04
WHAT WE LOVE vs WHAT WE'D CHANGE
35×
more "garbage" than "clean"

We love our people. We hate our garbage.

← what people LOVEwhat they want to CHANGE →
Cleanliness
37
1273
Community
288
211
Green / trees
140
438
Transport
127
109
Safety
121
109

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.

05
EVERY AREA HAS ITS THING
5
distinct "love" personalities

Don't pitch the same thing to every area.

TRANSPORT
39%
Khar Road East
love it for buses, trains, easy reach
COMMUNITY
67%
Vile Parle
love it for people, helpful neighbours
SAFETY
40%
Naigaon, Dadar East
love it because it feels safe and quiet
GREEN
44%
Kharghar
love it for trees, parks, open spaces
SHOPS
32%
Lalbaug
love it because everything is right there
COMMUNITY
47%
Mahim Koliwada
love it for the bond, the tradition
GREEN
32%
Sion
love it for greenery, breathing space
GREEN
26%
Wadala
love it for parks and open spaces
COMMUNITY
52%
Raheja Vihar, Powai
love it for the gated community life
TRANSPORT
36%
Chembur
love it for connectivity

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.

06
A GENERATIONAL INVERSION
+0.51
youth score above elders in Raheja Vihar

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

1.52.02.53.03.5
Raheja Vihar, Powaigated community life
2.85
elders
3.35
youth
+0.50
Khetwadi (Charni Rd)dense old market lanes
2.14
elders
2.54
youth
+0.40
Lower Parelmixed old + new
2.81
elders
2.96
youth
+0.15
Dharaviold neighbourhood, deep roots
2.47
elders
2.2
youth
-0.27
Youth (12–24)
Elders (41+)
+ means youth see it better than elders

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.

07
OLD RESIDENTS HAVE THE MOST TO SAY
79%
of the most detailed writers have lived here 10+ years

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

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.

08
CIVIC LITERACY IS NOT SPREAD EVENLY
40×
gap between most and least talkative areas

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

Talked about many issues at once →
Chembur
2.56
Khetwadi
1.84
Vikhroli (West)
1.59
Lower Parel
1.5
Mahim Koliwada
1.44
Wadala
1.21
Talked about almost nothing
Bandra Colony
0.06
Bandra
0.45
Dharavi
0.65
Sion
0.83
Dombivli
0.84
Borivali
0.84

What we found

We counted how many different civic issues an average citizen mentioned in one response. In Chembur: 2.56 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.

09
A PARAGRAPH MEANS OKAY, AN ESSAY MEANS UPSET
2.97
hope score of essay writers (3.18 for short writers)

The most expressive citizens are the most frustrated.

What we found

WISE scores actually go UP as people write more — from silent (2.64) to vocal (2.74). But the tiny group who wrote essays (more than 500 characters, only 68 people) drops back to 2.62, with hope falling to 2.97 and voice to 2.44. 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.

10
MUMBAI'S BUSY 30s
9.9%
use action words at 31-40 (~16% at every other age)

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

16.2%
12-17
Teen
16.7%
18-24
Young
14.4%
25-30
Working
9.9%
31-40
Busy 30s
14.1%
41-50
Mid-life
15.8%
51-60
Pre-retire
15.7%
61+
Senior
↑ how often each age group used action words (build, plant, clean, stop) when asked "what would you change?"

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 9.9%. 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.

11
REMOVAL vs CREATION
4×
more "remove" words than "create" words

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

REMOVAL words — clean up, stop, change
Clean
6.0%
Stop
1.4%
Change
2.3%
CREATION words — build, plant, teach, unite
Build
2.3%
Plant
1.7%
Give
1.5%
Unite
0.4%
Teach
0.1%

What we found

When citizens described what they'd change, they said clean (6.0%), stop, change. They almost never said build (2.3%), 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.

12
A SIGNAL OF ABSENCE
-0.13
inclusion score of inclusion-prioritisers (vs everyone else)

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

What we found

When citizens picked their top WISE priority (Wellbeing, 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. Wellbeing: same, -0.07 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 Wellbeing" (sounds like preference). Say "33% of citizens experience Wellbeing as the most-missing thing in their area" (sounds like demand). Same number. Sharper political meaning.

13
A SILENCE WE NEED TO ASK ABOUT
0
out of 2,712

Zero mentions of harassment in 2,712 responses. Something is wrong with our question.

0
mentions of harassment or eve teasing
across 2,712 responses, from both men and women

What we found

We searched every open-ended response, in English and Hindi, for any mention of harassment, eve teasing, molestation. From both men and women. Zero. Not one mention in 2,712 responses.

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.

HOW TO USE THIS BOOK

13 patterns. 13 separate questions for BRM.

Each pattern is sized for a single decision: should we do something about this?

The "what we could try" box on each pattern is just a starting suggestion. Some are operational (the next survey design). Some are programmatic (workshops, pilots, partnerships). Some are advocacy (changing how we present the data when we present it).

You can also raid this book — funder decks can lift any single pattern with its visual; coalition conversations can borrow the locality love-cards from Pattern 5; a v2 survey design can use Patterns 1, 13, and the methodology observations as its agenda.

The city averages flatten the story. Each pattern is a place where the texture shows through.

Method. All numbers come from the CCF 16 WISE Survey CSV. We worked with 2,712 responses (3,447 minus 611 from M/East and 131 ambiguous "M" ward tags). Govandi and Mankhurd were deliberately left out because they have their own separate report — the patterns here are what the rest of Mumbai is telling us.

Other reports in this series: v1 main report · v2 ward charts · heat brief

Built 2026-06-08. Plain-language patterns book companion to the WISE Quotient 2026 v2 work.

Patterns Book WISE 2026 Blue Ribbon Movement