Clustering Themes

From scattered observations to working themes

Following the toolkit naming conventions, this file is named exp-06-cluster-themes-2025-03-12.qmd.

Overview

In this demo, we take the mountain of raw data from conversations and observations and cluster it into themes.

We demonstrate how we used a Google Sheet (one observation per row) imported into a Miro board for sticky clustering. It pairs with the Toolkit: Clustering Guide.

The goal is to see what frictions, routines, and emotional cues repeat often enough to point toward hypotheses of pain.

Outputs you’ll see here
– Screenshots of the clustering wall (before/after)
– The theme labels we settled on
– Narrative explanations for each cluster
– Core vs. edge distinction, so you can see which themes anchor hypotheses and which are “refrigerated” for later surprises

1. Clarify the Unknown

  • Question: What unmet needs (pains) emerge repeatedly in women’s commuting experiences?
  • Why clustering now: We’ve finished exploratory conversations and observations. Novelty is dropping; it’s time to converge by turning fragments into patterns.

2. Inputs We Clustered

We combined quotes from interviews and notes from observations. Each was reduced to a single movable unit (one per sticky).

Examples of raw observations (short & concrete):

  • “I text my roommate when I leave campus so she can watch my ETA.”
  • Avoids shortcut street; takes a longer lit route; arrives 12–15 minutes later.
  • “Crowded trains make me vigilant; I grip my bag and plan my exit two stops early.”
  • Keys between fingers; bag positioned tightly under arm.
  • Family texts: “Ping me when you’re home.” If no ping by 10:30pm, they call.

These are examples only. The full set is in the sample sheet.

3. Method: Miro + Sheet Import

  1. Prepare the Sheet
    • Column A = observation (one per row).
    • Optional columns: source (interview/observation), time, context.
  2. Import into Miro
    • In Miro: Apps → Sticky Note Import (or Paste as sticky notes).
    • Select Column A; each row becomes a sticky note.
    • Arrange stickies loosely; don’t categorize yet.
  3. Affinity Map
    • Drag stickies into small groups where similar meaning emerges.
    • Start new clusters when a sticky doesn’t fit an existing group.
    • Keep moving until repetition and coherence feel strong.
  4. Name Clusters
    • Add a short label (2–4 words) above each group.
    • Write a one-sentence “why this belongs together” note under the label.

Paper option: write each observation on a sticky note; cluster on a wall; snap photos at each stage.

4. Before / After (Screenshots)

Raw, ungrouped stickies
Raw scattered stickies before clustering

After grouping & naming clusters
Clusters labeled and arranged

5. Resulting Clusters

After clustering, we sorted themes into core clusters (strong repeated signals) and edge clusters (smaller, but worth keeping).

Core = likely to anchor hypotheses.
Edge = stored in the “refrigerator” for surprises later.

Core Clusters

  1. Reassurance Routines
    • Inside: “Text when you leave/arrive,” timed check-ins, live-tracking links.
    • Why it holds: Shared acts that transfer anxiety from walker to trusted other.
    • Signal: Frequent; across late-evening and unfamiliar routes.
  2. Social Spillover
    • Inside: Family anxiety, roommate stress, “call me if no ping.”
    • Why it holds: Pain extends beyond the walker to their network.
    • Signal: Moderate; strongest after 9:00pm.
    • Note: Could be treated as a sub-cluster of Reassurance, but distinct enough to stand on its own.
  3. Route Choice & Environment
    • Inside: Choosing lit/busier streets, crossing street to manage spacing, avoiding alleys.
    • Why it holds: Micro-decisions that trade convenience for predictability and visibility.
    • Signal: Strong; often co-occurs with Reassurance.
  4. Crowding & Vigilance
    • Inside: Gripping bags, scanning, discomfort in forced closeness.
    • Why it holds: Overcrowding heightens vigilance and amplifies stress.
    • Signal: Moderate-high.
  5. Predictability & Control
    • Inside: Frustration at detours, anxiety when late, recalibrating when routine breaks.
    • Why it holds: Security comes from routine; disruption sparks unease.
    • Signal: Strong, consistent across sources.
  6. Body Management & Micro-frictions
    • Inside: Footwear discomfort, juggling bags, fatigue, instinctive flinches.
    • Why it holds: Small bodily frictions accumulate into real stress.
    • Signal: Smaller, but recurring.

Edge Clusters

  1. Stranger Dynamics
    • Inside: Respectful distance, offers of help, ignoring slower commuters.
    • Why it holds: Social interactions with strangers shape comfort.
    • Signal: Small, situational.
  2. Respite (Personal)
    • Inside: Podcasts, daydreaming, phone scrolling, headphones as shield.
    • Why it holds: Solo commuters carve out mini “bubble zones.”
    • Signal: Individual but common.
  3. Connection (Social)
    • Inside: Chats with friends, shared meals, group laughter.
    • Why it holds: Commute as social ritual, not just transit.
    • Signal: Small, but meaningful.
  4. Street Ecology
  • Inside: Vendors, buskers, security staff.
  • Why it holds: Environmental features that color mood, sometimes add safety.
  • Signal: Thin cluster, but vivid.
  1. Conflict/Frustration
  • Inside: Heated discussions, blocked paths.
  • Why it holds: Social friction; not large, but worth noting.
  • Signal: Tiny; could merge with Connection.

6. What We Learned

  • Patterns, not anecdotes: Seeing multiple notes echo the same idea makes these clusters reliable.
  • Core vs. edge: Core clusters give us the best candidates for unmet needs. Edge clusters are weaker signals now but may resurface as hidden opportunities later.
  • Variety matters: Conversations gave access to feelings/thoughts; observations gave behaviors/body language. Together, they balanced the picture.

7. Next Steps

  1. Draft one persona per major core cluster (unless one persona naturally spans multiple).
  2. Build experience maps for each persona’s key activities.
  3. Highlight emotional peaks and valleys to surface candidate pains.
  4. Keep edge clusters visible — don’t lead with them, but don’t throw them away.
Tip

The Refrigerator Rule
Edge clusters are like leftovers: not dinner tonight, but they keep well. Store them in your “refrigerator” so you can revisit when surprises appear in testing or solution design.

Traceability