Raw observations are not intelligence. Intelligence is produced when observations are structured, compared against a baseline, and evaluated against defined thresholds. This lesson introduces the three primary collection tools adapted from FM 2-22.3 and FM 34-2-1 for civilian use: the Activity Register, the Pattern Analysis Plot Sheet, and the Event Template. Together, these tools transform field notes into a collection system that produces usable analytical outputs.
From Observation to Intelligence
The gap between what you see and what you know is closed by method. A capable intelligence system does not depend on observers with exceptional memory or intuition. It depends on observers using consistent tools that produce data in a format an analyst can compare, trend, and evaluate.
In a community intelligence context, the observer and the analyst are often the same person. That makes the tools even more important — they serve as the structural discipline that prevents the observer from filling the analytical role with bias.
FM 2-22.3 Chapter 8 describes the standard HUMINT collection tools used at the ground level. FM 34-2-1 Appendix B describes the Pattern Analysis Plot Sheet and activity register formats for reconnaissance operations. The tools presented in this lesson are direct civilian adaptations of those formats, scaled for MAG and neighborhood-level use.
Tool 1: The Activity Register
A time-ordered log of observations at a defined location during a defined collection period. Captures: observer ID, location, date and time, observation type (human/vehicle/infrastructure/communications), count or status, behavioral notes, and deviation flag.
Primary domains: Human activity, infrastructure and lifeline behavior
Output: A chronological record that supports trend analysis and baseline comparison over time.
The Activity Register is the first tool you build and the one you will use most frequently. Its strength is simplicity: any observer with a notebook and a clock can maintain one. Its value compounds over time — the longer you maintain it, the more precisely you can define normal and identify deviations.
Activity Register Field Structure
| Field | What to Record | Example |
|---|---|---|
| Observer | Consistent ID (initials, callsign) | NM |
| Location | Named point or grid | Elm/Route 9 intersection |
| Date / Time | 24hr format, consistent timezone | 2026-07-14 0715 |
| Domain | H / I / V / C | V (vehicle) |
| Count / Status | Numeric count or NORM / DEGRADE / FAIL | 14 (baseline: 3–6) |
| Notes | Configuration, behavior, direction | Mix of passenger and pickup, eastbound |
| Dev Flag | Y / N | Y |
A deviation flag does not mean an alert. It means the observation warrants comparison to the baseline and potential escalation. The threshold for escalation is defined by your team in advance — not in the moment.
Tool 2: The Pattern Analysis Plot Sheet
A time-by-location matrix that plots observations across multiple collection periods to reveal volume trends, clustering, and gap patterns. Most effectively used for vehicle and movement domain data, though applicable to any domain with countable indicators.
Primary domains: Vehicle and movement, human activity
Output: A visual representation of activity patterns across time that makes anomaly clusters immediately apparent.
The Plot Sheet is the tool that turns a collection of activity registers into trend intelligence. Where the register answers “what happened at this time and place,” the plot sheet answers “what pattern do these observations reveal over time.”
At its simplest, a plot sheet is a grid: locations across the top, time blocks down the left side, observation counts in the cells. After four to six collection periods, visual patterns emerge — and deviations from those patterns become visible without calculation.
A spiral notebook with a hand-drawn grid is a fully functional plot sheet. The format is not the point. The discipline of recording observations in a consistent location-and-time structure is the point. Build the habit on paper first, then automate if capacity allows.
Tool 3: The Event Template
A structured record used to document a specific deviation event in detail: what was observed, when, by whom, in which domain, what baseline it deviated from, what the estimated significance is, and what follow-on collection is warranted.
Primary domains: All four (triggered by any domain deviation)
Output: A formatted deviation record that feeds directly into HUMINT reporting and, where relevant, the Community SITREP.
The Event Template is your escalation document. When an Activity Register entry or Plot Sheet observation crosses your defined deviation threshold, an Event Template is opened. It captures the deviation formally and ensures that the follow-on analytical steps are taken before any reporting or action decision is made.
Deviation Threshold Framework
Define your thresholds in advance, by domain. A simple three-level framework adapted from military reporting standards:
| Level | Threshold | Action |
|---|---|---|
| NOMINAL | Observation within 10–15% of baseline norm | Log in Activity Register. No follow-on required. |
| AMBER | Observation 15–50% above or below baseline norm, or first occurrence of an unfamiliar indicator | Open Event Template. Initiate corroboration collection. Monitor for 24–48 hours. |
| RED | Observation greater than 50% above or below baseline, recurrence of an AMBER indicator, or multi-domain concurrent deviation | Complete Event Template. Brief MAG leadership. Evaluate for SITREP integration. |
A MAG observer at a rural crossroads logs 22 vehicle passes between 0600 and 0800 on a Monday morning. His Activity Register baseline for that location, that day, that time window is 5–9. He flags the deviation (Y) and opens an Event Template. The template prompts him to document the vehicle types (predominantly loaded pickup trucks and one flatbed), direction of travel (south), and any behavioral indicators (none — traffic was moving normally at speed).
He transfers the observation to the Plot Sheet. It reveals that three of the last five Monday mornings have shown elevated vehicle counts at that location, trending upward. The pattern suggests a recurring activity, not a one-time event. He updates the threshold status to AMBER and requests corroboration from a second observer on the southern route.
This is the three-tool cycle working as designed: Register captures, Plot Sheet reveals pattern, Event Template escalates and drives follow-on collection.
Maintaining Baseline Integrity
A baseline that is not maintained is worse than no baseline at all — it creates false confidence. Baselines degrade for three primary reasons:
- Collection gaps. Missed observation periods introduce uncertainty about whether a deviation is real or the product of a gap in the record. Define a minimum collection frequency and flag missed periods explicitly in the register.
- Environmental drift. Normal changes. Seasonal traffic patterns, new construction, school schedule changes, and business openings all shift the baseline. Review and update baseline definitions quarterly at minimum.
- Observer inconsistency. Different observers using different criteria produce incomparable data. Standardize your field structure and run occasional calibration checks — two observers covering the same location for the same period and comparing registers.
Teams frequently abandon collection tools after the first few weeks because they feel like they are “not seeing anything.” This is backwards. A collection system that is producing NOMINAL readings consistently is working correctly. It is building the baseline that makes future deviations detectable. The absence of deviations is not a sign that the system is unnecessary. It is a sign that you now have a comparison point.
Build your first Activity Register entry. Choose one observable location you pass through routinely. For the next five days, log one observation per day at the same time using the field structure above: Observer, Location, Date/Time, Domain, Count/Status, Notes, Dev Flag.
At the end of five days, you have the beginning of a baseline. Note whether any observations felt like deviations before you had the data, and whether the data confirms or contradicts that feeling.