Routine Activity Triangle Elements Presence Checker
Research-premise record for routine-activity theory, not infer an actual motivated offender or future crime. Independently established feature columns: motivated-actor research premise independently annotated, suitable-target research premise independently annotated, guardianship absence research premise independently annotated. Unknown evidence and overlapping candidates retained; not semantic parsing, identity, causation, diagnosis, lawfulness, authenticity or safety determination.
Description
Routine-activity theory describes co-occurring actor, target and guardianship premises.
Routine-activity theory describes co-occurring actor, target and guardianship premises.1
Recording those premises does not predict an event or identify an offender. Use this tool to compare independently annotated known features with fixed candidate rules.
When to use Routine Activity Triangle Elements Presence Checker
Use this tool to compare independently annotated known features with fixed candidate rules. Establish the jurisdiction or research scope, observation and event definitions, reference date, matched period and denominator basis.
How to use Routine Activity Triangle Elements Presence Checker and understand its results
- Establish the jurisdiction or research scope, observation and event definitions, reference date, matched period and denominator basis.
- Enter observedFeatureFlags, featureKnownFlags, using the field meanings and rules below, then run the tool.
- Read result.compatibleClasses, result.witnessedClasses, result.unknownFeatureIndices. Interpret each field using the units, signs and evidence rules below.
- If input validation fails, correct the reported field and run again. Before applying a successful result, check the stated reference scope, units, assumptions and any retained unknown evidence.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| observedFeatureFlags | Observed feature flags; binary 0/1 in the order documented below | list of number | Required |
| featureKnownFlags | Feature known flags; 1 means known and 0 means unknown; keep the same item order | list of number | Required |
Enter binary observed and known flags in the exact column order below. A known mismatch with a constrained rule excludes a candidate; unknown constrained evidence leaves it compatible but prevents full witnessing. A −1 rule ignores that column, including unknown evidence there. Return all compatible and witnessed candidates and zero-based unknown indices, preserving overlap. The comparison records supplied evidence without inferring facts, identity, diagnosis or legal status.
| Zero-based column | Observed feature |
|---|---|
| 0 | Motivated-actor research premise independently annotated |
| 1 | Suitable-target research premise independently annotated |
| 2 | Guardianship absence research premise independently annotated |
| Candidate | Motivated-actor research premise independently annotated | Suitable-target research premise independently annotated | Guardianship absence research premise independently annotated |
|---|---|---|---|
| routine-activity-premises-cooccur | Known present (1) | Known present (1) | Known present (1) |
compatible_c=no known constrained mismatch; witnessed_c=all constrained features known and matching; ignored columns impose no condition Apply the field units and exact ordering, boundary and evidence rules above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Structured result containing the calculation or selected reference fields below | object |
| result.compatibleClasses | All candidates consistent with known features; may include candidates with missing evidence | list of string |
| result.witnessedClasses | Candidates with all required feature constraints actually known and satisfied | list of string |
| result.unknownFeatureIndices | Zero-based positions of unknown features | list of values |
{
"input": {
"observedFeatureFlags": [
1,
1,
1
],
"featureKnownFlags": [
1,
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [
"routine-activity-premises-cooccur"
],
"witnessedClasses": [
"routine-activity-premises-cooccur"
],
"unknownFeatureIndices": []
}
}
}Limitations and assumptions
Feature and criterion flags are independent annotations. Unknown evidence remains unknown; matching rules do not establish a diagnosis, cause, prediction or approval.
Every listed input requires an explicit value. Unexpected fields, incorrect value types, nonfinite numbers and sparse numeric arrays fail validation; tool-specific bounds and alignment rules apply as described above. Extremely large inputs or outputs can also be rejected. An input error is distinct from a successful calculation that reports a false comparison or retains unknown evidence.
Alternative or Complementary methods
Use Crime Attractor vs Generator Place Classifier to examine a related aspect: Crime-generator and crime-attractor concepts describe research evidence about places and activity.
Use Defensible Space Eyes On Street Ratio Lookup where its separate inputs and scope apply: Defensible-space visibility metrics use specific site and observation definitions.
References
Similar or alternative tools
- Crime Attractor vs Generator Place Classifier
Crime-generator and crime-attractor concepts describe research evidence about places and activity.
- Defensible Space Eyes On Street Ratio Lookup
Defensible-space visibility metrics use specific site and observation definitions.