Crime Attractor vs Generator Place Classifier
Research-context place observations, not presume unlawful behavior from venue type. Independently established feature columns: independently established visitor/activity generator evidence, independently established purposeful offender-attraction evidence. Unknown evidence and overlapping candidates retained; not semantic parsing, identity, causation, diagnosis, lawfulness, authenticity or safety determination.
Description
Crime-generator and crime-attractor concepts describe research evidence about places and activity.
Crime-generator and crime-attractor concepts describe research evidence about places and activity.1
Venue type alone does not establish unlawful behavior. Use this tool to compare independently annotated known features with fixed candidate rules.
When to use Crime Attractor vs Generator Place Classifier
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 Crime Attractor vs Generator Place Classifier 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 | Independently established visitor/activity generator evidence |
| 1 | Independently established purposeful offender-attraction evidence |
| Candidate | Independently established visitor/activity generator evidence | Independently established purposeful offender-attraction evidence |
|---|---|---|
| generator-evidence-candidate | Known present (1) | Ignored |
| attractor-evidence-candidate | Ignored | 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
],
"featureKnownFlags": [
1,
1
]
},
"output": {
"result": {
"compatibleClasses": [
"generator-evidence-candidate",
"attractor-evidence-candidate"
],
"witnessedClasses": [
"generator-evidence-candidate",
"attractor-evidence-candidate"
],
"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 Routine Activity Triangle Elements Presence Checker to examine a related aspect: Routine-activity theory describes co-occurring actor, target and guardianship premises.
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
- Routine Activity Triangle Elements Presence Checker
Routine-activity theory describes co-occurring actor, target and guardianship premises.
- Defensible Space Eyes On Street Ratio Lookup
Defensible-space visibility metrics use specific site and observation definitions.