Journey To Crime Distance Decay Buffer Calculator
Conditional distance-decay weights on independently measured distances with explicit below-buffer exclusion. Not geographic offender identification, normalized residence probability or police targeting recommendation.
Description
Distance-decay models assign relative weights using a calibrated distance scale.
Distance-decay models assign relative weights using a calibrated distance scale.1
A caller-defined exclusion buffer modifies the model without establishing an offender's location. Use this tool to calculate the stated observation or conditional model from supplied measurements.
When to use Journey To Crime Distance Decay Buffer Calculator
Use this tool to calculate the stated observation or conditional model from supplied measurements. Establish the jurisdiction or research scope, observation and event definitions, reference date, matched period and denominator basis.
How to use Journey To Crime Distance Decay Buffer Calculator and understand its results
- Establish the jurisdiction or research scope, observation and event definitions, reference date, matched period and denominator basis.
- Enter independentlyMeasuredDistancesKilometers, callerCalibratedDecayScaleKilometers, callerExclusionBufferKilometers, using the field meanings and rules below, then run the tool.
- Read result.conditionalRelativeDistanceWeights. 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 |
|---|---|---|---|
| independentlyMeasuredDistancesKilometers | Independently measured distances kilometers | list of number | Required |
| callerCalibratedDecayScaleKilometers | Caller calibrated decay scale kilometers | number | Required |
| callerExclusionBufferKilometers | Caller exclusion buffer kilometers | number | Required |
Enter finite nonnegative distances, positive decay scale and nonnegative exclusion buffer in kilometers. For distances strictly below buffer return zero; otherwise return exp(−distance/scale), including the buffer boundary. An empty distance list is allowed and exact exponential underflow returns zero. No normalization, calibrated probability or inferred location is supplied.
relativeWeight(d)=d<buffer ? 0 : exp(−d/scale) 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.conditionalRelativeDistanceWeights | Conditional Relative Distance Weights; use the units, sign convention and calculation stated above | list of number |
{
"input": {
"independentlyMeasuredDistancesKilometers": [
0,
1,
2
],
"callerCalibratedDecayScaleKilometers": 1,
"callerExclusionBufferKilometers": 1
},
"output": {
"result": {
"conditionalRelativeDistanceWeights": [
0,
0.36787944117144233,
0.1353352832366127
]
}
}
}Limitations and assumptions
A caller-defined exclusion buffer modifies the model without establishing an offender's location. Results depend on the independently established measurement basis and the exact model assumptions above.
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 Repeat Call Address Concentration Curve Tool where its separate inputs and scope apply: An address concentration curve ranks recorded call counts and accumulates their shares.
References
Similar or alternative tools
- Crime Attractor vs Generator Place Classifier
Crime-generator and crime-attractor concepts describe research evidence about places and activity.
- Repeat Call Address Concentration Curve Tool
An address concentration curve ranks recorded call counts and accumulates their shares.