Repeat Call Address Concentration Curve Tool
Actual repeat-call concentration curve by independently grouped pseudonymous address IDs, stable input-order ties. Calls do not establish crimes, individual identity or permission to target a location.
Description
An address concentration curve ranks recorded call counts and accumulates their shares.
An address concentration curve ranks recorded call counts and accumulates their shares.1
The result describes the submitted address inventory rather than a population-wide risk ranking. Use this tool to calculate the stated observation or conditional model from supplied measurements.
When to use Repeat Call Address Concentration Curve Tool
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 Repeat Call Address Concentration Curve Tool and understand its results
- Establish the jurisdiction or research scope, observation and event definitions, reference date, matched period and denominator basis.
- Enter callerAddressIds, independentlyRecordedCallCounts, using the field meanings and rules below, then run the tool.
- Read result.descendingCallerAddressIds, result.cumulativeAddressFractions, result.cumulativeObservedCallFractions. 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 |
|---|---|---|---|
| callerAddressIds | Caller address ids | list of string | Required |
| independentlyRecordedCallCounts | Independently recorded call counts | list of number | Required |
Require nonempty unique nonblank address IDs with aligned nonnegative finite call counts and positive total. Sort indices by descending count and break ties by original index. Return ordered IDs, cumulative address shares (i+1)/addressCount and cumulative call shares. Fractional counts are accepted; no catalog retrieval or risk interpretation occurs.
addressFraction_i=(i+1)/N; cumulativeCallFraction_i=Σfirst(i+1)sortedCounts/ΣallCounts 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.descendingCallerAddressIds | Descending Caller Address Ids; use the units, sign convention and calculation stated above | list of string |
| result.cumulativeAddressFractions | Cumulative Address Fractions; use the units, sign convention and calculation stated above | list of number |
| result.cumulativeObservedCallFractions | Cumulative Observed Call Fractions; use the units, sign convention and calculation stated above | list of number |
{
"input": {
"callerAddressIds": [
"a",
"b",
"c"
],
"independentlyRecordedCallCounts": [
1,
3,
2
]
},
"output": {
"result": {
"descendingCallerAddressIds": [
"b",
"c",
"a"
],
"cumulativeAddressFractions": [
0.3333333333333333,
0.6666666666666666,
1
],
"cumulativeObservedCallFractions": [
0.5,
0.8333333333333334,
1
]
}
}
}Limitations and assumptions
The result describes the submitted address inventory rather than a population-wide risk ranking. 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 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.