What do community-submitted reports reveal about phone activity in South Australia? This dashboard tracks classification trends, lookup anomalies and risk signals across SA-allocated numbers. Allocation reflects numbering origin - not subscriber ownership.
Data as of · Dataset established 2014
28% scam ratio (↑1pp above national avg)
Risk intensity 0.6x national baseline
Reporting ↑113% vs 30-day avg
233 active numbers in last 30 days
South Australia Telecommunications Risk Context
SA reporting intensity, scam prevalence and submission velocity - benchmarked against national figures. Where does this state sit relative to the rest of Australia?
Risk Intensity Index
0.6x
Ratio of state scam-report density (per 100K pop.) to national average, derived from ABS population estimates.
A value below 1.0 means fewer scam reports per capita than the national average.
SA Scam Ratio
28%
Proportion of reports classified as “scam” by contributors. Classification is user-assigned and unverified.
National avg: 27%
↑ 1pp above
Reports per 100K Pop.
998.3
National avg: 1641.8
· Rank #6 of 8
Reporting Velocity
148 reports (last 7 days)
299 total reports (30d). Volume of approved submissions within the stated window.
SA National Share
4.2%
of all Australian reports. Reflects reporting concentration relative to population share.
Active Numbers (30d)
233
Distinct phone numbers with new reports in the last 30 days. Indicates current reporting breadth across SA.
Emerging Risk Signals in SA
Rapid report accumulation or shifting classifications on the same number - these patterns stand out from the baseline and warrant closer attention.
Rapid Report Accumulation
Two or more reports filed within seven days. When multiple people independently flag the same number in a short window, the signal carries more weight.
Some contributors classify these as scam while others disagree. That split makes the number harder to categorise definitively - watch for how the balance shifts as new reports come in.
Ranked by all-time report count. Independent reports from different contributors on the same number build a stronger signal - though classifications are user-assigned and can shift as new data arrives.
Lookup spikes often precede formal reports - a surge in searches for a particular number suggests people are noticing something before they submit.
Lookups (7d)
421
Weekly avg: 383
+10% vs avg
Lookups (30d)
1,645
Lookup Density per 100K
89.2
Lookup-to-Report Ratio
0.1x
30-day lookups per report filed. Below 1.0 means more reports than lookups - contributors are actively engaged.
High Lookup / Low Report Anomalies
Frequently searched but rarely reported - these numbers sit in a gap between public curiosity and formal submission. High lookup volume alone does not confirm any risk.
Higher values suggest concentrated reporting on fewer numbers rather than broad coverage.
Dominant Classification
Scam
28% of all classified reports in SA.
Dataset Established
2014
Reporting Model
Community-submitted
Classification Method
Rule-assisted aggregation
Localised Reporting Concentration
Larger population centres naturally produce more submissions, so raw volume alone does not indicate disproportionate risk. Select any locality below to see its number-level breakdown.