Metric Explorer
Deterministic, reproducible metrics derived from directly observed source data. Every metric is labelled DERIVED and links to its formula, source fields, methodology and calculation audit. No predictions. No causation. No private intelligence scoring.
- Evidence class
- derived metric (deterministic)
- Institutional use
- public_observability
concentration
density
(sum(egm_count_at_period_end where lga = LGA) / lga_adult_population) * 1000
count(canonical_venues where current_lga = LGA AND venue_stream = STREAM)
financial
Direct observed value from NSW LGA quarterly/bi-annual gaming-machine reports.
lga_net_profit_aud / SUM(lga_net_profit_aud across all observed LGAs in the same reporting period)
Direct observed value from NSW LGA quarterly/bi-annual gaming-machine reports.
sum(venue_net_profit in LGA) / lga_adult_population
lga_net_profit_aud / egm_count
lga_tax_paid_aud / egm_count
venue_net_profit / sum(venue_net_profit in LGA)
ranking
percentile_rank(venue.egm_at_period | universe = LGA × stream × period)
percentile_rank(venue.egm_at_period | universe = NSW × stream × period)
temporal
egm_at(latest_period) - egm_at(closest_observed_period ≈ latest_period - 36 months)
egm_at(latest_period) - egm_at(closest_observed_period ≈ latest_period - 60 months)
(egm_latest - egm_previous_observed_period) where periods are adjacent observed periods
mean(egm_count_at_period_end over the four most recent observed periods for ENTITY)
coefficient_of_variation(egm_count_series_observed) = stddev / mean, classified into LOW (<5%), MODERATE (5–10%), ELEVATED (10–20%), HIGH (>20%)
egm_at(latest_period) - egm_at(closest_observed_period ≈ latest_period - 12 months)