Identify Multi-Criteria Hotspots

Identifies parcels ranking in the top percentile across multiple ecosystem service families, revealing areas with exceptional multi-functional value.

Usage

identify_hotspots(units, families = NULL, threshold = 80, min_families = 3)

Arguments

units

sf object with computed family indices (family_*)

families

Character vector of family column names to analyze. If NULL (default), uses all columns starting with "family_"

threshold

Numeric percentile threshold (0-100) for defining "high" values. Default: 80 (top 20 percent)

min_families

Minimum number of families in which a parcel must rank above threshold to be classified as a hotspot. Default: 3

Value

sf object with original data plus three new columns: hotspot_count (number of families where parcel ranks above threshold), hotspot_families (comma-separated list of family names above threshold), is_hotspot (logical indicating if parcel meets min_families criterion)

Details

The function identifies multi-criteria hotspots by: 1. Computing percentile thresholds for each family index 2. Counting how many families each parcel ranks above threshold 3. Flagging parcels exceeding `min_families` as hotspots

**Use cases**: - Conservation prioritization (high biodiversity + age + connectivity) - Risk mitigation (high vulnerability across fire + storm + drought) - Multi-objective optimization (balancing competing services)

Bilingual Support

This function supports bilingual messages via `nemeton_set_language()`.

See also

[compute_family_correlations()], [plot_correlation_matrix()]

Other analysis: compute_family_correlations()

Examples

if (FALSE) { # \dontrun{
# Load demo data with family indices
data(massif_demo_units)
units <- massif_demo_units
units$family_B <- runif(nrow(units), 30, 90)
units$family_T <- runif(nrow(units), 40, 85)
units$family_C <- runif(nrow(units), 45, 80)
units$family_W <- runif(nrow(units), 35, 75)

# Identify hotspots: top 20\
hotspots <- identify_hotspots(
  units,
  threshold = 80,
  min_families = 3
)

# View hotspot parcels
hotspot_parcels <- hotspots[hotspots$is_hotspot, ]
print(hotspot_parcels[, c("parcel_id", "hotspot_count", "hotspot_families")])

# Conservative threshold: top 10\
elite_hotspots <- identify_hotspots(
  units,
  threshold = 90,
  min_families = 4
)

# Analyze specific families only
biodiversity_hotspots <- identify_hotspots(
  units,
  families = c("family_B", "family_T"),
  threshold = 75,
  min_families = 2
)
} # }