diff --git a/sql/trending.py b/sql/trending.py
new file mode 100755
index 0000000..5aa1a2d
--- /dev/null
+++ b/sql/trending.py
@@ -0,0 +1,68 @@
+#!/usr/bin/env python3
+
+import sqlite3
+
+
+def print_trends(cur):
+    cur.execute("""
+        SELECT t.base, COUNT(DISTINCT tr.site_id) AS num_sites
+        FROM tracking tr
+        JOIN scan ON scan.id = tr.scan_id
+        JOIN tracker t ON t.id = tr.tracker_id
+        WHERE scan.no_blocking = 1 AND scan.daily_scan = 1
+            AND scan.date >= DATETIME('now', '-14 day')
+            AND scan.date < DATETIME('now', '-7 day')
+        GROUP BY t.base
+        ORDER BY num_sites DESC""")
+
+    prev = { row[0]: row[1] for row in cur.fetchall() }
+    if not prev:
+        print("Not enough past data for selected date range")
+        return
+    top_prevalence_prev = next(iter(prev.values()))
+
+    cur.execute("""
+        SELECT t.base, COUNT(DISTINCT tr.site_id) AS num_sites
+        FROM tracking tr
+        JOIN scan ON scan.id = tr.scan_id
+        JOIN tracker t ON t.id = tr.tracker_id
+        WHERE scan.no_blocking = 1 AND scan.daily_scan = 1
+            AND scan.date >= DATETIME('now', '-7 day')
+        GROUP BY t.base
+        ORDER BY num_sites DESC""")
+
+    top_prevalence = None
+
+    for row in cur.fetchall():
+        if not top_prevalence:
+            top_prevalence = row[1]
+
+        rel_prevalence = row[1] / top_prevalence
+
+        if rel_prevalence < 0.01:
+            continue
+
+        delta = 1.0
+        prevalence_prev = prev.get(row[0], None)
+        if prevalence_prev:
+            rel_prevalence_prev = prevalence_prev / top_prevalence_prev
+            delta = rel_prevalence - rel_prevalence_prev
+
+        if abs(delta) < 0.03:
+            continue
+
+        print("  "
+            # num sites
+            f"{row[1]:>{len(str(top_prevalence))}}  "
+            # relative prevalence
+            f"{round(rel_prevalence, 2):.2f}  "
+            # change from previous
+            f"{round(delta, 2) * 100:>3.0f}%  "
+            # tracking domain
+            f"{row[0]}")
+
+
+if __name__ == "__main__":
+    with sqlite3.connect("badger.sqlite3", detect_types=sqlite3.PARSE_DECLTYPES) as db:
+        cur = db.cursor()
+        print_trends(cur)