القائمة
Icelandic Tourist Board — Keflavík passenger counts by nationality and month, flights, accommodation, tourism stats via Power BI.
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| name | ferdamalastofa |
| description | Icelandic Tourist Board — Keflavík passenger counts by nationality and month, flights, accommodation, tourism stats via Power BI. |
Inbound tourism statistics via Power BI dashboard scraping. Passenger counts through Keflavík airport by nationality, month, and year.
Dashboard URL: https://www.maelabordferdathjonustunnar.is/
Embed backend: ferdapbi.azurewebsites.net (Azure web app generating Power BI embed tokens)
The dashboard embeds Power BI reports via ferdapbi.azurewebsites.net/embed/{reportId}. Data must be extracted by intercepting Power BI API calls within the iframe.
| Report ID | Page | Description |
|---|---|---|
1fa56a04-3340-46c5-a36b-f9dde4ce0b92 | /fjoldi-farthega-um-keflavik | Passenger counts by nationality |
34af65b4-b68d-4309-a17a-5e9d4632b55c | /hotel | Hotel guest nights and occupancy |
7cf5f866-459c-4873-947c-082d8a216ea9 | /allir-gististadir | All accommodation types |
ae74ab2c-4b1a-4a5c-bf4f-a8ffdda1801f | /dvalarlengd-og-gistimati | Length of stay and accommodation type |
| Page | URL path | Description |
|---|---|---|
| Þjóðernaskipting og fjöldi um Keflavík | /fjoldi-farthega-um-keflavik | Primary — passenger counts by nationality |
| Framboð á flugi frá Keflavík | /frambod-a-flugi-fra-keflavik | Flight supply from Keflavík |
| Verð á flugi | /verd-a-flugi | Flight prices |
| Tölfræðivísar norrænna flugfélaga | /tolfraedivisar-norraenna-flugfelaga | Nordic airline statistics |
| Skemmtiferðaskip | /skemmtiferdaskip | Cruise ships |
| Norróna | /norrona | Norröna ferry |
| Page | URL path | Description |
|---|---|---|
| Aldur, kyn og fleiri bakgrunnsbreytur | /aldur-kyn-og-fleiri-bakgrunnsbreytur | Age, gender, demographics |
| Tilgangur ferðar og heimsóttir landshlutar | /tilgangur-ferdar-og-heimsottir-landshlutar | Purpose of travel, regions visited |
| Ánægja og upplifun | /anaegja-og-upplifun | Satisfaction and experience |
| Fjöldi gesta á áfangastöðum | /fjoldi-gesta-a-afangastodum | Visitors at destinations |
| Umferðarslys erlendra ferðamanna | /umferdarslys-erlendra-ferdamanna | Tourist traffic accidents |
| Spá um fjölda erlendra farþega | /spa-um-fjolda-erlendra-farthega | Forecast of foreign passengers |
| Page | URL path | Description |
|---|---|---|
| Hótel | /hotel | Hotel statistics |
| Verð á hótelgistingu | /verd-a-hotelgistingu | Hotel prices |
| Allir gististaðir | /allir-gististadir | All accommodation types |
| Dvalarlengd og gistimáti | /dvalarlengd-og-gistimati | Length of stay and accommodation type |
(Available but not yet explored)
| Metric | Icelandic | Description |
|---|---|---|
| Heildarfjöldi | Total passengers | All passengers through Keflavík in period |
| Fjöldi erlendra farþega | Foreign passengers | Non-Icelandic passengers (count + %) |
| Fjöldi Íslendinga | Icelandic passengers | Icelandic passengers (count + %) |
| Uppsafnaður heildarfjöldi | Cumulative YTD | Year-to-date total |
| Filter | Icelandic | Values |
|---|---|---|
| Markaðssvæði | Market area | All, Europe, North America, Asia, etc. |
| Ár | Year | 2016–2026+ |
| Mánuður | Month | janúar–desember |
| Tab | Description |
|---|---|
| Fjöldi í [mánuður] | Monthly breakdown by nationality with YoY comparison |
| Yfirlit | Overview/summary |
| Árstíðardreifing | Seasonal distribution |
Data includes passenger counts by nationality with flags. Top nationalities (Jan 2026 sample): Ísland, Bandaríkin, Bretland, Kína, Ítalía, Þýskaland, Frakkland, Pólland, Suður-Kórea, Ástralía/Nýja-Sjáland, Japan, Spánn, Suður-Ameríka, Kanada, etc.
Report ID: ae74ab2c-4b1a-4a5c-bf4f-a8ffdda1801f
Tourist length of stay and accommodation type from Ferðamálastofa's border survey (landamærarannsókn).
| Metric | Icelandic | Description |
|---|---|---|
| Meðalfjöldi gistinátta | Average guest nights | Monthly average nights stayed, 2024–2026 |
| Sundurliðun eftir bakgrunni | Breakdown by demographics | Average nights by age group (15–24, 25–34, ..., 65+) |
| Dvalarlengd | Length of stay distribution | % of tourists by stay duration (0, 1, 2–3, 4–5, 6–8, 9–12, 13+ days) |
| Sundurliðun eftir tegund gistingar | By accommodation type | Usage % and median stay by type |
| Icelandic | English | Category |
|---|---|---|
| Vinir/ættingjar, möbilhúsi… | Friends/family, motorhome | Ekki greitt fyrir náttuvöl |
| Tjaldstæði, ekki greitt f… | Campsite (unpaid) | Ekki greitt fyrir náttuvöl |
| Önnur gisting | Other accommodation | Ekki greitt fyrir náttuvöl |
| Hótel, gistiheimili | Hotel, guesthouse | Greitt fyrir náttuvöl |
| Ibúðagisting | Apartment rental (Airbnb etc.) | Greitt fyrir náttuvöl |
| Hostel | Hostel | Greitt fyrir náttuvöl |
| Tjald greitt fyri… | Campsite (paid) | Greitt fyrir náttuvöl |
| Húsbíll/tjaldv… | Campervan/motorhome | Greitt fyrir náttuvöl |
| Sumarhús eða skálar | Summer houses or huts | Greitt fyrir náttuvöl |
| Húsbíll greitt fyri… | Campervan (paid) | Greitt fyrir náttuvöl |
| Filter | Icelandic | Values |
|---|---|---|
| Bakgrunnsbreytur | Background variable | Aldur (age), Kyn (gender), etc. |
| Ár | Year | Multiple selections (2024, 2025, 2026) |
| Mánuður | Month | All, or specific months |
age_group,avg_nights
15-24 ára,6.8
25-34 ára,7.0
35-44 ára,6.7
45-54 ára,6.8
55-64 ára,6.9
65 ára og eldri,7.2
stay_duration,pct
Gisti ekki,0.6%
1 dagur,2.7%
2-3 dagar,16.8%
4-5 dagar,25.2%
6-8 dagar,30.6%
9-12 dagar,16.0%
13 dagar eða meira,8.2%
Use Playwright to load the parent page and intercept Power BI executeQueries API calls:
import asyncio
import json
from playwright.async_api import async_playwright
BASE_URL = "https://www.maelabordferdathjonustunnar.is"
async def scrape_passenger_data(page_path="/fjoldi-farthega-um-keflavik"):
async with async_playwright() as p:
browser = await p.chromium.launch(headless=True)
page = await browser.new_page()
query_results = []
async def handle_response(response):
url = response.url
# Power BI data queries go through these endpoints
if 'querydata' in url.lower() or 'public/reports' in url.lower():
try:
if response.status == 200:
content_type = response.headers.get('content-type', '')
if 'json' in content_type:
body = await response.json()
query_results.append({
'url': url,
'data': body
})
except Exception:
pass
page.on('response', handle_response)
await page.goto(
f"{BASE_URL}{page_path}",
wait_until='networkidle',
timeout=60000
)
# Power BI reports load async — wait for data queries
await asyncio.sleep(10)
await browser.close()
return query_results
Power BI embedded reports use a compressed DSR (DataShapeResult) format:
def parse_powerbi_results(results):
"""Extract tabular data from Power BI query results."""
rows = []
for result in results:
data = result.get('data', {})
if 'results' not in data:
continue
for res in data['results']:
dsr = res.get('result', {}).get('data', {}).get('dsr', {})
for ds in dsr.get('DS', []):
for ph in ds.get('PH', []):
dm = ph.get('DM0', [])
# Value dictionaries for compressed references
value_dicts = dsr.get('ValueDicts', {})
for row in dm:
# G0, G1, ... = dimension values (nationality, month, etc.)
# C = compressed reference indices into ValueDicts
# X[n].M0 = measure values
# R = repeat flags (inherit from previous row)
rows.append(row)
return rows
def decompress_dsr(dsr_data):
"""Decompress Power BI DSR format with ValueDicts and repeat flags.
Power BI compresses data by:
1. ValueDicts: shared string arrays referenced by index (C field)
2. R (repeat): bitmask indicating which G values repeat from previous row
3. Ø (null): marks null/missing values
"""
value_dicts = dsr_data.get('ValueDicts', {})
all_rows = []
for ds in dsr_data.get('DS', []):
for ph in ds.get('PH', []):
dm = ph.get('DM0', [])
prev_values = {}
for row in dm:
current = {}
repeat_mask = row.get('R', 0)
# Resolve G (group/dimension) values
for i in range(10): # G0..G9
key = f'G{i}'
if key in row:
current[key] = row[key]
prev_values[key] = row[key]
elif repeat_mask & (1 << i) and key in prev_values:
current[key] = prev_values[key]
# Resolve C (compressed dict reference) values
if 'C' in row:
for idx, val in enumerate(row['C']):
dict_key = f'D{idx}'
if dict_key in value_dicts and isinstance(val, int):
current[f'C{idx}'] = value_dicts[dict_key][val]
else:
current[f'C{idx}'] = val
# Extract X (measure) values
x_data = row.get('X', [])
for xi, x in enumerate(x_data):
if isinstance(x, dict):
for mk, mv in x.items():
current[f'X{xi}_{mk}'] = mv
all_rows.append(current)
return all_rows
The dashboard has multiple tabs (Fjöldi, Yfirlit, Árstíðardreifing). To load data from different tabs, click the tab buttons:
async def click_tab(page, tab_name):
"""Click a Power BI report tab within the iframe."""
# Find the Power BI iframe
iframe_element = await page.query_selector('iframe[src*="ferdapbi"]')
if not iframe_element:
return
frame = await iframe_element.content_frame()
if not frame:
return
# Click the tab button by text
button = await frame.query_selector(f'button:has-text("{tab_name}")')
if button:
await button.click()
await asyncio.sleep(5) # Wait for new data to load
To scrape different time periods, interact with the Power BI slicers:
async def set_year_filter(frame, year):
"""Change the Ár (year) slicer in the Power BI report."""
# Find and click the year dropdown
year_slicer = await frame.query_selector('[aria-label*="Ár"]')
if year_slicer:
await year_slicer.click()
await asyncio.sleep(1)
option = await frame.query_selector(f'[title="{year}"]')
if option:
await option.click()
await asyncio.sleep(3)
month,year,nationality,passengers,yoy_change,yoy_pct
2026-01,2026,Ísland,43809,-3611,-8%
2026-01,2026,Bandaríkin,24069,-9066,-27%
2026-01,2026,Bretland,20973,+2244,+12%
2026-01,2026,Kína,8908,-764,-8%
2026-01,2026,Ítalía,7555,+5262,+229%
2026-01,2026,Þýskaland,6626,-1260,-16%
2026-01,2026,Frakkland,5803,-115,-2%
2026-01,2026,Pólland,3860,+654,+20%
2026-01,2026,Suður-Kórea,2921,+1304,+81%
2026-01,2026,Ástralía/Nýja-Sjáland,2751,+127,+5%
maelabordferdathjonustunnar.is) fetches a Power BI embed token from ferdapbi.azurewebsites.net. Direct Power BI API calls require this token.ValueDicts, R (repeat) flags, and C (compressed references). Must decompress before use.mkdir -p data/processed/ferdamalastofa
# After extraction:
# data/processed/ferdamalastofa/passengers_by_nationality.csv
# data/processed/ferdamalastofa/monthly_totals.csv
# data/processed/ferdamalastofa/stays_by_duration.csv
# data/processed/ferdamalastofa/stays_by_accommodation.csv
# data/processed/ferdamalastofa/hotel_occupancy.csv