ferdamalastofa

name	ferdamalastofa
description	Icelandic Tourist Board — Keflavík passenger counts by nationality and month, flights, accommodation, tourism stats via Power BI.

Ferðamálastofa (Icelandic Tourist Board)

Inbound tourism statistics via Power BI dashboard scraping. Passenger counts through Keflavík airport by nationality, month, and year.

Data Source

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.

Power BI Report IDs

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

Available Dashboards

Samgöngur (Transport)

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

Ferðamenn (Tourists)

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

Gisting (Accommodation)

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

Rekstur & efnahagur (Operations & Economy)

(Available but not yet explored)

Key Data: Passenger Numbers (fjoldi-farthega-um-keflavik)

Metrics

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

Filters/Slicers

Filter	Icelandic	Values
Markaðssvæði	Market area	All, Europe, North America, Asia, etc.
Ár	Year	2016–2026+
Mánuður	Month	janúar–desember

Report Tabs

Tab	Description
Fjöldi í [mánuður]	Monthly breakdown by nationality with YoY comparison
Yfirlit	Overview/summary
Árstíðardreifing	Seasonal distribution

Nationality Breakdown

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.

Key Data: Stays (dvalarlengd-og-gistimati)

Report ID: ae74ab2c-4b1a-4a5c-bf4f-a8ffdda1801f

Tourist length of stay and accommodation type from Ferðamálastofa's border survey (landamærarannsókn).

Metrics

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

Accommodation Types (tegund gistingar)

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

Filters/Slicers

Filter	Icelandic	Values
Bakgrunnsbreytur	Background variable	Aldur (age), Kyn (gender), etc.
Ár	Year	Multiple selections (2024, 2025, 2026)
Mánuður	Month	All, or specific months

Sample Output

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%

Extraction Method

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

Parsing Power BI Response

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

Switching Report Tabs via Interaction

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

Changing Filters (Year/Month)

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)

Sample Output

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%

Data Caveats

1. Embed token auth: The parent page (maelabordferdathjonustunnar.is) fetches a Power BI embed token from ferdapbi.azurewebsites.net. Direct Power BI API calls require this token.
2. Rate limiting: Don't scrape too frequently — the embed tokens have limited validity.
3. DSR compression: Power BI uses a compressed format with ValueDicts, R (repeat) flags, and C (compressed references). Must decompress before use.
4. Icelandic number format: Uses dots as thousands separators (163.175 = 163,175).
5. Monthly updates: Data is updated monthly after Isavia publishes new figures.
6. Historical data: Available from ~2016 onward via year slicer.

Alternative Sources

• Hagstofan: Has some tourism statistics but less granular nationality breakdown
  • SAM01601: Passengers through Keflavík by nationality (annual)
• Isavia: Source of the raw data, publishes monthly press releases
• UNWTO: International comparison data

Save Output

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