1. Enabling a national road and street database in population statistics: Commuting distances for all employed persons and other accessibility statistics Pasi Piela pasi.piela@stat.fi European Forum for Geostatistics Sofia Conference 2013

2. Three accessibility applications 1) Commuting distances (with discussion on commuting time) – General annual update for the Social Statistics Data Warehouse 2) Travel time accessibility of public hospitals – ESSnet Geostat IB –project 3) Distances to Elementary schools – Within selected 64 municipalities, for the Association of Finnish Local and Regional Authorities.

3. Data and contextual issues Digiroad, National Road Database of TraFi – accurate data on the location of all roads and streets in Finland Social Statistics Data Warehouse of StatFi – Dwelling coordinates and work place coordinates along with a variety of demographic features. – Coordinate coverage for the ”workplace” was 91.2% of all employed inhabitants in 2010. – Aggregated statistics: the Grid Database (99 %) chargeable product of StatFi

4. Pairwise computing of commuting distances 1/2 2.1 million coordinate pairs i: ((x id, y id ), (x iw, y iw )) Computational complexity is high – ”takes several weeks” ESRI ArcGIS® and Python™ – number of available licenses is not high – Network Analyst Route Solver Documentation is not satisfactory but it works: common Dijkstra’s algorithm with hierarchical routing (7-class functional classification: Class I main road, Class II main road, Regional road etc.) impedance attribute is the length of a route

5. Pairwise computing of commuting distances 2/2 The solution: 45,000 pairs of points (90,000 datarows) for one program run. 3 parallel runs per one standard computer. For 2 computers (2 licenses): 270,000 distances in about 3 days.

6. Distance statistics in kilometers TypeMedianMeanQ1Q3QCD Linear6.0213.342.0515.220.76 Route7.6615.922.6718.640.75 Q1 = 25th percentile, Q3 = 75th percentile Mean of 0 – 200 km distances Deviation measure here: QCD = (Q3 – Q1) / (Q3 + Q1) Quartile coefficient of dispersion

7. Median and QCD for populations of the sub-regions (LAU 1)

8. Travel time: 24/7 emergency accessibility Data – manual geocoding of certain emergency rooms – Population aggregated to 1 km x 1 km grids Travel time – Speed limits for each traffic element (smallest unit of Digiroad, speed limits dynamically segmented). – Functionality class of a traffic element Target: 30 and 60 minutes detailed service areas for each unit

9. Relative 30 minutes drive time coverage by age groups Hospital Dist.1 – 1516 - 6465+All 0.8960.8940.8460.885 Min, Kainuu 0.5910.5810.5190.570 Max, Helsinki 0.991 0.9860.991

11. Elementary school accessibility Distance enough? This approach estimates distances directly from each dwelling unit to the nearest school – Schools: lower comprehensive, upper comprehensive and general schools including both – Special education schools are excluded (only 3.5 % in Finland) Non-hierarchical approach, allowing pedestrian districts as part of the route optimisation

12. Relative distances to the nearest lower/upper comperehensive school For the permanent population aged 7 to 12 and 13 to 15 in 2012 Municipality< 1 km< 3 km< 5 kmPopulation Helsinki lower s. 0.8110.9961.00029,246 Helsinki upper s. 0.5520.9881.00014,647 Espoo lower s.0.6690.9840.99418,799 Espoo upper s.0.4170.9340.9828,779

13. Further research and conclusions As mentioned earlier, travel time is a highly relevant part of accessibility studies and further research is needed. Many accessibility challenges are related to rush hour traffic. Hence, for example, the estimation of the commuting travel time becomes much more complex than the general 24/7 emergency accessibility. The extensive and detailed national road network has been proven useful in population statistics and will be seen as part of the social statistics data warehouse based applications in forthcoming years. Благодаря ви за вниманието! pasi.piela@stat.fi