enabling a national road and street database in population statistics: commuting distances for all...
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Enabling a national road and street database in population statistics: Commuting distances for all employed persons and other accessibility statistics
Pasi [email protected]
European Forum for Geostatistics Sofia Conference 2013
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.
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
Pairwise computing of commuting distances 1/2
• 2.1 million coordinate pairs i: ((xid, yid), (xiw, yiw))• 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
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.
Distance statistics in kilometersType Median Mean Q1 Q3 QCD
Linear 6.02 13.34 2.05 15.22 0.76
Route 7.66 15.92 2.67 18.64 0.75
• Q1 = 25th percentile, Q3 = 75th percentile• Mean of 0 – 200 km distances• Deviation measure here:
• QCD = (Q3 – Q1) / (Q3 + Q1)• Quartile coefficient of dispersion
Commuting distance
Median in kilometers
2.4 - 4.5
4.6 - 6.5
6.6 - 10.5
10.6 - 15.5
15.6 - 24.5
Commuting distanceQuartile coefficientof dispersion
0.51 - 0.70
0.71 - 0.79
0.80 - 0.85
0.86 - 0.90
0.91 - 0.96
Median and QCD for populations of the sub-regions (LAU 1)
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
Relative 30 minutes drive time coverage by age groups
Hospital Dist. 1 – 15 16 - 64 65+ All
All 0.896 0.894 0.846 0.885
Min, Kainuu 0.591 0.581 0.519 0.570
Max, Helsinki 0.991 0.991 0.986 0.991
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Drive time accessibility to public hospitals
Hospital
Hospital District
0 - 30 minutes
31 - 60 minutes
> 60 minutes
G1 km Population Grids
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Service Area Polygons
30 min.
60 min.
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
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 km Population
Helsinki lower s. 0.811 0.996 1.000 29,246
Helsinki upper s. 0.552 0.988 1.000 14,647
Espoo lower s. 0.669 0.984 0.994 18,799
Espoo upper s. 0.417 0.934 0.982 8,779
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[email protected]