1. Calculating commuting distance – a straight forward process?
Ingrid Kaminger
GISCO meeting
Luxemburg
March 2nd 2015
Calculating commuting distance – a straight forward process?

2. Motivation 2011: change to the register based census
Most commuting variables published in previous censuses are not included in administrative data sources (commuting time, commuting distance, commuting frequency, means of transport used for commuting)
For the Census 2011 publications very coarse estimates for some of these variables were derived from a testing file of a LAU2-to-LAU2 matrix

3. Grant & Goal
Eurostat Grant 2013: Merging statistics and geospatial information in Member States
Title: Census Enriching Commuter Statistics
Goal 1: Model the variables road distance and commuting time using
georeferenced register data
and a road network .

4. Concept and functional possibilities
Commuters: self-employed and employed persons as well as pupils or students, who have to travel between their place of residence and their place of work or educational institution
Data source: register based census 2011, includes building ID of home and work resp. school for every resident
Available for residents of Austria working resp. going to school in Austria
Georeference for each building in the building and dwellings register
Routing network (street classification, turning relations, speed, distance, …)

5. Working steps
Prepare data and feed it into system ( residents  routes)
Georeference missing objects
Create point layer of living and working addresses
Add transit routes to street network
Network Analyst – load points as locations
Routing network – adapt necessary settings (speed, restrictions, hierarchy,…)
Run the scripts (this takes about 6 weeks!)
Evaluate results
Find solution for unplausible results
create tables,…

6. Use case story/ example
Statistics
Minutes
Kilometer
Mean
22,77
28,00
Q0=min
0,05
0,03 Q1
8,93
7,97
Q2=median
12,56
12,42 Q3
25,69
31,13
Q4=max
422,33
666,77
Statistics
Minutes
Kilometer
Mean
25,38
31,22
Q0=min
0,02
0,01 Q1
5,49
3,04
Q2=median
10,19
6,09 Q3
26,91
26,44
Q4=max
360,85
581,35

7. Technologies and Standards used
ESRI ARCGIS on PC and in a Network Environment
Operating System: Windows Server 2008 R2 Enterprise; RAM: 16 GB; Processor: 2,4 GHz Quad Core
ESRI Network Analyst Extension
Adapted Routing network 2013 based on TomTom (Company Geomarketing for Austrian streets), missing transit streets outside Austria)
Scripts in Python 2.7
SQL-scripts for tabular results

8. Modelling a speed profile
Missing traffic light information
 modelled speed depending on max speed and location of road section (e.g. built-up areas)
Modell is based on selection of routes calculated in various programs and derived from that in comparison to experienced time.
ArcGIS (Min opt)
Google Maps
Bing Maps
OSRM
Graphhopper maps
Speed Profile: ArcGIS (Minutes STAT)

9. Unplausible results
all pupils of a primary school travel more than 15km  coordinate of that school was wrong  corrected coordinates and recalculated routes.
Distance 0,0 km for
commuters ?
Buildings on the same
roadsection  used
euclidian distance
Problems with the
road network…

10. Unplausible results 2
Problem: unplausible commuting time/distance within a municipality
commuters commuting within home municipalities had distances of >60km
Various Reasons
road network
mountainous areas
in border regions…

11. Unplausible results 2 - situation

12. Solution for unplausible results 2
For each municipality calculate
Q1, Q3 … first and third quartile
IQR=Q3-Q1 … interquartile range
of commuting distances travelled within this municipality
Distance considered unplausible if
Distance > Q3 + 1,5 * IQR
Solution: use 1,5 x length of the diagonale of the minimum bounding rectangle of the municipality as substitute value
ArcGIS: minimum bounding geometry
– rectangle by area

13. Comparison Results M1(LAU2 matrix) and M2(now)

14. Potential Reuse Repeatable action
algorithms developed for 2011 data can be reused in the years to come
already calculated data from 2012
just started calculations for data from 2013
Benefit for the ESS
STAT will document the work
STAT will provide the model and routing script
Countries having a similar data basis only have to organise the routing system for their country and should be able to derive comparable results.

15. Resume
The crux of it is the routing network and technical infrastructure
Results for „local“ commuters (within municipality or vienna district) were not available before
Results for distances are much more precise (in particular for short distances < 30km)
A straight forward process?
No, it is cumbersome, but it is worth it!

16. Calculating commuting distance – a straight forward process?
Contact:
Ingrid Kaminger
Guglgasse 13, 1110 Wien
Tel: +43 (1)
Fax: +43 (1)
Calculating commuting distance – a straight forward process?