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

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

Grant & Goal Eurostat Grant 2013: Merging statistics and geospatial information in Member States Title: Census 2011 - Enriching Commuter Statistics Goal 1: Model the variables road distance and commuting time using georeferenced register data and a road network .

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, …)

Working steps Prepare data and feed it into system (8.401.940 residents  4.270.049 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,…

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

Technologies and Standards used ESRI ARCGIS 10.1. 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

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)

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…

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…

Unplausible results 2 - situation

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

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

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.

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!

Calculating commuting distance – a straight forward process? Contact: Ingrid Kaminger Guglgasse 13, 1110 Wien Tel: +43 (1) 71128-7773 Fax: +43 (1) 7128622 Ingrid.Kaminger@statistik.gv.at Calculating commuting distance – a straight forward process?