11.2.1 Austria


The following report describes the workflow for calculation of indicator 11.2.1 within the framework of the GEOSTAT 3 project, work package 2, by Statistics Austria.

Data status

The following data sources have been used:

  • Data on officially recognized public transportation stops was not available to STAT for all of Austria in time for this report. However the city of Vienna and Carinthia (Land Kärnten) provide useful open data including coordinates of transport stops and the city of Vienna including type of means of transport for each stop as well as the location of elevators. Data is available under CC BY 3.0 AT license and provided under the Austrian open data initiative https://www.data.gv.at/. Data as of 21.6.2018 has been used, the update frequency is unclear, so this is the data of download.
  • Population data from the statistical population register as of 1.1.2017 has been used. In Austria, the statistical population register is based on administrative data from the national central register of residents, which is geocoded by a direct link to the address buildings and dwellings register (point of entry validation).
  • Routing Network: the 2015 commercial street network based on TomTom was used.
  • Geographic delineation of urban centres following national methodology, produced by Statistics Austria. The urban-rural typology of Statistics Austria is available as inspire data under http://www.statistik.at/gs-inspire/www/inspire2/download/feed_77679c34-302c-11e3-beb4-0000c1ab0db6.xml . The delineation 2015 (based on data from 2013) has been used. (http://www.statistik.at/web_en/classifications/regional_breakdown/urban_rural/index.html)


The steps to calculate the indicator comprised six different phases described in detail below:

a) Prepare geocoded population data

The population data used for statistical analyses at Statistics Austria is based on administrative data from the national central register of residents, which is transmitted to Statistics Austria on a quarterly basis. The data itself is collected by the municipalities and transferred directly into the central register of residents hosted by the ministry of interior as service provider to the registration authorities. Registrations can only be entered choosing a valid building respectively dwelling id from the address buildings and dwellings register (A-BDR) and are saved together with the A-BDR-IDs.

The address buildings and dwellings register also contains coordinates for each address respectively building. These coordinates are in the local surveying projection system (three Gauss Krueger stripes), so for the use as a national dataset in combination with other national geodata the coordinates have to be transformed and reprojected. The population data can then be geocoded by linking it with the A-BDR through the common IDs. Statistics Austria assembles this geo-enabled dataset once a year including for each building the total numbers of residents with main and secondary residence, total number of dwellings, total number of units of employment and employees.

This file is stored as point-geometry in the “Geodatabase” and also as basic processing file for standard point in polygon analysis, such as the service of analysing customer defined polygons.

Various unit record datasets containing the building ID are available for this service. The files are stored in a postgres database and can be joined and aggregated to user demands using SQL scripts. Files on age and sex, education, economic activity and nationality of each individual as well as data on households and families were used for the exercise.

Some 99.9 percent of the 2017 population could be directly geocoded to the level of building location. In absolute values some 9.400 persons out of the 8.772.865 could not, which can have different reasons. E.g. old buildings do not necessarily have geocodes or registrations before the start of the central register of residents (on valid addresses) may have invalid addresses. However every resident (100% of the population) has the code of the enumeration district and municipality ID in which it is registered. When conducting the calculations on access to public transportation stops, only population accurately assigned to address location was used.

b) The Urban-rural typology of Statistics Austria:

This step has already been completed prior to the indicator analysis.

For statistical purposes Statistics Austria developed the urban-rural typology in 2015 and defined urban and rural areas (based on 2013 data) for the first time. The 2015 definition of urban regions was developed on the premise of maximum continuity with the previous definitions of urban regions (delineated from 1971 until 2001).

In a first step highly densified areas were delineated based on 500m grid cells and urban and regional centres were defined on municipality level. For the definition of regional centres the existence of infrastructure facilities was taken into consideration. In a next step municipalities outside of centres were classified according to commuter interrelations and accessibility of centres. The results are 4 major classes: urban centres (urban regions), regional centres, rural areas surrounding centres (urban regions outer zone) and rural areas. These classes are subdivided into a total number of 11 classes according to the accessibility of urban and regional centres (central, intermediate, peripheral). Additionally, the importance of tourism was evaluated for each municipality (additional layer of information). More information and data is available under


As data on officially recognized public transportation stops was only available for the city of Vienna and Carinthia only the urban-rural typology of Statistics Austria was evaluated and no further comparison with other urban concepts done.

c) Selection and preparation of public transportation stops:

A complete national dataset on transport stops and timetables of provided transport modes is gathered by the organisation VAO Verkehrsauskunft Österreich and provided to various routing applications for intermodal door to door routing. However the access for statistical purposes has not been clarified yet. So for this exercise data on officially recognized public transportation stops was only available for the city of Vienna and Carinthia as open data. For Carinthia including coordinates of transport stops and for the city of Vienna including coordinates, type of means of transport for each stop as well as the location of elevators.

Data as of 21.6.2018 was downloaded and point-geometries were created for each public transportation stop. The databases include coordinates in WGS84, which had to be transformed and reprojected to be used in combination with the point based population data, the road network and basemap.

In the case of Vienna a filter was created in order to select only those public transportation stops that are regularly serviced by the main means of transport, such as buses, municipal railways, tramways and the underground. Only these stops were selected for further processing.

d) Preparation of location of elevators:

Data as of 21.6.2018 was downloaded and point-geometries were created for each location of elevators (in comabination with public means of transport stops). The databases includes coordinates in WGS84, which had to be transformed and reprojected to be used in combination with the point based population data, the road network and basemap.

e) Computation of service areas

Service areas were computed using a Euclidian distance buffering operation (250m and 300m for Vienna and 300m for the urban areas of Kärnten) based on the public transportation stops that were selected in the previous step. A separate layer of buffers was created for the locations of elevators in Vienna. Buffering was undertaken in ArcGIS and the resulting buffers were then used for a spatial join with the point locations of the population data. In addition the buffer of 250m was chosen for comparison, since this might be closer to the services area delineation along a routing network.

A test was also conducted on network distance using the national road network. The outcome of this test is described further below.

f) Calculation of the population within service areas:

Once all the previous steps were completed, the share of population within service areas could be calculated. By doing a point in polygon analysis of address points and the buffers, each point was given a new attribute to indicate whether it lies within the buffer and which buffer (250m, 300m, and/or elevator). Using these spatial relationships between population (points) and area features (polygons) any combination of variables could be calculated.

Table 1: concept of the master table from which the spatial relation of building.ID and distance to stops respectively elevators (lifts) can be used for the final calculations

Building-ID Municipality-ID Total pop2017 Buffer Lift
ID 1 90101 4 250
ID 2 90101 0 300
ID 3 90101 15 250 250
ID 4 90101 14 250 250
ID 5 90101 7 250 250
ID 6 90101 14 300
ID 7 90101 12 250 300
ID 8 90101 34 250
ID 9 90101 16 300 250


Network distance

A small test was conducted on using network distance instead of Euclidian distance buffers. The test was conducted using ArcGIS Network Analyst and the TomTom Street network.

The street network includes numerous variables on each street section to describe it, like one way and turning regulations, type of road, length of section, speed limit, and more. However as we were interested in walking distance, most restrictions were irrelevant and not put in place. However the resulting service areas were still not useful for the calculations, as the street network is missing pedestrian routes (through parks, building blocks etc.), which clearly would be first choice for pedestrians. Hence the test was not followed any further.


As expected, the share of the urban population with convenient access to public transportation is far higher in Vienna than in the urban areas of Carinthia. While in Vienna this share is close to 100% only 82% of the Carinthian population live closer to a stop than 300m. The share of population living close to elevators/lifts is much lower. This may only partly be an indicator for barrier free transport, since a lot of the busses and trams have lowered doors and entrances and most of them are on street level and no lifts are needed to use them.

As shown in the following tables, no significant difference can be seen in the numbers for males and females. The share for the age group of 15-24 year olds is slightly higher both in Vienna and the urban areas of Carinthia. Again the share for the age groups 15-24 as well as 25-64 with convenient access to elevators is slightly higher.

Table 2: Share of population with convenient access to public transportation, disaggregated by sex

Vienna Carinthia


  stop Lift
<= 300m 99,7% 23,3% 82,3%
male 99,7% 23,5% 82,1%
female 99,7% 23,1% 82,5%
> 300m 0,3% 76,7% 17,7%
male 0,3% 76,5% 17,9%
female 0,3% 76,9% 17,5%


Table 3: Share of population with convienient access disaggregated by age

Age 0-14 Age 15-24 Age 25-64 Age 65-
<= 300m 99,7% 99,8% 99,7% 99,7%
> 300m 0,3% 0,2% 0,3% 0,3%
Vienna Lift
<= 300m 21,6% 24,4% 23,9% 21,9%
> 300m 78,4% 75,6% 76,1% 78,1%
Carinthia urban
<= 300m 81,56% 83,45% 82,53% 81,54%
> 300m 18,44% 16,55% 17,47% 18,46%


In the case of Statistics Austria, many of the crucial elements suggested by GEOSTAT 3, with relevance for the calculation of this indicator, have already been put in place.

Most significantly the strengths recognised are:

  • Availability of authoritative, point-based location data for geocoding
  • Availability of population data from administrative sources, enabling easy, annual updates of the indicator without having to use population estimations
  • Use of point-of-entry validation of address information in population registry providing very good conditions for geocoding and few non-matching observations


Contact information: ingrid.kaminger@statistik.gv.at