1. Ingrid Kaminger
(and Sibylle Saul)
GISCO Meeting
Luxembourg
11 April 2018
Grid-based indicators of accessibility of public utility infrastructure Merging statistics and geospatial information in Member States

2. Motivation
Development of new statistical data products using geoinformation and registers
Availability of road network
Availability of point based registers
Availability of nationwide georeferenced datasets
Accessibility as the ability of people to reach and participate in activities

3. Principal components analyses (PCA) Indicators of accessibility
Objectives
Population
objects
Statistical grids
Network analysis
Calculation of distances
Infrastructure
Principal components analyses (PCA)
Aggregation of indicators
Indicators of accessibility

4. Working steps
Development of model (define topics, define tools and modelling parameters)
Preparation of input datasets (selection of relevant data sets)
Create point layers for each public infrastructure variable (geocoding)
Calculations in GIS: network analysis using python scripting
Aggregations in R statistical environment
Check of results

5. Data Sources Data on population: Data on infrastructure:
Register-based Labour Market Statistics ( )
Data on infrastructure:
Census of Enterprises and their Local Unit of Employment (LUE) ( )
School and Pre-School Statistics (2014/15)
External data sources
Spatial information:
Register of Building and Dwellings
Road network

6. Approach: road distance to selected local units of employment by OENACE
Education
Health
Retail Sale
Leisure
Security
pre-primary education,
compulsory schools,
secondary schools
hospitals (acute care),
General practitioners,
Medical specialists,
pharmacies,
residential care
Groceries,
Clothes/footwear,
Postal/financial activities,
petrol station
Museums/ libraries, botanical/ zoological gardens, sport facilities, cinemas, restaurants
Police
Selection of local units of employment by OENACE

7. Principal components analyses  indicators
Education
Health
Retail Sale
Leisure
Security
pre-primary education,
compulsory schools,
secondary schools
hospitals (acute care),
General practitioners,
Medical specialists,
pharmacies,
residential care
Groceries,
Clothes/footwear,
Postal/financial activities,
petrol station
Museums/ libraries, botanical/ zoological gardens, sport facilities, cinemas, restaurants
Police
Topic indicators
Topic indicators
Topic indicators
Topic indicators
Topic indicators
Indicators by topic
and building & grid cell
Total indicator

8. 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 2016 based on TomTom (Company GeoMagis)
Model builder and scripts in Python 2.7
SQL-scripts for tabular results
R statistical software (methods department)

9. Results and Potential Reuse
New grid based dataset
independent of administrative changes
Description and visualisation of results
Repeatable action
algorithms reusable in the years to come
Potential improvement for statistical registers
Benefit for the ESS
work is documented
model and scripts are provided
Countries having a similar data basis should be able to derive comparable results.
statistical product

10. Results: total indicator, quantiles

11. Results: total indicator, <, =, > mean

12. Total indicator: Population vs. Grid cells (1km)

13. Comparing the indicator around Wien for 1km, 500m and 250m grids

14. Results: indicator retail sale

15. Results: indicator education

16. Results: indicator health

17. Results: indicator leisure

18. Results: indicator security

19. Grid-based indicators of accessibility of public utility infrastructure
Contact:
Ingrid Kaminger
Sibylle Saul
Guglgasse 13, 1110 Wien
Tel: +43 (1) or -8024
Fax: +43 (1)
The project documentation and results for 1km grid
(as of 31 October 2014) are available for download here.