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

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

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

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

Data Sources Data on population: Data on infrastructure: Register-based Labour Market Statistics (31.10.2014) Data on infrastructure: Census of Enterprises and their Local Unit of Employment (LUE) (31.10.2014) School and Pre-School Statistics (2014/15) External data sources Spatial information: Register of Building and Dwellings Road network

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

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

Technologies and Standards Used ESRI ARCGIS 10.4. 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)

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

Results: total indicator, quantiles

Results: total indicator, <, =, > mean

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

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

Results: indicator retail sale

Results: indicator education

Results: indicator health

Results: indicator leisure

Results: indicator security

Grid-based indicators of accessibility of public utility infrastructure Contact: Ingrid Kaminger Sibylle Saul Guglgasse 13, 1110 Wien Tel: +43 (1) 71128-7773 or -8024 Fax: +43 (1) 7128622 geoinformation@statistik.gv.at The project documentation and results for 1km grid (as of 31 October 2014) are available for download here.