Dijkstra, L. - Access to services by settlement size in Europe
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Access to services by settlement size in Europe - Lewis Dijkstra, European Commission. Workshop on Modernising Statistical Systems, OECD 2018.
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Dijkstra, L. - Access to services by settlement size in Europe
- 1. Modelled access to services and the (refined) degree of urbanisation By Lewis Dijkstra, Head of Economic Analysis, DG REGIO Analysis done jointly with Mert Kompil from JRC Ispra
- 2. Local, sub-regional and regional services
- 3. Requirements per service Type of service Ideal population Minimum population Ideal distance Maximum distance Local (neighborhood) facilities Schools, small health facilities, childcare services, small shops 10.000 5.000 2.5 km 5 km Subregional (municipal) facilities High schools, hospitals, theatres, super markets 100.000 50.000 10 km 25 km Regional facilities Specialized services: education, health, culture, public administration, financial and business services 1.000.000 500.000 50 km 100 km
- 6. Degree of urbanization: Level 1 & 2 Rural area Mostly un- inhabited Dispersed rural area Village Towns & suburbs Suburb Town City City
- 9. Areas outside settlements Settlements by population size Small Medium Large 500-5,000 5,000-50,000 >50,000 Celllevelcriteria residentspersqkm High density >1500 Not applicable Villages Dense Towns City Moderate density 300- 1500 Suburb Semi-dense Towns Low density 50-300 Rural dispersed area Very low density <50 Mostly uninhabited area
- 10. Land and population in the EU by refined degree of urbanisation 0 10 20 30 40 50 60 70 80 90 Mostly uninhabited Dispersed rural area Village Suburb Town City Shareofland,in% Land, 2012 0 5 10 15 20 25 30 35 40 Mostly uninhabited Dispersed rural area Village Suburb Town City Shareoftotalpopulation,in% Population, 2011
- 11. Villages offer potential for walking and public transport 0% 10% 20% 30% 40% 50% 60% 70% Mostly uninhabited Dispersed rural areas Villages Suburbs Town Cities Population within 1 km of local services sub-regional services regional services 0 10 20 30 40 50 60 70 80 90 100 Mostly uninhabited * Dispersed rural area Village Suburb Town City Stopsper10,000residents Modelled public transport stops to serve 80% of the population * 375
- 12. Less travel demand in larger settlements 0 10 20 30 40 50 60 70 Mostly uninhabited Dispersed rural areas Villages Suburbs Town Cities Distanceinkm Modelled distance to local services sub-regional services regional services Type of grid cell Km a year Mostly uninhabited 21,029 Dispersed rural areas 14,417 Villages 11,588 Suburbs 6,227 Town 4,172 Cities 3,034 Assuming 1.5 trips a day to local services, 2 trips a week to sub-regional services and 1 trip a month to a regional service
- 13. City size has no impact on distance to closest local or –sub-regional services
- 14. Walking most realistic for local services
- 15. Conclusion • Modelling of access to services can fill data gaps and provides good simulation of actual distribution • It can be used to estimate impact of demographic change on where services will be easier/harder to maintain • It can help to estimate travel demand and the potential for modal shifts in different locations