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Ana Moreno Monroy - Global definition of cities and their areas of influence

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Presentation by Ana Moreno Monroy, OECD at the OECD Workshop on Spatial Dimensions of Productivity, 28-29 March 2019, Bolzano.

More info: https://oe.cd/GFPBolzano2019

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Ana Moreno Monroy - Global definition of cities and their areas of influence

  1. 1. A global definition of cities and their areas of influence Ana I . More no Monroy, OE CD/CFE /RDT Workshop on Spat ial Dim e nsions of Produ ct iv it y Bol zano, March 2 8 th , 2 0 1 9
  2. 2. Many cities do not match their respective administrative boundaries
  3. 3. Note: Metro large regions performed above national levels (1.13) and metros close to national levels (0.98) Spatial productivity comparisons require a consistent definition of cities and their area of influence 0.84 0.86 0.88 0.9 0.92 0.94 0.96 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 GDPperworkerrelativetocountry(weightedaverage) Non-Metro Close toMetro Non-Metro Close toSmall & Medium Non-Metro Remote
  4. 4. Functional Urban Areas (FUA) or urban agglomerations demarcate the space encompassed by daily commuting flows Extending the definition of urban agglomerations globally Number OECD-EC FUAs Estimated FUAs FUAs 1 191 ~10 000 Countries 34 178 Data Global grid + National census (travel-to-work flows) Global grids Method Commuting intensity Probabilistic
  5. 5. OECD-EC FUAs
  6. 6. 1. Identification of densely inhabited and large places (cities or “urban centres”) 2. Definition of commuting zone (area of influence) linked by commuting flows to cities 3. The sum of city area and surrounding commuting zone area is the Functional Urban Area How to define OECD-EC FUAs City Commuting zone City FUA
  7. 7. • 83 FUAs • FUA population (2011) 85,000 - 11.7 million • 65% people live in FUAs (19% in Greater Paris) Example OECD-EC FUAs - France Core Commuting zone
  8. 8. Visit the updated web-site: https://measuringurban.oecd.org/# The OECD Metropolitan eXplorer
  9. 9. Estimating FUAs globally
  10. 10. Extracting meaningful information from existing FUAs Cells with 300 people or more Source: The pudding
  11. 11. Global FUA method in two steps Country with commuting flows data Country without commuting flows data Step 2: CLASSIFY each pixel as within or outside FUA based on actual FUA borders (left) or estimated probability (right) City 1 City 2 Based on predicted probabilities from logistic regression (on 500K obs.) on travel time to closest city, city size and country characteristics City 1 City 2 Estimate travel time from each pixel to every city in the country using travel impedance grid. Choose smallest Step 1: ASSIGN each pixel with 300+ people to the closest city
  12. 12. Good/Excellent predictive capacity and no notable biases by country characteristics Robust to alternative proxies and acceptable predictive power (100 training and test sets based on random samples of ~1 400 cores) Model performance and validation Proxy for core size AUROC Population 0.862924 Area 0.861503 Night-time lights 0.862459 AUROC : 0.5 = no distinction cells inside/outside FUAs; 1= perfect distinction
  13. 13. Draw borders based on pixels with predicted probability > optimal threshold (~0.73) Merge FUAs with touching boundaries within 5km from each other into polycentric FUAs Implementation and borders Actual vs estimated FUA border, Bogotá, Colombia
  14. 14. Administrative estimated FUAs and external validity tests Jaccard = 1  Two maps are the same. Threshold = 15% of population in municipality falls in estimated FUA(s)
  15. 15. Global suburbanisation trends
  16. 16. Global comparison results 0% 10% 20% 30% 40% 50% 60% 70% Least Developed Less Developed, excluding least developed More Developed Commuting Pop. % in FUA FUA % in Total Pop (millions of persons) 0% 10% 20% 30% 40% 50% 60% 70% 80% Africa Asia Europe Latin America & Caribbean North America Oceania Commuting Pop. % in FUA FUA % in Total Pop (millions of persons) 54% of the world’s population live in FUAs (3.6 billion). 12% of them live in commuting zones
  17. 17. The ratio of people living in commuting zones over people living in urban centres is highest for the richest countries Amongst large countries, USA has the largest share of population in commuting zones (30%) Suburbanisation is higher in high- income countries
  18. 18. Thank you ana.morenomonroy@oecd.org paolo.veneri@oecd.org

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