IAOS-OECD conference "Better statistics for better lives",19-21 September 2018, Paris. More information at: http://www.oecd.org/IAOS2018/

1. IAOS Conference
Paris, 21 September 2018
Ana Moreno, Marcello Schiavina, Paolo Veneri
Presented by Paolo Veneri
HeadofTerritorialAnalysisandStatisticsUnit
CentreforEntrepreneurship,SMEs,RegionsandCities
Defining the economic
boundaries of cities. A global
application

2. 2
1. The EC-OECD city definition
2. The OECD Metropolitan databases
3. Evidence on OECD cities
OUTLINE

3. Many cities do not match their respective
administrative boundaries
Source: OECD calculations based on population density disaggregated with Corine Land Cover.

4. Why do we need a harmonised definition
of cities and functional urban areas?
• To make sound comparisons of city indicators (i.e. SDG 11)
• To answer questions such as:
– How many cities are there in a specific country?
– Is Istanbul bigger than Paris?
– Is a specific city growing or shrinking?
– Is the growth of cities occurring in the centres or in the
suburban areas (i.e. commuting zones)?
• To improve urban investments and development strategies

5. The concept of functional urban area (FUA)
Urban centre
Commuting zone
Functional urban area
How to define functional urban areas:
1. Identification of densely inhabited and large places (urban centres or cores).
2. Definition of the commuting zone (hinterland) linked by commuting flows to the
city centre.
3. The sum of urban centre and surrounding commuting zone is the functional urban
area
Identified 1,130 FUAs in 33 OECD countries + Colombia

6. A map of French FUAs
• In France our method
allows us to identify 83
FUAs
• Total population in
2011 ranges from
85,000 to 11.7 million
(Paris)
• 65% of French
population live in FUAs
(Paris represents 19%)

7. Where to find the metropolitan database?
- OECD.Stat  http://stats.oecd.org/Index.aspx?Datasetcode=CITIES
- Metropolitan explorer  http://measuringurban.oecd.org/

8. DEFINING FUAS
AT THE GLOBAL SCALE

9. • Problem: Find a way to define commuting zones in places
where there is no commuting data
• Approach:
– Step 1: Assign every 1km2 cell with at least 300 people to a
unique core
– Step 2: Estimate border of commuting zone of each core
using the estimated probability of belonging to a FUA
A Global application of functional urban areas
Problem and approach

10. • Global Human Settlements Population Layer, containing
population in each 1km2 cell (1975-2015)
• Population model vector database (pre-release), containing
polygons for Towns and LDCs following a simple 4-cell
contiguity rule and UC cells
Inputs
Small Medium Large
500-5,000 5,000-50,000 >50,000
High
density
>1500 Not applicable Town City
Medium
density
300-1500 Village Suburb
Low density 50-300
Rural dispersed
areas
Very low
density
<50
Mostly uninhabitated
areas
Settlements by population size
Celllevelcriteria
inhabitanspersqkm
Areas outside settlements

11. We obtain travel times between the edge of each core and each
cell within country borders following the Dijsktra algorithm using:
the Global travel impedance grid (https://map.ox.ac.uk/):
– Represents time associated with moving through grid cells,
quantified as a movement speed within a “friction” grid (30
arcsec resolution). Unit of measurement in grid is minutes
required to travel one kilometre
– Information on roads (and speed limits), railroads, water bodies
and movement over land is used to characterize each cell
Inputs: Travel impedance grid to estimate travel
times
Open!

12. Method steps: Estimation
1. Subset cells with population >300
inhabitants in each country
2. Identify cells falling within FUA
borders (FUA dummy=1 (black), 0
(blue) otherwise)
3. Calculate the distance of each cell
to all cores and assign to closest
4. Pool data for all countries (~ 0.5
million obs.) to estimate a logistic
regression of a FUA dummy on distance
+ size of the core + size of cell + country
controls + polynomial terms
Example country: Slovenia

13. Model estimation and selection
Variable importance score
(absolute value of the Z-stats) The proportion of cells within FUAs
(1s) is 48.4%, ensuring a balanced
sample
The BIC supports the inclusion of
polynomial terms for distance, core
population, national GDP per capita
and cars per 10 000 inhabitants in
country
N=498,702; C= 31

14. Model performance
• Performance diagnosis based on Area Under the Relative Operation Curve (AUROC),
which plots the true positive rate versus the false positive rate
• To guide our final model choice, we build 100 training and test sets based on random
samples of 1,413 cores in our sample and compute median performance measures
(probability threshold = 0.75; set to optimal = 0.72 for the implementation)
AUROC by country

15. IMPLEMENTATION AND RESULTS

16. General results
We obtain 9,895 FUA
borders based on
11,343 cores in 179
countries, covering
1,711,827 sq. km.
United States is the
country with the largest
FUA area coverage
(449,177 sq. km).
Detail, North America

17. Global suburbanisation trends
• In 2015, 54% of the total population across countries lived in
FUA (3.6 billion). 12% lived in commuting zones
• Suburbanization accelerated the most in Europe and Latin
America between 2000 and 2015

18. Suburbanisation is higher in high-income
countries
The ratio of people living
in commuting zones over
people living in cores is
highest in countries with
the highest income per
capita
Amongst large countries,
USA has the largest share
of population in
commuting zones (30%)
GDP per capita vs ratio of commuting population over core population

19. Suburbanisation is highest in medium-sized
cities
FUA pop. by size class
% FUA pop.
in
commuting
zones 2015
%
Commuting
pop. in
compact
suburbs 2015
Less than 100 K 7.11 89.07
Between 100K and 1 million 12.66 85.47
Between 1 and 10 million 13.83 83.75
Larger than 10 million 7.89 87.70
All size classes 11.85 85.14
Suburbs become part of
cores as cities expand.
Most suburbanization
happens in compact
suburbs
Share of FUA population in commuting zones and share of
commuting population in compact suburbs by FUA size class

20. Thank you!
ana.morenomonroy@oecd.org
paolo.veneri@oecd.org