Presentation by John Östh, Aura Reggiani & Laurie Schintler Advanced Brainstorm Carrefour (ABC): ‘Smart People in Smart Cities’ Matej Bel University, Banská Bystrica, Slovakia (August, 2016)

1. Resilience in Spatial and Urban
Systems 2
John Östh, Aura Reggiani
& Laurie Schintler
Smart People in Smart Cities
Faculty of Economics, Matej Bel University &
Regional Science Academy & The City of
Banská Bystrica

2. Presentation
• The main idea
• Theoretical framework
– Central Place Theory
– Self-Organization Theory
• Questions
• Data
– Mobile phone data
– GIS data
• Methods
– Setting up a a self-organizing BigData dataset
• Results
• Conclusions

3. The main idea
• There is an increasing amount of papers
discussing urban and regional resilience.
• However, most times the geography of urban
areas and regions are taken for granted – i.e. the
spatial administrative organization of urban areas
and regions may or may not be mismatching the
functional regions.
• The main idea is to make use self-organization
methods to trace the spatial patterns of the
urban and regional fabric

4. Central Place Theory
• Invented the study of systems of cities and the
interrelationship between cities.
– Assuming that:
• Space is flat, population and resources evenly
distributed.
• Competition, cost and direction for transport, etc.
identical throughout space
– Concepts
• Threshold – minimum population needed for x
• Range – maximum distance population is willing to
commute
Christaller, W (1933), Die zentralen Orte in Süddeutschland. Gustav Fischer, Jena.

5. Central Place Theory and Sweden
• Year 1962-1971, a municipality reform redrew the borders
of Sweden
• Central for the process was Christaller and the CPT –
especially the idea about the administrative principle (k=7)
• This means that between 1962 and 1971, all Swedish
municipalities were redrawn so that:
– Central places became municipalities and gained control over
smaller urban areas and rural areas being near.
– Metropolitan areas were set aside due to the administrative
complexity and population size (became too populous to
administer as “local”)
– Some very remote areas were also set aside (threshold not met
but municipalities needed for administrative reasons).
Set aside ~ regions not determined on the basis of threshold and range

6. Self-Organization theories
”…finding that in certain situations external
forces acting on the system do not
determine/cause its behavior, but instead trigger
an internal and independent process by which
the system spontanelosuly self-organizes itself.”
(Portugali, 2000)

7. Self-Organization of Regions
• There is a large body of literature working on
self-organization – the amount of self-
organization literature that deals with regions
is smaller.
• However, using a wide definition…

8. Self-Organization of Regions
• Has been studied for a very long time:
– Von Thünen and the annuluses of economic
activities
– Alonso – bid/rent
– Christaller (1933) and Lösch (1940) – hierarchies
of activities
– Burgess (1925) and Hoyt (1939) – the morphology
of the urban landscape

9. Self-Organization of Regions
• Self-organizing methods are borrowed from
chemistry, physics, computer science and
math including:
– Fractals and related – i.e. sand pile cities, cellular
automata,…
– Game-related methods (see for instance Schelling)
(further reading Portugali; Batty)

10. Our approach to Self-Organization
• Starts with inspiration from Kohonen (1982, 2001) and
Self-Organizing Maps – where (at least) two interacting
subsystems are used to reposition neurons using a
spatially restricted and iterative learning process.
• We set up a method where mobile phones are
clustered using an iterative learning process where a
hypothetical gravitational force determines the spatial
realms of influence
• Why is this smart?
– Ai, factual flows, responsive and dynamic (not historical
data)…

11. Questions
• Overarching questions:
– Since CPT was used for the construction of
Swedish municipalities - can SO methods be
employed to determine CP?
– Can the Self-Organization of Phones be used to
delineate functional regions of today…tomorrow?
– Can regions of scales be constructed?

12. Data
• Comes from one of the major Swedish mobile
phone operators (among the largest 5)
• Network Driven Records (NDR) stored at the
MIND database at Uppsala University.
• Record all events (silent handovers, text, Internet,
Calls, etc.) and codes each event temporarily to
the nearest 5min interval – 288 temporal units in
24h
• Geography is restricted to mast-level
• Data drawn from a Tuesday in January in 2016

13. Data
• Used dataset contains:
– The average position of each phone and hour
(allowing for positions between masts)
– Each phone can appear in the dataset 24 times -
this is however unusual – in most cases phones
are idle for at least a few hours per 24h.
• Since we don’t want to introduce spurious locations
(i.e. back-tracking and assuming that phones are at the
same location at time t as at time t-1) – we only
position active phones.
– No data of activity or holder is included

14. Data
• To make handling of data easier, all average
coordinates are aggregated to the nearest
100m x 100m coordinate. The dataset still
contains of more than 1.6 million unique
locations of which the majority have more
than one phone

15. Data
• GIS data used to validate our SO-results
– GIS-layers depicting the distribution of urban
areas, municipality borders and of major water-
bodies

16. Methods: - setting up a SO dataset
• Assumption:
– Each phone exerts gravity.
– The gravitational force is modelled to decay
exponentially
– Decay parameter is derived mathematically using
a HLM design on observed mobility
(see Östh et al. 2016)
– Decay parameter value in this case = 0.00166
– Gravity is used as weight at distance dij
Alternative assumption: using Boolean k-borders (0|1) for the construction of thresholds
proved not to work – images available in the post-presentation section

17. Methods: - setting up a SO dataset
• The iterations are conducted using EquiPop
– K-nearest neighbour “contextualizer” for very large
datasets.
– In this study we set up EquiPop to retrieve the distance-
decay weighted average Y-coordinate (first) from the k
nearest neighbours, than the X-coordinate (second) from
the k nearest neighbours.
– We manipulate the outdata, constructing a new file with
updated Y and X coordinates and iterate the procedure
– In our studies, iterations were terminated at iteration 20
because there was no significant difference in cluster
mobility from previous state*
*for k = 50 000, the rule was thereafter applied to all ks

18. Methods: - setting up a SO dataset
• Determining k-values.
– Doubling sequences of k can
roughly be associated with
varying neighbourhood
functions
(Östh 2014; Östh et al. 2015)
– By applying the same
strategy to our SO regions
dataset, CP hierarchies can
be defined crudely
We constructed the following
k-phone regions:
6 250 phones
12 500 phones
25 000 phones
50 000 phones
100 000 phones

19. Methods – setting up a SO dataset
• Next slides will show how the 20 iterations
clustered the phones in the greater Stockholm
region

20. K = 50 000

21. K = 50 000

22. K = 50 000

23. K = 50 000

24. K = 50 000

25. K = 50 000

26. K = 50 000

27. K = 50 000

28. K = 50 000

29. K = 50 000

30. K = 50 000

31. K = 50 000

32. K = 50 000

33. K = 50 000

34. K = 50 000

35. K = 50 000

36. K = 50 000

37. K = 50 000

38. K = 50 000

39. K = 50 000

40. K = 50 000

41. K = 50 000

42. K = 50 000

43. K = 50 000

44. K = 50 000

45. Converting points to areas

46. Creating phone areas surrounding each phone at initial
position is conducted using Thiessen polygon
techniques.
Using each area as a
building-block, and by
keeping trace of its
mobility over
iterations we may
piece together (dissolve)
areas that contribute to
a self-organized cluster
for each k at iteration 20

47. Results
• First section
– Self-organization of phones compared to the
spatial distribution of urban areas
• Second section
– Comparison of the spatial realms of municipalities
and the spatial realms of phone-origins for the
creation of self-organized clusters.

48. Self-organization of phones compared to
the spatial distribution of urban areas

49. Self-organization of phones compared to
the spatial distribution of urban areas
• How many of the phone clusters end up
within urban areas?
– After iteration 20 and k=6250
(the most wide spread), including both clusters
reaching k and not reaching k:
• 8.3% of all phones end up in locations being more than
1000m from the nearest urban area
• 91.7% end up within or close to urban areas.
– Using only clusters reaching k:
• 100% of all phones end up in urban areas.
Since CPT was used for the construction
of Swedish municipalities - can SO
methods be employed to determine CP?

50. Comparing spatial realms
• The 1962 municipality delineation idea means
that very rural and very urban areas will not
match SO regions.
• Midsized municipalities will display strong
similarities with SO regions
• Can the Self-Organization of Phones be
used to delineate functional regions of
today?
• Can regions of scales be constructed?

51. Comparing spatial realms

52. Comparing spatial realms

53. Comparing spatial realms

54. Conclusion
• Self-organization of phones can be used to
create functional regions.
• Using phones of specific hours or using the
trajectories of phones could help to construct
different functional regions

55. Post-presentation section

56. K = 15 000

57. K= 2500

58. K = 500