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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
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
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
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.
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
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)
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…
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
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)
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)…
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?
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
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
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
Data
• GIS data used to validate our SO-results
– GIS-layers depicting the distribution of urban
areas, municipality borders and of major water-
bodies
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
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
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
Methods – setting up a SO dataset
• Next slides will show how the 20 iterations
clustered the phones in the greater Stockholm
region
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
K = 50 000
Converting points to areas
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
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.
Self-organization of phones compared to
the spatial distribution of urban areas
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?
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?
Comparing spatial realms
Comparing spatial realms
Comparing spatial realms
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
Post-presentation section
K = 15 000
K= 2500
K = 500

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Resilience in Spatial and Urban Systems 2

  • 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
  • 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?
  • 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
  • 56. K = 15 000