Resilience in Spatial and Urban Systems 2

Resilience in Spatial and Urban Systems 2

John Östh, Aura Reggiani

& Laurie Schintler

Smart People in Smart CitiesFaculty of Economics, Matej Bel University & Regional Science Academy & The City ofBanská 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 tocommute

Christaller, W (1933), Die zentralen Orte in Süddeutschland. Gustav Fischer, Jena.

Central Place Theory and Sweden

• Year 1962-1971, a municipality reform redrew the bordersof 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 whichthe 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 withvarying neighbourhoodfunctions(Östh 2014; Östh et al. 2015)

– By applying the same strategy to our SO regions dataset, CP hierarchies canbe defined crudely

We constructed the followingk-phone regions:6 250 phones12 500 phones25 000 phones50 000 phones100 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

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Converting points to areas

Creating phone areas surrounding each phone at initial position is conducted using Thiessen polygon

techniques.

Using each area as abuilding-block, and by keeping trace of its mobility over iterations we maypiece 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