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Resilience in Spatial and Urban Systems 2

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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)

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Resilience in Spatial and Urban Systems 2

  1. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Methods – setting up a SO dataset • Next slides will show how the 20 iterations clustered the phones in the greater Stockholm region
  20. 20. K = 50 000
  21. 21. K = 50 000
  22. 22. K = 50 000
  23. 23. K = 50 000
  24. 24. K = 50 000
  25. 25. K = 50 000
  26. 26. K = 50 000
  27. 27. K = 50 000
  28. 28. K = 50 000
  29. 29. K = 50 000
  30. 30. K = 50 000
  31. 31. K = 50 000
  32. 32. K = 50 000
  33. 33. K = 50 000
  34. 34. K = 50 000
  35. 35. K = 50 000
  36. 36. K = 50 000
  37. 37. K = 50 000
  38. 38. K = 50 000
  39. 39. K = 50 000
  40. 40. K = 50 000
  41. 41. K = 50 000
  42. 42. K = 50 000
  43. 43. K = 50 000
  44. 44. K = 50 000
  45. 45. Converting points to areas
  46. 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. 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. 48. Self-organization of phones compared to the spatial distribution of urban areas
  49. 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. 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. 51. Comparing spatial realms
  52. 52. Comparing spatial realms
  53. 53. Comparing spatial realms
  54. 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. 55. Post-presentation section
  56. 56. K = 15 000
  57. 57. K= 2500
  58. 58. K = 500

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