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1 The Zipf Seminars at EMU-UM Sunday, April 06, 2014 After Zipf: From City Size Distributions to Simulations Or why we find it hard to build models of.

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Presentation on theme: "1 The Zipf Seminars at EMU-UM Sunday, April 06, 2014 After Zipf: From City Size Distributions to Simulations Or why we find it hard to build models of."— Presentation transcript:

1 1 The Zipf Seminars at EMU-UM Sunday, April 06, 2014 After Zipf: From City Size Distributions to Simulations Or why we find it hard to build models of how cities talk to each other Michael Batty & Yichun Xie UCL EMU m.batty@ucl.ac.ukm.batty@ucl.ac.uk yxie@emich.eduyxie@emich.edu http://www.casa.ucl.ac.uk/http://www.casa.ucl.ac.uk/ http://www.ceita.emich.edu/http://www.ceita.emich.edu/

2 2 The Zipf Seminars at EMU-UM What we will do in this talk 1. 1.Continue Tom and Johns discussion of Zipfs Law in particular and scaling in urban systems in general from last week 2. Review very briefly what this area is about from last week 3. Review the key problems – power functions v. lognormal, fat tails, thin tails, primate cities 4. Note the basic stochastic models where cities do not talk to each other but do produce good simulations. Illustrate such a simulation.

3 3 The Zipf Seminars at EMU-UM What we will do in this talk 5. Outline some more examples of Zipfs Law in terms of data applications – countries, spatial partitions, telecoms systems, the geography of citations 6. Note how connectivity or interaction is entering the debate through social networks and the web 7.

4 4 The Zipf Seminars at EMU-UM Zipfs Law … Says that in a set of well-defined objects like words (or cities ?), the size of any object (is inversely proportional to its size; and in the strict Zipf case this inverse relation is This is the strict form because the power is -1 which gives it somewhat mystical properties but a more general form is the inverse power form

5 5 The Zipf Seminars at EMU-UM In one sense, this is obvious – in a competitive system where resources are scarce, it is intuitively obvious that there are less big things than small things And when you have a system in which big things grow from small things, this is even more obvious But why should the slope be -1 and why should the form be inverse power In fact as we shall see and as Tom intimated last week this is highly questionable

6 6 The Zipf Seminars at EMU-UM Here are some classic examples from last week First from Zipf (1949)

7 7 The Zipf Seminars at EMU-UM Now from Tom (2003) – top 135 cities

8 8 The Zipf Seminars at EMU-UM As you can see the curve is not quite straight but slightly curved – this is significant but there are some obvious problems Most researchers have taken the top 100 or so cities– they have disregarded the bottom but what happens at the bottom is where it all begins – where growth starts – the short tail Cities are not well defined objects – they grow into each other 3. Cities do not keep their place in the rank order –but shift but the order stays stable – how ? 4 Primate cities are problematic at the top of the long tail

9 9 The Zipf Seminars at EMU-UM Lets look at some cities, countries, & spatial partitions USA-3149 cities R-sq = 0.992 = -0.81 Mexico-36 cities R-sq = 0.927 = -1.27 World-216 countries R-sq = 0.708 = -2.26 UK-459 areas R-sq = 0.760 = -0.58

10 10 The Zipf Seminars at EMU-UM Basically what these relations show is that as soon as you define something a little bit different from cities, you get Zipf exponents which are nowhere near unity. In fact it would seem that for countries we have much greater inequality than cities which in turn is much greater than for exhaustive spatial divisions Now to show how different this all is, then I will show yet another set of countries where there are now only 149 countries, not 216 – from another standard data set (MapInfo)

11 11 The Zipf Seminars at EMU-UM Log Population Log Rank

12 12 The Zipf Seminars at EMU-UM Log Population Log Rank The King or Primate City Effect Scaling only over restricted orders of magnitude A different regime in the thin tail

13 13 The Zipf Seminars at EMU-UM

14 14 The Zipf Seminars at EMU-UM Related Problems Scaling - many indeed most distributions are not power functions The events are not independent - in medieval times they may have been but for the last 200 years, cities have grown into each other, nations have become entirely urbanized, and now there are global cities - the tragedy in NY tells us this - where more than half of those killed were not US citizens Should we expect scaling ? We know that cities depend on history as well as economic growth

15 15 The Zipf Seminars at EMU-UM Confusion over Zipf exponents and their value Why should we expect no characteristic length scale - when the world is finite ? We should avoid the sin of Asymptopia. As scaling is often said to be the signature of self- organization, why should we expect disparate and distant places to self-organize ? The primate city effect is very dominant in historically old countries BUT should we expect these differences to disappear as the world becomes global ?

16 16 The Zipf Seminars at EMU-UM Lets first look at arbitrary events - An Example for the UK based on Administrative Units, not on trying to define cities as separate fields These are 458 admin units, somewhat less than full cities in many cases and some containing towns in county aggregates - we have data from 1901 to 1991 so we can also look at the dynamics of change - traditional rank size theory says very little about dynamics

17 17 The Zipf Seminars at EMU-UM Log Population Log Rank

18 18 The Zipf Seminars at EMU-UM

19 19 The Zipf Seminars at EMU-UM This is what we get when we fit the rank size relation P r =P 1 r - to the data. The parameter is hardly 1 but it is more than 1.99 which was the value for world population in 1994

20 20 The Zipf Seminars at EMU-UM A Digression – Many other systems show such rank size – here we will look at geography of scientific citation –the Highly Cited

21 21 The Zipf Seminars at EMU-UM

22 22 The Zipf Seminars at EMU-UM Rank-Size Distributions of Highly Cited Scientists red institution, black place, grey by country straightline fits by institution (red) by place/city (black) by country (grey)

23 23 The Zipf Seminars at EMU-UM The Highly Cited By Place

24 24 The Zipf Seminars at EMU-UM Explaining City Size Distributions Using Multiplicative Processes The last 10 years has seen many attempts to explain scaling distributions such as these using various simple stochastic processes. Most do not take any account of the fact that cities compete – talk to each other. In essence, the easiest is a model of proportionate effect or growth first used for economic systems by Gibrat in 1931 which leads to the lognormal distribution

25 25 The Zipf Seminars at EMU-UM The key idea is that the change in size of the object in question is proportional to the size of the object and randomly chosen, that is This leads to the log of differences across time being a function of the sum of random changes This gives the model of proportionate effect

26 26 The Zipf Seminars at EMU-UM Heres a simulation which shows that the lognormal is generated with much the same properties as the observed data for UK Note how long it takes for the lognormal to emerge, note also the switches in rank – too many I think for this to be realistic

27 27 The Zipf Seminars at EMU-UM t=1000 t=900 Log of Rank t=1000 Population based on t=900 Ranks Log of Population Shares

28 28 The Zipf Seminars at EMU-UM This is a good model to show the persistence of settlements, it is consistent with what we know about urban morphology in terms of fractal laws, but it is not spatial. In fact to demonstrate how this model works let me run a short simulation based on independent events – cities on a 20 x 20 lattice using the Gibrat process – here it is

29 29 The Zipf Seminars at EMU-UM Other Stochastic Processes which have been used to explain scaling 1. The Simon model - birth processes are introduced 2. Multiplicative random growth with constraints on the lowest size - size is not allowed to become too small otherwise the event is removed: Solomons model; Sornettes work 3. Work on growth rates consistent with scaling involving Levy distributions – Stanleys work 4. Economic variants – Gabaix, Krugman, LSE group, Dutch group, Reed etc

30 30 The Zipf Seminars at EMU-UM Dynamics of Rank-Size: Applications We will now look again at countries and population change and then at penetration of telecoms devices by country We have country data from 1980 to 2000, and telecoms data over the same period – we are interested in the dynamics – we can measure changes using the so-called Havlin plot defined as

31 31 The Zipf Seminars at EMU-UM This is the average difference in ranks over N cities or countries with respect to two time periods j and k. So at each time we can plot a curve of differences away from that time in terms of every other time period. This lets us identify big shifts in rank and thus unusual dynamics.

32 32 The Zipf Seminars at EMU-UM This is population of countries

33 33 The Zipf Seminars at EMU-UM And the average rank distances

34 34 The Zipf Seminars at EMU-UM This is the telecoms data

35 35 The Zipf Seminars at EMU-UM And the average rank distances

36 36 The Zipf Seminars at EMU-UM Some More Issues Note the way systems grow in terms of the telecoms data Note the fact that there is no connectivity at all in these systems Lets finish by looking at connectivity – how cities talk to each other – can we say anything at all about models that take such interactions into account – its another seminar but let us sketch some ideas

37 37 The Zipf Seminars at EMU-UM Networks and Scaling These are distributions where the events are unambiguous or less ambiguous - the distribution of links in and out of nodes defining networks have been shown to be scaling by many people over the last four years, notably by Barabasi and his Notre Dame group and by Huberman and his Xerox Parc now HP Internet Ecologies group Here we take a look at the distribution of in-degrees and out-degrees formed by links relating to web pages - a web page is pretty unambiguous, and s is a link unlike a city. This is some work that we did in 1999 at CASA.

38 38 The Zipf Seminars at EMU-UM This is based on some network data produced by Martin Dodge and Naru Shiode in CASA from their web crawlers

39 39 The Zipf Seminars at EMU-UM Number of Web Pages and Total Links - indegrees and outdegrees These are taken from relevant searches of AltaVista for 180 domains in 1999 Note the notion of a system which is immature – in terms of the lognormal form

40 40 The Zipf Seminars at EMU-UM Number of Web Pages,Total Links, GDP and Total World Populations

41 41 The Zipf Seminars at EMU-UM As a general conclusion, it does not look as though the event size issue has much to do with the scaling or lack of it.We urgently need some work on spatial systems with fixed event areas, thus shifting the focus to densities not distributions

42 42 The Zipf Seminars at EMU-UM Two regimes for the in- degrees and out-degrees

43 43 The Zipf Seminars at EMU-UM The Key Issues: Where do we go from here? Scaling can be shown to be consistent with more micro-based, hence richer, less parsimonious models; but there is a disjunction between work on spatial fractals such as in our 1994 book Fractal Cities (Academic Press) and the rank size rule – very hard to know how to build consistent models that work at the spatial level and give fractal relations which translate into city size distributions

44 44 The Zipf Seminars at EMU-UM Resources References Papers Web Resources We will assemble a list and put these on a web site. I will out this power point on the China Data Center site like Tom and Johns from last week if I can penetrate the Chinese walls of EMU ! Take a look at our web site where at least the web paper can be downloaded from the publications section and some of the work on cyberspace is reported http://www.casa.ucl.ac.uk/ http://www.cybergeography.org/ http://www.casa.ucl.ac.uk/citations/

45 45 The Zipf Seminars at EMU-UM Network Approaches to Scaling Here we take a look at the distribution of indegrees and outdegrees formed by links relating to web pages - a web page is pretty unambiguous. There is a lot of work on this produced during the last three years, notably the Xerox Parc group & the Notre Dame group let me start with some notions of about graphs

46 46 The Zipf Seminars at EMU-UM On the left a random graph, whose distribution of the numbers/density of links at each node is near normal - this has a characteristic length - the average On the left, what is much more typical - a graph which is scaling - one whose distribution is rank size, following a power law P(k) ~ k - 2.5

47 47 The Zipf Seminars at EMU-UM Not only does the topology of web pages follow power laws so does the physical hardware - the routers and wires This and the last diagram are taken from the article by Barabasi called The Physics of the Web printed in the July 2001 issue of Physics World

48 48 The Zipf Seminars at EMU-UM Some statistics from Steves work - which imply scale free networks Lots and lots of issues here - we need models of how networks grow and form, how does the small world effect mesh into scale free networks ? We need to map cyberspace onto real space and back, and this is no more than mapping social space onto real space and back - its not new. I will finish

49 49 The Zipf Seminars at EMU-UM

50 50 The Zipf Seminars at EMU-UM

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52 52 The Zipf Seminars at EMU-UM

53 53 The Zipf Seminars at EMU-UM Links as indegrees and outdegrees compared to the Total Links

54 54 The Zipf Seminars at EMU-UM

55 55 The Zipf Seminars at EMU-UM

56 56 The Zipf Seminars at EMU-UM

57 57 The Zipf Seminars at EMU-UM Number of Web Pages and Total Links - indegrees and outdegrees These are taken from relevant searches of AltaVista for 180 domains in 1999

58 58 The Zipf Seminars at EMU-UM Network Approaches to Scaling Here we take a look at the distribution of indegrees and outdegrees formed by links relating to web pages - a web page is pretty unambiguous. There is a lot of work on this produced during the last three years, notably the Xerox Parc group & the Notre Dame group let me start with some notions of about graphs

59 59 The Zipf Seminars at EMU-UM As an introductory example, I will repeat what I say in the editorial I handed out on small worlds. You can read this later The term small worlds was first coined in psychology and sociology in the 1960s by Stanley Milgram but remained a talking point only, for 30 years largely because there was 1. No technical apparatus to measure connectivity in very large graphs - where you have say more than 1 million nodes 2. There was no real way in which one could handle processes taking place on graphs 3. There was not much thinking about how real graphs structures evolved - through time

60 60 The Zipf Seminars at EMU-UM All these points needed to be resolved before one could get anywhere and they are slowly being resolved. An example of a small world - a kind of connectivity in graphs

61 61 The Zipf Seminars at EMU-UM Examples: Evolution of transport systems in big cities What makes small spaces in cities attractive and livable in Spread of disease - foot and mouth for example How social systems hold together Academic communities, like us Nervous systems, how particles interact, WWW etc

62 62 The Zipf Seminars at EMU-UM Some of the most interesting work is being done in virtual space - in cyberspace not in real space. Here is an example of such a network

63 63 The Zipf Seminars at EMU-UM The world wide web is a small world as are most systems that dont break apart under tension - thing about cities that break apart - London currently with the fact that no decent freeway system was built in the automobile age and the subway hasnt been fixed for 50 years. Global cities are small worlds. However there is a much more general theory of networks being devised which examines regularity and processes in such structures. Recently it looks as though most stable networks are scale free - this means that when you examine their structure, there is no characteristic length scale - they are fractal - moreover as they grow, they grow through positive feedback - dense clusters get denser - the rich get richer - again think of cities - in short they do not grow randomly

64 64 The Zipf Seminars at EMU-UM On the left a random graph, whose distribution of the numbers/density of links at each node is near normal - this has a characteristic length - the average On the left, what is much more typical - a graph which is scaling - one whose distribution is rank size, following a power law P(k) ~ k - 2.5

65 65 The Zipf Seminars at EMU-UM Not only does the topology of web pages follow power laws so does the physical hardware - the routers and wires This and the last diagram are taken from the article by Barabasi called The Physics of the Web printed in the July 2001 issue of Physics World

66 66 The Zipf Seminars at EMU-UM Here is some work that Steve Coast in our group at CASA is doing on detecting and measuring the hardware of the web and visualizing it - all this is prior to measuring its properties - i.e. is it scaling, is it a small world and so on Challenge is to map real space onto cyberspace and that so far has not really been attempted in these new ideas about how network systems grow This is the cluster of routers, and hubs and machines in UCL

67 67 The Zipf Seminars at EMU-UM Some more fancy visualizations of these networks

68 68 The Zipf Seminars at EMU-UM Some statistics from Steves work - which imply scale free networks Lots and lots of issues here - we need models of how networks grow and form, how does the small world effect mesh into scale free networks ? We need to map cyberspace onto real space and back, and this is no more than mapping social space onto real space and back - its not new………………… I will finish

69 69 The Zipf Seminars at EMU-UM Some references - Martin Dodge and Rob Kitchins new book Steve Coasts web site www.fractalus.com/steve/ Our web site www.casa.ucl.ac.uk and drill down to get to Martins www.cybergeography.org

70 70 The Zipf Seminars at EMU-UM

71 71 The Zipf Seminars at EMU-UM 1901 1991 Log of Rank 1991 Population based on 1901 Ranks Log of Population Shares Here is an example of the shift in size and ranks over the last 100 years in GB

72 72 The Zipf Seminars at EMU-UM

73 73 The Zipf Seminars at EMU-UM


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