the computer and the geographer€¦ · geographer, but i believe that many features of the problem...

The Computer and the GeographerAuthor(s): Torsten HägerstrandReviewed work(s):Source: Transactions of the Institute of British Geographers, No. 42 (Dec., 1967), pp. 1-19Published by: Blackwell Publishing on behalf of The Royal Geographical Society (with the Institute ofBritish Geographers)Stable URL: http://www.jstor.org/stable/621369 .Accessed: 26/07/2012 11:22

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The Computer and the Geographer' TORSTEN HAGERSTRAND, FIL. DR.

(Professor of Social and Economic Geography, University of Lund, Sweden)

MS. received 6January 1967

IF WE look away from certain rather new sorts of purely theoretical investigations which can be undertaken without very much of an observational base, geographical research almost by definition has to be founded on empirical data. However, we do not need just a few scattered observations but, more often, information in very large quantities. We further adhere to the habit of depicting on maps the information we have obtained, both for the sake of analysis and for the sake of communication. This habit forces us to assemble complete information concerning the area under observation to a degree which can often be avoided in other branches of research. Undoubtedly we have all at times met a situation where mapping or calculation was never accomplished for the simple reason that the amount of work proved to be insuper- able.

Judging from the content of current geographical periodicals, it is in my opinion still not very common for geographers in this situation to turn to the modern powerful data- processing techniques for help. In fact we have the means at our disposal ready to take over much tedious manual work and above all to make possible exercises which simply could not have been undertaken earlier unless it were possible to command regiments of clerks, and perhaps not even then.

Automatic data-processing is undoubtedly still an innovation in the professional work of the geographer. It is of course quite a normal state of affairs for opinions to be divided about the value of innovations. So, for example, Professor Bowden in his book Faster than Thought reports that Sir Winston Churchill once said: 'We must beware of needless innovations, especially when guided by logic.' On the other hand, the Duke of Cambridge who-if I am correct-was not known to adapt willingly to new conditions, is said to have admitted that 'The right time for making changes is when you cannot help it.'2 I am inclined to believe that we have now come very close to the second situation as regards the handling of information, and therefore I propose to talk about automatic data-processing in geography.

I have no desire to present a philosophical essay on the geographer's situation in the coming age of automatic processing of information. I leave out entirely such important matters as the staffing and instrumentation of departments, and the future training of students for a career in geography. My task is much more limited in scope: I just want to show that we have here a tempting opportunity and that there is an importantjob for us to carry out. I should like to do this by demonstrating how in my country we have attacked some problems basic to the long- term development of geographical research and planning and how we have been experimenting under present conditions. My point of view, of course, is that of the social and economic geographer, but I believe that many features of the problem are common to all branches of geography.3

I think that the computer can do three different and useful things for us. The first and simplest operation is straightforward descriptive mapping, either by locating figures directly on

I

a base map as they emerge from some source of data, or by converting them into symbols such as dots, isolines or shaded areas. The computer can also be programmed to draw the base map on a suitable scale.4 The maps are even printable without redrawing if we are willing to accept their peculiar aesthetic characteristics.

The second and more important function is the analytical one. The computer can evaluate locational relationships for us, calculate indices, correlate different sets of phenomena, draw boundary lines according to specified rules, classify places and regions, apply the sieve method, pick out samples, and so forth. It is not even necessary to draw the maps underlying the analysis. It may be sufficient that they are properly represented inside the processing unit.

The third kind of service is perhaps the most interesting and promising. This service is to run process models by which we might try to reproduce observed or create hypothetical chains of events of a geographical nature. I believe that vast possibilities lie ahead in this field, both in physical and social geography. Numerical weather forecasting provides an excellent example of what I have in mind from a science which is closely related to geography. I further believe that it will be efforts in this particular field which may help us to renew the attack on the ancient and fundamental problem of geographical synthesis.

Later on, I am going to comment briefly on some practical cases, representing the different levels of computation. But before that, I will examine a basic problem which has to be solved if we aim to progress beyond merely carrying out research faster and with less effort.

In the long run, the computer cannot give full service to geographical research and related planning unless we make a united and vigorous attack on the way in which official statistics are collected and published. Many difficulties of interpretation of statistical data emanate from the under-developed way in which spatial dimensions are handled. Thus, as every geographer in the social and economic field knows by frustrating experience, census-takers usually aggregate data over political areas of varying size, form and overlap, a practice that gives by far too low a degree of geographical precision for many kinds of research problems and too few degrees of freedom to rearrange data for special research purposes not foreseen at the time of the census. Overlap between conflicting systems of regions also makes it difficult to correlate various sets of data. Changes of boundaries obstruct evaluation of time-series of local and regional developments. I do not deny, of course, that there is a need for administrators to have data for their respective areas of responsibility. But since they-at least in my experience-use rather few statistics, there is no strong reason why the political system should dominate the organization of such a big investment as a census.

The problem has been felt for a long time. As early as I870, A. Haviland complained of the matter to The British Association for the Advancement of Science and suggested the use of watersheds as universal statistical units.5 He argued that 'Were a natural system substituted for the present one, and our country divided into districts regulated by its watershed and river system, we should then have in every district a focus of scientific inquiry, whether it be as to rainfall, temperature, prevalence or strength of wind, agricultural statistics, the produce of our fields, our mines, or our rivers, or for the purpose of registering the occupations, the diseases, mines, or the deaths of our people. Moreover, such a system would form the best basic map for every future census, and being once established upon a well-considered and natural plan, would do away with the necessity of those eternal alterations which are now year by year going on, to the utter confusion of the scientific student'. Having seen few improvements since Haviland's time, we have still, I think, to support his criticism if not any longer his solution.

T. HAGERSTRAND 2

THE COMPUTER AND THE GEOGRAPHER

The production of dot-maps of the distribution of population is one of the ways used by geographers for decades to obtain a partial solution in a restricted field. But for further advances in the geographical analysis of such things as movements of population, transportation, land use, and urban activities including industry, we need an entirely new approach to the collection and rendering of information. Perhaps an entirely new philosophy of the responsibilities of census bureaux has to come forth. One may argue that their main function should not be to

publish masses of all-purpose statistical tables-of which many are never used-but instead to collect and store data and then hand them out processed according to the specifications of the individual consumers, be they public authorities or research establishments.

If we look back on the history of census-taking, we find that demographers and econo- mists have been much more successful in getting official data adapted to the conceptual requirements of their disciplines. To take an elementary case, it is a universal principle when measuring age to use the year and parts of it-that is to say, physical units-as the measure of time. Through this principle we can derive all sorts of correlations and relative locations in the time dimension.

Today, this seems too self-evident to need pointing out. But note that geographical position in space is still handled in so primitive a way that corresponding correlations and relative locations in this dimension are very difficult or sometimes impossible to derive.6 In moments of dissatisfaction with the state of geography, it is comforting to meditate over what kind of

demography or econometrics we would have had if number of births or volume of production were to be reported for periods of office of prime ministers or mayors or some similarly arbitrary unit of time.

In view of the historical past of geography it is only natural that we have had a much more fruitful relationship with the map-producing offices of our countries than with the census-takers. But I feel that today we are approaching a level of abstraction which makes the time-honoured distinction between, on one hand, the physical features depicted on topographic maps and, on the other, objects and events accounted for in statistical publications, a rather artificial one. In my department with its strong double interest in social geography and planning we have over more than a decade been advocating the idea that the production of maps and the collecting of statistical information should be looked upon as one closely connected function.7 Just now we seem to be very close to complete success in Sweden. In October I966, a Royal Commission submitted to the Government a proposal for the foundation of a national data-system which will create a full connection between map and census and go far toward meeting the requirements of geographers and planners as to a consistent and flexible treatment of the space dimension.8 This system, if adopted-and it turns out to be surprisingly inexpen- sive to establish and maintain-could start a revolution in both empirical and theoretical geographical research. But this will happen under one condition only, namely, that the geographer accepts the computer as an everyday aid in his work.

In fact to us as geographers the computer is a friendly animal because it likes to handle information in the way that we prefer to have it. So, for example, when it comes to the location of data, the use of ordinary geographical names of places and areas is very inconvenient, but the use of x-y co-ordinates works excellently. And such co-ordinates, of course, are the spatial equivalents of the physical units which are used for measuring time.

Let me now go into some detail about how the proposed system works. Existing conditions are favourable for it in Sweden. For centuries, public documents concerning, for example, registration of population, taxation, land use, or real estate, have used as legal addresses the cadastral indices both in rural areas and in towns. Road and street addresses have been added

3

in recent times only for the convenience of the postal service. So, by this principle, we have for a long time been able manually to collect data from various sources and pin them down

.... 263

\ .... .

(O . k.. \.

S ''***\.~~~?? ^~ ~ -.~ ~? \ ''i

FIGURE I-The selection of 'central (or identification) points', representing location of cadastral units. The left half of the Figure (this page) shows boundary lines and conventional designations of cadastral units. The right half (p. 5) shows in the same area how the central points have been inserted. The distance between vertical lines 1616 and I617 is I km.

on maps in considerable detail. But the legal designations ofcadastral units are very old-fashioned and to find only one single unit on the map can sometimes involve hours of search.

What has been suggested now is to select inside every cadastral unit in the country a point

T. HAGERSTRAND 4


of identification and then to enter the x-y co-ordinates from the national grid-system of this

point in the land register. A precision of 10 m is found to be sufficient even in urban areas

where lots sometimes are very narrow. These points of identification will be continuously up-dated by the land surveyors as a normal part of their duties, and thus at all times a correct

picture of the set of points will be available. Further, the land register will be kept on both

ordinary registration cards and on computer tape. All this means that all sources of information which use the cadastral units as addresses can automatically become loaded with very precise locational information by the use of the land-register tape as a translating device.

5

6 T. HAGERSTRAND

When the system was first suggested in a geographical paper a little more than io years ago, we had in mind a register giving identification co-ordinates to all buildings. This proved to be too ambitious for the national system, but the solution which is now under consideration

is so flexible that local authorities, say ^ ^ ^ ^ ^ ^ ^ ^ ^^ ^ e ^ ^: " big cities or county councils, can them-

389 ' 4r selves add more information if they ,67 Pv 1s219 need to.

'"63 \ .5 6 .) a,' ,/ I 52> ^ A map (Fig. i) will show more

34 )3

2 (740

c 4

2 )12 3 2

1 clearly how the system works. We see, 31 \9 5

16/8 7,J.-. in two versions, the fringe area

29 16)6 1 ') 37 26T \ 9 6,.. between an urban agglomeration and

028 3o10 4 1.2141712149\ / 2 4 the1l2 28 7

7 4 42317 1

93 4191 open countryside, to the left without, 26

/6 5 3 44

006 4 8_i18-14 1

1 and to the right with, identification f 5 6 ' 8S 13. 41/1 lo, 9 -

4325 ,28 2 6 A2o 3620 6 2, 2 1 32 ,-31,-i points. Dashed lines are property 23\ 7311 / 24 43',3,17, 3 1- boundaries, and names and numbers

21 4 1\211 12 1 7 13 55098 20 1418o il2 34 \/ show how units are designated accord- 32 5 5310 ) 19, 27 (0 (2 922(C107 6 5 9'13 11 40 15

/ 74 ,520 25 10 310 27 (18 5 ing to traditional principles. Apart 189 .4t

,' ~ ~'*.~-- '-.o..-% 4e 2 356 "/21

'

'15 7 -- 8

141 8 1 -

16 from minor additional rules, identifi- 1 17 'I / cto 16 ,?5/" \/

^1 cation points will be chosen in two 4635 L( 2

3 ways. When the units have buildings, FIGURE 2-Inhabitants per km2 in the parish of Moheda according the point is located over the main to the I940 census. Isarithms for I, 50 and Ioo inhabitants per km2. building. When there is no building

on the unit, a 'central point' is chosen =^^^ = ^^^ n H.c- - H instead. It is in itself an interesting 6359

38 2 2_ problem how best to represent the 57 2, - 'I

"6 " ; '/ 2 o4 position of an irregular surface by one 6335 77 8? 15 single central point.9 In practice, to

34 1 5 3 3

5 2 4 _ speed up operations, the identification

32 ' \2 3 11 1I point will be located at the index

3 4 1 4 1

6250 61 2 6 25312 3,, figure of the unit on the base map. 28 1 2 1 3 1

4 4 5( This is always by eye, given a central 26

1 1 5 2 5 1 2 2e location. All this means that the surfaces /5' 2 11 3 4 1 . 51

5 5 4 16

2 2 19 3 t6 -./ depicted on the left-hand map and all 2 3 43

3 3 4' 7 4 9 information subsequently attached to

2 2 9 1 4 1 34 1 1 4.4 6 5

13 ' 6

9.5 J 22

' 91 5 4 1 20 5 2 .2 1.3 65

them are to be located according to 6320 1 3 Jq6) 5 2 6 (16,5 5O 23 4 4

2 4 1 , 2 24 L 1 83 '4 16 the distribution of black dots on the 19 ,0 I - '. _

iS 42 2 \

__ X __ 2 J,616

7 t right-hand map. For each dot the

16, 2; 2/ 2. , \ x-y co-ordinates are entered in the land 3165 16/ register along with the ordinary infor-

FIGURE 3-Children under is years of age in 1940. mation on area, owner, or planning regulations. For a few sample areas,

the system has been implemented and later some experimental computer work from these areas will be presented.

While waiting for this new national data-system to be adopted and organized, the geography departments in the country have made some joint preparations on a wider scale to facilitate geographical computer work based on ordinary statistical information as it is


available today. This work involves the selection of one identification point and measuring its x-y co-ordinates to an accuracy of I km for each local political unit and each urban agglomeration recognized by the census bureau.0l Some local political units are very big and thus not well represented by only one identification point even on the national scale. Therefore an approximate numerical population map has been prepared, based on the settlement pattern as shown on topographical maps in combination with the I960 census. This map gives the number of inhabitants in io km squares. All these figures are available in mimeographed form and are widely used. The material forms the base for some kinds of analysis within the degree of accuracy which the original permits. A couple of cases in which it has been used.will also be demonstrated here.

As already pointed out, the computer can help us to do three different kinds of work: descriptive mapping, analytical investigations and running process models. I should like to take the opportunity to demonstrate and discuss examples of all three types. s ^ as s e N

t ~ ~ * e . . ^ ^ The exercises are based on the different 6339 kinds of data just described, partly 37 -..- '/"

36 ,,

very accurate information arranged- 6(335

according to the new data-bank / principles, partly the cruder informa- 5 , 32s

tion adjusted to automatic processing. o3 \ "' 2 '

Already at the stage of descriptive 20

mapping, the computer-oriented data- 27 1

bank shows its advantages because it 6325 1 2 . brings in an enormous increase in 24 2 320 1

flexibility and precision. On the map 22 /' (Fig. 2), a rural population has been 6320 1 3

/ cross-tabulated with reference to a \ 1

grid with a cell-width of I km. The 17 '

figures in the resulting 'geographical 6315 , matrix' give the absolute number of 14

inhabitants living in the cells, but FIGURE 4-Workers in mechanical industry in I940.

since these are of equal size, the matrix at the same time is a density map. To underline this, isolines for ten, fifty and a hundred inhabitants per km2 have been interpolated. The interpolation can also be made automatically. 1

We are not, however, always interested in total population. We may want to know the distribution of some sub-group, perhaps farms of a certain size or, in the city, houses of a certain age or, very often, some age-group of the population. The next map (Fig. 3) shows for the same area as before the distribution of children up to the age of I5. Specified occupational groups can also be plotted very quickly as, for example, workers in mechanical industry (Fig. 4). One can also pick out combinations of data in a very large number of permutations, all depend- ing on one's particular research problem. We are also free to decide what degree of topographical detail we want to have. It is obvious that we can aggregate over larger and larger units. But we can also go farther down the scale to the village or block level by applying a more finely meshed grid. Figure 5 is a small part of the earlier area presented with reference to a Ioo m grid. Notice here the sprawl along road lines. It has to be emphasized that the use of a

7

square grid is often convenient, but it is not itself part of the system. Data can be aggregated over all kinds of areas which we happen to be interested in. Such areas may, for example, be zones of distance from some central point or bands along road lines, zones of different height above sea-level, or regions with different degrees of air pollution.

One other thing has to be said about this system. Since in my country we have continuous registration of population as well as ownership of real estate, of individual income and a number of similar things, the proposed system is very flexible in the sense that we can at any time get an up-to-date view of the situation. This of course is of particular interest to planners and administrators but it will also give the research worker a unique chance to follow change through time. So for example it will become quite possible to follow migration on the individual level even inside administrative areas, or to see how ownership of land evolves.

Apart from considerations other than those

X~ S 3~ pertaining to scientific research and planning, 920 however, a data-system of the kind just described

32

S 5 3 7 9 7 may seem quite unacceptable. It may appear , 6 5 to be far too intrusive from the point of view 4

3 3 of the individual or firm. One general answer 2

4

to this very serious question would be that the 1 42

7 7 point is not to extract individual information 21

e 5 as such but to retain the fullest possible freedom '68

5 6 72 a8 6 5 to aggregate data in many more ways than is

S 5 .34 25 l 5' 5' 8 feasible apart from published statistics. The

7 2 3 , 6 integrity of the individual and firm can still be 6 6

4 , safeguarded by legal means if that is required.

2 4 5 Q,_ i 5(26 1. A more specific answer would be that these 2 861 .

~ . 305o 3 4 , 6W20 2,,4 3/ things are looked upon very differently in

4 2 4 6

4 different social environments. In my country for 76

4 example, we are not going to include data other ~~5 31 ~ 8 than those that are already accessible to the

4 , 1 9 3 1 research worker. The difference is only that it 2 3 2 C6 takes a prohibitive amount of time to pick them

<5lto out today. Population registers, taxable income, FIGURE 5-Inhabitants per h in the village of ownership of land and such-like are already Moheda. Size of cell ioox 100 m. For location of area, see co-ordinates on Figure 2. public data according to our basic philosophy

that the more information is available, the easier it is to control in the public interest how different authorities carry on their business.

Proceeding to more analytical types of work, I should first of all like to point out that our most common case is perhaps when we calculate the geographical distribution of some part of the population as a fraction of some other part of the population. It may concern any- thing from the number of workers in an industry in relation to the total number of workers, to the area of a certain crop in relation to arable land. Such calculations when computerized and based upon data-bank information can be performed much faster and thus with much greater variety. They also become much more satisfactory from the geographer's point of view because he is free to handle the territorial base in a much more sophisticated way than is possible today. So, for example, a floating circle could be used as a unit. In addition, the data- bank will give access to more adequate base populations than are available at present. In medical

8 T. HAGERSTRAND


geography, for example, one could calculate the occurrence of some disease in relation to just that part of the population which seems to be concerned. But all this is for the future: I have no actual cases of interest to demonstrate.

Instead I am now proceeding to types of large-scale analysis in which conventional statistical data have been used. Since my cases are closely related to current planning problems a few words of orientation have to be said first. An overriding problem for planning on both the national and regional level in Sweden is the very low density of population. On a land area which is nearly twice that of the United Kingdom we have not quite eight million inhabitants. As the agricultural population including forestry workers is diminishing in number there is a growing concentration in urban areas, but apart from the three leading urban regions, the towns are all rather small and rather scattered over the country. Since the ambition of the welfare state is to give all citizens as far as possible equal opportunities and standards concerning housing, schooling, security of employment, commercial service and medical and social care, it is a fundamental requirement to understand the implications of our peculiar distribution of

population. Under prevailing conditions of economics of scale, not only in industry but also in services, it is difficult or even impossible for many establishments to find a sufficient population base within reasonable travelling distance. Our rural legacy is certainly an expensive one in an age of rapid technological reorganization.

One obvious way to get a summary measure of the characteristics of our distribution of population from the locational point of view would be to compute the population potential in some detail as defined by J.Q. Stewart and W. Warntz.12 This has been tried but the trouble is that the result is almost impossible to explain to the policy makers, especially as no one quite understands what the population potential concept really means as a device for a locational policy. To overcome this difficulty, at least partly, we decided in my department, which is involved in a research project on the matter for the government, to produce a simpler and more practical version of the potential concept. I should like to demonstrate how this was done because the case shows very clearly that calculations need not be particularly complicated to justify the use of a computer. Here, it is rather its capacity of fast mass-production which comes to the fore.

A simple way to describe the overall locational relationships of any place is to calculate a device which might be called its 'locational profile'. This is done by observing in what quantities surrounding objects gradually pile up in number as a function of distance (road- or time- or cost-distance) from the place. A set of diagrams illustrates what this means (Fig. 6). Consider two arbitrary points A and B situated in a hypothetical coastal area including two small urban places and a scattered rural population. As can be seen from the lower graphs, the 'locational profiles' of the two points are functions which grow by distance in very different ways. From point A, I0,000 inhabitants can be reached within a radius of less than 30 km. From point B, a radius of more than 60 km is required to cover the same number of people. If, on the other hand, an individual is searching for recreation at the waterfront within a maximum distance of, say, 50o km from the starting point, then he has a choice along only 4 km of coastline if he sets out from point A, but 20 km if he sets out from point B. In the same way, the 'locational profile' can be estimated in relation to all other elements of interest, such as places of work, educational establishments, service facilities or raw materials. In passing, it is worth noting that the 'locational profile' of a place may become modified over time owing to two different sets of alterations in the surroundings. One of these consists of the relocation of establishments and population. The other consists of such improvements in transport

B

9

as give new values to time- and cost-distances. Present-day developments, of course, follow both lines simultaneously and in general tend to increase the concavity of the profiles. During

*e100 inv. Kumutativ summering av befolkning med stigande avstand fran A resp. B

1000-tal

18-

16-

14-

12- ~/I 8 A/ B/ 6-'

,2- .

0 1 20 30 40 50 60 7 0 km

Kumulativ summering av kust- stricka med stigande avstind fran A resp.B

Km kust

30

20

- B A

10 20 30 50 60 70

0 10 20 30 40 50 60 70km

FIGURE 6-Principles for the calculation of 'locational profiles' with reference to a hypothetical coastal area with two small urban places and a scattered rural population. (Translation of Swedish text: Ioo inv. = Ioo inhabitants

Kumulativ summering av befolkning = Cumulative addition of popu- med stigande avstand fran A resp. lation with increasing distance B from points A and B respectively. Kumulativ summering av kuststracka = Cumulative total length of med stigande avstand fran A resp. coastline with increasing dis- B tance from points A and B

respectively. iooo-tal = thousands

Km kust = kilometres of coastline)

the process of urbanization we are trying to pack establishments and people closer together in order to facilitate co-operation.

Recently we have produced an atlas of 'locational profiles' covering the entire country.

T. HAGERSTRAND IO

o -+ 0 (C ~ AC0

7600 1 'x |7600 7600 ., v -* % X )0 4 4

\C ~X Totalbefolkning 1960 i 1000-toat / nom nedan angivna yta.Motsvarar 22 ,

en radie om c: a30 km. 0

75007 ^' 7500 L L//

LrL +10 26 4 \

i r ?- , >. *

F- I

7400 7

7300 I

730 -0 - A oN

72 00,/ N ;7200 7200' 'r

N

\

t\ v k- C-

10

711

\, "\

(>

6700

/ / r/x

4\

\ rl

\1

N

N

\ I

' - - \

6500

30

6300

0 100 km I I I I I I

/0 0

0 0 q-

CO (7

FIGURE 7-Number of inhabitants in I960 within commuting reach of any point, assuming that 30 km is the maxilum distance. The polygon in the upper left corner shows how the 'floating circle' was approximated.


As the base, the grid-map of the 1960 population was used. For about 1300 points, regularly spaced, we have computed how population accumulates with gradually widening circles. Straight-line geometrical distance had to be used. Checks seem to indicate that this introduces about the same amount of error everywhere compared with actual road-distances. Specific operational rules had to be introduced to create detours around our big lakes in central Sweden which of course cause very considerable deviations from straight-line distances.

TABLE I

Selected 'Locational Profiles'from Sweden with reference to the Distribution of Population in I960

Size ofpopulation within specific distancesfrom Distances required to cover specific point numbers of inhabitants

Urban Rural Size of Urban Rural Total Km population population Total population population population km

km km

Point 7180/I560, IO o 800 8oo 3,000 20 26 15 Northern Sweden 30 3,700 3,700 7,400 5,000 38 34 20

50 7,500 9,800 17,300 io,ooo 64 50 35 100 24,700 40,Ioo 64,800 25,000 Ioi 76 62 150 54,000 96,100 150,Ioo 50,000 145 114 82 200 213,000 214,700 427,700 Io00,oo 176 I52 127 250 352,600 302,300 654,900 250,000 208 218 I75 300 460,200 350,500 810,700 500,000 322 458 2I 350 533,I00 413,000 946, IO I,OOO,OOO 532 670 375

Point 6480/1500, 10 34,800 5,900 40,700 3,000 I 5 I Middle Sweden 30 175,000 37,200 212,200 5,000 I 8 I

50 248,600 79,700 328,300 I0,00o 3 13 2 Ioo 536,000 225,500 761,500 25,000 7 24 6 150 1,172,100 452,400 1,624,500 50,000 14 36 12 200 2,868,600 750,500 3,619,100 I0o,ooo 22 59 20 250 3,816,000 1,166,800 4,982,800 250,000 50 105 37 300 4,251,IOO 1,388,000 5,639,100 500,000 96 160 73 350 4,578,500 1,542,200 6,120,700 I,o00,000 134 232 113

The computer produced its output both in the form of curves, tables and maps. Table I

gives two condensed sets of profiles, one taken from the northern and one from the central

part of the country. These examples will at the same time illustrate the principle and give some

impression of what a low population density actually means. The graphs contain three curves, one for urban, one for rural and one for total population. We can read off two things, either how many people there are within specified distances or how far out we have to go in all directions to cover specified numbers of inhabitants. In the case from upper Norrland, for

example, we have to go out about 35 km in all directions in order to find Io,000 inhabitants, whereas at the point farther south the corresponding distance is only about 2 km. Again in the northernmost case, a radius of about 375 km is required to gather one million, whereas this can be done from the second point within 113 km. Also the difference in urbanization comes out very clearly. In the northern case, the rural population dominates over the urban

up to a distance of 200 km when the coastal towns come within reach. In the central case, the urban population outweighs the rural many times even from the outset.

The maps of course give better general views. We have it in mind to see if some sort of

taxonomy can be established among the curves. Then it would become feasible to map the

II

T. HAGERSTRAND

FIGURE 8-Variation in hours of travel-time by car required to cover one million inhabitants. The Sound is regarded as a complete barrier between Denmark and Sweden.

FIGURE 9-Variation in hours of travel-time by car required to cover one million inhabitants, assuming ferries between Copenhagen and Malmi and between HelsingBr and Halsingborg.

distribution of types. So far we have only made breaks in the profiles at various levels. One of these breaks is of particular interest in regard to the distribution of population when we take the possibilities of commuting into account. The question is simply that of finding out how

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FIGURE o1-Variation in hours of travel-time by car required to cover one million inhabitants, assuming a bridge between Copenhagen and Malm6, and a ferry between Helsingor and Hilsingborg.

FIGURE I I-Variation in hours of travel-time by car required to cover one million inhabitants, assuming a ferry between Copenhagen and Malm6, and a bridge between Helsingor and Hilsingborg.

many people could reach each of the sample points within reasonable commuting distance. Of course no definite limit for commuting exists in terms of pure geometrical distance. But we think that 30 km represents an acceptable approximation. On this assumption, Figure 7

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T. HAGERSTRAND

FIGURE I2-Variation in hours of travel-time by car required to cover three million inhabitants. The Sound is regarded as a complete barrier between Denmark and Sweden.

FIGURE I3-Variation in hours of travel-time by car required to cover three million inhabitants, assuming ferries between Copenhagen and Malmo and between Helsingor and Halsingborg.

has been constructed on the basis of the individual values computed for each of the 1300 sample points. The curves then indicate how the number of inhabitants varies over the country with reference to a 'floating circle' with a radius of 30 km. In practice, the circle had to be approxi-

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FIGURE 14-Variation in hours oftravel-time by car required to cover three million inhabitants, assuming a bridge between Copenhagen and Malmo, and a ferry between Helsing6r and Halsingborg.

FIGURE IS5-Variation in hours of travel-time by car required to cover three million inhabitants, assuming a ferry between Copenhagen and Malmi, and a bridge between Helsing6r and Hilsingborg.

mated with a surface made up of square cells. This configuration can be read off in the upper left corner of Figure 7. Although we are mainly interested in techniques, some comments on the actual results

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I6 T. HAGERSTRAND

are of interest. There is only one single area where the commuting circles cover more than one million inhabitants. There is also only one area where the number of potential commuters is between 500,000 and one million. oo00,000 inhabitants within commuting reach appear in a number of scattered regions but nowhere in the northern half of the country. Islands of 50,000 are the largest there and a vast part lies below the limit of I0,000. The next step in this work will be to estimate in the same way the distances to employment opportunities in various types of industries, to educational facilities, to commercial services, hospitals, libraries and theatres. This will give us maps of supply and demand which we hope to be able to compare. We want to know where it seems to be most economic to try to maintain or reach a certain standard. We know in advance that vast areas have too few inhabitants. But we do not know at what level of population density conditions begin to compare fairly well with our best equipped regions.

As was said before, the locational profiles can be modified in two ways, through relocation of establishments and population, and through improvements in transport. So far, we have been concerned with the relocation of establishments and population. But there is also a large transport project under investigation which has been analysed by computer. This is the proposed bridge between Sweden and Denmark over the Sound. The purpose of this project may be said to have two different geographical aspects. A bridge could make movement between Scandinavia as a whole and the European continent faster and more convenient. But it could also strengthen the upper level of our common urban hierarchy by unifying Copenhagen with the fairly large cities on the Swedish side of the Sound. There is controversy and uncer-

tainty as to the precise location of the bridge. It will be shortest and cheapest to construct it at the northern end of the Sound where the distance between Halsingborg and Helsing0r is only about 4 km. But the two big cities Copenhagen and Malm6 are both situated at the southern end. Here, the distance is over 20 km and in addition, this location would lead to some

lengthening of the road from central Sweden to Denmark. In order to assess the social and economic effects of a bridge between Denmark and southern

Sweden, we have calculated how the 'locational profiles' vary over the whole region with different arrangements of transport. One of the questions has been how long does it take by truck or car to cover a series of defined population thresholds, from half a million up to four million. The output consists of more than a hundred different maps. This is, incidentally, one of the problems with automatic data-processing: the output can easily become unmanageable in its quantity. My demonstration must be limited to a very small sample. First let us take a look at the driving times required to cover one million inhabitants (Figs. 8-II). The program was rather difficult in the Danish part of the area because of the many ferry-crossings and

bridges between islands which had to be taken into account. The computer also had to find the shortest route possible in all respects.

If we look first at a situation in which Denmark and Sweden are assumed to be two entirely separate markets (Fig. 8) then we also arrive at two separate areas of optimal accessibility. In Denmark, Copenhagen is the most central point where one million inhabitants could be covered in about 25 minutes. In western Zealand it would take almost 2 hours. But the same amount of time would be about the optimum in Sweden where we have to go inland to find the best central position. The present ferry situation (Fig. 9) gives no advantage for Denmark at the one million level but it improves the accessibility of population in southern Sweden at the same time as the optimal point moves towards the ferry-terminal at Halsingborg where the route is shortest.


If we locate a bridge in the southern position (Fig. Io), Malmo will benefit greatly. The time to cover one million inhabitants will be reduced from over 2 hours to about 50 minutes. It is interesting to see that a bridge in the northern position (Fig. ii) does not provide much

improvement compared with the present ferry situation. The reason is, of course, that the

crossing is already so fast that not much can be gained in time by use of a bridge. Passing over intervening population thresholds and going up to three million (Figs. I2-I5)

gives a very different picture. In the first case of entirely isolated market areas the best locations are seen to move away from the Sound in both Denmark and Sweden. In the present ferry situation, however (Fig. 13), the northern part of the Sound forms one area with 3 hours as

travelling time to cover three million inhabitants. But there is also a second one a little farther north in Sweden because here also G6teborg comes within reach. Finally we can again easily see what the alternative positions of the bridges would mean at various points in terms of

time-saving (Figs. I4-I5). There are several possible ways of condensing the content of the many separate maps in

order to obtain a synoptic view of how different places in the region will be affected with reference to all population thresholds considered in the calculations. It is impossible here to describe all these methods, but some of the results may be summarized.

The main points seem to be the following. Today, Copenhagen and Malmi have a very different position up to a market size of about three million inhabitants. Above that level, the difference is very small and in both countries the optimal points do in fact move away inland from the Sound because there is so much uninhabited water-area around. A bridge would have far-reaching effects, in particular on the Swedish side, but effects which differ very much with the size of population sought. Up to a threshold of one million, the bridge is of little importance to Copenhagen because a local population of this size already exists. At a level of four million, there is no improvement on either side. The effects fade away in Sweden rather quickly with increasing distance from the Sound. After a little more than Ioo km it is quite unimportant, a surprising fact. Of course, people and goods could reach Copenhagen much more quickly than today but access to a large population would not be much affected by the existence of a bridge.

I am not going to demonstrate the corresponding calculations of transport costs under various assumptions. We performed a number of such.13 My point here is not to stress the problem of the proper location of the bridge but just to indicate that exercises of this kind, whatever value they might have, could not possibly be performed by hand calculations because there are hundreds of thousands of separate computations behind each individual map.

We now come finally to the third type of work which the computer could do for us, the running of process models. Here, we approach a field which to a large degree still has to be explored. The field I have in mind is, on the physical side, numerical weather forecasting or the simulation of the development of river patterns, for instance. On the social side, there are, for example, simulations of diffusion, models concerned with central place theory, or models tackling the question of urban land-use development.14 What all these experiments and exercises have in common is that one starts with a set of units, be it air-masses, streams, winds, human beings, establishments, or pieces of land, for which one can prescribe certain rules of behaviour according to pre-existing empirical knowledge or else purely hypothetically. The behaviour can be described with the aid of probability distributions or with rigid systems of rules or strategies. What we are then interested to find out is the joint outcome over time and space when the units of behaviour start to interact and the behaviour of one unit influences the

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I8 T. HAGERSTRAND

behaviour of others in a chain of events. Sometimes the calculations are simple enough to be undertaken manually. But more often the units are so many and the rules of behaviour so complex that use of a computer is justified. A further reason for this is that one normally wants not just a single result but a series for comparison.

It is not possible here to enter into the somewhat complicated technical details concerning the setting up and operation of the kinds of process model which I have in mind. Instead, I will attempt to give a closer idea of the nature of the approach by outlining a problem which, in a fairly straightforward way, would lend itself to this type of analysis. This particular example concerns a planning problem in Sweden which, so far, has not been investigated, but could be tackled appropriately by computer. The background to the problem is as follows. A majority of farms in Sweden are very small and therefore uneconomic under present conditions. The Government has for a number of years carried on a policy which is aimed at enlarging the size of farms up to a certain minimum size. This is achieved by means of certain administrative and economic rules applicable to all parts of the country. Thus, a State agency has always the first right to buy small farms as soon as a farmer leaves, provided that no close relatives are planning to take over. When, in a given area, the State agency has succeeded in acquiring a number of such units reasonably close together, they are amalgamated into one new larger unit which is offered to someone who is willing to take up farming under the new conditions. Alternatively, a small unit can be added straight away to some farm in the immediate vicinity that needs to be enlarged.

When looking at the rules one may well ask how long it will take for the Government to achieve its goals in respect of farm size. One might want to go even further and investigate the different timing of events under the different physical circumstances found in various parts of the country. Here, I think, is a case well fitted for the setting up of a process model of the stochastic type. The data-bank will provide basic information, such as the size and precise location of existing individual farm units and the age and family characteristics of individual farmers. Then we need to express in quantitative terms the probabilities of farmers going out of business because of retirement or death, and the probabilities of sons, sons-in-law or neigh- bours being ready to take over. In addition, we have to specify the maximum tolerable distance for amalgamation. These probabilities and conditions could be established by empirical estimates based on what has happened in recent years. After these preliminaries, the computer can do the work of infusing artificial life in the system.

Repeated projections along this pattern in a number of sample areas would provide an insight into the time-efficiency of prevalent policy as well as into the influence of the physical environment. It must, for example, be quite different reorganizing farm-units on an open plain where there is freedom to amalgamate in all directions, from the situation in narrow valleys where one can only operate up or down the river-course. Thus, this kind of model should interest anyone who is concerned with the relations between physical features and human conditions, and perhaps also give politicians some useful hints about the degree of success which might be expected from the adopted policy.

To the geographer, process models of the kind just indicated are also interesting for a more fundamental reason, namely, because of their contribution to methodology. They seem to open a way to a more rigorous attack on something which the traditional regional geographer has always tried to achieve in a more intuitive way: an understanding of complex regional situations. I think that probably the ability to handle complex regional situations is what the outside world expects from the geographer, rather than some specific topical


knowledge-at least this is the case in the planning field. I believe that it is important for us to try to live up to that expectation and therefore I submit the suggestion that computer- oriented data-banks and process models tailored to handle complex regional situations are proper means to help to approach that end.

Conclusion

The fast developing data-processing technology can undoubtedly offer great potential aid to the geographer. In my opinion, we have to prepare ourselves for this in a number of ways. First of all, it is essential for us to insist that statistical and cartographical information be

arranged and integrated for automatic processing in a manner which fully meets geographical demands. Secondly, we have to develop sophisticated and efficient geographical techniques which fully match the new standards of observation and computation. Not only systematic but also regional geography is involved here. Finally, there are also some obvious and far- reaching consequences, which I have not dealt with explicitly, for the training of students, consequences which perhaps should be followed up even in the teaching of geography in schools.

These are all demanding tasks, but they will surely prove to be of great benefit, both in the field of pure research and in the field of applied geography.

NOTES 1 A lecture delivered at the invitation of the Institute at its Annual Conference at Sheffield, 3-7 January, 1967. 2 B. V. BOWDEN (Ed.), Faster than Thought. A symposium on digital computing machines (I953). 3 The problem of geographical data-processing is also discussed in R. C. KAO, 'The use of computers in the process-

ing and analysis of geographic information', Geogr. Rev. 53 (I963), 530-47. 4 W. R. TOBLER, 'Automation in the preparation of thematic maps', Cartogr. J., 2 (I965), 32-8. 5 A. A. HAVILAND, 'A proposed rearrangement of the registration districts of England and Wales, for the purpose

of facilitating scientific inquiry', Rep. 40th Meeting, Br. Ass. Advmt Sci., Liverpool (I870). 6 See for example, J. T. COPPOCK, 'The relationship of farm and parish boundaries-a study in the use of agricultural

statistics', Geogr. Stud., 2 (I955), I2-26. 7 T. HXGERSTRAND, 'Statistiska primaruppgifter, flygkartering och "data-processing"-maskiner. Ett kombinerings-

projekt', Svensk geogr. Arsb. 3I (I955), 233-55; A. JAKOBSON, 'Befolkningsforandrigar i vastra Smaland', Plan (I959); S. NORDBECK, 'Framstallning av kartor med hjalp av siffermaskiner', Meddn Lunds geogr. Instn, 40 (I964).

8 Fastighetsregistrering, Statens Offentliga Utredningar (I966); Forsdksverksamhet met koordinatmetoden, Bilaga (I967). 9 S. NORDBECK, 'Location of areal data for computer processing', Lund Stud. Geogr., C, 2 (I962), 4I P. 10 A. MICKLANDER in I. TORSTENSSON, Koordinatregister over Sveriges forsamlingar och tdtorter (Uppsala, I964;

mimeo.). 11 B. E. BENGTSSON and S. NORDBECK, 'Construction of isarithms and isarithmic maps by computer', Nord. Tidskr.

informationsbehandl., 4, 2 (I964). 12 J. Q. STEWART, 'Empirical mathematical rules concerning the distribution and equilibrium of population', Geogr.

Rev., 37 (I947), 461-85; W. WARNTZ, 'A new map of the surface of population potentials for the United States, I960', Geogr. Rev., 54 (I964), I70-84; K. NORBORG, 'Potentialbegreppet. Ett viktigt hjalpmedel vid lokaliseringsdiskussioner; Svensk geogr. Arsb. 38 (I962), 23-36.

13 The problem of the variation of transport costs in Sweden has also been studied by computer by G. T6RNQUIST, 'Transport costs as a location factor for manufacturing industry', Lund Stud. Geogr., B, 23 (I962), 37-60.

14 See for example, T. HAGERSTRAND, 'A Monte Carlo approach to diffusion', Eur. J. Sociol. 6 (I965), I; R. L. MORRILL, 'Migration and the spread and growth of urban settlement', Lund Stud. Geogr., B, 26 (I965), 208 p.; R. MALM, G. OLSSON and 0. WXRNERYD, 'Approaches to simulations of urban growth', Geogr. Annlr, 48 B (I966), 9-22.

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the computer and the geographer€¦ · geographer, but i believe that many features of the problem...

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