Information technology has had a substantial impact on methods of geographical study. Although this varies between different application fields, effective use of the new technology requires careful consideration of the underlying concepts of geographic data representation. Here, an improved data model is described by means of raster techniques to represent population-related data in the form of surfaces. This type of model is seen as having broad applicability to the representation of socioeconomic information taken, for example, from a population census or from residential postcoded records. In this paper an extensive application of this approach to large geographical scales is described by reference to Britain, and suggestions are made as to the advantages of such a data model for socioeconomic visualisation and analysis.
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References
1.
BartelsD, 1982, “Geography: Paradigmatic change or functional recovery? A view from West Germany”, in A Search for Common Ground Eds GouldPOlssonG (Pion, London) pp 24–33
2.
BaxterR S, 1976Computer and Statistical Techniques for Planners (Methuen, Andover, Hants)
3.
BowmanA W, 1973, “A comparative study of some kernel-based nonparametric density estimators”Journal of Statistical Computation and Simulation21313–327
4.
BowmanA W, 1984, “An alternative method of cross-validation for the smoothing of density estimates”Biometrika71353–360
5.
BrackenI, 1989, “The generation of socioeconomic surface for public policy making”Environment and Planning B: Planning and Design16307–326
6.
BrackenIMartinD, 1989, “The generation of spatial population distributions from census centroid data”Environment and Planning A21537–543
7.
BrowneJ JMillingtonA C, 1983, “An evaluation of the use of grid squares in computerised choropleth maps”The Cartographic Journal20(2) 71–75
8.
BurroughP A, 1986Principles of Geographical Information Systems for Land Resources Assessment (Oxford University Press, London) pp 81–102
9.
ChrismanN R, 1987, “Fundamental principles of geographic information systems”, in Proceedings Auto Carto 8 Ed. ChrismanN R (American Society for Photogrammetry and Remote Sensing, Falls Church, VA) pp 32–41
ClarkeMWilsonA, 1989, “Mathematical models in human geography: 20 years on”, in New Models in Geography. Volume 1 Eds PeetRThriftN (Unwin Hyman, London) pp 30–40
CRU, 1980People in Britain—A Census AtlasCensus Research Unit, University of Durham (HMSO, London)
14.
DevereuxB J, 1986, “The integration of cartographic data stored in raster and vector formats”, in Proceedings, Auto Carto London Ed. BlakemoreM J (The Royal Institute of Chartered Surveyors, 12 Great George Street, London SW1P 3AE) pp 257–266
15.
EpanechnikovV A, 1969, “Non-parametric estimation of a multidimensional probability density”The Theory of Probability and Its Applications14153–158
16.
FlowerdewRGreenM, 1989, “Statistical methods for inference between incompatible zonal systems”, in Accuracy of Spatial Databases Eds GoodchildM FGopalS (Taylor and Francis, London) pp 239–248
17.
ForbesJ, 1984, “Problems of cartographic representation of patterns of population change”The Cartographic Journal21(2) 93–102
18.
GatrellA CDunnC EBoyleP J, 1991, “The relative utility of the Central Postcode Directory and Pinpoint Address Code in applications of geographical information systems”Environment and Planning A231447–1458
19.
GoodchildM F, 1989, “Modeling error in objects and fields”, in Accuracy of Spatial Databases Eds GoodchildM FGopalS (Taylor and Francis, London) pp 107–114
20.
HaggettP, 1983Geography: A Modern Synthesisthird edition (Harper and Row, New York)
21.
IisakaJHegedusE, 1982, “Population estimation from Landsat imagery”Remote Sensing of Environment12259–272
LangfordMUnwinD JMaguireD J, 1990a, “Generating improved population density maps in an integrated GIS”, in Proceedings of the First European Conference on Geographical Information SystemsEGIS Foundation, Faculty of Geographical Sciences, PO Box 80.115, 3508 TC, Utrecht, pp 651–660
24.
LangfordMUnwinD JMaguireD J, 1990b, “Mapping the density of population: Continuous surface representations as an alternative to choroplethic and dasymetric maps” RR-8, Midlands Regional Research Laboratory, University of Leicester LE1 7RH
25.
LoP, 1989, “A raster approach to population estimation using high-altitude aerial and space photographs”Remote Sensing of Environment2759–71
26.
MackayJ R, 1951, “Some problems and techniques in isopleth mapping”Economic Geography271–9
27.
MaffiniG, 1987, “Raster versus vector data encoding and handling: A commentary”Photogrammetric Engineering and Remote Sensing531397–1398
28.
MartinD, 1989, “Mapping population data from zone centroid locations”Transactions of the Institute of British Geographers New Series1490–97
29.
MartinD, 1991Geographic Information Systems and Their Socioeconomic Applictions (Routledge, Chapman and Hall, Andover, Hans)
30.
MartinDBrackenI, 1991, “Techniques for modelling population-related raster databases”Environment and Planning A231069–1075
31.
MennimK, 1986, “Raster techniques in geological map production”, in Proceedings, Auto Carto London Ed. BlakemoreM J (RICS, London) pp 267–271
32.
NordbeckSRystedtB, 1970, “Isarithmic maps and the continuity of reference interval functions”Geografiska Annaler B5292–123
33.
ObergSSpringfeldtP (Eds), 1991The Population Atlas of Sweden (Swedish National Atlas Publications, Stockholm)
34.
OpenshawS, 1984The Modifiable Areal Unit Problem (Geo Books, Norwich)
35.
OpenshawS, 1989, “Learning to live with error in spatial databases”, in Accuracy of Spatial Databases Eds GoodchildM FGopalS (Taylor and Francis, London) pp 263–275
36.
PeuckerT KFowlerR JLittleJ JMarkD M, 1978, “The triangulated irregular network”, in Proceedings of the DTM Symposium American Congress on Surveying and Mapping, St Louis, MO, pp 24–31
37.
PeuquetD J, 1988, “Representations of geographic space: Toward a conceptual synthesis”Annals of the Association of American Geographers78375–394
38.
PiwowarJ MLeDrewE FDudychaD J, 1990, “Integration of spatial data in vector and raster formats in geographical information systems”International Journal of Geographical Information Systems4429–444
39.
RobbinsR GThakeJ E, 1988, “Coming to terms with the horrors of automation”The Cartographic Journal25(2) 139–142
40.
RobinsonA HPetchenikB B, 1975, “The map as a communication system”The Cartographic Journal12(1) 7–14
41.
SametH, 1984a, “Algorithms for the conversion of quadtrees to raster”Computer Graphics and Image Processing261–16
42.
SametH, 1984b, “The quadtree and related hierarchical data structures”ACM Computing Survey16187–260
43.
SchmidC FMacCannellE H, 1955, “Basic problems, techniques and theory in isopleth mapping”Journal of the American Statistical Association50220–239
44.
ShirayevE E, 1987Computers and the Representation of Geographical Data translated from Russian (John Wiley, Chichester, Sussex)
45.
SilvermanB W, 1986Density Estimation for Statistics and Data Analysis (Routledge, Chapman and Hall, Andover, Hants)
46.
TeicholzEBerryB J L (Eds), 1983Computer Graphics and Environmental Planning (Prentice-Hall, Englewood Cliffs, NJ)
47.
TheobaldD, 1989, “Accuracy and bias in surface representation”, in Accuracy of Spatial Databases Eds GoodchildM FGopalS (Taylor and Francis, London) pp 99–106
48.
ToblerW R, 1967, “Of maps and matrices”Journal of Regional Science17(2) 275–280
49.
ToblerW R, 1979, “Smooth pycnophylactic interpolation for geographic regions”Journal of the American Statistical Association74519–530
50.
TomlinC D, 1983, “A map algebra”, presented at the Harvard Computer Conference, 31 July-4 August, Cambridge, MA; copy available from Department of Computer Sciences, Harvard, Cambridge, MA
51.
TrotterC M, 1991, “Remotely-sensed data as an information source for geographical information systems in natural resource management: A review”International Journal of Geographical Information Systems5(2) 225–239
