PT3 WU088/Kelly February 19, 2004 18:49 Char Count= 0

PART IIISPATIAL MODELLING OF URBAN

SYSTEM DYNAMICS

197

Spatial Modelling of the Terrestrial Environment. Edited by R. Kelly, N. Drake, S. Barr.C© 2004 John Wiley & Sons, Ltd. ISBN: 0-470-84348-9.

PT3 WU088/Kelly February 19, 2004 18:49 Char Count= 0

Editorial: Spatial Modelling ofUrban System Dynamics

Stuart L. Barr

With the world’s urban population expected to double between the years 2000 and 2020(Harrison and Pearce, 2000), many urban areas will become increasingly dynamic envir-onments, experiencing growth that will result in changes in their social and economicstructure, spatial pattern of land use and their physical appearance. A consequence of suchgrowth will be that an increasing amount of the Earth’s natural and financial resourceswill have to be directed towards urban areas, in order to supply the increased demandfor energy, food and water, as well as to provide the required housing stock and relatededucation and health services. Concerns over how to effectively manage such resources, inorder to minimize the negative impacts of urban growth, have increased the awareness thatsustainable urban planning policies need to be adopted that allow resources to be spatiallytargeted and managed effectively.

Computational mathematical models of urban systems provide one potential mechanismby which sounder theories of sustainable urban development may be developed as theyallow the likely effects of population growth on the physical, functional and socio-economicstructure of urban areas to be estimated (Wilson, 2000). Such models, whether coarse-scalemodels of urban growth (Batty and Longley, 1994) or small-area level models of urbanactivity and function, require a range of detailed geographically referenced informationfor their initialization, calibration and validation. In many cases, particularly in developednations, such information is provided in the form of national censuses, which often providethe most complete, consistent and objective information at a single point in time on thesocio-economic structure of urban areas (Wilson, 2000). A number of concerns have beenraised, however, in relation to the suitability of certain national censuses for studying andmodelling urban systems; these include, the long time period between censuses (often

Spatial Modelling of the Terrestrial Environment. Edited by R. Kelly, N. Drake, S. Barr.C© 2004 John Wiley & Sons, Ltd. ISBN: 0-470-84348-9.

199

PT3 WU088/Kelly February 19, 2004 18:49 Char Count= 0

200 Spatial Modelling of the Terrestrial Environment

10 years), the course level of spatial aggregation often employed, their inability to describedirectly the general physical form and land use function of urban areas, and their poorcoverage or lack of availability in many developing nations (Openshaw, 1995).

Potentially, recent developments in sensor technology within field Earth observation al-low a number of the above-mentioned concerns to be addressed. For example, very highspatial resolution (2–4 m) optical satellite images (i.e., IKONOS and QuickBird) and highspatial-density airborne LiDAR (Light Detection and Ranging) data, potentially allow ac-curate, consistent and timely information on the physical form and land use organizationof urban areas to be obtained at scales of between 1:10 000 and 1:25 000 (Ridley et al.,1997). However, if the full potential of Earth-observed images for studying and modellingurban systems is to be realized, sensor development needs to be matched not only by im-proved approaches to the inference of policy-relevant information, but also by an improvedintegration of such information into the current computational methodologies employed inurban system modelling (Donnay et al., 2001).

In this section on urban systems two studies are presented that address the above obser-vation. In the chapter by Barr and Barnsley (Chapter 10) the spatial topological structureof urban land cover information, such as one may typically try to derive directly from veryhigh spatial resolution remotely sensed images, is studied in order to ascertain whether itprovides a relatively simple means by which to infer land use. They show for the urbanarea under investigation, that while the spatial topology of land cover allows the broadland use categories present to be distinguished (e.g. residential versus industrial), it doesnot allow a more detailed land use typology to be characterized (e.g. different types ofresidential development). The chapter by Devereux et al. (Chapter 11) demonstrates howEarth-observed information and UK census data can be integrated in order to spatiallymodel sustainable traffic emission scenarios for the county of Cambridgeshire, UK. Theydemonstrate that land cover information derived from a Landsat-TM image can be used toderive traffic emission coefficients for the parameterization of a spatial interaction modelof traffic emissions. Moreover, the utility of LiDAR data for visualizing the relationshipbetween modelled traffic emissions and urban density is also demonstrated.

References

Batty, M. and Longley, P.A., 1994, Fractal Cities: A Geometry of Form and Function(London: Academic Press).

Donnay, J.P., Barnsley, M.J. and Longley, P.A., 2001, Remote sensing and urban analysis, inJ.P. Donnay, M.J. Barnsley, and P.A. Longley (eds), Remote Sensing and Urban Analysis(London: Taylor and Francis), pp. 3–18.

Harrison, P., and Pearce, F., 2000, AAAS Atlas of Population and Environment (Berkeley,California: University of California Press).

Openshaw, S., 1995, The future of the census, in S. Openshaw (ed.), Census Users Handbook(Cambridge: Geoinformation International) pp. 389–411.

Ridley, H., Atkinson, P. M., Aplin, P., Muller, J.-P., and Dowman, I., 1997, Evaluating thepotential of forthcoming commercial U.S. high-resolution satellite sensor imagery at theOrdnance Survey, Photogrammetric Engineering and Remote Sensing, 63, 997–1005.

Wilson, A.G., 2000, Complex Spatial Systems: The Modelling Foundations of Urban andRegional Planning (London: Prentice Hall).