The Dictionary of Human Geography (44 page)

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Authors: Michael Watts

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diffusion
The spread of a phenomenon (including ideas, objects and living beings) over space and through time. There is a long tradition of diffusion studies in American cultural geography, most closely associated with the work of Carl Ortwin Sauer (1889 1975) and Fred B. Kniffen (1900 93). According to Sauer (1941), it was Friedrich Ratzel (1844 1904) who ?founded the study of the diffusion of cultural traits, presented in the nearly forgotten second volume of his Anthropogeographie? published in 1891 (see anthropogeography). In Sauer?s view, diffu sion ?the filling of the space of the earth? was a ?general problem of social science?: ?A new crop, craft or technique is introduced to a culture area. Does it spread, or diffuse vigorously or does its acceptance meet resist ance?? The specific contribution of geography was to reconstruct diffusion pathways and to evaluate the influence of physical barriers (Sauer, 1952; Wagner and Mikesell, 1962). Both tasks were pursued by various members of the Berkeley school, but they reappeared in a starkly different guise in the much more formal study of innovation diffusion inaugu rated by Torsten Hagerstrand (1916 2004). (NEW PARAGRAPH) One of Sauer?s closest associates introduced Hagerstrand?s Swedish monograph to Anglo American geography: ?No one who essays in the future to interpret the distribution of culture elements in the process of diffusion can afford to ignore Hagerstrand?s methods and conclusions? (Leighly, 1954). Even so, it was some fourteen years before an English translation of Hagerstrand?s Innovation diffu sion as a spatial process appeared (Hagerstrand, 1967; see Duncan, 1974). Hagerstrand?s work had two catalytic consequences: it set in motion the frozen worlds of spatial science, and it opened the door to sophisticated computer modelling of spatial processes. The theoretical structure of his original model is shown in the figure. An interaction matrix provides the contours of a generalized or mean information field, which structures the way in which information circulates through the population in a regional system. These flows are modulated by physical barriers and indi vidual resistances, which together check the transformation of information into innovation and so shape the successive diffusion waves that break on to the final adoption surface. (NEW PARAGRAPH) Most immediate discussion focused on the operationalization of the model on the use of simulation methods, the comparison of ?observed? and ?predicted? patterns of adop tion, and the detection of a localized neigh bourhood effect. Within this modelling tradition, the most important developments included the following: (NEW PARAGRAPH) A formalization of the mathematical rela tionships between the structure of the mean information field and the form and velocity of diffusion waves, revealing the connections between different distance decay curves and the classic neighbour hood effect (although it is scarcely sur prising that a distance bound interaction matrix should generate a contagious pattern of adoptions). (NEW PARAGRAPH) A demonstration that the Hagerstrand model is only a special instance of the simple epidemic model, and the subsequent derivation of more complex epidemic models, particularly through the remark able contributions of Cliff, Haggett, Ord and Versey (1981), whose replication of a range of ?spatial processes? (see process) confirmed: (NEW PARAGRAPH) the recognition of hierarchical diffusion, typically through central place sys tems, and frequently operating alongside the distance bound, contagious diffusion of the classical model (Hudson, 1969; Pedersen, 1970); (NEW PARAGRAPH) the incorporation of rejection and re moval processes and the modelling of competitive diffusions (Webber, 1972). (NEW PARAGRAPH) These changes entailed a move away from simulation techniques towards more analytical methods, which have been of immense importance in the increased traffic between epidemiology and medical geography (see disease, modelling of). This is now the major focus of diffusion theory in human geography, although spatial models of infor mation circulation and innovation diffusion are important in marketing research too. (NEW PARAGRAPH) Haggett (1992) claimed to see parallels between Sauer?s original prospectus and the contemporary modelling of disease, particularly his use of ?controlled speculation? and his focus on ?hearths and pathways?. Ironically, however, it was precisely these features that caused diffusion theory to fall from grace in most other areas of human geography. There were sev eral brilliant studies that wired diffusion into larger social transformations (e.g. Pred, 1973; Blaikie, 1975), but these were the exception to a cascade of studies using available data sets merely to ?fit? or ?test? diffusion models. Just ten years after the translation of Hagerstrand?s magnum opus, Blaikie (1978) could speak of a ?crisis? in diffusion research, which he said arose from its preoccupation with spatial form and space time sequence, while Gregory (1985) attributed the ?stasis? of diffusion theory to a pervasive unwillingness to engage with social theory and social history to explore the conditions and the consequences of diffusion processes. Critics argued that the spatial cir culation of information remained the strate gic element in most applications of the Hagerstrand model and its derivatives, and while flows of information through different propagation structures and contact networks were exposed in more detail, the primacy accorded to the reconstruction of these spatial pathways obscured a crucial limitation of the Hagerstrand model: it operated within what Blaut (1977) called a ?granular region?, ?a sort of Adam Smithian landscape, totally without macrostructure?. In particular: (NEW PARAGRAPH) The Hagerstrand model begins with a pool of ?potential adopters? and does (NEW PARAGRAPH) not explain the selective process through which they are constituted in the first place. This suggests the need for a model of biased innovation, where (for example) class or gender circumscribes access to innovations. ?Non diffusion? is then not a passive but an active state arising directly from the structures of a particular society (Yapa and Mayfield, 1978). Critiques of this sort required dif fusion theory to be integrated with fields such as political economy and femi nist geography that pay attention to the social as well as the spatial. (NEW PARAGRAPH) The Hagerstrand model assumes a ?uniform cognitive region? and does not explain the selective process through which information flows are interpreted. This matters because ?resistance? to in novation is not invariably a product of ignorance or insufficient information: it may signal a political struggle by people whose evaluation of the information is strikingly different to that of the ?poten tial adopters?. Critiques of this sort re quired diffusion theory to be re connected to a more general cultural geography (Blaut, 1977). (NEW PARAGRAPH) These critiques served largely to divert attention to other projects, however, and there has been little advance in the architecture of diffusion theory in recent years. Interest in the detailed reconstruction of specific diffusion sequences as key moments in processes of economic and cultural transformation has continued in cultural historical geography and environmental history (e.g. Jordan, 1993; Overton, 1996), and there is also a growing interest in the circulation of informa tion, including the transmission of scientific knowledge and the formation of creative econ omies (Kong, Gibson, Khoo and Semple, 2006). But these enquiries rarely refer to, let alone rely on, classical diffusion theory. The tension between diffusion modelling on the one side and cultural historical and politico economic studies of diffusion on the other (and the versions of human geography that each represents) is exemplified by the study of AiDs. Mapping and modelling the spread of the disease has been a major focus of geo graphical enquiry, but this has been under taken largely in isolation from studies of its social and cultural geography (cf. Brown, (NEW PARAGRAPH) . It is in the space between these two intellectual traditions that diffusion theory currently languishes, but some small steps (NEW PARAGRAPH) towards bridging the gap have been made in studies of disease diffusion and war (e.g. Smallman Raynor and Cliff, 2004). dg (NEW PARAGRAPH) Suggested reading (NEW PARAGRAPH) Blaikie (1978); Blaut (1977); Haggett (1992). (NEW PARAGRAPH)
digital cartography
The use of numerical coding, electronic media and digital com puters to collect, manipulate, manage and dis play geographical data. Coined in the 1970s, the term has largely replaced the older rubric ?computer assisted cartography?, because almost all production cartography is now computer assisted. ?Digital cartography? seems destined for obsolescence insofar as most geospatial data are now ?born digital?; that is, originally captured as numbers using a global positioning system (gps) receiver, aerial photogrammetry or remote sensing, rather than converted from an existing map image by scanning or a process of electronic tracing known as digitizing. In this sense, geograph ical data acquired by updating, transforming or otherwise enhancing the attributes (descrip tions) of cartographic objects such as street segments and land parcels are also born digital. Because hard copy, analogue maps have become more the exception than the rule, ?digital cartography? seems likely to sur vive only when writers need an opposite for ?analogue cartography?. Even so, born digital materials, especially useful because they are readily searchable and easily updated, challenge conventional practices of map preservation and copyright (Varian, 2005). (NEW PARAGRAPH) As an endeavour, digital cartography is closely related to geographic information (NEW PARAGRAPH) systems (gis). In general, the former focuses on data acquisition, data management and the generation of reproducible images, while the latter emphasizes data retrieval and specialized analysis. These overlapping disciplines share a common interest in automated map gener alization and the display of terrain data. In addition, digital cartography has a dynamic, interactive component linked to scientific visu alization and computer animation. Other shared concerns include standardized terms and definitions, flexible exchange formats, efficient methods for ensuring reliability, and the development of metadata describing a file?s origin, contents and fitness for use (Nogueras Iso, Zarazaga Soria, Lacasta, Bejar and Muro Medrano, 2004). (NEW PARAGRAPH) As with GIS, digital cartography recognizes two principal types of data, raster and vector. Examples of raster data include land cover data acquired from space with a multi spectral scanner, digital elevation models (DEMs) consisting of surface elevations sam pled for a grid with rows and columns spaced 30 m apart, and images scanned from histor ical topographic maps and nautical charts. The spatial quality of a raster data set is described by the spatial resolution of the sen sor or scanner. By contrast, vector data rely on lists of point coordinates to describe the shape and position of boundary lines, streets and other linear features, as well as lists of linkages or adjacencies to specify the boundaries of polygons representing, for example, counties or census tracts. In addition, attribute data describe the type of feature, its name or iden tifying number, and specialized characteristics such as the population of a census tract or the width and left and right hand address ranges of a street segment. (NEW PARAGRAPH) Each format has particular advantages. Maps in raster format, for instance, are well suited for print on demand distribution as well as dissemination over the iNTERNET (Evans and Vickers, 1995). By contrast, vector data are especially useful for creating map dis plays at different scales on diverse projections (see map projection). Mathematical formulae afford ready conversion between spherical and plane coordinates or from one projection to another. Indeed, tailored map projections that control distortion for a region of interest or provide insightful views, including dynamic oblique views or flyovers, are a prime asset of digital cartography. (NEW PARAGRAPH) Digital cartographic research has also pro duced algorithms for map generalization and map labelling, both of which support the dynamic, interactive change of scale for street maps, topographic maps and electronic atlases. Generalization is especially relevant for maps displayed at scales smaller than that for which the data were originally acquired (McMaster and Shea, 1992). Because map symbols are more likely to overlap as scale decreases, a generalization algorithm must first identify areas where overlap will occur and then avoid aesthetically awkward (and sometime confusing) graphic conflict by sup pressing less important features or shifting them apart one of several ethically respon sible trade offs that human mapmakers call ?cartographic license? (Li and Su, 1995). Generalization must also smooth out intricate coastlines and streams with tight meander loops, both of which can yield ambiguous and unappealing blobs at substantially reduced scales. Automated map labeling is similar to map generalization insofar as the algorithm must determine potential conflict between the allegedly ideal positions of map labels (Zoraster, 1997). If the conflict cannot be resolved by moving one or more labels to a less desirable location, the algorithm might need to leave a feature unlabeled or use a thin leader line to link a symbol with a label that cannot be adjacent. More straightforward is the attachment of text to curved features such as roads and streams. (NEW PARAGRAPH) Another common display task in digital cartography is the realistic viewing of digital elevation models. Typical strategies include low angle oblique views, which require a geo metric transformation as well as the identifica tion and suppression of symbols where the surface is hidden from view (De Floriani and (NEW PARAGRAPH) Magillo, 2003) and the addition of shadows, or hill shading, in areas not illuminated by a hypothetical light source located in the upper left or upper right (Tuhkanen, 1987). More intriguing are maps formed by draping satellite imagery, land cover classifications or topo graphic symbols over obliquely viewed digital elevation models (Banerjee and Mitra, 2004). (NEW PARAGRAPH) Although digital cartography has largely displaced its non electronic counterpart as the principal means of acquiring and storing geospatial data, paper maps thrive in diverse ways: in newspapers, magazines, atlases and geography textbooks; as mass marketed way finding and recreation maps; and as custom ized, one off artefacts downloaded over the Internet (Peterson, 2003) or created using commercial, off the shelf mapping software (Longley, Goodchild, Maguire and Rhind, (NEW PARAGRAPH) pp. 157 75). Everyday experience sug gests, not without irony, that the majority of paper maps nowadays are specially tailored renderings based on digital data and produced on a laser or ink jet printer. MM (NEW PARAGRAPH) Suggested reading (NEW PARAGRAPH) DeMers (2002); Longley, Goodchild, Maguire and Rhind (2005); McMaster and Shea (1992); Peterson (2003). (NEW PARAGRAPH)

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