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Simulation methodologies for observing large-scale urban structures
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Landscapeand Urban Planning, 26 (1993) 231-250 Elsevier Science Publishers B.V., Amsterdam
Simulation methodologies for observing large-scale urban structures John Decker School ofplanning and Research Associate with the Centerfor Urban Design, College ofDesign. Architecture, Art and Planning, University of Cincinnati, Cincinnati, OH 45221-0073, USA
Abstract
The arrival of advanced and widely available graphic computing has made simulation a tool for both practice and research in all areas of design. The large-scale and complex nature of urbanization has long required some type of simulation to facilitate external observation and analysis. The flexibility and expandability of computer models promises phenomenal potential when used towards this end. This paper discusses simulation methodology in terms of the descriptive basis for simulation constructions and the analytical methods that can be applied to models once they exist. Woven around a series of images created from studies produced at the University of Cincinnati’s Center for Urban Design and by students in the School of Planning, the paper discusses specifically examined issues illustrated by each of the images. It is important to note that simulation activity is a very broad arena and that one single narrative could not possibly discuss all of the activity at the Center and in the School of Planning, therefore this paper focuses on the issues of spatial and structural modeling and the resulting types of analysis associated only with the physical aspects of urbanization.
Introduction: Simulation and analogy
The extent and complexity of the modem urban entity makes it both dificult to observe and to comprehend. The urban designer is faced with design tasks that can be no less than integrally linked with processes of analysis. Any proposals not derived and based on such analysis are doomed to failure as they will be overtly alien to the social and functional logic of the surrounding city. Much of this observation and analysis needs to be carried out from objective viewpoints; however, owing to the size of urban areas, these viewpoints can be relatively remote from groundplane city experience. Direct objective sources of data are available in the form of maps and aerial photographs; however, these sources are themselves often interpretative, or at the very least, static representations of something intrinsically dy-
namic. Further, to be used effectively they must ultimately be combined with those internal viewpoints that are reflective of how one lives and moves in a city. A simulation, by substituting the functioning of one system to represent another, can serve as an accessible surrogate for the city’s complex systems, extensive spatial structure, or environmental influences. Proposals can also be tested in the simulated context. Traditionally, urban simulation has taken the form of physical models. These can be extremely convincing when built to sufficient extent and detail. They can be useful for the study of environmental factors such as shadowfall or airflow turbulence under laboratory conditions. The cinematic use of models for observing unbuilt or fantastic cities such as the disturbing future image of Los Angeles that is background to the film ‘Blade Runner’ can be extremely effective, particularly when opti-
0 1993 Elsevier Science Publishers B.V. All rights reserved 0169-2046/93/$06.00
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tally or electronically montaged with human actors (Shay, 1982). However, these types of simulation are expensive and difficult to construct and, therefore, inaccessible in general situations of practice. Models also normally provide only external views, short of using sophisticated optical instruments, and this sharply limits their analytical flexibility. A further problem noted with models is that simplification or small size makes them look ‘cute’ or toy-like (Hedman and Jazewski, 1984). Digital computing has created whole new categories of simulation possibilities for both observation and projection of urban processes. These range from purely mathematical models of a statistical or economic nature to sophisticated electronic montages of three-dimensionally represented proposals placed into photographic context. Combinations of the mathematical models with graphic capability can create advanced dynamic simulations. This type of simulation is currently available, albeit at a low level, in the form of the program ‘Simcity’. In what are termed vector graphic environments, simulations of the physical city can be constructed as a database having a spatial nature. In that they are spatial, these constructions resemble physical models, yet their virtual nature allows for an infinite flexibility in representation of detail, representation of space (paraline projection or perspective), and internal or external points of view. By using advanced display devices, such as a ‘virtual reality’ helmet, the models can be ‘entered’ by an observer for street-level experience. With reference to a relational database and some type of controlling device (mouse, joystick, spaceball, etc.), the models can be interactive to allow the user direct manipulation of the experience. For these simulations to be useful beyond the level of electronic games, they must be constructed at sufficient levels of spatial and representational accuracy to create models that are evocative of real urban space. They must tit
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into recognizable semiotic and cognitive nomenclature, behave in ways that are consistent with expectations of physical reality, and be based on generally accepted urban analogies. A first level analogy for the physical city is that it is an engine, an array of systems and structures having processes of input-output and redistribution of mass, resulting in the consumption of energy and generation of heat. The systems can be analyzed in terms of sets of finite elements having thermodynamic and mechanical relationships and thoroughly subject to physical law. A more comprehensive analogy is that the city is an organism, indeed an engine, yet one having the means (social and political) for the self-regulation of its processes of growth and replication of its tissues. In the attempt to describe a city in more comprehensive terms, other systems of analogy have been devised which combine cultural and historic influences with observable physical formations. Rossi has described the city as a kind of repository of collective memory, with all events and structures leaving a permanent trace accumulating into the whole. This fabric starts from a base of the uniqueness of the geographical location and proceeds up to the most recent intervention. As for this uniqueness of geographic loci, Norberg-Schultz ( 1980) speaks of an almost metaphysical quality attached to the specific (and distinct) point in space which he terms the ‘genius loci’. This quality fundamentally influences and is present in all subsequent action and built forms. Trancik’s ( 1986) system of analysis looks at the city in isolated layers of buildout or tigureground array, linkages or movement systems, and a ‘place’ layer. Place is possibly related to the concept of geographic-spatial uniqueness advanced by Rossi and Norberg-Schultz, combined with culturally derived architectural qualities. Whether attempting value-free description (Gosling and Maitland, 1984) or not, most systems of urban analysis are based significantly on the issue of movement and move-
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ment systems, both as behavioral and spatial generators of growth, decay, and other urban processes. Appleyard and Myer ( 197 1) studied the cognition of movement and external or environmental clues which lend meaning to the process of moving. Bacon ( 1967) states the historic significance of the specific axes that have been generators to the development of great world cities such as Paris and Rome. He further describes the application of these stated principles to design, particularly in reference to his own work in Philadelphia. Lynch ( 1960) analytically divides the city into those entities which give legibility or meaning to urban experience. Proceeding from a point and line diagramming of the movement system which he terms ‘nodes and paths’, inclusive of significant landmarks, his system divides the city matrix into observably different districts and the edges between them. This system creates highly effective diagrams in that hierarchically and typologically different nodes, paths, landmarks, districts and edges can be noted and accounted for. The movement diagram and district diagram further relates literally to the spatial array of urban structures and can be rationally derived from maps. Observation, which creates the diagram, can be highly personal and value-laden, particularly when edges of a non-physical (cultural, social or psychological) nature are diagrammed. Consequently, any two observers may differ in stating both hierarchy and location. As with Trancik’s system, symbolic diagrams must be combined with, and supported by, more literal data to achieve the comprehensive understanding necessary to drive effective design. Therefore, designers analyze the city in as many ways as possible, often summing various descriptive analysis into a systemic synthesis of their own. Although the full extent of the urban entity is currently a matter of some debate, the physical entity can be regionally observed as fairly continuous settlement having multiple centers and an amorphous edge. If one ignores the use-
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relative patterns of fragmentation created by economic and political processes of “the pulverization of homogenous space” (Harvey, 1985, p. 13 ) one can observe a generalized continuity in the urban fabric marked here and there by consolidations of large-mass buildings observable from high altitude. This fabric is generally stated to be arrayed in concentric regions around a particular dense original center, often the symbolic downtown. Major axes of movement extend outward from this center into the surrounding belts, tending to array radially to make triangular connection to more distant centers, despite any local grid-iron tendency. Commercial development and all subsequent urbanization is observable as propagating along these axes and along concentric connections between the axes. Often large consolidated beltways and peripheral freeways have been constructed in response to concentric demand between the radiating corridors (Branch, 1988). In my computer simulation studies at the Center for Urban Design under Dr. David Gosling’s direction, I have found that I am often modeling two distinct large-scale urban structures, either individually or in various combinations, which I have termed ‘cores’ and ‘rays’. Cores are singular, identifiable consolidations of structures such as downtown areas, airports, campuses, industrial parks, and large retail districts. Though they may initially be created by a process of accumulation, they generally bear the mark of intentional planning. Rays are the hierarchically superior connections and associated development that form the fabric of linkage between cores. Rays are often intrusions into the subdominant city matrix which is generally composed of various types of housing. Development along rays is movement driven and often clearly automobile oriented. It has scattered transitional uses first (residential structures as offices, etc. ), but tends to concentrate and formalize at points where rays cross. Any technological enhancement of the ray’s movement efficiency, such as
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Fig. 1. Three forms of graphic representation illustrating the core area of downtown Cincinnati: (a) figure-ground; (b) United States Geographical Survey Map; (c) exploded axonometric of movement systems and landforms. Artwork by the author (ink on mylar, 1992), except USGS map (Covington Quadrangle, 1 : 24 000 series, dated 1984 revision).
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widening of the road or increased connection with other movement system components, will stimulate additional development. Expressways, owing to their limited access and grade separation, act as super:rays, inducing development of various types into the adjacent rays linked to the expressway by interchanges. This development is often scaled larger than other commercial types owing to the regional significance of expressways in the movement system. As described, a graphically represented array of cores and rays in any given settlement would resemble Lynch’s path and node diagrams of which this nomenclature is derived. The difference is that the computer models include specific structural detail such as building locations and mass in combination with the systemic array of the movement system, thus combining layers derived from Trancik’s theories with Lynch-type diagrams. Figure 1 shows the area around the dense downtown core of Cincinnati in three types of graphical abstraction. The top two frames show the figure-ground of the immediate core area in comparison with the United States Geographical Survey (USGS) map of streets, railroads, expressways, and geographical features. Rays can be observed in the figure-ground as linear areas of intensilication associated with major visible axial spaces. The bottom diagram illustrates the movement structure of the core and surrounding rays in an exploded axonometric of isolated layers, starting with the landform and vegetation, and proceeding to the expressways, and surface streets. Scale, structure, and complexity
Those models of sufficient scope to extend beyond the limits of a single district and made to such levels of detail that one begins to encounter issues associated with the whole of the urban entity, are what are termed here ‘largescale simulations’. The ideal is simulations that are sufficiently extensive to study actual largescale urban structure or those entities associ-
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ated with the metropolitan extents of an urban area beyond the often artificial limits of political boundaries. As the scale of what is observed increases, certain structures and associated detail become more apparent as others drop out of relevance. This is true for graphical representation as well as actual aerial views, although with graphic forms and simulations unnecessary detail can be selectively deleted. Figure 2 illustrates this scale-detail relationship, by a zoom from left to right, through metropolitan, urban, and district scales. At the metropolitan scale, the geometric array of the movement system is the most visible feature. At urban scale, the actual buildout represented as ligureground and landform topography become relevant. Streets and roads would be visible, but here are selectively deleted to clarify buildout and landform relationships. At district scale, buildings, streets, and sidewalks are seen in full three-dimensional form. In constructing simulations, the choice of which scale-dependent detail is relevant, has importance for reasons of clarity as well as efficiency of computer memory. Carrying unnecessary detail quickly overwhelms computer memory as the scale of a study is increased, while at the same time, the perceptual clarity of the simulation is diminished. Urban structure is traditionally described as occurring in three layers. The city is seen to exist in terms of buildings and structures on the surface, supportive infrastructure below the ground, and air-rights overhead (Branch, 1988). These layer descriptions can be made more specific by isolation of layers for all identifiable environmental factors, geometric and physical structures, and influences of a nonphysical nature. This process of dissecting city structure, termed ‘delayering’ by Mario Gandelsonas ( 199 1), when applied to geometric structure allows for the independent observation of a complex entity or arrangement, normally visually buried in the whole of the city matrix. In Fig. 3, this process is shown applied
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Fig. 2. Comparison of visible detail relative to scale. Cincinnati area in terms of: (a) roads at metropolitan scale; (b) figureground and landform at urban scale; (c) architectonic detail at district scale. Scale factor changes by four in each successive zoom. Far left map is Hamilton County Road Map, xerographically altered by the author, updated 1988. Middle map is a drawing by the author (ink on mylar, 1992). Right-hand map is hand-altered plotter output from an Autocad model. Model and aherations by the author, 1992.
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Fig. 3. Series of images depicting process of delayering as an analytical technique. (a) the USGS map from Fig. 1. Other images are ink on mylar extractions: (b) slope and landform; (c) land lost to development; (d) movement geometries and ‘desire for extension’; (e) north-south movement in isolation; (f) east-west movement in isolation. All artwork by the author (ink on mylar, 1992 ) except USGS map (see Fig. 1).
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to the basin area around Cincinnati’s downtown core. The top-left image is the USGS source map, below are extractions of slope (middle-left), and area lost to development such as parks, railroads, expressways, and the river (bottom-left ). The right-hand sequence of images is more directly derivative of Gandelsonas’s type of analysis. The top image explores forces associated with the street geometry, metaphorically as desire for extension versus the disruptive presence of the landform. The bottom two images use differential filtering of the street grid showing north-south versus east-west and clearly illustrating multiple grids and fragmentation induced by the landform, compensation for the river and intrusion of railroad and expressway systems. Recombination of the extracted layers can create various forms of synthesized understanding leading to design solutions. This was the process used by Ian McHarg ( 1969) in his large-scale studies of conflicting land uses and environmental fragility by which negative impacts were visually summed to reveal more environmentally correct route locations and use arrangements. McHarg’s use of the technique was prior to generalized availability of graphic computers and was, therefore, executed manually. Owing to computer capability for selectable grouping of datasets, this process of delayering and recombination is made much more accessible for all types of urban study and forms the basis of geographic information or thematic mapping theory. Although graphically and artistically sophisticated, the computer-based analytical studies exhibited in Gandelsonas’s work ‘The Urban Text’ ( 199 1) are for the most part two-dimensional. Such exercises can be carried out just as easily by conventional means, yet the real power of vector computing lies in three-dimensional modeling, particularly when examining a city with a more complex base-plane geometry than Chicago. Figure 4, a simulation of the University of Cincinnati core and two surrounding commer-
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cial rays, illustrates the relationship between building volumes and the two-dimensional figure-ground. To improve visual differentiation, the campus buildings are represented as walls without roofs and the surrounding buildings simplilied to extruded masses. The volumetric model shows variation in building heights and form that would not be apparent in the figureground alone. Although the groundplane was not modeled, the buildings are placed at correct base elevations with the footprints remaining at sea level. The computer ability to model complex urban structures as independent entities selectable for observation alone, or in groupings up to the whole of a simulation, forms the basis for a technique that I call decompositional analysis. The method is useful for analyzing vertical portions of a city as seen in Fig. 5. This image of a high-rise portion of downtown Cincinnati shows vertical structure in terms of the street geometry and building footprints, the first-floor walls, the second-floor (skywalk associated) walls, the skywalk system, and the tower volumes, here expressed in the form of a conventional exploded axonometric. Figure 6 illustrates decomposition applied to the analysis of a group of commercial building elevations in the Ludlow Avenue business district of Cincinnati. The image shows (from top to bottom) solids and voids, transparency, commercial signage, architectural detail, composite masses, streetscape and utility clutter, and wireframe masses. This is a useful technique for examining the original architectural intentions in a streetscape before making proposals for inlill and alteration of existing structures. Another advantage of computer modeling is the capability for assembly of extensive models from any number of smaller subcomponents if scale and locational control is included in all portions. Figure 7 illustrates a large three-dimensional study of a proposed expressway route in west-central Hamilton County, assembled by students. The grid increment is 500 ft., making the total length of the model 32 000
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Fig. 4. Illustration depicting volumetric delayering, west campus of University of Cincinnati and surrounding neighborhoods. Autocad model by School of Planning students and the author ( 199 I), graphically altered by the author ( 1992).
ft. The expressway, surface roads, and building masses were modeled with buildings colored to designate use. The lower portion of the image shows how coordinated portions of the model were assembled. Controlling geometry was established on the source maps before they were divided up between student groups. The model is subsequently being used for animated movement studies along the proposed expressway. Terrain, earthworks, and vegetation, owing to their extreme complexity and consequent memory demand, are very difficult entities to simulate; yet, in many instances, they must be included for faithful representation of urban space. The graphic abstraction of contour lines is an effective technique for two-dimensional representation of landforms; computer repre-
sentation, however, requires interpolation into true surfaces. Computers represent continuously curving surfaces as an array of quadratic or triangular faces. Smaller unit faces create more accurate representation of surfaces; consequently, large or complex landforms quickly create large memory demand. A trade-off exists between representational sensitivity and the extent of a landform model. If viewpoints are previously selected, local surfaces can be modeled at higher accuracy and nested into low detail models of visible yet distant landforms. Landforms can be modeled by direct interpolation from contours or by construction of a series of profiles in a quadratic or parallel array and interpolation of the intervening surfaces. In the case of the terrain simulation of the
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Fig. 5. Illustration of the process of decompositional analysis applied to a portion of downtown Cincinnati. Autocad model originally produced by the author for the Center for Urban Design ( 1990). graphically altered by the author ( 1992).
campus of the University of Toledo, shown in the upper portion of Fig. 8, a system of parallel profiles was created as most changes in grade were perpendicular to the course of the small river. Since this model required an extremely complete simulation, several thousand individual tree models were included, which, even at high simplification, gave the model the largest single memory of any produced at the Center. In the bottom frame a composite view is
seen showing all surrounding buildings and roads, railroads, high-tension towers, sub-station areas, campus trees and campus buildings. The campus buildings were individually modeled to high detail including roof forms, changes in material and transparency. Buildings around the campus were modeled as walls without roofs. The model was used for various types of three-dimensional analysis and for the
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Fig. 6. Decompositional analysis applied to a series of elevations in the Ludlow Neighborhood Business District, figure-ground map to the left shows location of buildings. The decompositions are (from top to bottom): solid and void; transparency in isolation; commercial signage; architectural detail elements; composite masses of the buildings; streetscape utilities; wireframes of the buildings. Autocad model, map and graphic manipulations by the author ( 1992), for Center for Urban Design.
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Fig. 7. Autocad plot views of a large expressway simulation produced by School of Planning students and the author in 1992. (a) Two views of the extended model: because of the length of the model it was broken into two to avoid excessive graphic reduction. (b) View shows how model was composed from units produced by individual students.
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Fig. 8. Two views of a large simulation of the University of Toledo campus produced for a study at the Center for Urban design in 199 1. (a) Landform-earthwork model and trees. (b ) All builtform elements and trees; the land is excluded to enhance graphic clarity. Autocad model by the author and Dayana Salazar for the Center for Urban Design ( 199 1) , graphic manipulations by the author (1992).
testing of a series proposals.
of campus
planning
Sequence and time-dependent phenomena
It is a fact that, owing to the scale difference between humans and a city, most of city expe-
rience occurs at street level, making the most relevant portion of city buildings the immediate first floor street frontage (Gosling and Maitland, 1984). Moving around in the city reveals its greater structure only by degrees and as accumulated in memory. Views from high-
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rise buildings or elevated landforms can reveal some of the large plan intentions of the settlement: normally however, large-scale views will be limited to deep corridor perspective from a narrow straight street or skyline views from surrounding open land. It is for this reason that much of urban design concerns itself with street experience, either in the public realm of the streetscape or the private-realm edge of the buildings and their setbacks from the street. Similarly, the skyline, which is a common distant view in most cities, has considerable iconographic significance, particularly in high-rise districts, and is consequently a subject for design study. A complex spatial structure such as a skyline
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is best observed in motion, as is street experience. Though there may be one singular distinctive view, to be effective, three-dimensional iconography must be present in many views. Once assembled, computer models can be used to create sequential study within the computer space. The computer simply calculates the increments between any two spatial locations, and the result is an animated sequence. Figure 9 illustrates a skyline iconography study of the Cincinnati downtown. The images here represent the four extremes looking (from top to bottom) north, west, south, and east. The views are elevation projections rather than perspectives. The dotted lines show the pyramided height limits over the build-
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Fig. 9. Four elevation views of downtown Cincinnati showing building heights and locations ground landform. Views are looking: (a ) north, (b ) west, (c) south, and (d) east. Autocad
relative to zoning heights and backmodel by the author ( 1990- 1992 ).
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Fig. 10. Large simulation view of approach to downtown Cincinnati from the northwest. (a) Shows the building masses in isolation, illustrating the pyramid shape of the downtown core. (b) Shows building masses and the landform, showing the embracing nature of the land. Autocad model produced by the author and Kiril Stanislov at the Center for Urban Design ( 19901992). Graphic manipulation and animations by the author ( 1992).
ings, which when seen in this simultaneous view shows potentially under-utilized sites. Also observable is a potential for buildings to
infill sites up to the legal height, creating a kind of shelf threatening to negate the original pyramid intention of the height limits. Apparent
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Fig. 1I. Ink-traced frames of a computer film illustrating changes in downtown Cincinnati through time. (a) 1930; (b) 1940; (c) 1950; (d) 1960; (e) 1970; ( f) 1980; (g) I990-present. Film directed and animated by Frank Russell, Autocad models by the author, produced at Center for Urban Design ( 1992). Ink-tracing of frames by the author ( 1992).
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Fig. 12. Three views of a shadowfall study in downtown Cincinnati, from computer film. North is down and t o thee right. Views are (top to bottom): early morning, noon, and afternoon. Time of year is equinox. Computer film directed and animated by Fra nk Russell, Autocad models by the author, produced at the Center for Urban Design ( 1992). Images are dot. -matrix video pi-i1tts directly from computer tiles.
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in the background of these views is the embracing nature of the landform which surrounds the downtown tightly on three of its four sides. When one is approaching a city or moving around in it, the terrain forms the basis of the experience, with the city being only a modilication of the effect. The degree of modilication is dependent on the scale and pattern of the city structures (Hedman and Jazewski, 1984, p. 105). The use of computer animation combined with the decompositional capability for switching elements on and off can clarify which elements play the strongest modifying roles. A movement sequence can be observed as a composite simulation or just in terms of commercial signage or landform alone. Figure 10 shows a single frame of such a sequence of approach from the hills northwest of downtown Cincinnati. In the top view the city’s masses are seen in isolation, and in the bottom they are viewed in combination with the land to show its significance. Roads and expressways are turned off to accelerate computer calculation times. Visible grid lines on the land relate to the Jeffersonian control grid and the closest grids (flat land) have an increment of 1000 ft. In addition to movement, computer models can be used to observe other changes through time. Patterns of growth, land use, and distribution of population density can all be animated to observe patterns that may only become apparent in motion. This is analogous to using a movie camera with an intervalometer to accelerate imperceptibly slow events to visibility. Figure 11 shows a sequence of changes in vertical density in the core portion of Cincinnati’s downtown. The frames represent 1Oyear increments starting in 1930 and running to 1990, from top-left to middle-right to bottom-left. As with many cities, intense densification can be observed after 1970 with some of the largest floor-plate buildings appearing after 1980. To create the animation, demolished buildings were turned off in their correct
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year as replacement buildings were raised into place from below the model (Russell and Decker, 1992 ) . When combined with mathematical models, vector models can be used to observe and demonstrate various physical phenomena. These models can become highly calculation intensive and deviate significantly from reality if the descriptive mathematics involve too many variables. However, for observation of limited events in isolated circumstances, calculation driven models can provide extremely accurate representations. Figure 12 illustrates a shadow fall study produced for the same film that included the densification study above. The views shown are morning, noon and evening extremes on a single day. North is oriented down. The completed film showed the shadows dynamically sweeping from the base of the buildings through both solstices and the equinox. The model was used to further test shadow sweep from a number of high-rise proposals as well as existing structures. Unlike the other images, these are derived directly from the rendering created by the computer on video tape (Russell et al., 1992). At the present time, computer capability only allows frame-by-frame calculation of animations, and even though the process can be automated, sending a batch of frames to tape is still time intensive, requiring large calculations for the creation of each frame based on a complex model. Thus real-time or directly interactive experience of a simulation is still not a possibility. To be useful, tapes must be produced from the standpoint of consideration of as many possible conditions of observation required for any specific analytical study. Additional requirements or changes in viewpoint additional phases of generally require production. Conclusion Each of these studies was carried out within the framework and limits of a specific study or
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particular exercise, yet this is not the limit of their value. The power of computer simulation is that the activity of building models is accumulative, especially when many of the models are different portions of the same urban area. If consistent nomenclatural and scale control is applied to each exercise, the capability for combining all is only limited by the overhead limit of memory storage. The fact that the models are infinitely flexible and adjustable means that accuracy and detail can be continuously enhanced, as can simple updating to reflect a changing urban fabric. In my own research, I have begun to accumulate large portions of several urban areas and numerous detailed fragments of others. These pieces are extremely useful for comparative study as well as individual examination. They are consequently becoming a resource for teaching of urban design as well as for additional research into urban structure. The large database of Cincinnati that has been accumulated at the Center has been made generally available for public use and continues to grow. Looking towards the future, this database and others like it, will become powerful tools for urban management, particularly when combined with other thematic layering in systems having more intelligence or more complete inference engines. A goal of the Center is to continue to push the representational limits of simulation as well as its scale and dynamic capabilities. By combining other types of existing systems, we intend to produce hybrid systems capable of critical design analysis as well as projection of various urban processes. Ultimately intelligent systems will provide urban designers and managers, at the very least, with advanced and comprehensive instrumentation for controlling this complex, exciting, and often baffling entity which is the city. Acknowledgments
While I was on sabbatical, my Department Chair, Kenneth Pearlman, appointed me to co-
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chair the 34th Annual Conference of the Association of the Collegiate Schools of Planning (ACSP) along with Steven I. Gordon. Professor Gordon had missed the faculty meeting at which it was discussed. In planning the conference, Professor Gordon and I expressed an interest in having the call for papers allow for conference papers to be considered for a peerrefereed conference proceedings. Professor Pearlman supported the idea and took it to the ACSP executive committee, who rejected it. Shortly afterwards, Jon E. Rodiek, a co-editor of Landscape and Urban Planning contacted us with a offer to publish selected papers from the conference. Although Professor Gordon had lost interest, I felt a subset of papers on urban design research and environment-behavior studies might have some value. I contacted Professor Rodiek and he agreed to publish a peer-refereed proceedings in that area. When the call for papers for the conference went out, I sent out a special call for papers on environmental design research to be considered for publication in the special proceedings. Professor Gordon set up the database which allowed us to handle, process and organize all of the papers we received. Ellen Wallace handled the papers as they arrived, filed them, entered them on the database, sent acknowledgements, and worked with Professor Gordon and I on the schedule. The conference, which took place in Columbus, OH from 1 through 30 October 1992 owes much to her tireless efforts. With over 500 registrants and papers presented, the 34th ACSP had the largest attendance of any ACSP conference. After reading all abstracts and papers submitted and attending relevant sessions, I invited other presenters, whose work fit the theme, to submit papers. Each paper received a blind review by at least two reviewers. I thank the individuals who submitted papers, the reviewers, Richard Titus for the special role he played in handling the blind review of my paper, Ellen Wallace and Steven Gordon for their efforts up through the
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conference, and Kenneth Pearlman for getting me into this in the first place. The reviewers were: D. Amedeo, University of Nebraska; J. Carpenter, The Ohio State University; J. Decker, University of Cincinnati; M. Felson, University of Southern California; B. Fisher, University of Cincinnati; K. Foster, Pennsylvania State University; J. Gaber; University of Nebraska; L. Gerckens, The Ohio State University; R.V. George, University of Illinois, Champaign-Urbana; M. Grannis, The Ohio State University; D.L.A. Gordon, Harvard University; D. Gosling, University of Cincinnati; P.C. Gurstein, University of British Columbia; H.Heft, Dennison University; F. Hurand, Eastern Washington University; R.E. Lloyd, California State Polytechnic University, Pomona; R. Kaplan, University of Michigan; S. Kaplan, University of Michigan; M. Ilmonen, Helinski University of Technology; A. Jones, The Ohio State University; B. Jones, University of Colorado, Denver; A. Loukaitou-Sideris, University of California, Los Angeles; A.R. Madani Pour, University of Newcastle-Upon-Tyne; R. Marans, University of Michigan; J. Mayo, University of Kansas; M. Morrone, The Ohio State University; D.J. Nadenicek, Pennsylvania State University; S. Onal, University of Nottingham; P. Owens, University of California, Berkeley; F. Philips, The Ohio State University; J. Potter, The University of Nebraska; W.F.E. Preiser, University of Cincinnati; W.M. Robe, University of North Carolina, Chad Hill;
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F. Russell, University of Cincinnati; F. Sancar, University of Wisconsin, Madison; R.I. Selby, University of Illinois, Champaign-Urbana; J. Simpson, The Ohio State University; G.L. Spragge, Queen’s University; R. Taylor, Temple University; E.E. Tillman, Morgan State University; R. M. Titus, National Institute of Justice; J.K. Zacharias, Concordia University. R.M. Titus selected reviewers and administered the blind reviews on the paper by J.L. Nasar, B. Fisher, and M. Grannis. References Appleyard, D., Lynch, K. and Myer, J., 197 1.The View from the Road. MIT Press, Cambridge, MA. Bacon, E.N., 1967. Design of Cities. Viking Press, New York. Branch, M.C., 1988. Comprehensive City Planning. APA Press, Washington DC. Gandelsonas, M., 1991. The Urban Text. MIT Press, Cambridge, MA. Gosling, D. and Maitland, B., 1984. Conceits of Urban Design. Academy Editions, St. Martin’s Press, New York. Harvey, D., 1985. Consciousness and the Urban Experience. Johns Hopkins University Press, Baltimore, MD. Hedman, R. and Jazewski, A., 1984. Perception of the City. APA Press, Washington DC. Lynch, K., 1960. The Image of the City. MIT Press, Cambridge, MA. McHarg, I.L., 1969. Design with Nature. Natural History Press, Garden City, New York. Norberg-Schultz, C., 1980. Genius Loci. Rizzoli Press, New York. Rossi, A., 197 1.Architettura della Citta. Milan. Russell, F. and Decker, J., 1992. ‘Fountain Square West’ video. The Center for Urban Design, Cincinnati, OH. Shay, D., 1982. Blade Runner-2020 Foresight. Cinefex J. Cinematic Illusions, 9. Trancik, R., 1986. Finding Lost Space: Theories of Urban Design. Van Nostrand Reinhold, New York.
Landscapeand Urban Planning, 26 (1993) 231-250 Elsevier Science Publishers B.V., Amsterdam
Simulation methodologies for observing large-scale urban structures John Decker School ofplanning and Research Associate with the Centerfor Urban Design, College ofDesign. Architecture, Art and Planning, University of Cincinnati, Cincinnati, OH 45221-0073, USA
Abstract
The arrival of advanced and widely available graphic computing has made simulation a tool for both practice and research in all areas of design. The large-scale and complex nature of urbanization has long required some type of simulation to facilitate external observation and analysis. The flexibility and expandability of computer models promises phenomenal potential when used towards this end. This paper discusses simulation methodology in terms of the descriptive basis for simulation constructions and the analytical methods that can be applied to models once they exist. Woven around a series of images created from studies produced at the University of Cincinnati’s Center for Urban Design and by students in the School of Planning, the paper discusses specifically examined issues illustrated by each of the images. It is important to note that simulation activity is a very broad arena and that one single narrative could not possibly discuss all of the activity at the Center and in the School of Planning, therefore this paper focuses on the issues of spatial and structural modeling and the resulting types of analysis associated only with the physical aspects of urbanization.
Introduction: Simulation and analogy
The extent and complexity of the modem urban entity makes it both dificult to observe and to comprehend. The urban designer is faced with design tasks that can be no less than integrally linked with processes of analysis. Any proposals not derived and based on such analysis are doomed to failure as they will be overtly alien to the social and functional logic of the surrounding city. Much of this observation and analysis needs to be carried out from objective viewpoints; however, owing to the size of urban areas, these viewpoints can be relatively remote from groundplane city experience. Direct objective sources of data are available in the form of maps and aerial photographs; however, these sources are themselves often interpretative, or at the very least, static representations of something intrinsically dy-
namic. Further, to be used effectively they must ultimately be combined with those internal viewpoints that are reflective of how one lives and moves in a city. A simulation, by substituting the functioning of one system to represent another, can serve as an accessible surrogate for the city’s complex systems, extensive spatial structure, or environmental influences. Proposals can also be tested in the simulated context. Traditionally, urban simulation has taken the form of physical models. These can be extremely convincing when built to sufficient extent and detail. They can be useful for the study of environmental factors such as shadowfall or airflow turbulence under laboratory conditions. The cinematic use of models for observing unbuilt or fantastic cities such as the disturbing future image of Los Angeles that is background to the film ‘Blade Runner’ can be extremely effective, particularly when opti-
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tally or electronically montaged with human actors (Shay, 1982). However, these types of simulation are expensive and difficult to construct and, therefore, inaccessible in general situations of practice. Models also normally provide only external views, short of using sophisticated optical instruments, and this sharply limits their analytical flexibility. A further problem noted with models is that simplification or small size makes them look ‘cute’ or toy-like (Hedman and Jazewski, 1984). Digital computing has created whole new categories of simulation possibilities for both observation and projection of urban processes. These range from purely mathematical models of a statistical or economic nature to sophisticated electronic montages of three-dimensionally represented proposals placed into photographic context. Combinations of the mathematical models with graphic capability can create advanced dynamic simulations. This type of simulation is currently available, albeit at a low level, in the form of the program ‘Simcity’. In what are termed vector graphic environments, simulations of the physical city can be constructed as a database having a spatial nature. In that they are spatial, these constructions resemble physical models, yet their virtual nature allows for an infinite flexibility in representation of detail, representation of space (paraline projection or perspective), and internal or external points of view. By using advanced display devices, such as a ‘virtual reality’ helmet, the models can be ‘entered’ by an observer for street-level experience. With reference to a relational database and some type of controlling device (mouse, joystick, spaceball, etc.), the models can be interactive to allow the user direct manipulation of the experience. For these simulations to be useful beyond the level of electronic games, they must be constructed at sufficient levels of spatial and representational accuracy to create models that are evocative of real urban space. They must tit
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into recognizable semiotic and cognitive nomenclature, behave in ways that are consistent with expectations of physical reality, and be based on generally accepted urban analogies. A first level analogy for the physical city is that it is an engine, an array of systems and structures having processes of input-output and redistribution of mass, resulting in the consumption of energy and generation of heat. The systems can be analyzed in terms of sets of finite elements having thermodynamic and mechanical relationships and thoroughly subject to physical law. A more comprehensive analogy is that the city is an organism, indeed an engine, yet one having the means (social and political) for the self-regulation of its processes of growth and replication of its tissues. In the attempt to describe a city in more comprehensive terms, other systems of analogy have been devised which combine cultural and historic influences with observable physical formations. Rossi has described the city as a kind of repository of collective memory, with all events and structures leaving a permanent trace accumulating into the whole. This fabric starts from a base of the uniqueness of the geographical location and proceeds up to the most recent intervention. As for this uniqueness of geographic loci, Norberg-Schultz ( 1980) speaks of an almost metaphysical quality attached to the specific (and distinct) point in space which he terms the ‘genius loci’. This quality fundamentally influences and is present in all subsequent action and built forms. Trancik’s ( 1986) system of analysis looks at the city in isolated layers of buildout or tigureground array, linkages or movement systems, and a ‘place’ layer. Place is possibly related to the concept of geographic-spatial uniqueness advanced by Rossi and Norberg-Schultz, combined with culturally derived architectural qualities. Whether attempting value-free description (Gosling and Maitland, 1984) or not, most systems of urban analysis are based significantly on the issue of movement and move-
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ment systems, both as behavioral and spatial generators of growth, decay, and other urban processes. Appleyard and Myer ( 197 1) studied the cognition of movement and external or environmental clues which lend meaning to the process of moving. Bacon ( 1967) states the historic significance of the specific axes that have been generators to the development of great world cities such as Paris and Rome. He further describes the application of these stated principles to design, particularly in reference to his own work in Philadelphia. Lynch ( 1960) analytically divides the city into those entities which give legibility or meaning to urban experience. Proceeding from a point and line diagramming of the movement system which he terms ‘nodes and paths’, inclusive of significant landmarks, his system divides the city matrix into observably different districts and the edges between them. This system creates highly effective diagrams in that hierarchically and typologically different nodes, paths, landmarks, districts and edges can be noted and accounted for. The movement diagram and district diagram further relates literally to the spatial array of urban structures and can be rationally derived from maps. Observation, which creates the diagram, can be highly personal and value-laden, particularly when edges of a non-physical (cultural, social or psychological) nature are diagrammed. Consequently, any two observers may differ in stating both hierarchy and location. As with Trancik’s system, symbolic diagrams must be combined with, and supported by, more literal data to achieve the comprehensive understanding necessary to drive effective design. Therefore, designers analyze the city in as many ways as possible, often summing various descriptive analysis into a systemic synthesis of their own. Although the full extent of the urban entity is currently a matter of some debate, the physical entity can be regionally observed as fairly continuous settlement having multiple centers and an amorphous edge. If one ignores the use-
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relative patterns of fragmentation created by economic and political processes of “the pulverization of homogenous space” (Harvey, 1985, p. 13 ) one can observe a generalized continuity in the urban fabric marked here and there by consolidations of large-mass buildings observable from high altitude. This fabric is generally stated to be arrayed in concentric regions around a particular dense original center, often the symbolic downtown. Major axes of movement extend outward from this center into the surrounding belts, tending to array radially to make triangular connection to more distant centers, despite any local grid-iron tendency. Commercial development and all subsequent urbanization is observable as propagating along these axes and along concentric connections between the axes. Often large consolidated beltways and peripheral freeways have been constructed in response to concentric demand between the radiating corridors (Branch, 1988). In my computer simulation studies at the Center for Urban Design under Dr. David Gosling’s direction, I have found that I am often modeling two distinct large-scale urban structures, either individually or in various combinations, which I have termed ‘cores’ and ‘rays’. Cores are singular, identifiable consolidations of structures such as downtown areas, airports, campuses, industrial parks, and large retail districts. Though they may initially be created by a process of accumulation, they generally bear the mark of intentional planning. Rays are the hierarchically superior connections and associated development that form the fabric of linkage between cores. Rays are often intrusions into the subdominant city matrix which is generally composed of various types of housing. Development along rays is movement driven and often clearly automobile oriented. It has scattered transitional uses first (residential structures as offices, etc. ), but tends to concentrate and formalize at points where rays cross. Any technological enhancement of the ray’s movement efficiency, such as
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Fig. 1. Three forms of graphic representation illustrating the core area of downtown Cincinnati: (a) figure-ground; (b) United States Geographical Survey Map; (c) exploded axonometric of movement systems and landforms. Artwork by the author (ink on mylar, 1992), except USGS map (Covington Quadrangle, 1 : 24 000 series, dated 1984 revision).
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widening of the road or increased connection with other movement system components, will stimulate additional development. Expressways, owing to their limited access and grade separation, act as super:rays, inducing development of various types into the adjacent rays linked to the expressway by interchanges. This development is often scaled larger than other commercial types owing to the regional significance of expressways in the movement system. As described, a graphically represented array of cores and rays in any given settlement would resemble Lynch’s path and node diagrams of which this nomenclature is derived. The difference is that the computer models include specific structural detail such as building locations and mass in combination with the systemic array of the movement system, thus combining layers derived from Trancik’s theories with Lynch-type diagrams. Figure 1 shows the area around the dense downtown core of Cincinnati in three types of graphical abstraction. The top two frames show the figure-ground of the immediate core area in comparison with the United States Geographical Survey (USGS) map of streets, railroads, expressways, and geographical features. Rays can be observed in the figure-ground as linear areas of intensilication associated with major visible axial spaces. The bottom diagram illustrates the movement structure of the core and surrounding rays in an exploded axonometric of isolated layers, starting with the landform and vegetation, and proceeding to the expressways, and surface streets. Scale, structure, and complexity
Those models of sufficient scope to extend beyond the limits of a single district and made to such levels of detail that one begins to encounter issues associated with the whole of the urban entity, are what are termed here ‘largescale simulations’. The ideal is simulations that are sufficiently extensive to study actual largescale urban structure or those entities associ-
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ated with the metropolitan extents of an urban area beyond the often artificial limits of political boundaries. As the scale of what is observed increases, certain structures and associated detail become more apparent as others drop out of relevance. This is true for graphical representation as well as actual aerial views, although with graphic forms and simulations unnecessary detail can be selectively deleted. Figure 2 illustrates this scale-detail relationship, by a zoom from left to right, through metropolitan, urban, and district scales. At the metropolitan scale, the geometric array of the movement system is the most visible feature. At urban scale, the actual buildout represented as ligureground and landform topography become relevant. Streets and roads would be visible, but here are selectively deleted to clarify buildout and landform relationships. At district scale, buildings, streets, and sidewalks are seen in full three-dimensional form. In constructing simulations, the choice of which scale-dependent detail is relevant, has importance for reasons of clarity as well as efficiency of computer memory. Carrying unnecessary detail quickly overwhelms computer memory as the scale of a study is increased, while at the same time, the perceptual clarity of the simulation is diminished. Urban structure is traditionally described as occurring in three layers. The city is seen to exist in terms of buildings and structures on the surface, supportive infrastructure below the ground, and air-rights overhead (Branch, 1988). These layer descriptions can be made more specific by isolation of layers for all identifiable environmental factors, geometric and physical structures, and influences of a nonphysical nature. This process of dissecting city structure, termed ‘delayering’ by Mario Gandelsonas ( 199 1), when applied to geometric structure allows for the independent observation of a complex entity or arrangement, normally visually buried in the whole of the city matrix. In Fig. 3, this process is shown applied
a
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Fig. 2. Comparison of visible detail relative to scale. Cincinnati area in terms of: (a) roads at metropolitan scale; (b) figureground and landform at urban scale; (c) architectonic detail at district scale. Scale factor changes by four in each successive zoom. Far left map is Hamilton County Road Map, xerographically altered by the author, updated 1988. Middle map is a drawing by the author (ink on mylar, 1992). Right-hand map is hand-altered plotter output from an Autocad model. Model and aherations by the author, 1992.
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Fig. 3. Series of images depicting process of delayering as an analytical technique. (a) the USGS map from Fig. 1. Other images are ink on mylar extractions: (b) slope and landform; (c) land lost to development; (d) movement geometries and ‘desire for extension’; (e) north-south movement in isolation; (f) east-west movement in isolation. All artwork by the author (ink on mylar, 1992 ) except USGS map (see Fig. 1).
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to the basin area around Cincinnati’s downtown core. The top-left image is the USGS source map, below are extractions of slope (middle-left), and area lost to development such as parks, railroads, expressways, and the river (bottom-left ). The right-hand sequence of images is more directly derivative of Gandelsonas’s type of analysis. The top image explores forces associated with the street geometry, metaphorically as desire for extension versus the disruptive presence of the landform. The bottom two images use differential filtering of the street grid showing north-south versus east-west and clearly illustrating multiple grids and fragmentation induced by the landform, compensation for the river and intrusion of railroad and expressway systems. Recombination of the extracted layers can create various forms of synthesized understanding leading to design solutions. This was the process used by Ian McHarg ( 1969) in his large-scale studies of conflicting land uses and environmental fragility by which negative impacts were visually summed to reveal more environmentally correct route locations and use arrangements. McHarg’s use of the technique was prior to generalized availability of graphic computers and was, therefore, executed manually. Owing to computer capability for selectable grouping of datasets, this process of delayering and recombination is made much more accessible for all types of urban study and forms the basis of geographic information or thematic mapping theory. Although graphically and artistically sophisticated, the computer-based analytical studies exhibited in Gandelsonas’s work ‘The Urban Text’ ( 199 1) are for the most part two-dimensional. Such exercises can be carried out just as easily by conventional means, yet the real power of vector computing lies in three-dimensional modeling, particularly when examining a city with a more complex base-plane geometry than Chicago. Figure 4, a simulation of the University of Cincinnati core and two surrounding commer-
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cial rays, illustrates the relationship between building volumes and the two-dimensional figure-ground. To improve visual differentiation, the campus buildings are represented as walls without roofs and the surrounding buildings simplilied to extruded masses. The volumetric model shows variation in building heights and form that would not be apparent in the figureground alone. Although the groundplane was not modeled, the buildings are placed at correct base elevations with the footprints remaining at sea level. The computer ability to model complex urban structures as independent entities selectable for observation alone, or in groupings up to the whole of a simulation, forms the basis for a technique that I call decompositional analysis. The method is useful for analyzing vertical portions of a city as seen in Fig. 5. This image of a high-rise portion of downtown Cincinnati shows vertical structure in terms of the street geometry and building footprints, the first-floor walls, the second-floor (skywalk associated) walls, the skywalk system, and the tower volumes, here expressed in the form of a conventional exploded axonometric. Figure 6 illustrates decomposition applied to the analysis of a group of commercial building elevations in the Ludlow Avenue business district of Cincinnati. The image shows (from top to bottom) solids and voids, transparency, commercial signage, architectural detail, composite masses, streetscape and utility clutter, and wireframe masses. This is a useful technique for examining the original architectural intentions in a streetscape before making proposals for inlill and alteration of existing structures. Another advantage of computer modeling is the capability for assembly of extensive models from any number of smaller subcomponents if scale and locational control is included in all portions. Figure 7 illustrates a large three-dimensional study of a proposed expressway route in west-central Hamilton County, assembled by students. The grid increment is 500 ft., making the total length of the model 32 000
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Fig. 4. Illustration depicting volumetric delayering, west campus of University of Cincinnati and surrounding neighborhoods. Autocad model by School of Planning students and the author ( 199 I), graphically altered by the author ( 1992).
ft. The expressway, surface roads, and building masses were modeled with buildings colored to designate use. The lower portion of the image shows how coordinated portions of the model were assembled. Controlling geometry was established on the source maps before they were divided up between student groups. The model is subsequently being used for animated movement studies along the proposed expressway. Terrain, earthworks, and vegetation, owing to their extreme complexity and consequent memory demand, are very difficult entities to simulate; yet, in many instances, they must be included for faithful representation of urban space. The graphic abstraction of contour lines is an effective technique for two-dimensional representation of landforms; computer repre-
sentation, however, requires interpolation into true surfaces. Computers represent continuously curving surfaces as an array of quadratic or triangular faces. Smaller unit faces create more accurate representation of surfaces; consequently, large or complex landforms quickly create large memory demand. A trade-off exists between representational sensitivity and the extent of a landform model. If viewpoints are previously selected, local surfaces can be modeled at higher accuracy and nested into low detail models of visible yet distant landforms. Landforms can be modeled by direct interpolation from contours or by construction of a series of profiles in a quadratic or parallel array and interpolation of the intervening surfaces. In the case of the terrain simulation of the
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Fig. 5. Illustration of the process of decompositional analysis applied to a portion of downtown Cincinnati. Autocad model originally produced by the author for the Center for Urban Design ( 1990). graphically altered by the author ( 1992).
campus of the University of Toledo, shown in the upper portion of Fig. 8, a system of parallel profiles was created as most changes in grade were perpendicular to the course of the small river. Since this model required an extremely complete simulation, several thousand individual tree models were included, which, even at high simplification, gave the model the largest single memory of any produced at the Center. In the bottom frame a composite view is
seen showing all surrounding buildings and roads, railroads, high-tension towers, sub-station areas, campus trees and campus buildings. The campus buildings were individually modeled to high detail including roof forms, changes in material and transparency. Buildings around the campus were modeled as walls without roofs. The model was used for various types of three-dimensional analysis and for the
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Fig. 6. Decompositional analysis applied to a series of elevations in the Ludlow Neighborhood Business District, figure-ground map to the left shows location of buildings. The decompositions are (from top to bottom): solid and void; transparency in isolation; commercial signage; architectural detail elements; composite masses of the buildings; streetscape utilities; wireframes of the buildings. Autocad model, map and graphic manipulations by the author ( 1992), for Center for Urban Design.
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a
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Fig. 7. Autocad plot views of a large expressway simulation produced by School of Planning students and the author in 1992. (a) Two views of the extended model: because of the length of the model it was broken into two to avoid excessive graphic reduction. (b) View shows how model was composed from units produced by individual students.
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Fig. 8. Two views of a large simulation of the University of Toledo campus produced for a study at the Center for Urban design in 199 1. (a) Landform-earthwork model and trees. (b ) All builtform elements and trees; the land is excluded to enhance graphic clarity. Autocad model by the author and Dayana Salazar for the Center for Urban Design ( 199 1) , graphic manipulations by the author (1992).
testing of a series proposals.
of campus
planning
Sequence and time-dependent phenomena
It is a fact that, owing to the scale difference between humans and a city, most of city expe-
rience occurs at street level, making the most relevant portion of city buildings the immediate first floor street frontage (Gosling and Maitland, 1984). Moving around in the city reveals its greater structure only by degrees and as accumulated in memory. Views from high-
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rise buildings or elevated landforms can reveal some of the large plan intentions of the settlement: normally however, large-scale views will be limited to deep corridor perspective from a narrow straight street or skyline views from surrounding open land. It is for this reason that much of urban design concerns itself with street experience, either in the public realm of the streetscape or the private-realm edge of the buildings and their setbacks from the street. Similarly, the skyline, which is a common distant view in most cities, has considerable iconographic significance, particularly in high-rise districts, and is consequently a subject for design study. A complex spatial structure such as a skyline
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is best observed in motion, as is street experience. Though there may be one singular distinctive view, to be effective, three-dimensional iconography must be present in many views. Once assembled, computer models can be used to create sequential study within the computer space. The computer simply calculates the increments between any two spatial locations, and the result is an animated sequence. Figure 9 illustrates a skyline iconography study of the Cincinnati downtown. The images here represent the four extremes looking (from top to bottom) north, west, south, and east. The views are elevation projections rather than perspectives. The dotted lines show the pyramided height limits over the build-
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Fig. 9. Four elevation views of downtown Cincinnati showing building heights and locations ground landform. Views are looking: (a ) north, (b ) west, (c) south, and (d) east. Autocad
relative to zoning heights and backmodel by the author ( 1990- 1992 ).
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Fig. 10. Large simulation view of approach to downtown Cincinnati from the northwest. (a) Shows the building masses in isolation, illustrating the pyramid shape of the downtown core. (b) Shows building masses and the landform, showing the embracing nature of the land. Autocad model produced by the author and Kiril Stanislov at the Center for Urban Design ( 19901992). Graphic manipulation and animations by the author ( 1992).
ings, which when seen in this simultaneous view shows potentially under-utilized sites. Also observable is a potential for buildings to
infill sites up to the legal height, creating a kind of shelf threatening to negate the original pyramid intention of the height limits. Apparent
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Fig. 1I. Ink-traced frames of a computer film illustrating changes in downtown Cincinnati through time. (a) 1930; (b) 1940; (c) 1950; (d) 1960; (e) 1970; ( f) 1980; (g) I990-present. Film directed and animated by Frank Russell, Autocad models by the author, produced at Center for Urban Design ( 1992). Ink-tracing of frames by the author ( 1992).
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Fig. 12. Three views of a shadowfall study in downtown Cincinnati, from computer film. North is down and t o thee right. Views are (top to bottom): early morning, noon, and afternoon. Time of year is equinox. Computer film directed and animated by Fra nk Russell, Autocad models by the author, produced at the Center for Urban Design ( 1992). Images are dot. -matrix video pi-i1tts directly from computer tiles.
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in the background of these views is the embracing nature of the landform which surrounds the downtown tightly on three of its four sides. When one is approaching a city or moving around in it, the terrain forms the basis of the experience, with the city being only a modilication of the effect. The degree of modilication is dependent on the scale and pattern of the city structures (Hedman and Jazewski, 1984, p. 105). The use of computer animation combined with the decompositional capability for switching elements on and off can clarify which elements play the strongest modifying roles. A movement sequence can be observed as a composite simulation or just in terms of commercial signage or landform alone. Figure 10 shows a single frame of such a sequence of approach from the hills northwest of downtown Cincinnati. In the top view the city’s masses are seen in isolation, and in the bottom they are viewed in combination with the land to show its significance. Roads and expressways are turned off to accelerate computer calculation times. Visible grid lines on the land relate to the Jeffersonian control grid and the closest grids (flat land) have an increment of 1000 ft. In addition to movement, computer models can be used to observe other changes through time. Patterns of growth, land use, and distribution of population density can all be animated to observe patterns that may only become apparent in motion. This is analogous to using a movie camera with an intervalometer to accelerate imperceptibly slow events to visibility. Figure 11 shows a sequence of changes in vertical density in the core portion of Cincinnati’s downtown. The frames represent 1Oyear increments starting in 1930 and running to 1990, from top-left to middle-right to bottom-left. As with many cities, intense densification can be observed after 1970 with some of the largest floor-plate buildings appearing after 1980. To create the animation, demolished buildings were turned off in their correct
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year as replacement buildings were raised into place from below the model (Russell and Decker, 1992 ) . When combined with mathematical models, vector models can be used to observe and demonstrate various physical phenomena. These models can become highly calculation intensive and deviate significantly from reality if the descriptive mathematics involve too many variables. However, for observation of limited events in isolated circumstances, calculation driven models can provide extremely accurate representations. Figure 12 illustrates a shadow fall study produced for the same film that included the densification study above. The views shown are morning, noon and evening extremes on a single day. North is oriented down. The completed film showed the shadows dynamically sweeping from the base of the buildings through both solstices and the equinox. The model was used to further test shadow sweep from a number of high-rise proposals as well as existing structures. Unlike the other images, these are derived directly from the rendering created by the computer on video tape (Russell et al., 1992). At the present time, computer capability only allows frame-by-frame calculation of animations, and even though the process can be automated, sending a batch of frames to tape is still time intensive, requiring large calculations for the creation of each frame based on a complex model. Thus real-time or directly interactive experience of a simulation is still not a possibility. To be useful, tapes must be produced from the standpoint of consideration of as many possible conditions of observation required for any specific analytical study. Additional requirements or changes in viewpoint additional phases of generally require production. Conclusion Each of these studies was carried out within the framework and limits of a specific study or
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particular exercise, yet this is not the limit of their value. The power of computer simulation is that the activity of building models is accumulative, especially when many of the models are different portions of the same urban area. If consistent nomenclatural and scale control is applied to each exercise, the capability for combining all is only limited by the overhead limit of memory storage. The fact that the models are infinitely flexible and adjustable means that accuracy and detail can be continuously enhanced, as can simple updating to reflect a changing urban fabric. In my own research, I have begun to accumulate large portions of several urban areas and numerous detailed fragments of others. These pieces are extremely useful for comparative study as well as individual examination. They are consequently becoming a resource for teaching of urban design as well as for additional research into urban structure. The large database of Cincinnati that has been accumulated at the Center has been made generally available for public use and continues to grow. Looking towards the future, this database and others like it, will become powerful tools for urban management, particularly when combined with other thematic layering in systems having more intelligence or more complete inference engines. A goal of the Center is to continue to push the representational limits of simulation as well as its scale and dynamic capabilities. By combining other types of existing systems, we intend to produce hybrid systems capable of critical design analysis as well as projection of various urban processes. Ultimately intelligent systems will provide urban designers and managers, at the very least, with advanced and comprehensive instrumentation for controlling this complex, exciting, and often baffling entity which is the city. Acknowledgments
While I was on sabbatical, my Department Chair, Kenneth Pearlman, appointed me to co-
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chair the 34th Annual Conference of the Association of the Collegiate Schools of Planning (ACSP) along with Steven I. Gordon. Professor Gordon had missed the faculty meeting at which it was discussed. In planning the conference, Professor Gordon and I expressed an interest in having the call for papers allow for conference papers to be considered for a peerrefereed conference proceedings. Professor Pearlman supported the idea and took it to the ACSP executive committee, who rejected it. Shortly afterwards, Jon E. Rodiek, a co-editor of Landscape and Urban Planning contacted us with a offer to publish selected papers from the conference. Although Professor Gordon had lost interest, I felt a subset of papers on urban design research and environment-behavior studies might have some value. I contacted Professor Rodiek and he agreed to publish a peer-refereed proceedings in that area. When the call for papers for the conference went out, I sent out a special call for papers on environmental design research to be considered for publication in the special proceedings. Professor Gordon set up the database which allowed us to handle, process and organize all of the papers we received. Ellen Wallace handled the papers as they arrived, filed them, entered them on the database, sent acknowledgements, and worked with Professor Gordon and I on the schedule. The conference, which took place in Columbus, OH from 1 through 30 October 1992 owes much to her tireless efforts. With over 500 registrants and papers presented, the 34th ACSP had the largest attendance of any ACSP conference. After reading all abstracts and papers submitted and attending relevant sessions, I invited other presenters, whose work fit the theme, to submit papers. Each paper received a blind review by at least two reviewers. I thank the individuals who submitted papers, the reviewers, Richard Titus for the special role he played in handling the blind review of my paper, Ellen Wallace and Steven Gordon for their efforts up through the
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conference, and Kenneth Pearlman for getting me into this in the first place. The reviewers were: D. Amedeo, University of Nebraska; J. Carpenter, The Ohio State University; J. Decker, University of Cincinnati; M. Felson, University of Southern California; B. Fisher, University of Cincinnati; K. Foster, Pennsylvania State University; J. Gaber; University of Nebraska; L. Gerckens, The Ohio State University; R.V. George, University of Illinois, Champaign-Urbana; M. Grannis, The Ohio State University; D.L.A. Gordon, Harvard University; D. Gosling, University of Cincinnati; P.C. Gurstein, University of British Columbia; H.Heft, Dennison University; F. Hurand, Eastern Washington University; R.E. Lloyd, California State Polytechnic University, Pomona; R. Kaplan, University of Michigan; S. Kaplan, University of Michigan; M. Ilmonen, Helinski University of Technology; A. Jones, The Ohio State University; B. Jones, University of Colorado, Denver; A. Loukaitou-Sideris, University of California, Los Angeles; A.R. Madani Pour, University of Newcastle-Upon-Tyne; R. Marans, University of Michigan; J. Mayo, University of Kansas; M. Morrone, The Ohio State University; D.J. Nadenicek, Pennsylvania State University; S. Onal, University of Nottingham; P. Owens, University of California, Berkeley; F. Philips, The Ohio State University; J. Potter, The University of Nebraska; W.F.E. Preiser, University of Cincinnati; W.M. Robe, University of North Carolina, Chad Hill;
J. Decker/Landscape
and Urban Plannrng 26 (1993) 231-250
F. Russell, University of Cincinnati; F. Sancar, University of Wisconsin, Madison; R.I. Selby, University of Illinois, Champaign-Urbana; J. Simpson, The Ohio State University; G.L. Spragge, Queen’s University; R. Taylor, Temple University; E.E. Tillman, Morgan State University; R. M. Titus, National Institute of Justice; J.K. Zacharias, Concordia University. R.M. Titus selected reviewers and administered the blind reviews on the paper by J.L. Nasar, B. Fisher, and M. Grannis. References Appleyard, D., Lynch, K. and Myer, J., 197 1.The View from the Road. MIT Press, Cambridge, MA. Bacon, E.N., 1967. Design of Cities. Viking Press, New York. Branch, M.C., 1988. Comprehensive City Planning. APA Press, Washington DC. Gandelsonas, M., 1991. The Urban Text. MIT Press, Cambridge, MA. Gosling, D. and Maitland, B., 1984. Conceits of Urban Design. Academy Editions, St. Martin’s Press, New York. Harvey, D., 1985. Consciousness and the Urban Experience. Johns Hopkins University Press, Baltimore, MD. Hedman, R. and Jazewski, A., 1984. Perception of the City. APA Press, Washington DC. Lynch, K., 1960. The Image of the City. MIT Press, Cambridge, MA. McHarg, I.L., 1969. Design with Nature. Natural History Press, Garden City, New York. Norberg-Schultz, C., 1980. Genius Loci. Rizzoli Press, New York. Rossi, A., 197 1.Architettura della Citta. Milan. Russell, F. and Decker, J., 1992. ‘Fountain Square West’ video. The Center for Urban Design, Cincinnati, OH. Shay, D., 1982. Blade Runner-2020 Foresight. Cinefex J. Cinematic Illusions, 9. Trancik, R., 1986. Finding Lost Space: Theories of Urban Design. Van Nostrand Reinhold, New York.