Dynamic Patterns

Visualizing Landscapes in a Digital Age

Karen M'Closkey, Keith VanDerSys

• Éditeur: Routledge
• Année: 2017
• ISBN:
  9781317401414
• Édition: 1
• Sujet: Architecture
• Sous-sujet: Urban Planning & Landscaping

Dynamic Patterns explores the role of patterns in designed landscapes. Patterns are inherently relational, and the search for and the creation of patterns are endemic to many scientific and artistic endeavors. Recent advances in optical tools, sensors, and computing have expanded our understanding of patterns as a link between natural and cultural realms.

Looking beyond the surface manifestation of pattern, M'Closkey and VanDerSys delve into a multifaceted examination that explores new avenues for engagement with patterns using digital media. Examining the theoretical implications of pattern-making, they probe the potential of patterns to conjoin landscape's utilitarian and aesthetic functions.

With full color throughout and over one hundred and twenty images, Dynamic Patterns utilizes work from a wide range of artists and designers to demonstrate how novel modes of visualization have facilitated new ways of seeing patterns and therefore of understanding and designing landscapes.

Topological Patterns

1. Photomicrography of fossilized stromatolite.

2. Satellite radar image of Keraf Suture, Sudan, 2009.
Buried beneath layers of sand, this Precambrian geologic feature was recently unearthed through new imaging technology.

A topological description of a pattern is concerned not with exact values of distances and angles but rather with the number of connections.¹

—A. L. Loeb

The pervasiveness of data and sensing technologies has led to an increased ability to visualize the physical processes that give rise to identifiable patterns in landscapes. Both visible and invisible characteristics are catalogued and translated into pixels, points, and lines so that the description of a terrain is no longer limited to mapping its spatial structure or perceptible features alone; rather, the description also represents its topological properties, which chart relationships among a variety of processes that flow through the landscape.² This chapter delineates two kinds of topological patterns: divisible and accretive. Although the two are not mutually exclusive, divisible patterns define the spatial structure of a surface, such as its topography, whereas accretive patterns simulate processes upon or near that surface, such as water flow. In computational models, both divisible and accretive patterns are shaped as much by data as by the direct manipulation of geometries. That is, many of the patterns described in this chapter are indirectly structured and numerically informed as much as they are directly drawn. They are generated by algorithms in which one or more constraints, such as slope or distance, govern their entire shape and organization. Consequently, patterns are imbued with quantitative information in ways that were not previously possible. As both “datascapes” and landscapes, these patterns are inseparable from the virtual and physical milieus from which they arise.³

Typology (Things) and Topology (Relations)

The study and creation of patterns involves the transfer of organized information from one medium to another.⁴ This transfer is aided by procedure-based computer modeling, which facilitates recursive design methods. Recursion is embedded in both computational techniques, where sequences of operations are used as feedback loops, and the resultant forms, i.e., patterns. Recursion provided the foundation of Gregory Bateson’s ecological epistemology; he believed that, since the development of the universe and of life is recursive, our methods and models should also be recursive so that they will best reflect how communication happens. In the natural world, for example, form is a process of differentiation that occurs incrementally during an organism’s development instead of being established from the outset.⁵ Differentiation takes place through the relationships among the various parts in conjunction with the external environmental forces that affect these relationships. Although the various stages of development share common characteristics, the differences among them are most significant, since they produce diversity of form. Thus, form is an embodiment of difference. As architect Stan Allen summarizes, “Differences of configuration, pattern, or shape make sense only when put into play within a larger field of differences. Change is redefined as difference overtime, and all form becomes relational, based on interval and change.”⁶

3. Joshua Freese and Jieping Wang, 2016, Various spiral patterns traced through different step functions. In each sample, the structure of the model remains constant,

4. Spiral pattern of sunflower.

5. MIT Media Lab, Illuminating Clay, 2004.
This technology uses open-source geospatial information to generate real-time analysis of changes made to a terrain. These models combine the physical media of clay with computational imagery. Physical changes made to the clay are captured, analyzed, and projected back to the model’s surface.

Using morphogenetic processes in nature as an inspiration for thinking about computational techniques in design, patterns can be understood as relational and dynamic ways of organizing rather than static ones.⁷ As Bateson explains:

We have been trained to think of patterns, with the exception of those of music, as fixed affairs. It is easier and lazier that way but, of course, all nonsense. In truth, the right way to begin to think about the pattern which connects is to think of it as primarily … a dance of interacting parts and only secondarily pegged down by various sorts of physical limits.⁸

Computational models should have particular relevance for landscape architecture, given the temporal and relational qualities inherent in the landscape medium. Even though much has been written about the importance of determining better ways to engage such qualities, there has been little change in the analytical or representational techniques that should accompany such a shift. The media used for design have changed profoundly in the last fifteen years, yet few landscape architects have taken up the challenge of investigating the potentials and limitations associated with such changes. Apart from spatial analytics and GIS, digital media used in landscape architecture have remained largely within the realm of two-dimensional explorations that replicate manual drawing techniques, such as mapping and montage.⁹ Even vector-based GIS software utilizes pre-classified, two-dimensional geometric entities. This overreliance on two-dimensional geometry and raster-based image making reinforces typological thinking because the supporting design methods are based on simple classifications, such as layering previously established shape files or images. The intention behind, and the consequence of, classification is often replication. This results in the reproduction of recognizable landscape types, such as wetlands, and the uncritical transfer of elements from one place to another without significant alteration or correlation with their specific circumstances. The implicit assumption is that, if a landscape is drawn to look similar to a type, it will behave accordingly. This limits pattern to its common association as “a form or mode proposed for imitation.”¹⁰ In contrast, understanding patterns topologically, through three-dimensional computational models, liberates them from this restricted definition

Topology is the mathematical study of shapes and spaces that retain properties of connectedness while undergoing continuous deformation, such as curving and bending Topological patterns, therefore, comprise an ordered array of such shapes and spaces in which all entities combine to create a network. The etymology of topology derives from the Greek topos, “place,” and logos, “speaking, discourse, treatise, doctrine, theory, science.”¹¹ In Latin, the term was analysis situs, which emphasizes the study of a situation.¹² In the late nineteenth to early twentieth century, topology became identified within a branch of mathematics as the study of continuity.¹³ In contrast to Euclidean geometry, topology describes relationships among entities that remain unaltered through change. Topological surfaces are formed by interconnected points and vectors rather than by discrete coordinates, and by calculus and differential geometry (the study of change) rather than by fixed geometric shapes (the study of constants). In topology, metrics like length or area are not stable, yet the connectivity among elements is preserved. For instance, the points in a bent lattice will have different locations and distances between them (variances) yet retain an equal degree of connection (invariance).¹⁴ Given that landscape design involves the manipulation of interrelated surfaces and materials that are bound to physical localities yet open to environmental influences, topology is an apt framework for thinking about and working with landscapes.¹⁵

6. A. L. Loeb, 1971. The top ...