Extended Abstract
Introduction
Although almost everyone acknowledges the importance of “scale” in scientific research, over the past few decades, our understanding of the concept itself, its impact on our analyses, and how we use it have faced significant fundamental challenges. These challenges have provided the basis for some debates and questions in this field; including whether scale is an ontological and epistemological tool or merely a methodological tool? Is it a material feature of the world or a mental feature of humans? And whether we fundamentally need scale or not?
Such discussions and questions have led the discussion of scale to move beyond classical perspectives and concerns, such as scale errors and observer biases, the problem of the modifiable area unit problem (MAUP), and finding the optimal or ineffective scale (Openshaw, 1984), and move towards complex discussions of scale as a dynamic parameter. In other words, scale effect analysis no longer seeks to simply identify statistical biases resulting from changes in administrative boundaries or unit sizes (with the epistemological assumption that phenomena have a fixed and unified nature and that changing the scale only changes the “lens” of observation), but rather seeks to understand the nature of the “phenomenological transformation” of phenomena under the influence of different scales. On this basis, the more important question is not whether scale affects spatial analyses (or not), but how changing scale redefines and redraws the nature of the phenomenon itself, its phenomenological structure, epistemological validity, and the cause-effect relationships between phenomena.
Moreover, today, with the advent of “big data,” “agent-based and cell-based modeling,” and a deeper understanding of “complex systems,” the paradigm of scale as a tool or lens seems to have faced a fundamental impasse. Empirical evidences and theoretical arguments show that changing scale does not simply lead to a change in the precision of observation, but also to a “phenomenological transformation” of the nature of the phenomenon. Hence, the main issue will be that in many social, spatial and ecological systems, phenomena at different scales exhibit different qualitative and structural properties, which are defined not as “errors” but as “emergent properties.” In other words, when we move from the microscale (individual/unit) to the macroscale (collective, structure), we are faced with a new spatial entity whose governing rules are fundamentally different from those at lower scales (Batty, 2013).
In light of the latest findings in the field of scale over the past decades, in the six sections: "semantics", "typology", "metaphorization", "theorization", "methodology" and "synthesis", this article attempts to explain why it is necessary to move beyond "classical" approaches to scale and how we can seek to develop an understanding of scale in which scale is considered not merely as a tool, lens or interfering variable in modeling, but rather as a structural and phenomenological aspect of complex spatial systems. Based on the research results and findings of the most important theorists in this field, it will be argued that in order to achieve a true understanding of geographical phenomena, we must move from static, simple, and linear scale analysis to dynamic, complex, and nonlinear scale analysis; where our geographical knowledge is derived not from the truth of the phenomenon at a given scale, but from the logic of the phenomenological transition of that phenomenon between multiple scales.
Materials and Methods
The methodology of the present study is a “documentary review” and is based on “content analysis” or “thematic analysis”. By this method, in the first step, key studies related to the topic and problem (in global and Iran level) were collected; in the second step, the main contents and themes in them were extracted and compiled in the form of “semantics”, “typology”, “metaphorical”, and “theoretical” categories. In the third step, the implications, effects, and consequences of each of the above aspects were analyzed in modern spatial analyses. Finally, the results of the content analysis of the research were presented with reference to the themes addressed in the article.
Results and Discussion
The results of this article show that recent analyses of scale effects have moved beyond the perspective of “correcting scale error” and toward “understanding the phenomenological structure of scale.” In other words, scale is no longer a nuisance variable that needs to be rectified, but is central to understanding the complexities of complex, nonlinear, and emergent systems. For example, recent analyses, combining machine learning, agent-based modeling, and graph theory, attempt to reconstruct the connections between different scales, phenomenological structures, and processes of the production of space. The ultimate goal here is to arrive at a model that can understand the variation in the quality of phenomena across scales not as an error, but as an intrinsic and fundamental feature of the spatial aspect of the world.
The classical paradigm's concern with scale as a measurement error or statistical bias was based on the assumption that a phenomenon (for example, crime rates or population density) has an eternal truth at different scales, and that changing the boundaries or size of the units only makes us see that truth as true or false. From this perspective, the goal was to find the scale that had the least error. But new analyses challenge this naïve realism. These analyses are based on the assumption that phenomena are not independent of scale; when the scale changes, we do not just change our perspective, but we also change the “operational space” of the phenomenon. For example, a phenomenon is defined at the microscale (individual) by psychological and biological rules, at the mesoscale (neighborhood) by social rules, and at the macroscale (nation) by political-economic rules. Hence, scale is not the lens of the phenomenon, but rather the “constitutive” of the phenomenon.
The constitutive nature of scale causes a kind of phenomenological transformation in the phenomenon, in which the nature of what is experienced or observed, will differ not only quantitatively but also qualitatively at each scale. For example, at the micro scale, the phenomenon may be an “event” or individual behavior, but as the scale increases, different events come together and create a “trend” or structure. This trend or structure is something more than the sum of the events; it is an emergent property. Hence, the phenomenon at the micro scale will be ontologically different from the phenomenon at the macro scale.
This result also shows that scale is not a peripheral parameter, but a condition for the possibility of the existence of a phenomenon. In classical philosophy, it was assumed that the phenomenon or object has a single, fixed and independent existence and that scale is only a method of observing it. However, the above analyses claim that the phenomenon is an emergent entity at any scale. On the other hand, the phenomenon manifests a different “self” at different scales. When we change the scale, we actually change the existential and ontological structure of the phenomenon. On this basis, phenomena have multiple, scale-based entities. (For example, a “city” is existentially and ontologically different from a “collection of houses,” a city is an entity that makes sense on a macro scale.) So the truth of a phenomenon lies not in a specific point or scale, but in the spectrum of its scale transitions.
Conclusion
The final conclusion of this article for the analyst of space is the need to go beyond static, linear, and single-scale modeling and move towards dynamic, nonlinear, and multi-scale modeling. For the expert of the spatial analysis, scale is not just a method and tool of observation, but an integral part of the ontological structure that constitutes the nature of the phenomenon, and our knowledge of the phenomenon is always a relational act in which truth is the product of interaction between different existential domains. Another consequence of such a conclusion, as the theorist of space Doreen Massey (2005) repeatedly reminded us, would be that the researcher and analyst of space needs to learn that spatial phenomena find meaning not in points but in interactions and relationships between scales. Hence, an effective spatial analysis is an analysis of how different scales interact with each other, where micro-laws collide with macro-structures and new phenomena are born and brought into being from these encounters.