Algorithmic Landscapes and the Logic of the Collection

Abstract

Technologies such as cellular automata, genetic algorithms, and artificial intelligence have greatly expanded the range of possible architectural and urban solutions. The challenge is no longer only how to generate form, but how to interpret, evaluate, and select among multiple outcomes produced within computationally open-ended systems. When such generated alternatives are understood as collections, selection can be approached as a curatorial act. This article introduces the concept of algo-scapes: potentially extensive algorithmic landscapes whose meaning emerges only through processes of human discernment, comparison, and choice. To examine the broader logic of such selection, the study adopts a two-phase comparative design based on two parallel surveys. In the first phase, responses from 32 collectors of material objects are used to examine how collections are expanded and evaluated under conditions of differentiation, coherence, and spatial limitation. In the second phase, responses from 50 architects, urban planners, and interior designers are analyzed in order to determine whether analogous patterns can be identified in the ways spatial systems are developed, modified, and brought to temporary states of completion. The findings suggest that the strongest similarity between the two domains lies not in identical criteria of evaluation, but in a shared structure of selective growth based on addition, differentiation, limitation, and temporary completeness. While collectors place greater emphasis on uniqueness, aesthetic value, and personal attachment, architects, urban planners, and interior designers prioritize systemic fit, improvement, and contextual coherence. The article argues that the logic of collection can serve as a useful interpretive model for understanding architectural, urban, and algorithmic design systems, provided that it is understood structurally rather than literally. In this sense, algo-scapes are not meaningful simply because they can generate many alternatives, but because they require meta-level criteria through which meaningful configurations can be selected from potentially open-ended fields of possibility.

1. Introduction

In recent decades, global cities have increasingly adopted architecture as a strategic instrument of urban development, cultural positioning, and economic branding. Within this context, the commissioning of buildings designed by internationally recognized “starchitects” has become a recurrent mechanism through which cities seek to enhance visibility, distinction, and symbolic capital [1]. Although cities do not literally collect such works, the selective pursuit of high-profile architectural authorship resembles a curatorial logic in which architecture operates as a form of cultural currency. This tendency is embedded in the broader condition of inter-urban competition, in which cities act within a global marketplace of investment, tourism, talent, and prestige. Signature buildings—especially museums, cultural centers, infrastructural hubs, and mixed-use complexes—function as highly legible markers of ambition and identity. The widely cited example of the Guggenheim Museum in Bilbao demonstrated the capacity of iconic architecture to contribute to the rebranding of post-industrial landscapes, stimulate urban regeneration, and reshape international perception. Beyond branding itself [2], the attraction of starchitects also reflects the desire of political and cultural institutions to accumulate symbolic capital and assert relevance within broader cultural networks [3].

By embedding architecturally distinctive objects into the urban fabric, cities effectively assemble portfolios of visual and cultural assets that support narratives of modernity, innovation, and cosmopolitan identity. In this sense, the built environment may be understood not only as an expression of civic ambition, but also as the result of selective processes through which certain forms, authorships, and spatial interventions are privileged over others. This perspective opens a broader question that exceeds city branding alone: according to what logic are certain additions incorporated into a larger whole, while others remain excluded? This question becomes even more relevant in the context of computational design. Technologies such as cellular automata, genetic algorithms, and artificial intelligence have dramatically expanded the field of possible architectural and urban solutions [4]. Under such conditions, the central problem is no longer only the generation of form, but also the establishment of criteria through which alternatives can be evaluated, compared, and selected. If computational outputs are understood as potentially open-ended landscapes of possibilities, then choosing among them begins to resemble the logic of collection: not accumulation alone, but selective growth governed by relations of coherence, differentiation, limitation, and temporary completeness. In this sense, algorithmic design does not merely produce options; it intensifies the need for explicit frameworks of selection. The present article approaches this problem through the concept of algo-scapes: potentially extensive algorithmic landscapes whose meaning does not arise automatically from computational generation, but through processes of human discernment, comparison, and choice. Rather than treating the collection merely as a metaphor, the article asks whether the logic of collecting can function as an interpretive model for understanding how spatial systems are incrementally assembled and transformed. The aim is not to claim that collections and architectural–urban systems are identical, but to test whether they share a comparable structure of selective growth.

To address this question, the study adopts a two-phase comparative design. In the first phase, it examines how collectors of material objects select, expand, and evaluate collections under conditions of differentiation, coherence, and spatial limitation. In the second phase, it investigates whether analogous patterns can be identified in the ways architects, urban planners, and interior designers approach the development, modification, and completion of spatial systems. By comparing these two domains, the article seeks to clarify whether the collection can be understood as a structural model of selection that helps explain not only material accumulation, but also spatial development under conditions of finitude.

Research Questions and Hypotheses

This article examines whether the logic of collecting can serve as a meaningful interpretive model for understanding how architectural and urban systems are incrementally assembled, expanded, and transformed. More specifically, it asks whether material collections and spatial systems share comparable principles of selection, differentiation, limitation, and temporary completeness. The main research question is: Can the logic governing the formation and expansion of material collections be meaningfully recognized in the logic through which architectural and urban systems are developed? This question is supported by three subsidiary questions: Which criteria most strongly shape the selection of a new element in a collection or spatial system? Is the introduction of new elements primarily driven by continuity and controlled differentiation, or by radical novelty? What role does spatial capacity play in shaping decisions about growth, modification, and perceived completeness?

The study tests four hypotheses:

Hypothesis 1 (H1). 

In both groups, new elements will be selected primarily according to their contribution to the coherence of the collection or system.

Hypothesis 2 (H2). 

In both groups, controlled differentiation will be preferred more frequently than either simple repetition or radical novelty.

Hypothesis 3 (H3). 

Spatial capacity will significantly influence decisions regarding the introduction, modification, and configuration of elements in both domains.

Hypothesis 4 (H4). 

Collectors and architects, urban planners, and interior designers will differ in the relative weight they assign to individual selection criteria, with collectors placing greater emphasis on uniqueness, future value, and personal attachment, and spatial practitioners placing greater emphasis on systemic fit, functional justification, and contextual coherence.

Together, these questions and hypotheses position the article as an inquiry into the possible structural analogy between collecting and spatial development, with particular attention to how selective growth operates under conditions of spatial finitude.

2. Theoretical Background

This section develops a theoretical framework for examining how urban environments are shaped through processes of selection and differentiation. It distinguishes analytically separate but interrelated levels at which these processes operate. Urban development is approached as a multi-layered process in which material conditions, representational practices, and evaluative decisions shape the city’s composition and transformation. Attention is given to the city’s finitude as a bounded spatial system, the construction and communication of urban identity, and the criteria governing selection, prioritization, and exclusion of elements. The logic of collecting is introduced not as a mere analogy, but as an explanatory model for urban development, based on the assumption that both cities and collections operate through bounded selection and structured inclusion. This raises the question of whether urban growth and transformation may similarly be governed by logics of controlled addition within constraints.

2.1. City as a Finite Assemblage

When cities are conceptualized as collections, urban theory offers a well-established framework for understanding them as materially finite and selectively curated assemblages. Aldo Rossi’s notion of the city as a repository of collective memory emphasizes the persistence of material artifacts within a limited urban fabric, where permanence and transformation coexist through processes of accumulation and replacement [5]. Similarly, Walter Benjamin’s reading of the city as an archive—composed of fragments, remnants, and historical layers—aligns with the idea of urban space as a palimpsest shaped by continuous acts of selection and erasure [6]. Kevin Lynch further underscores the constraints of urban space by addressing the cognitive and spatial limits through which cities are structured, perceived, and organized [7]. In addition, Rem Koolhaas’s reflections on congestion and accumulation reveal how density intensifies the necessity of choices within finite spatial conditions rather than eliminating them [8]. Viewed through these theoretical lenses, the city operates as a materially bounded collection, comparable to other forms of material assemblage, and fundamentally distinct from digital space, which is often theorized as potentially limitless. This distinction reinforces the relevance of analytical frameworks grounded in spatial finitude, while positioning the investigation of digital spatial abundance as a subject for future research. Within this framework, finitude is not only a spatial condition but also a principle that shapes how urban elements are continuously re-evaluated and reconfigured over time. This opens up the question of how selection operates in practice when cities are actively produced and represented through contemporary planning and cultural strategies. In this sense, processes of differentiation and emphasis become central to the way urban meaning and form are articulated.

2.2. City Branding, Iconicity and Placemaking

The concept of the city as a collection may first be understood through the framework of city branding, as the construction of place identity emerges through a sequence of decisions made by various actors involved in governance and urban development [9]. Within contemporary global economic flows, the differentiation of individuals and spaces has become increasingly imperative, positioning branding as a central mechanism of transformation. According to Klingmann [10], the focus shifts away from objects themselves and their intrinsic values toward the effects they produce on people and their surrounding environments. In this context, architecture undergoes a fundamental transformation: rather than merely designing static forms, it becomes the conscious production of contexts in which collective desires and self-awareness intersect. This shift marks a departure from the traditional understanding of architecture as an aesthetic style and redefines it through the lens of lived experience. It is within this transition from form to experience that the internal logic of the collection can be identified. The discourse surrounding place branding frequently appears through the concept of Competitive Identity, which, according to Anholt [11], denotes the integration of branding strategies with public diplomacy and the active promotion of trade, investment, tourism, and exports. Within this framework, the cultural and artistic sphere emerges as a key factor in reshaping the perception of the city. As an indispensable component of urban planning, culture directly influences local identity, economic development, social cohesion, ultimately contributing to a higher quality of life within urban environments [12].

Particularly significant is the emergence of the progressive wave of city branding that has developed since 2010. This approach is grounded in the principle of “co-creation,” through which numerous actors collectively shape the image of the city [13]. This phenomenon manifests through networks of emotional relationships that individuals and groups establish with localities, while time becomes the crucial factor in its formation. Sense of place functions as both a personal and collective construct, accumulating within communities over decades and being transmitted across generations [14]. Rather than representing a static image of the past, it evolves alongside broader societal changes [15]. When considered through the lens of co-creation, city branding begins to resemble the logic of a collection, as it presupposes a shared vision of the desired urban image.

Objects interpreted as components of urban collections are often described as iconic. Architectural iconicity combines public recognizability, symbolic meaning, and aesthetic value [16]. However, Sklair [17] questions what constitutes an iconic object in the contemporary context, particularly as digital media have shifted evaluations of iconicity beyond professional circles into wider public discourse. Simultaneously, globalization has encouraged the transformation of urban environments and the creation of new spatial narratives, especially in cities where public spaces increasingly function as spaces of consumption [18]. Within this context, placemaking has emerged as a holistic approach to creating meaningful places [19]. Although the concept continues to evolve and requires stronger theoretical foundations [20], it functions as a tool of the urban imaginary that reshapes perceptions of the city while influencing everyday life [21,22]. This research therefore investigates whether city branding, and placemaking in particular, operate according to an identifiable logic. Since previous studies associate both concepts with predefined notions of urban character and emphasize the complexity of urban relationships [23], the logic of collecting is examined as a way of identifying underlying mechanisms at a smaller scale.

2.3. Collecting as a Logic of Selection

A collection should not be understood as a mere accumulation of objects. Rather, it constitutes a structured and reflexive sequence of decisions through which objects are selected, differentiated, valued, and integrated into a coherent whole. Contemporary scholarship on collecting emphasizes that collections emerge through processes of ordering and classification that establish relationships among objects and define conceptual boundaries of the assemblage [24]. In this sense, a collection is not characterized by quantity but by the logic of inclusion and exclusion that governs its formation. The distinction between a collection and an undifferentiated aggregation is epistemic. Collecting entails the attribution of meaning and the establishment of criteria through which objects acquire relevance within an interpretive framework [25]. As museum and collection studies have demonstrated, collections are produced through selection, documentation, categorization, and contextualization that transform objects into components of a larger system of knowledge [26,27]. The value of a collection resides in the relationships curated between its items [28]. Furthermore, collections require boundaries. Decisions regarding what belongs to a collection and what does not are integral to its identity and the construction of the so-called “ideal collector self” [29,30]. Without such boundaries, collecting risks devolving into indiscriminate accumulation. As Kilroy-Marac [31] argues, organization, differentiation, and coherent ordering separate meaningful collecting from mere possession.

Selection is never arbitrary; it is guided by criteria that define the identity and purpose of a collection [32]. These criteria may be historical, aesthetic, scientific, thematic, functional, or provenance-based [33,34,35]. What distinguishes a collection from indiscriminate accumulation is the existence of evaluative frameworks that establish which objects are relevant or irrelevant. Selection criteria determine what enters a collection and shape interpretive relationships between its constituent objects, producing coherence and meaning. Museum scholars have shown that collection development policies translate institutional values and knowledge priorities into acquisition and deaccessioning practices [27,36]. Consequently, collections should be understood not as neutral repositories but as material manifestations of intellectual, cultural, and value-based choices. Through selection criteria, collectors construct distinctions, define significance, and establish the conceptual architecture that transforms objects into an integrated whole [31]. A collection, therefore, is best understood as a curated structure of value and meaning: an ordered assemblage generated through continuous acts of choice, differentiation, and boundary-making that together produce an integrated and intelligible whole.

2.4. From Collecting to Spatial Systems

The purpose of this paper is not to conceptualize the city as a collection in a literal or ontological sense. Instead, it examines whether the processes through which urban identities are formed and urban environments evolve exhibit structural similarities to the logic of collecting. In this framework, the city is approached not as a collection itself, but as a socio-spatial assemblage whose development is shaped by selective processes of valuation, differentiation, preservation, and exclusion [31,37,38]. This perspective is particularly relevant in the context of urban branding and placemaking. Contemporary cities increasingly compete for investment, tourism, talent, and cultural visibility through the strategic construction of distinctive identities [39]. Such identities are not inherent attributes of places but are actively produced through selective representations of urban space [40]. Architectural landmarks, historic districts, waterfront redevelopments, cultural institutions, and public spaces are frequently mobilized as emblematic elements that communicate a desired image of the city, while other aspects of the urban environment remain less visible within official narratives [41]. In this sense, city-making involves processes of differentiation and selection analogous to those observed in the formation of collections.

The analogy becomes particularly evident when considering how urban identity is assembled through the attribution of significance. Just as objects acquire value through their relationship to a collection, buildings, districts, and urban spaces derive symbolic importance through their integration into broader narratives of place [42]. Urban branding and placemaking can therefore be understood as curatorial practices that organize spatial elements into coherent frameworks of meaning. The resulting urban identity is not simply discovered but selectively constructed through choices regarding representation, preservation, investment, and visibility [43]. From this perspective, the question is not whether cities are collections, but whether the processes through which urban identities are produced exhibit a logic comparable to that of collecting: a continuous practice of selection through which coherence, distinction, and value are generated at the scale of the city.

3. Materials and Methods

This study employed a two-phase comparative survey design in order to examine whether the logic of collecting can serve as an interpretive model for understanding the incremental formation and transformation of architectural and urban systems. The two questionnaires [44] were developed as parallel survey instruments rather than identical forms. Construct comparability was addressed through author-led expert review and analytical alignment of items during questionnaire design, so that domain-specific wording could be adapted to collectors and spatial practitioners while preserving the same underlying analytical dimensions. Accordingly, concepts such as coherence, novelty, controlled differentiation, spatial limitation, and completeness were operationalized through corresponding sets of items designed to capture analogous evaluative processes in the two groups. The comparative analysis therefore rests on conceptual and analytical correspondence, rather than on strict item-level identity. The first questionnaire targeted collectors of material objects and focused on the logic of collection growth, selection criteria, spatial constraints, and perceptions of completeness. The second targeted architects, urban planners, and interior designers and examined analogous dimensions in relation to spatial systems, groups of buildings, and urban interventions. Although the wording of some items was adapted to the specific domain, the overall structure of the two questionnaires was kept parallel in order to enable comparison between collecting practices and architectural–urban decision-making. Both instruments included sections on growth strategy, the degree of differentiation of new elements, criteria of selection, dynamics of replacement and change, the role of spatial capacity, and the possibility of completeness or further branching into subcollections or subsystems.

Data were collected through purposive sampling between 15 April 2026 and 25 April 2026. The survey was conducted anonymously, with informed consent presented at the beginning of the questionnaire [44]. Participants were invited directly via email on the basis of prior knowledge that they belonged to one of the two target groups: collectors of physical objects, or professionals working in architecture, urbanism, or interior design. This sampling strategy was adopted because the study did not seek to capture general public opinion, but rather to examine decision-making patterns among respondents with relevant practical experience in collection-building or spatial-system development. The achieved sample comprised 32 collectors of material objects and 50 architects, urban planners, and interior designers. Because the two groups differ in size and because some corresponding questions were phrased in domain-specific terms, the comparative analysis relied on normalization through within-group percentages and analytically matched categories rather than on direct comparison of raw frequencies alone.

A clear distinction was made between material collections and digital collections. Unlike physical collections, digital collections do not require the same direct storage and display capacity and are shaped by different conditions of accessibility, reproducibility, and platform dependency. Since the present study focuses specifically on spatial limitation, accumulation, and selective expansion under materially finite conditions, the empirical sample was intentionally restricted to collectors of physical objects. This methodological distinction is also supported by the collectors’ responses concerning the growth of storage and display space over time. As shown in Figure 1, the reported increase in available physical space over one-, five-, and ten-year periods confirms that material collections are directly conditioned by spatial capacity in a way that digital collections are not. The comparison with digital collecting practices is therefore deferred to future research.

The key conceptual dimensions of the study—coherence, novelty, controlled differentiation, spatial limitation, and completeness—were operationalized through corresponding sets of survey questions adapted to the two respondent groups. For transparency, the full questionnaires are available via the Figshare repository [44], and

Table 1. Hypotheses, corresponding survey items, analytical variables, and statistical procedures. Note: The category spatial practitioners included architects, urban planners, and interior designers. Q18 and Q19 were retained for within-group interpretation only, since these items are not directly equivalent across the two questionnaires.

Hypothesis	Substantive Focus	Collectors Items	Spatial Practitioners Items	Variables/Indices	Statistical Procedure
H1. In both groups, new elements will be selected primarily according to their contribution to the coherence of the collection or system.	Priority of coherence over novelty	Q9, Q13, Q16 vs. Q11, Q12, Q15, Q17; Q18 and Q19 used only for within-group interpretation	Q9, Q13, Q16 vs. Q11, Q12, Q15, Q17; Q18 and Q19 used only for within-group interpretation	Coherence index = mean (Q9, Q13, Q16); Novelty index = mean (Q11, Q12, Q15)	Descriptive statistics; Cronbach’s alpha; Wilcoxon signed-rank test or paired-samples t-test
H2. In both groups, controlled differentiation will be preferred more frequently than either simple repetition or radical novelty.	Preference for moderate difference	Q5, Q6; supplementary Q4, Q22	Q5, Q6; supplementary Q4, Q22	Categorical preference for degree of difference; reported percentage of difference	Frequencies and percentages; chi-square goodness-of-fit; Kruskal–Wallis test or ANOVA; chi-square test of independence
H3. Spatial capacity will significantly influence decisions regarding the introduction, modification, and configuration of elements in both domains.	Effect of spatial limitation and capacity	Q25–Q31; supplementary Q32–Q34	Q25–Q29; supplementary Q30–Q32	Space influence variables; perceived limiting role of space; effect of increased space; abandonment due to space; percentage impact on configuration	Spearman correlations; chi-square tests; ordinal logistic regression and/or linear regression where appropriate
H4. Collectors and spatial practitioners will differ in the relative weight they assign to individual selection criteria.	Between-group differences in emphasis	Q9–Q17; Q18–Q19 only for within-group interpretation	Q9–Q17; Q18–Q19 only for within-group interpretation	Corresponding criterion scores; coherence and novelty indices; preference type from Q5	Mann–Whitney U tests or independent-samples t-tests; chi-square test of independence; comparison of composite indices

summarizes how these item groups relate to the hypotheses, analytical variables, and statistical procedures used in the study.

The collectors’ questionnaire included items concerning the type and size of the collection, growth strategy, preference for continuity or novelty when adding new elements, the relative importance of specific selection criteria, the dynamics of replacement and restructuring, the influence of spatial capacity, and the perceived completeness of the collection. The questionnaire for spatial practitioners followed the same overall structure, but adapted the wording of items to the domain of spatial practice, addressing the development of spatial systems, typological fit, functional justification, contextual coherence, spatial limitation, and system completion. In both cases, most closed-ended items used ordinal response formats, including categorical multiple-choice items and five-point importance scales. Each questionnaire also included open-ended questions inviting respondents to explain what completeness meant in their domain and to provide a concrete example of introducing a new element into a collection or system.

Before comparison, the survey data were normalized to account for differences in sample size and question formulation between the two groups. Categorical responses were therefore analyzed as percentages within each group, while corresponding domain-specific answers were mapped onto shared analytical categories. Likert-scale items were compared through descriptive statistics, and open-ended responses were coded thematically in order to support the interpretation of the quantitative findings.

The statistical analysis was conducted in three steps. First, descriptive statistics were used to summarize the structure of both samples and the distribution of responses across questionnaire items. Second, the internal consistency of the block of selection criteria was assessed using Cronbach’s alpha, while composite indices were constructed to compare coherence-related and novelty-related orientations. Third, the hypotheses were tested through a combination of within-group and between-group analyses. Hypotheses concerning the relative importance of coherence-related versus novelty-related criteria were examined by comparing composite indices within each group. Hypotheses regarding controlled differentiation were tested through frequency distributions, goodness-of-fit tests, and comparisons between categorical preferences and reported percentages of difference. Hypotheses concerning spatial capacity were examined through correlations and, where appropriate, regression models. Finally, between-group differences were assessed through comparative analyses of corresponding questionnaire items and composite indices.

Given the ordinal nature of most questionnaire items and the possibility of non-normal distributions, non-parametric procedures were treated as the primary analytical framework. Accordingly, Wilcoxon signed-rank tests [45] were used for within-group comparisons, Mann–Whitney U tests [45] for between-group comparisons, chi-square tests [46] for categorical distributions, and Spearman correlation coefficients [45] for examining relationships involving spatial capacity. Parametric alternatives were considered only where distributional assumptions were acceptable. Open-ended responses were not included in the formal statistical testing of hypotheses, but were analyzed qualitatively in order to contextualize the quantitative findings and identify recurrent explanatory patterns concerning completeness, replacement, and the introduction of new elements. Figure 2 illustrates a simplified schematic overview of the research procedure.

4. Results

The results are presented in accordance with the comparative analytical sequence defined in the methodological framework, moving from descriptive normalization to hypothesis testing and qualitative interpretation. The analysis first provides a descriptive overview of the two survey datasets, with particular attention to the normalization of response categories and the use of percentages within each respondent group, since the two samples differ in size. This step enables the initial Google Forms outputs to be transformed from separate descriptive summaries into comparable analytical indicators.

The presentation then proceeds from descriptive distributions to statistical interpretation. The main emphasis is placed on the comparison between collectors of material objects and architects, urban planners, and interior designers, focusing on selection criteria, coherence versus novelty, controlled differentiation, spatial capacity, and the perceived completeness of collections or spatial systems. Open-ended responses are used as a qualitative layer that supports the interpretation of the quantitative findings rather than as a separate form of statistical evidence.

4.1. Descriptive Survey Results and Normalization of Response Categories

The empirical material consists of two parallel survey datasets. The first dataset includes responses from collectors of material objects, while the second includes responses from architects, urban planners, and interior designers. Since the two respondent groups differ in size, the descriptive results are presented primarily through percentages within each group rather than through absolute frequencies alone. The structure of the two samples and the analytical blocks used for the comparative reading of the survey data are summarized in

Table 2. Survey samples and analytical blocks.

Respondent Group	N	Main Survey Focus	Use in Analysis
Collectors of material objects	32	Collection growth, selection criteria, spatial limitation, replacement, completeness, and branching into subcollections	Descriptive statistics, normalized categorical comparison, hypothesis testing, and qualitative interpretation
Architects/urban planners/interior designers	50	Spatial systems, groups of buildings, urban interventions, capacity, contextual fit, configuration, completeness, and branching into subsystems	Descriptive statistics, normalized categorical comparison, hypothesis testing, and qualitative interpretation

.

Before interpretation, the response categories were reviewed and normalized where necessary. This was particularly important for questions in which the two questionnaires used domain-specific wording but addressed comparable analytical dimensions, such as similarity, continuity, controlled differentiation, radical novelty, spatial limitation, reconfiguration, and completeness. The normalized distribution of selected categorical indicators is shown in Figure 3, which illustrates how the initial survey outputs were transformed into comparable percentages within each respondent group.

4.2. Comparative Analysis and Hypothesis Testing

The comparative analysis was structured around the four hypotheses defined in the methodological framework. After the normalization of response categories, the results were examined through four analytical dimensions: coherence versus novelty, controlled differentiation, spatial capacity, and between-group differences in selection criteria. The aim of this section is to identify the main empirical patterns, while their broader theoretical implications are addressed in the discussion.

In order to move from descriptive comparison to formal hypothesis testing, non-parametric and categorical procedures were used. Wilcoxon signed-rank tests were used for within-group comparisons of coherence and novelty indices. Chi-square goodness-of-fit tests were used to examine dominant categorical preferences within each group, while chi-square tests of independence were used to compare categorical distributions between the two respondent groups. Mann–Whitney U tests were used for ordinal between-group comparisons. Effect sizes are reported as r for Wilcoxon and Mann–Whitney tests, Cohen’s w for chi-square goodness-of-fit tests, Cramer’s V for chi-square tests of independence, and rank-biserial r for criterion-level Mann–Whitney comparisons.

Before testing the hypotheses, the internal consistency of the selection-criteria block was examined using Cronbach’s alpha. The results of this reliability analysis are presented in

Table 3. Internal consistency of selection criteria and composite indices.

Respondent Group	Scale/Index	Items	Cronbach’s Alpha *	Interpretation
Collectors	Selection criteria block	Q9–Q17	0.76	Acceptable internal consistency
Architects/urban planners/interior designers	Selection criteria block	Q9–Q17	0.83	Good internal consistency
Collectors	Extended selection criteria block	Q9–Q19	0.75	Acceptable internal consistency
Architects/urban planners/interior designers	Extended selection criteria block	Q9–Q19	0.87	Good internal consistency
Collectors	Coherence index	Q9, Q13, Q16	0.58	Moderate/heterogeneous
Collectors	Novelty index	Q11, Q12, Q15	0.62	Moderate/heterogeneous
Architects/urban planners/interior designers	Coherence index	Q9, Q13, Q16	0.76	Acceptable internal consistency

* Cronbach’s alpha values are used here to assess internal consistency, not to define the conceptual meaning of the indices.

. For the corresponding items Q9–Q17, reliability was acceptable among collectors and good among architects, urban planners, and interior designers. When the broader block Q9–Q19 was considered, the values remained acceptable for collectors and increased further among architects, urban planners, and interior designers. These results indicate that the selection criteria form a sufficiently coherent evaluative block for comparative analysis.

The smaller composite indices used for H1 showed different levels of internal consistency, as also shown in Table 3. Among architects, urban planners, and interior designers, both the coherence index and the novelty index reached acceptable reliability. Among collectors, the values were lower, suggesting a more heterogeneous evaluative structure. For this reason, the indices are interpreted as analytical composites rather than as strict psychometric scales.

The comparison of the coherence and novelty indices shows that the two respondent groups follow different evaluative logics. The descriptive values and Wilcoxon test results for these indices are reported in

Table 4. Descriptive values and Wilcoxon tests for coherence and novelty indices.

Respondent Group	Index	Items	Mean	Median	SD	Wilcoxon Comparison	p	Effect Size r
Collectors	Coherence index	Q9, Q13, Q16	3.43	3.67	1.05	Novelty > coherence, W = 65.00	0.009	0.53
Collectors	Novelty index	Q11, Q12, Q15	3.88	4.00	0.98
Architects/urban planners/interior designers	Coherence index	Q9, Q13, Q16	4.33	4.67	0.79	Coherence > novelty, W = 169.00	<0.001	0.57
Architects/urban planners/interior designers	Novelty index	Q11, Q12, Q15	3.69	3.67	0.96

, while the same relationship is visualized in Figure 4. Among architects, urban planners, and interior designers, the coherence index was higher than the novelty index (M = 4.33 vs. M = 3.69). A Wilcoxon signed-rank test confirmed that this difference was statistically significant, W = 169.00, p < 0.001, r = 0.57. By contrast, among collectors, the relationship was reversed: the novelty index was higher than the coherence index (M = 3.88 vs. M = 3.43), W = 65.00, p = 0.009, r = 0.53. This indicates that H1 is only partially supported: coherence is particularly important in the spatial-system group, while collectors place stronger emphasis on uniqueness, aesthetic value, and personal attachment.

The analysis of the preferred degree of difference shows that new elements are rarely understood as radically disruptive. The categorical distribution of responses and the corresponding statistical tests for H2 are presented in

Table 5. Preferred degree of difference and statistical tests for H2.

Category/Test	Collectors	Architects/Urban Planners/Interior Designers
Similarity/repetition	6.2%	10.0%
Controlled differentiation	46.9%	26.0%
Radical novelty	6.2%	2.0%
Context-dependent choice	31.2%	60.0%
Other domain-specific answer	9.4%	2.0%
Chi-square goodness-of-fit	chi-square (4, N = 32) = 21.44, p < 0.001, w = 0.82	chi-square (4, N = 50) = 59.60, p < 0.001, w = 1.09
Between-group comparison	chi-square (4, N = 82) = 9.26, p = 0.055, V = 0.34
Controlled differentiation vs. all other responses	chi-square (1, N = 82) = 3.78, p = 0.052, V = 0.21

, while the comparison between the two groups is illustrated in Figure 5. Collectors most frequently selected controlled differentiation (46.9%), followed by context-dependent choice (31.2%). In the spatial-system group, context-dependent choice was most frequent (60.0%), followed by controlled differentiation (26.0%). Radical novelty remained marginal in both groups.

Chi-square goodness-of-fit tests showed that the distribution of responses was not uniform in either group, as reported in Table 5. Among collectors, the distribution differed significantly from an equal distribution across categories, chi-square (4, N = 32) = 21.44, p < 0.001, w = 0.82. Among architects, urban planners, and interior designers, the distribution was also significantly uneven, chi-square (4, N = 50) = 59.60, p < 0.001, w = 1.09. A chi-square test of independence comparing the two respondent groups across the five normalized categories approached but did not reach conventional significance, chi-square (4, N = 82) = 9.26, p = 0.055, Cramer’s V = 0.34. A supplementary comparison of controlled differentiation versus all other responses showed a similar near-significant tendency, chi-square (1, N = 82) = 3.78, p = 0.052, Cramer’s V = 0.21. The reported percentage of difference in Q6 did not significantly differ between groups, Mann–Whitney U = 883.50, p = 0.188, r = 0.15.

H2 is therefore partially supported. It is supported in the broader sense that both groups avoid radical rupture and simple repetition as dominant strategies. However, it is not supported in the sense that controlled differentiation is the dominant category in both groups. As shown in Figure 5, collectors show the clearest preference for controlled differentiation, while spatial practitioners more frequently select context-dependent choice.

Spatial capacity produced the clearest contrast between the two groups. The main spatial-capacity indicators and statistical tests are summarized in

Table 6. Spatial-capacity indicators and statistical tests for H3.

Indicator	Collectors	Architects/Urban Planners/Interior Designers	Test	p	Effect Size
Spatial capacity affects choice/type at least partly	34.4%	96.0%	chi-square (1, N = 82) = 36.72	<0.001	V = 0.67
Abandonment due to spatial limitation	21.9%	82.0%	chi-square (1, N = 82) = 29.06	<0.001	V = 0.60
Influence of increased available space	M = 2.56	M = 3.54	U = 520.50	0.006	r = 0.30
Estimated impact of spatial capacity on final configuration	Not directly equivalent	M = 63.30%	W = 766.50 against 50% midpoint	<0.001	-

and visualized in Figure 6. For collectors, spatial limitation appears mainly as a practical condition related to storage and display. In this group, 34.4% stated that spatial capacity affects what they collect at least partly, and 21.9% reported abandoning an acquisition due to lack of space. In the spatial-system group, these values were much higher: 96.0% stated that spatial capacity affects the type of element introduced at least partly, and 82.0% reported abandoning an intervention due to spatial limitations.

A chi-square test of independence confirmed a strong between-group difference in whether spatial capacity affects the type of elements selected or introduced, chi-square (1, N = 82) = 36.72, p < 0.001, Cramer’s V = 0.67. A second chi-square test confirmed a similarly strong between-group difference in abandonment due to spatial limitations, chi-square (1, N = 82) = 29.06, p < 0.001, Cramer’s V = 0.60. These results are reported in Table 6. The influence of increased available space also differed significantly between the two groups. Architects, urban planners, and interior designers reported a stronger influence of increased space (M = 3.54, Mdn = 3.50) than collectors (M = 2.56, Mdn = 2.00), Mann–Whitney U = 520.50, p = 0.006, r = 0.30. Among spatial practitioners, the estimated impact of spatial capacity on the final configuration of the system was also high (M = 63.30%, Mdn = 70.00%) and statistically significantly above the midpoint of 50%, W = 766.50, p < 0.001.

Spearman correlations further support this interpretation. Among spatial practitioners, the perceived limiting role of space was positively correlated with both the influence of increased space and the reported impact of spatial capacity on final configuration, rho = 0.49 and rho = 0.46, respectively, both p < 0.001. Among collectors, the influence of available space on acquisition decisions was positively correlated with abandonment due to lack of space, rho = 0.58, p < 0.001. Together, the indicators presented in Table 6 and Figure 6 show that H3 is supported. Spatial capacity influences decisions in both domains, but its role is much stronger in the architectural and urban domain, where space functions as a systemic and configurational condition rather than only as storage or display capacity.

Finally, the comparison of selection criteria confirms between-group differences in emphasis. Criterion-level Mann–Whitney U tests were conducted for Q9–Q17, and the results are presented in

Table 7. Mann–Whitney U tests for corresponding selection criteria.

Criterion	Mean Difference, Spatial Minus Collectors	U	p	FDR- Adjusted p *	Rank- Biserial r	Direction
Contribution to coherence	+1.19	411.00	<0.001	<0.001	0.49	Spatial practitioners higher
Recognizability	+0.27	766.50	0.735	0.781	0.04	No significant difference
Innovation	+0.19	772.00	0.781	0.781	0.04	No significant difference
Boundary expansion	−0.10	839.50	0.702	0.781	−0.05	No significant difference
Improvement of existing system	+0.87	509.00	0.003	0.010	0.36	Spatial practitioners higher
Aesthetic value	−0.25	930.50	0.131	0.197	−0.16	No significant difference
Uniqueness	−0.64	1093.00	0.003	0.010	−0.37	Collectors higher
Fit with existing logic/typology	+0.66	590.00	0.035	0.064	0.26	Marginal after FDR correction
Cost	+0.56	573.50	0.025	0.057	0.28	Marginal after FDR correction

* FDR-adjusted p values were calculated using the Benjamini–Hochberg procedure. Positive rank-biserial r values indicate higher scores among spatial practitioners, while negative values indicate higher scores among collectors.

. Because multiple corresponding criteria were tested, Benjamini–Hochberg FDR-adjusted p values were also calculated. Positive rank-biserial r values indicate higher scores among architects, urban planners, and interior designers, while negative values indicate higher scores among collectors. The mean differences between the two groups are also visualized in Figure 7.

The strongest and most robust differences were found for contribution to coherence, improvement of the existing system, and uniqueness. Architects, urban planners, and interior designers assigned significantly higher values to contribution to coherence, U = 411.00, p < 0.001, FDR-adjusted p < 0.001, rank-biserial r = 0.49, and improvement of the existing system, U = 509.00, p = 0.003, FDR-adjusted p = 0.010, rank-biserial r = 0.36. Collectors assigned significantly higher values to uniqueness, U = 1093.00, p = 0.003, FDR-adjusted p = 0.010, rank-biserial r = −0.37. These findings are reported in Table 7 and visually summarized in Figure 7.

Fit with the existing logic or typology and cost also showed unadjusted between-group differences, with spatial practitioners assigning higher values to both criteria, but these effects did not remain statistically significant after FDR correction. Aesthetic value was higher among collectors descriptively, but the difference was not statistically significant. At the composite level, the coherence index was significantly higher among architects, urban planners, and interior designers than among collectors, U = 368.50, p < 0.001, r = 0.46. The novelty index did not significantly differ between groups, U = 895.00, p = 0.365, r = 0.10. This indicates that the between-group contrast is driven less by a general opposition between coherence and novelty, and more by the specific weighting of individual criteria. H4 is therefore supported.

A concise overview of the hypothesis testing is provided in

Table 8. Summary of hypothesis testing.

Hypothesis	Main Statistical Evidence	Result	Interpretation
H1	Collectors: novelty > coherence, W = 65.00, p = 0.009, r = 0.53. Spatial practitioners: coherence > novelty, W = 169.00, p < 0.001, r = 0.57.	Partially supported	Coherence dominates in the spatial-system group, while collectors emphasize novelty-related and affective criteria.
H2	Goodness-of-fit tests significant in both groups. Between-group difference approached significance, chi-square (4, N = 82) = 9.26, p = 0.055, V = 0.34.	Partially supported	Both groups avoid radical novelty, but controlled differentiation is dominant only among collectors.
H3	Spatial capacity affecting choice/type: chi-square (1, N = 82) = 36.72, p < 0.001, V = 0.67. Abandonment due to space: chi-square (1, N = 82) = 29.06, p < 0.001, V = 0.60.	Supported	Space has a stronger systemic and configurational role in the architectural and urban domain.
H4	Criterion-level Mann–Whitney tests show robust differences for coherence, improvement, and uniqueness. Composite coherence index is higher among spatial practitioners, U = 368.50, p < 0.001, r = 0.46.	Supported	The two groups assign different weights to selection criteria, although both operate through selective growth.

. This summary shows that H1 and H2 are partially supported, while H3 and H4 are supported. Taken together, the results indicate that the two domains share a comparable structure of selective growth, but differ in the criteria through which new elements are evaluated and justified.

4.3. Thematic Interpretation of Open-Ended Responses

The open-ended responses were analyzed as a qualitative layer supporting the interpretation of the quantitative findings. They were not used for statistical hypothesis testing, but were coded thematically in order to clarify how respondents understand completeness, limitation, coherence, and the introduction of new elements. The main qualitative relationship between the two datasets is summarized in Figure 8.

Several recurrent themes emerged from the responses. Among collectors, completeness was often understood either as an unattainable condition or as the closure of a predefined series, theme, or personal set. Many responses emphasized that a collection can always be extended, especially when new objects continue to appear or when personal interest remains active. Among architects, urban planners, and interior designers, completeness was more frequently associated with functional, spatial, contextual, and aesthetic coherence. However, many respondents also described spatial systems as open, temporary, or subject to future transformation.

The qualitative interpretation suggests that the strongest similarity between the two groups lies not in the criteria they use, but in the structure of the decision-making process itself. As illustrated in Figure 8, respondents in both domains describe the introduction of new elements through a comparable logic of addition, differentiation, limitation, completion, and branching. The main difference lies in the criteria through which this logic is interpreted: collectors rely more strongly on aesthetic, personal, and rarity-based criteria, while architects, urban planners, and interior designers rely more strongly on functional, contextual, spatial, and configurational criteria.

Figure 8 therefore provides a schematic synthesis of the relationship between the two datasets. It shows that collectors and spatial practitioners interpret growth through a similar structural logic, but justify selection through different domain-specific criteria. This supports the broader argument that the analogy between collections and spatial systems should be understood structurally rather than literally.

5. Discussion

5.1. Shared Structure, Different Criteria

The findings suggest that the analogy between collecting and architectural–urban development is meaningful, but not literal. The two domains do not operate through identical evaluative criteria. Rather, they share a comparable structure of selective growth. In both cases, new elements are added, differentiated, limited, interpreted as complete or incomplete, and sometimes reorganized into subcollections or subsystems. What differs is the evaluative framework through which these operations are justified. Collectors assign greater importance to uniqueness, aesthetic value, and personal attachment, whereas spatial practitioners place greater emphasis on coherence, improvement, and contextual fit. The strongest similarity between the two groups therefore lies not in the content of their criteria, but in the structure of the decision-making process itself.

This distinction is crucial because it prevents the concept of collection from being reduced to a simple metaphor of accumulation. The results indicate that a collection is not merely a set of accumulated elements, but a structured field of decisions through which elements are selected, compared, included, excluded, replaced, or reorganized. A similar logic can be recognized in architectural and urban systems, where new elements are never introduced in isolation, but always in relation to an existing whole [47]. What the surveys reveal is therefore not a direct equivalence between the two domains, but a structural analogy grounded in selective growth under conditions of limitation.

The qualitative interpretation strengthens this argument. Open-ended responses show that both groups describe growth through comparable structural operations—addition, differentiation, limitation, completion, and branching—even though they evaluate those operations differently. As summarized in Figure 8, collectors interpret growth more strongly through aesthetic, personal, and rarity-based criteria, while spatial practitioners interpret it through functional, contextual, spatial, and configurational criteria. This confirms that the most stable point of comparison between the two datasets lies in formal decision structure rather than in identical substantive priorities.

5.2. Partial Confirmation of Coherence and Controlled Differentiation

The results only partially support H1 and H2, and this partiality is theoretically important rather than problematic. H1 assumed that both groups would prioritize coherence over novelty. This expectation was confirmed among architects, urban planners, and interior designers, for whom the coherence index was significantly higher than the novelty index. Among collectors, however, the opposite pattern emerged: novelty-related criteria were rated higher than coherence-related ones. The difference does not mean that collectors disregard coherence altogether, but rather that coherence in collecting is often achieved through the acquisition of unique, visually distinctive, or emotionally significant objects. In other words, novelty can itself function as a mode of strengthening the collection, whereas in spatial practice novelty is more often subordinated to systemic consistency.

This difference helps explain the partial support for H2 as well. In both groups, radical novelty remained marginal, which confirms that neither collecting nor spatial practice is primarily driven by disruptive rupture. However, controlled differentiation was not dominant in both groups. Among collectors it emerged as the leading category, while among spatial practitioners context-dependent choice was more frequent. This suggests that spatial practitioners do not reject controlled differentiation, but they evaluate it more situationally, in relation to contextual, programmatic, and configurational conditions. Collectors, by contrast, appear more willing to describe their choices through a stable preference for moderated difference. The implication is that both groups avoid pure repetition and radical rupture, but they stabilize acceptable difference in different ways.

Taken together, the partial confirmation of H1 and H2 refines the core argument of the article. The two domains do not converge around identical criteria or identical formulas of acceptable novelty. Instead, they converge around the fact that growth is selective, negotiated, and limited. What varies is the way in which the balance between continuity and difference is interpreted. This is precisely why the logic of collection is more useful as a structural model than as a literal transfer of criteria from one field to another.

5.3. Spatial Capacity as the Strongest Point of Analogy

H3 is the most strongly supported hypothesis and arguably the most revealing point of comparison between the two groups. In both domains, spatial capacity matters, but it does not matter in the same way. Among collectors, space appears primarily as a practical condition related to storage, display, preservation, and the decision whether another object can be acquired. Among spatial practitioners, by contrast, space functions as a systemic and configurational condition. It affects not only whether something can be added, but also how that addition transforms density, circulation, typological relations, functional organization, and contextual balance. The strong between-group differences in the main spatial indicators confirm that spatial limitation is more deeply constitutive in the architectural and urban domain.

This asymmetry is theoretically productive. It shows that the analogy between collections and spatial systems should not be framed as a similarity of scale or content, but as a similarity of constrained growth. In collections, space limits accumulation. In spatial systems, space actively organizes the logic of the whole. A new element cannot simply be placed next to existing ones; it changes the relations among them. This is why the architectural–urban domain displays stronger sensitivity to increased space, stronger rates of abandonment due to limitation, and stronger correlations between perceived limitation and configurational impact. The role of spatial capacity thus becomes the clearest point at which the collection model can illuminate spatial development without collapsing the distinction between objects and systems.

The methodological decision to restrict the empirical study to material collections gains further support from this finding. The growth of storage and display space reported by collectors over one-, five-, and ten-year periods confirms that physical collections remain directly tied to finite spatial conditions. This makes them a more appropriate comparative domain than digital collections, which do not impose equivalent material constraints. The comparison therefore rests not only on conceptual analogy, but also on a shared condition of physical limitation, even if that limitation operates differently in each field.

5.4. Between-Group Differences and the Weighting of Criteria

H4 is supported and provides the clearest evidence that the two domains assign different weights to corresponding criteria even when they operate through a comparable logic of selective growth. Criterion-level tests show that spatial practitioners assign significantly higher values to contribution to coherence and improvement of the existing system, while collectors assign significantly higher values to uniqueness. At the composite level, the coherence index is also significantly higher among spatial practitioners, whereas the novelty index does not differ significantly between groups. This is an important result because it shows that the contrast is not simply one of “coherence versus novelty” in the abstract. Rather, it is produced by the selective weighting of specific criteria.

This finding helps clarify why the article insists on a structural, not literal, reading of collection logic. If the two domains shared the same criteria, the argument could be reduced to a simple claim that architecture behaves like collecting. The evidence does not support such a claim. Instead, the two groups justify growth differently. Collectors rely more heavily on rarity, aesthetic attraction, and personal attachment, while spatial practitioners rely more heavily on systemic fit, improvement, and contextual reasoning. The comparison therefore reveals both convergence and irreducible difference: convergence at the level of selective operations, and difference at the level of evaluative priorities.

The fact that some criterion-level differences weakened after FDR correction is also useful rather than disappointing. It suggests that the strongest contrasts are not distributed evenly across all dimensions, but are concentrated in a smaller set of highly consequential criteria. This makes the overall pattern more robust: the distinction between the two groups is not a diffuse impressionistic contrast, but a more focused divergence centered on coherence, improvement, and uniqueness.

5.5. From Selective Growth to Algo-Scapes

The relevance of the findings extends beyond the comparison between collectors and spatial practitioners and becomes particularly visible when they are related to the expert-derived corpus of algorithmic design systems summarized in

Table 9. Anonymized expert collection of algorithmic design systems and selective-growth operations *.

System Code	Temporal Placement	Operational Logic/Intent	Dominant Selective-Growth Operation	Distinctive Contribution to the Expert Collection
A01	2011	Urban surface articulation	Differentiation	Introduces visual variability through rule-based pattern generation.
A02	2012	Perceptual exploration	Addition/differentiation	Extends algorithmic work into spatial and perceptual experience.
A03	2014	Emergence and evolution modelling	Branching/limitation	Simulates informal urban growth under spatial and rule-based constraints.
A04	2015	Public space patterning	Differentiation/completion	Tests controlled variation within an urban field.
A05	2015	Spatial connectivity research	Branching	Produces relational networks through distance-based connections.
A06	2015	Layout optimization	Limitation/completion	Generates typological configurations under rule-based constraints.
A07	2016	Spatial relationship modelling	Limitation	Configures spatial relations through adjacency and constraint logic.
A08	2016	Iterative spatial adaptation	Differentiation/completion	Produces progressive mutations of floor plans.
A09	2017	Graphic experimentation	Branching/differentiation	Explores emergent line systems and visual networks.
A10	2017	Parametric object design	Addition/coherence	Translates brand logic into an algorithmically generated object system.
A11	2018	Residential layout generation	Limitation/differentiation	Produces variations in housing units within programmatic limits.
A12	2019	Corridor-based institutional generation	Completion/limitation	Automates linear institutional typologies through spatial rules.
A13	2021	Algorithmic branding	Differentiation	Enables interactive variation within an identity-generation system.
A14	2021	Rule-based visual identity construction	Coherence/limitation	Constructs visual identity through controlled rule-based operations.
A15	2022	Context-responsive urban patterning	Differentiation/contextual completion	Applies site-specific generative logic to an urban design context.

* The systems are presented through neutral codes in order to foreground their analytical role within the expert corpus rather than their individual project identity. The table presents documented expert corpus of algorithmic design systems developed within the authors’ broader design-research practice. Full project references can be provided in the final version of the manuscript if required.

. This corpus is not introduced as a third empirical dataset equivalent to the two surveys. Rather, it functions as an interpretive extension of the main results. By organizing documented algorithmic systems according to their dominant selective-growth operations—such as addition, differentiation, limitation, completion, branching, and coherence—Table 9 shows that the same structural logic identified in the survey material can also be recognized within long-term computational design practice.

Read in this way, Table 9 performs an important bridging role. Material collections consist of distinguishable elements accumulated over time, while architectural and urban systems are structured by contextual, functional, and configurational relations. The expert corpus occupies an intermediate analytical position: like collections, it is composed of discrete and historically accumulated elements; like spatial systems, it is governed by operational coherence, constraint, adaptation, and branching. Each new algorithmic system therefore contributes to the corpus not only by adding another example, but also by differentiating the whole, redefining what counts as completion, and opening new generative directions. In this sense, the expert corpus can itself be read as a curated collection of design logics.

This is precisely where the concept of algo-scapes becomes analytically useful. If algorithmically generated environments are understood as potentially open-ended fields of alternatives [48], then the central issue is no longer generation alone, but the logic through which generated options acquire value. The survey results show that such value does not emerge from a single universal criterion. Instead, it depends on a structured process of selection through which alternatives are compared, differentiated, limited, and provisionally stabilized. Table 9 extends this argument by demonstrating that computational design systems themselves can be organized according to these same operations. Algorithmic landscapes are therefore meaningful not simply because they contain many possible outcomes, but because they require meta-level criteria through which meaningful configurations can be selected from open-ended fields of possibility [49].

The main contribution of the article thus lies in showing that the logic of collection can illuminate architectural, urban, and algorithmic design systems only when it is understood structurally rather than literally. Collections, spatial systems, and algorithmic corpora do not share identical criteria of evaluation, but they do share a logic of selective growth under conditions of finitude. What changes from one domain to another is not the presence of selection, but the criteria through which selection is justified. In that sense, Table 9 does not merely append an additional example to the study; it shows how the structural findings of the survey can be extended into the domain of computational design and interpreted through the concept of algo-scapes.

6. Concluding Remarks

This article set out to examine whether the logic of collecting can serve as a meaningful interpretive model for understanding how architectural and urban systems are incrementally assembled, differentiated, limited, and brought to temporary states of completion. The findings indicate that the analogy is meaningful, but not literal. Material collections and spatial systems do not share identical evaluative criteria; however, they do share a comparable structure of selective growth. In both domains, new elements are introduced through processes of addition, differentiation, limitation, completion, and branching, even though the criteria through which these operations are justified differ substantially.

The quantitative results show that spatial practitioners assign greater weight to coherence, improvement, and contextual fit, while collectors assign greater importance to uniqueness, aesthetic value, and personal attachment. The clearest point of comparison between the two domains is spatial capacity. In both groups, growth is conditioned by limitation, but the role of space is stronger and more structurally constitutive in the architectural and urban domain, where it shapes not only accumulation but also configuration. In this sense, the strongest contribution of the study lies in showing that the collection can be understood not simply as an accumulation of objects, but as a system of selective decisions operating under finite conditions.

The qualitative findings further reinforce this interpretation. Although the two groups do not speak in the same evaluative language, they describe growth through a similar sequence of structural operations. This supports the argument that the relationship between collecting and spatial development should be understood structurally rather than metaphorically. The collection is therefore useful not because cities or spatial systems literally behave like collections of objects, but because the logic of collection clarifies how elements are admitted into larger wholes, how acceptable difference is negotiated, and how temporary completeness is established.

This argument becomes especially relevant in relation to computational design. When algorithmically generated alternatives are understood as potentially extensive fields of possibilities, the central problem is not only generation but selection. In this regard, the expert-derived corpus of algorithmic design systems presented in Table 9 extends the survey findings by showing that the same selective-growth operations identified empirically can also be recognized in computational design practice. The concept of algo-scapes thus refers not merely to the abundance of generated alternatives, but to the need for meta-level criteria through which meaningful configurations are distinguished from open-ended possibility fields.

The study has several limitations. The sample was purposive rather than statistically representative, and the two respondent groups were unequal in size. In addition, the comparison necessarily relied on analogous rather than identical questionnaire items, which means that some domains could be compared more directly than others. The expert-derived corpus was used as an interpretive extension rather than as a third equivalent empirical dataset, and the present analysis was explicitly restricted to material rather than digital collections. Future research could therefore expand the model by including larger and more differentiated samples, more detailed collecting typologies, and a separate comparative analysis of digital collections and computational platforms. An additional methodological limitation concerns the design of the survey instruments. The two questionnaires were designed as parallel rather than identical instruments. Their comparability was established through author-led expert review and analytical alignment of items, but not through formal cognitive interviewing or invariance testing. Some between-group differences may therefore partly reflect differences in the interpretation of domain-specific terminology. The findings should thus be read as evidence of structural correspondence across analogous dimensions, rather than of perfectly identical underlying constructs. Beyond these methodological considerations, the study is also subject to limitations related to sample composition and generalizability. The group of spatial practitioners included respondents working in architecture, urban planning, and interior design, but all shared the same architectural educational background. The collectors’ group was more occupationally heterogeneous, and detailed professional background data were not systematically collected. In addition, the sample was purposive, relatively small, and recruited through the authors’ professional networks, which limits generalizability. The study should therefore be read as an exploratory comparison rather than a representative population study. Future research could address these limitations by employing larger and more diverse samples, clearer subgroup differentiation, and additional comparison groups outside design practice. Beyond expanding the sample, further studies could also strengthen the empirical validation of the proposed framework by examining how the identified selection principles are manifested in actual collecting and spatial design practices. Combining questionnaire-based evidence with empirical observations, case studies, or process-oriented analyses would provide a richer understanding of decision-making dynamics and allow the proposed relationships to be evaluated in more naturalistic settings.

Despite the outlined limitations, the article contributes a conceptually and methodologically productive framework for linking collecting, spatial development, and computational design. Its main claim is that the logic of collection can illuminate architectural, urban, and algorithmic systems when understood as a logic of selective growth under conditions of finitude. In that sense, the article argues for a shift from viewing collections as passive accumulations toward understanding them as structured systems of evaluative choice. This shift, in turn, helps explain how cities, spatial systems, and algorithmic landscapes are not only generated, but curated.