From traditional to digital contexts: new characteristics of the public's spatial perception of urban streets in the age of technology

The study examines how the rise of digital technologies (internet, smartphones, social media) has altered public interaction with urban space. Traditionally, street-space perception developed through multisensory, embodied movement, yielding three-dimensional experiences and attentiveness to spatial wholeness and utility. In the digital age, media mediate public sphere interactions and enable perceiving places via online imagery and mobile recording/sharing. The research asks: Have public street-space perception characteristics changed from traditional to digital contexts? What are the characteristics and reasons for any changes? What are the implications for spatial design? Streets are chosen as the focus due to their central role in urban life (movement, sociability, culture, commerce). The paper outlines a plan: review theory and prior work, synthesize traditional-context perception characteristics, empirically code social-media images for Xiaolouxiang (Wuxi, China) to characterize digital-context perception, and compare across contexts to derive design implications.

The review underscores streets as vital public spaces linked to well-being and health, with perception shaping environmental preferences relevant to planning. In traditional contexts, models (e.g., Nasar) relate environmental attributes to cognition, affect, and behavior. Influential elements include Lynch’s paths, edges, nodes, districts, and landmarks, later expanded to include objects and activities (Whyte, Gehl, Jacobs). Street perception concerns: buildings and spaces (nodes, paths, façades, colors, entrances, windows, skylines), objects (plants, skyscapes, signage, seating, food, display cases, awnings, bins, art, natural factors, lighting, vehicles), and spatial attributes/qualities (accessibility, disorder, motorization, functional diversity, openness, density, microclimate, enclosure, complexity; humanization, comfort, safety, friendliness, restoration). Activities include physical, social, recreational, and supervisory activities. Two synthesized traits of traditional-context perception are: focus on objects, buildings/spaces, and activities, and evaluation of spatial wholeness, utility, and experiential qualities. Methodologically, traditional studies used observations, interviews, and questionnaires; digital-era methods incorporate virtual tech, mobile app data, street-view imagery, and multisource big data but often overlook how social media and internet connectivity reshape perception. Digital-context studies increasingly use social media data, commonly textual UGC; however, text is often brief and less reflective of perceptual nuance. Image-based studies exist but frequently rely on coarse categorization. The paper identifies gaps: limited systematic comparison between traditional and digital contexts and underutilization of detailed image coding; it proposes in-depth NVivo coding of social-media images to address these gaps.

Design: Case study and comparative analysis grounded in grounded theory, using qualitative coding of user-generated images to examine digital-context perception and compare with synthesized traditional-context characteristics. Case: Xiaolouxiang historic district (Wuxi, China), a 900+ year-old, highly visited historic area with recent top-down renovation and limited community feedback, providing a rich site for people–space interaction. Data source: Sina Weibo posts with geotag or keyword “Xiaolouxiang.” Timeframe: Three years post-renovation, June 30, 2019–June 30, 2022, sampling during Chinese legal holidays (Qingming, Labor Day, Dragon Boat Festival, Mid-Autumn Festival, National Day, New Year’s Day, Spring Festival; 2019–2022). Rationale: High footfall (approx. 3 million annual visitors soon after reopening), pandemic-era social media use, and a baseline for future phase-II comparisons. Selection and validation: (1) Retrieve images clearly depicting Xiaolouxiang’s buildings/spaces; exclude unrelated images. (2) Deduplicate manually. (3) Exclude professional blogger/official images (identified by strong narrative series, professional composition, heavy post-processing, high saturation) to better capture everyday perceptions. (4) Member checking: multiple reviewers validate inclusions/exclusions. Final dataset: 1119 valid images. Coding and analysis: NVivo used for iterative coding. Open coding identified salient elements and features, yielding 209 free nodes and 1469 reference points. Axial coding merged nodes by attribute similarity into 12 subcategories (e.g., architectural details; courtyard, garden, street spaces; plants, animals, objects, food; festivals, daily activities). Selective coding integrated subcategories into three main categories: (1) Objects in buildings and spaces, (2) Buildings and spaces, (3) Activities in buildings and spaces. The most frequently referenced nodes (top 20) in each category were taken as principal elements of public preference. Reliability: Member checking with multiple coders and verification against original images. Bias and limitations acknowledged: subjective exclusion of professional images, finite sample; future work could add image-recognition pipelines to scale coding.

Framework continuity: In both traditional and digital contexts, perception organizes around three dimensions: (1) buildings and spaces, (2) objects in buildings/spaces, and (3) activities in buildings/spaces. Xiaolouxiang digital-context emphases (selected quantitative indicators from coding): Objects: • Place/name markers for space: Xiaolouxiang identifiers (5.65%). • Art installations and sculptures: red/orange lanterns (4.77%); warm light installations (3.13%); cool light installations (0.95%); Western-patterned ironwork and colorful light installations (1.36%); cultural-creative display cabinets embedded in walls (1.16%); modern-style cultural-creative sculptures (1.02%). • Ornamental plants (e.g., peonies, daffodils, moonflowers): 3.74%; botanical landscaping 2.45%; greenery on walls 1.09%; tree-branch-formed views 0.61%. • Distinctive food/beverages (milk tea, pizzas, crispy pancakes, hot-and-sour soups): 12.12%. • Cats: 1.29%. Buildings and spaces: • Unique perspectives/contents: narrow alleyways (0.82%), traditional buildings with modern high-rise background (3.00%), eaves/buildings/sky from an upward perspective (1.50%), windows with stacked tiles (0.95%). • Quiet street space: 3.74%; lively street space also present. • Clean plastered white walls: 3.06%; mottled old white walls: 1.57%. • Architectural details (eaves, ridges, horse-head walls, eaves tiles): approx. 2.8%. • Cultural/creative spaces and traditional elements (pavilions, ponds, rockery). Activities: • Two groups: festivals (e.g., tea ceremonies, parent–child activities, Chinese cultural performances, drama singing, National Day events, New Year activities) and daily activities (painting, bubble shows, children’s handicrafts). Histograms highlight frequent occurrences of cultural performances and community gatherings. Three notable shifts in digital-context perception (vs. traditional): 1) From 3D multisensory experiential focus to 2D visual aesthetics: preference for striking compositions, colors, light/shadow, and unique camera perspectives; functionality receives less emphasis. 2) From spatial totality to spatial details: heightened attention to architectural details (eaves, ridges, horse-head walls, tiles, door locks, stone carvings, stone lions), facilitating shareable and identity-expressive content. 3) From use/functionality to cultural characteristics: strong interest in culturally symbolic elements (lanterns, plaques, white walls with regional identity, named markers).

The study addresses its research questions by showing that while the core perceptual framework (space, objects, activities) persists, digital mediation reshapes emphases: toward two-dimensional visuality, detail-centric views, and cultural symbolization. Mechanisms include the affordances of mobile photography and social media dissemination, identity and distinction seeking in public spheres, and decentralized, participatory content production that elevates detail and novelty. The findings matter for urban design and planning: people’s online curation influences on-site expectations and behaviors, suggesting that visual capture points, detail-rich environments, and legible cultural symbols can enhance engagement. However, trade-offs arise: overemphasis on momentary visuals can undermine holistic experiential quality and functionality; detail-focused, screen-mediated “fragmented” spatial perception may weaken overall spatial legibility and attachment; and cultural symbol displays risk superficial, commercialized consumption and expectation–reality gaps. The results advocate design strategies that integrate visual appeal with coherent spatial narratives and robust usability, and that present cultural elements with authenticity and depth.

Using Xiaolouxiang as an empirical case, the paper compares traditional and digital contexts of public street-space perception. It confirms a stable three-part framework (space, objects, activities) but identifies three digital-era shifts: prioritization of two-dimensional visual aesthetics over multisensory 3D experience, greater focus on spatial details over overall spatial wholeness, and heightened attention to cultural symbols over pure functionality. Design implications include emphasizing visually compelling compositions, enhancing craft-based details with regional characteristics, and thoughtfully expressing cultural identity in space and programming. Future research should balance visual aesthetics with the integrity of holistic spatial experience, align cultural symbolism with functional performance, and scale empirical analysis (e.g., via image-recognition methods) while maintaining rigor in addressing social media data biases.

Methodological limitations include reliance on manual grounded-theory coding, constraining sample size and potentially limiting comprehensiveness; subjective exclusion criteria for professional/official images could introduce bias. Social media data pose challenges: possible fabricated posts/bots, lack of demographic metadata, selection bias toward younger, tech-savvy users, and difficulties defining control groups. The study mitigated risks via member checking, repeated screening, and using a literature-derived traditional-context framework as a comparative control, but generalizability remains limited. Future work should incorporate automated image recognition/filtering to expand datasets and develop strategies to compensate for social media biases.