(1) Background: The smart city management system, with GIS technology as its core, is based on realistic visualization of multiple types of 3D model data syntheses. However, the efficiency barriers to achieving smooth and continuous visualization from outdoor scenes to small indoor scenes remain a challenge. (2) Methods: This paper uses the visibility prediction method to obtain potential visual sets at three levels—outdoor, indoor and outdoor connection, and indoor—and constructs an R-tree spatial index structure for organizing potential visual sets. By integrating these potential visible sets with spatial indexes, scene visualization can be carried out effectively. (3) Results: A near-reality indoor and outdoor scene was used for experimentation, resulting in stable 10% fluctuation visual frame rates around 90 FPS. (4) Conclusions: Spatial indexing methods that combine potential visible sets can effectively solve the continuity and stability problem of indoor and outdoor scene visualization in smart city management systems.

Urban space exhibits marked heterogeneity in both form and function, yet how urban functions align with multilevel street structures remains insufficiently understood. This study investigates the coupling between street hierarchy and urban functions in Shenzhen through two complementary representations: (1) the relationship between street connectivity and POIs located near each street, and (2) the relationship between street nodes and POIs contained within street-node-based hotspots. Both datasets were hierarchically partitioned using head/tail breaks to reveal intrinsic scaling structures. Power-law detection shows that natural streets and hotspot clusters follow heavy-tailed distributions, forming nested living structures. The exponents for node- and POI-based clusters fall within the typical range of 2.0–2.2, whereas exponents for street connectivity and street-based POIs are higher, indicating stronger heterogeneity. Correlation analyses reveal consistent positive associations between POIs and street connectivity across all levels, with the strongest relationships in mid- to upper-level substructures. Toward finer levels, correlations weaken and become increasingly nonlinear, reflecting growing spatial irregularity. These findings demonstrate that Shenzhen’s urban functions are coherently organized along its street hierarchy, highlighting the fundamental role of multilevel street configurations in shaping functional spatial patterns.

China’s urban development is shifting from extensive expansion to intensive improvement, making the identification of inefficient stock land essential for sustainable urban renewal. Yet, existing approaches are often limited by incomplete data sources and low spatial precision. To address these issues, this study proposes a scalable framework that integrates multi-source big data, including land-use surveys, socioeconomic statistics, spatiotemporal trajectories, and ecological metrics. Using Shenzhen as a case study, we developed a multidimensional evaluation system across social, economic, and ecological dimensions, comprising eight specific indicators. Indicator weights were objectively determined using the entropy weight method, and GIS-based spatial analysis (mean-standardization and Moran’s I) was applied to characterize spatial patterns. Results identify 65.37 km² of inefficient land—about 7% of Shenzhen’s construction land in 2019—exhibiting an “edge aggregation and corridor extension” pattern, mainly distributed along urban–rural fringes and administrative boundaries. The spatial configuration is shaped by historical development, ecological constraints, and the city’s spatial structure. This study provides an objective and replicable framework for the precise identification of inefficient land, supporting data-driven urban renewal and spatial governance in high-density cities.

China’s urban development is shifting from extensive expansion to stock-oriented renewal, making the precise identification of inefficient land stock essential for sustainable spatial governance. To overcome the limitations of single-source data and coarse delineation, we propose a multi-source geospatial framework integrating land-use surveys, socioeconomic statistics, spatiotemporal mobility trajectories, and ecological indicators. Using Shenzhen as a case study, we construct an eight-indicator system across social, economic, and ecological dimensions and apply entropy-based objective weighting to support GIS-based weighted overlay evaluation. The results identify 65.37 km² of inefficient land in 2019, accounting for approximately 7% of Shenzhen’s construction land, with a distinctive “edge aggregation and corridor extension” pattern concentrated along urban–rural fringes and administrative boundaries. Inefficient land is highly uneven across districts, with Longgang (21.11 km²) and Bao’an (12.57 km²) contributing 51.5% of the total and exhibiting statistically spatial clustering (p < 0.01). The observed configuration reflects path-dependent historical development and policy–ecology constraints, including the interaction between ecological control boundaries and peripheral expansion. Overall, by integrating multi-source spatiotemporal big data within a multi-dimensional evaluation framework, the framework offers an objective and transferable approach for diagnosing inefficient land stock and informing targeted urban renewal strategies in high-density cities worldwide.

The topological structure of the underlying streets can help us better understand urban space and human activities therein. As human urban movements are inherently heterogenous in space and statistics, whether or not the network of streets holds a similar degree of heterogeneity worth being investigated. Relying on the graph theory and complex-network thinking, we adopted the street segment analysis-based methods and computed segment-based topological metrics in the downtown of two major cities in China: Beijing and Shanghai. More specifically, we used Flickr photo location data as a proxy of human urban activities and counted the movement flow at levels of both street-based communities and street segments. We measured the heterogeneity of each segment-based metric via the extent of being long-tailed in the rank-size distribution (long-tailedness). We found that segment-based betweenness was most long-tailed and was the best metric for capturing human activities within each community and that neither segment-based degree nor can closeness show a similar extent of long-tailedness and can have a good correlation with the segment-based flow. These findings point to the insight that the positive relationship between street structure and human activities is significantly shaped by their shared heterogeneous nature.

China's urban development is transitioning from an extensive expansion model to an intensive improvement approach. Promoting urban renewal through stock land resources has become a key focus in contemporary urban spatial governance. As cities enter the stock development era, accurately identifying the quantity and spatial distribution of inefficient stock land is crucial for improving land use efficiency. However, existing identification methods for inefficient existing space face challenges including limited data sources, over-reliance on expert experience and insufficient identification accuracy. These factors impede the realization of large-scale, high-precision, and universally applicable identification frameworks. This study proposes an identification method integrating multi-source big data, including land surveys, socioeconomic data, spatiotemporal trajectories, and air quality metrics. Using Shenzhen as a case study, we developed an index system with three dimensions (social, economic, and ecological) and eight indicators. The entropy weight method determined indicator weights, while mean-standardization and Moran’s I index characterized spatial distribution. The study identified a total of 65.37 km² of inefficient existing land in Shenzhen, accounting for approximately 7% of the city's construction land in 2019. Clusters mainly appear in the northwestern part of the city, including administrative boundary zones and urban fringe areas. Longgang District (21.11 km²) and Baoan District (12.57 km²) collectively contribute 51.5% of the inefficient land. Shenzhen's inefficient existing land exhibits an "edge aggregation and corridor extension" spatial pattern, and its formation is subject to the compound influence of historical development patterns, ecological control policies, and urban spatial structure. This study provides a scientific basis for urban renewal and inefficient land redevelopment through the integration of multidimensional big data and spatial statistical analysis. The proposed framework offers replicable technical support for sustainable urban governance and precision regeneration strategies in high-density Chinese cities.