Background: Artificial Light at Night (ALAN) is a growing public health concern, associated with circadian disruption, sleep disturbances, mental health issues, and increased risk for chronic conditions such as cancer and cardiovascular diseases. Despite a rapidly expanding epidemiological literature on ALAN-related health outcomes, there is limited consensus on standardized and comparable tools for assessing both how individuals perceive ALAN and their objective exposure at the individual and population levels. Methods: A scoping review was conducted using Scopus, PubMed, and Web of Science to identify studies published between 2010 and 2025. The aim was to systematically map the methods, tools, and frameworks used to evaluate both the perception of ALAN and objective ALAN exposure, and to examine their implications for public health. Eligibility was restricted to studies explicitly describing approaches to assess ALAN perception or exposure applied to human health research. Results: Most studies adopted cross-sectional designs and focused on urban contexts. Satellite-derived nighttime light data (62%) and GIS-based indicators (46%) were the most frequently used exposure proxies, while surveys and self-reported measures were commonly used to capture individual behaviors, health impacts and perceptions. Only 23% of studies employed wearable sensors or mobile applications, and architectural or building-level data were included in a single study (8%), highlighting a substantial gap in indoor exposure characterization. Conclusions: By critically reviewing current methods, tools, and frameworks, this work highlights the urgent need for harmonized, validated approaches that integrate objective environmental light measurements with human-centered data on how ALAN is perceived and experienced, thereby strengthening the evidence base for public health research and policy.
Urbanization is one of many factors leading to global biodiversity declines due to its negative effect on habitat area, isolation, and quality. Green roofs have been proposed to mitigate adverse environmental effects by increasing local urban biodiversity and buffering the urban heat island effect. Nevertheless, little is known about the feasibility of green roof implementation in areas where it may be most needed, i.e., cities of the Global South where biodiversity is under pressure and where climatic and economic constraints may impede green roof establishment. Here, we aim to provide a proof-principle for the effectiveness of (low cost) wicking beds planted with native wild plant mixtures for increasing biodiversity in Hebron, West Bank. Effectiveness was evaluated in terms of establishment success and biodiversity of plants, and their potential to increase insect biodiversity compared to conventional roofs. Wild plants established in high densities and a decent rage of species. Green rooftops also supported significantly higher insect richness and abundance than conventional roofs. Our results highlight that green roof design with wild plants can be an effective and affordable means for urban biodiversity conservation in dry and hot areas.
Urban services increasingly depend on interconnected sensing, communication, computing, decision-support, and response functions, yet technical performance alone does not establish service effectiveness. This structured integrative review examines WSNs, WBANs, V2X systems, 5G-enabled edge–cloud infrastructures, and prospective 6G capabilities across health, mobility, environmental monitoring, energy, water, infrastructure safety, and climate resilience. A five-database search covering January 2018–March 2025 was supplemented by citation tracing and a documented gap-directed update with a final cutoff of 1 July 2026. The analytical corpus comprised 40 peer-reviewed studies, five deployment cases, and one scope-boundary case. An outcome-mediated perception–network–edge/cloud–decision–response framework enabled categorical comparison of heterogeneous evidence without pooling non-comparable measures. Attribution was classified as T1 (comparatively evaluated downstream outcome), T2 (technical or bounded operational outcome), or T3 (conceptual linkage): three studies were T1, 34 T2, and three T3. Ten studies supported target-level SDG alignment, whereas none reached indicator-level correspondence. Evidence remained concentrated at communication, processing, decision-support, and bounded operational endpoints, with limited assessment of response availability and disruption–recovery conditions. Among the deployment cases, only SFpark supported T1 interpretation. These findings characterize the selected corpus and identify a persistent gap between technical performance and comparative, longitudinal, and distributionally assessed urban-service outcomes.
Urban traffic environments can elevate physiological stress, yet most existing studies assess this indirectly through infrastructural or traffic-related proxies rather than direct physiological measurement. This pilot study proposes a geographic information system (GIS)–Analytical Hierarchy Process (AHP) framework that integrates wearable heart-rate sensing with spatial analysis to identify localized physiological activation patterns at urban intersections. The proposed framework is presented as a methodological proof-of-concept and is not yet validated as a decision-support tool; application to urban health assessment or smart-city planning would require testing on a substantially larger and independently sampled spatial dataset. Data were collected from ten participants across 118 repeated commuting passes by private automobile at six intersections in Kragujevac, Serbia. An AHP-weighted urban stress index combining heart rate, the traffic-intensity proxy, time of day, and acceleration events (CR = 0.0115) was computed and mapped using inverse-distance-weighted interpolation. A linear mixed-effects model showed a significant positive association between an ordinal, time-of-day-based traffic-intensity proxy and heart rate across the 118 passes (8.90 bpm per ordinal unit, p < 0.001); because this proxy is derived from time-of-day categories, the association is best interpreted as an exploratory time-of-day–heart-rate relationship rather than a validated causal effect of traffic, and a sensitivity analysis confirmed that the same three intersections ranked highest across alternative weighting scenarios. The results indicate a consistent spatial relationship between intersections associated with higher traffic-intensity proxy values and elevated physiological activation. Although based on a limited pilot-scale dataset, the proposed framework demonstrates the feasibility of combining wearable physiological sensing with GIS–AHP spatial analysis and offers a methodological proof-of-concept for smart-city and urban-health research in medium-sized cities, pending validation on larger, independently sampled spatial datasets.
The licensing of trip-generating developments (TGDs) determines how mobility impacts are assessed and which mitigation measures are required, yet procedures remain heterogeneous and largely focused on roadway performance. No instrument was identified to assess their adherence to sustainable urban mobility (SUM) principles. This study proposes a maturity-based protocol for that assessment. It comprises four dimensions, sixteen components, and three maturity levels: N1 conventional, N2 in transition, and N3 SUM-oriented. It derives from an integrative review of the literature and technical and institutional documents. Expert content validation yielded item-level indices of 0.86–1.00 and a scale-level index of 0.94. Inter-rater assessment showed 81.3% exact agreement and an ordinal Krippendorff’s alpha of 0.83. Applied to Rio de Janeiro, it classified five components at N1, ten at N2, and one at N3. Sustainable mobility is reflected in the regulatory vocabulary, in the declared scope of the study, in measures for pedestrians and cyclists, and in the financial instrument, without reaching the metrics that structure the approval decision. By shifting the unit of analysis to the regulatory procedure and presenting a diagnostic profile rather than an aggregate score, the protocol provides a basis designed for replication and comparison across municipal licensing systems.
Metro station areas are transitioning from single-purpose transportation transfer nodes into multifunctional public spaces that integrate public services, cultural displays, and place-based experiences. However, there remains a lack of systematic evaluation tools for assessing public cultural service quality in metro station areas that integrate cultural meanings, public identity, and experiential expressions. An Analytic Network Process (ANP) network structure was established based on expert judgments to determine the weights, and Technigue for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied for comprehensive evaluation and ranking using the scores provided by professional evaluators for 13 sampled stations in Foshan, Xi’an, and Shenzhen. Analytic Hierarchy Process (AHP)-TOPSIS was used for robustness testing, and dimensional weight perturbation analysis was conducted to assess model stability. Six types of Point of interests (POIs) were further incorporated to characterize the external functional contexts of the sampled stations. The results showed that the weights of Authenticity, Legitimacy, and Theatricality were 0.498, 0.296, and 0.206, respectively. Traditional, Self-expressive, Charismatic, Exhibitionistic, and Utilitarian were identified as the key indicators with relatively high weights. The 13 sampled stations showed significant differences in overall performance. Higher-performing cases demonstrated more balanced performance across the three scene dimensions of Authenticity, Legitimacy, and Theatricality. In contrast, lower-performing cases showed relatively weaker performance on some key indicators. The robustness test based on AHP-substituted weights and Spearman correlation analysis verified the stability of the evaluation framework, while the sensitivity analysis showed that the evaluation results remained relatively stable under weight perturbations. This study extends the application of scene theory in metro station area research and provides an evaluation framework and practical reference for optimizing public cultural service spaces in transit-oriented urban environments.
Existing readiness indices for Connected and Automated Vehicles (CAVs) produce a single composite score per city. This conflates use cases with very different territorial requirements and, being compensatory, allows strong performance on one criterion to mask a deficiency that is, in practice, a hard prerequisite. This paper proposes and demonstrates an assessment framework with four elements. First, the verdict is stratified by Operational Design Domain (ODD) profile, so that one territory yields one verdict per use case. Second, each profile is assessed only on its eligible service area, the roads on which the corresponding service could actually operate. Third, a non-compensatory veto layer is overlaid on a compensatory segment-level score, which separates the readiness score from the fit share, the proportion of network length that satisfies every critical threshold. Fourth, the diagnosis is extended to specific corridors through a readiness-aware routing formulation. The framework is demonstrated on the Versailles, Satory, and Velizy perimeter of about 92 square kilometes, using a real OpenStreetMap network of 4834 nodes and 733 kilometres, combined with an explicitly illustrative synthetic indicator calibration. Restricting each profile to its service area changes one verdict outright. The campus shuttle and urban robotaxi profiles obtain near-identical scores, both close to 0.51, but fit shares differing seven-fold, a distinction that a single score cannot express. Readiness-aware routing reroutes up to 42 percent of origin and destination pairs onto better-equipped itineraries for about 1 percent additional travel length. Monte Carlo analysis and cross-method tests using TOPSIS and PROMETHEE-II leave all verdicts unchanged. We position the work as a methodological demonstration rather than a validated deployment tool: the indicator values are synthetic, and empirical calibration, expert weight elicitation, and field validation remain prerequisites for operational use.
Recent advances in spatial data and routing algorithms allow researchers to define the areas surrounding individuals using network distance rather than Euclidean distance. These differently constructed local environments then serve as inputs to spatial segregation measures, with network-based environments often producing higher segregation values than comparable Euclidean-based environments. These differences are frequently interpreted as evidence that mobility barriers embedded in the built environment shape patterns of racial isolation. However, distance-based local environments constructed over road networks typically contain fewer people than those constructed using Euclidean distance, raising the possibility that observed differences may reflect variation in the population composition of local environments rather than mobility constraints alone. This methodological study evaluates that possibility by constructing spatial segregation measures from both distance-based and population-based (k-nearest-neighbor) local environments defined using network and Euclidean distance. Whereas distance-based local environments built using network distance systematically include fewer people than those constructed using Euclidean distance, population-based local environments hold the size of local populations constant across measurement approaches. Holding constant the population of local environments reveals that differences between segregation measures based on network and Euclidean distance are substantially reduced across most U.S. places, although meaningful differences remain in some places. These findings suggest that previously documented differences between network- and Euclidean-based spatial segregation measures may partly reflect differences in the number of people included in local environments due to network-constrained reach.
Urban canal-edge spaces are increasingly recognised as important blue–green infrastructure for mitigating urban heat; however, the mechanisms governing pedestrian thermal comfort in these environments remain insufficiently understood. This study employed a validated ENVI-met model to investigate the effects of canal orientation, canal width, water bodies and riparian tree configuration on pedestrian thermal comfort under summer heatwave conditions in Manchester, UK. A total of 32 scenarios were simulated to quantify the individual and combined effects of these design factors. Canal water alone provided only moderate daytime cooling, with its effectiveness depending strongly on canal orientation and local ventilation conditions. In contrast, riparian trees substantially improved thermal comfort through reductions in mean radiant temperature associated with canopy shading. Furthermore, the study identified that thermal performance was influenced not simply by the presence of blue and green infrastructure, but by their interaction. Rather than acting as the dominant cooling source, canal water enhanced the cooling effectiveness of adjacent vegetation, while continuous canopy shading emerged as the principal mechanism controlling pedestrian thermal comfort. These findings provide new insights into the mechanisms of canal-edge cooling and offer quantitative evidence to support climate-responsive design of thermally comfortable and resilient urban waterfronts.
In recent years, gentrification has frequently been linked to housing inequality and mass evictions and portrayed in a negative light. This study defines gentrification in terms of neighborhood median household income, income diversity, and their level and trajectories over 14 years (2010–2023), and classifies neighborhood gentrification stages using hierarchical clustering. It then adopts a quantitative, data-driven approach to examine the pairwise statistical and geospatial relationships among gentrification stages, executed evictions (2023–2024), and other housing characteristics (rent, socioeconomic status, and demographics) across New York City neighborhoods. The results demonstrate that pre-gentrifying neighborhoods were 39.08 times more likely to have eviction rates 1.5 standard deviations above the mean, 14.19 times more likely to be geospatial eviction hotspots, and three times more likely to experience evictions per neighborhood than fully gentrified neighborhoods. Income-adjusted regression models indicate that gentrification-stage membership explains variation in eviction severity beyond median household income alone, increasing explained variance from 27.9% to 42.6%; late-stage gentrifying and stable low-change neighborhoods showed significantly lower eviction severity than fully gentrified neighborhoods at comparable income levels. Spatially, the Bronx, the most economically disadvantaged borough, contained 11 of 17 eviction-outlier NTAs and 37 of 48 geospatial eviction hotspots, making it the most eviction-concentrated area in the city. This clustering-based classification framework relies only on widely available Census and administrative data and can be applied to other metropolitan areas or adapted to related urban domains such as land-use and zoning analysis. Targeted, actionable recommendations are offered to direct eviction-prevention resources toward the most vulnerable communities.
The reliable long-term prediction of photovoltaic (PV) system performance is essential for optimizing operation, maintenance, and energy management in smart cities. Digital Twin (DT) technology has emerged as a promising approach for real-time monitoring and predictive analytics by continuously integrating physical system measurements with virtual models. However, many existing DT-based studies rely on limited validation periods, lack comprehensive uncertainty quantification, and provide insufficient comparisons with conventional forecasting approaches. To address these limitations, this study proposes a data-driven Digital Twin framework for daily photovoltaic energy production prediction for three silicon photovoltaic technologies (amorphous silicon, polycrystalline silicon, and monocrystalline silicon) operating under semi-arid climatic conditions in Morocco. The framework integrates data quality assessment and statistical production trend analysis based on linear regression, bootstrap confidence intervals, the Mann–Kendall trend test, and Sen’s slope estimator. In addition, the proposed DT model was benchmarked against persistence, linear regression, Random Forest, XGBoost, LightGBM, and Long Short-Term Memory (LSTM) models using a chronological training, validation, and independent testing framework. Model performance was evaluated over an independent test period of 602 consecutive days, representing approximately 1.65 years of continuous operation and more than one complete annual cycle. The proposed Digital Twin improved upon the standalone LightGBM model, achieving an RMSE of 1.3047 kWh/day, an MAE of 0.9255 kWh/day, a MAPE of 14.62%, and an R2 of 0.7015, compared with an RMSE of 1.3349 kWh/day, an MAE of 0.9637 kWh/day, and an R2 of 0.6875 for LightGBM. Over the 11-year monitoring period, the estimated long-term production trend rates were −0.566% yr−1 for a-Si, −0.335% yr−1 for pc-Si, and −0.260% yr−1 for mc-Si. Bootstrap analysis yielded median long-term production trend rates of −0.573, −0.326, and −0.251% yr−1, respectively, while Mann–Kendall tests indicated no statistically significant monotonic production trend for any technology (p = 0.1611, 0.2758, and 0.3502, respectively). The extended independent testing period, combined with statistical production trend analysis and comparative machine-learning evaluation, demonstrates the applicability of the proposed Digital Twin framework for photovoltaic performance monitoring and adaptive prediction under semi-arid climatic conditions.
Contemporary urban environments are characterized by intense and continuous activity, resulting in a significant transformation of the ambient acoustic environment. Noise pollution from sources such as traffic, construction, industry, and recreation propagate throughout these areas, although some residents may report a low perception of disturbance even under high exposure levels. This study investigates the moderating influence of housing conditions on the relationship between outdoor noise levels and perceived indoor acoustic environment in Coimbra, Portugal. The methodology combines primary data on housing characteristics and subjective indoor noise perception, obtained through a questionnaire survey, with secondary data from an environ-mental noise map. Logistic regression models, incorporating interaction terms, were employed to evaluate the moderating effect of housing conditions. The results demonstrate a statistically significant association between outdoor noise exposure and perceived indoor noise, with a limited moderating effect of housing conditions on this relationship.
Housing insecurity is one of the most urgent social problems in the United States, with eviction filings reflecting an important dimension of housing instability. This paper analyzes 75 monthly Florida eviction-filing observations from January 2019 through March 2025 to examine whether filing trajectories changed during and after pandemic-era tenant protections. Guided by an institutional temporality framework, we used descriptive analyses, a policy-guided interrupted time-series (ITS) model with Newey–West/HAC inference, a Fourier-seasonal dynamic regression with autoregressive errors, and exploratory segmented regression. The filing numerator covers all 67 Florida counties. Counts were the primary outcome; a secondary author-calculated rate used a fixed denominator of 2,794,102 renter-occupied housing units from the 2019–2023 American Community Survey 5-Year Table B25003. Mean monthly filings were 10,766 before March 2020, 6551 during March 2020–June 2021, and 11,701 during July 2021–March 2025. The primary ITS estimated an immediate March 2020 decline of 6854 filings (95% CI: 4541–9166 fewer; p < 0.001); the preferred AR(2) dynamic model also supported a large March decline (5957 fewer; p = 0.011). The July 2021 immediate change and direct tests of a higher post-period trajectory were not statistically significant. Overall annual seasonality was supported, but the apparent January and October peaks were not significant after adjustment. Florida filings therefore fell sharply around the onset of pandemic protections and subsequently returned toward the historical range, without statistically credible evidence that the post-moratorium trajectory became higher than the pre-pandemic trajectory.
Urban heat islands in densely built cities pose growing risks to public health and energy demand. Urban parks are key nature-based solutions, yet empirical evidence on park cool island (PCI) effects intensity and extent in compact cities remains limited. This study evaluated PCI for 12 pocket-to-medium/large urban parks in central Taipei, Taiwan. Land surface temperature (LST), mean radiant temperature (MRT), and effective temperature (ET) were measured along transects from park interiors into surrounding areas and linked to local environmental conditions. Parks showed PCI intensity in LST with an average cooling of 4.23 °C and an average cooling of 0.3 °C in ET; MRT cooling was weaker and spatially inconsistent. The cooling of LST extended to about 60 m beyond the park’s edges, while the cooling of ET reached approximately 150 m. In addition, the results show that shade and relative humidity are significant factors affecting PCI, and pervious pavement and wind speed also contribute to cooling LST and ET. The field observations provide evidence that urban parks contribute cooling effects and might reduce the risk of heat hazards.
This study examines the complex dynamics of water scarcity and infrastructure challenges in eThekwini Metropolitan Municipality, KwaZulu-Natal, South Africa. Despite its coastal location, the municipality faces persistent water shortages driven by rapid population growth, urbanization, climate change risks, aging and deteriorating infrastructure, and service delivery constraints. Communities including KwaXimba, Phoenix, Inanda, and Tongaat experience inconsistent access to clean water. The study aimed to identify root causes of scarcity, assess the effectiveness of existing water infrastructure, and propose sustainable, community-informed strategies to improve water resilience and equitable access. A qualitative research design was adopted to capture lived experiences and perceptions of water access across seven wards (1, 3, 52, 54, 56, 57, and 61), representing approximately 357,492 residents. Focus group discussions involved five randomly selected participants per ward, identified through councilor databases. Structured discussions were recorded for accuracy. Data were transcribed and analyzed using NVivo through systematic coding, thematic analysis, and organization of data into structured project folders aligned with research objectives. The analysis revealed recurring themes related to infrastructure decay, climate vulnerability, rapid urban expansion, and service delivery inefficiencies, highlighting significant barriers to reliable water access and community well-being. Findings provide evidence-based insights to guide policymakers, planners, and water authorities in strengthening infrastructure planning, climate resilience, and sustainable water governance.
Cities, being elementary concentrations of socio-economic activities and resource-environmental pressures, confront significant challenges in promoting new quality productive force (NQPF) development because technology, resource, and information conditions may be spatially associated across cities. Although the nexus among cities has gained widespread recognition in China’s high-quality development such as in innovations and low-carbon transformation, a critical gap exists in quantitatively assessing how model-estimated inter-city spatial correlations relate to high-quality development within an integrated framework. To bridge this gap, this study constructs an urban social correlation network (SCN) analysis framework, integrates spatial correlation methods to overcome the limitations of traditional heterogeneity assessment, and applies it to 283 Chinese cities from 2010 to 2023 to measure network characteristics and structural resilience related to NQPF. The results show that the network density rose from 0.0608 in 2010 to 0.1577 in 2023. By 2023, the SCN featured higher connectivity and reciprocity, comparatively high but lower efficiency, low hierarchy, and no obvious core–periphery structure. The 283 cities were divided into four blocks, exhibiting denser intra-regional than inter-regional links, and strengthened inter-regional interactions over time. The displayed node-removal trajectories declined faster under centrality-ordered removal than under one reported random-removal sequence. The network was more vulnerable to intentional attacks than to random attacks. Motif analysis indicates that M1 and M2 dominated and contributed to low network density, while the shift from open structural holes toward a mix of open and closed structures was associated with network evolution. These findings indicate that cultivating key node cities and improving inter-city coordination mechanisms to enhance network resilience are critical pathways for advancing NQPF development with Chinese characteristics, providing quantitative evidence for targeted inter-city coordination policymaking.
Whether economic growth decouples from municipal solid waste (MSW) generation in upper–middle-income economies remains contested. We test the Waste Kuznets Curve and a disposal-to-recovery substitution effect using a 13-year panel of 1101 Colombian municipalities, combining step-wise fixed-effects models with a non-parametric generalized additive model (GAM), a family of spatial specifications, and a selection-aware recovery model. We find no evidence of income-driven decoupling in landfilling. Once the urban density and demographic structure are controlled, the income terms lose significance, the non-parametric estimate is predominantly monotonic, and density emerges as the main structural driver. Material recovery grows faster than disposal with income (relative substitution), but this signal is concentrated where recovery is measured—only 27% of municipalities report it, and coverage falls from every metropolitan municipality to one in five in the rural periphery, and the income terms are stable across every spatial representation—so that once selection is corrected the recovery elasticity falls from about 5.9 to a non-significant 1.3. Rather than spontaneous decoupling, Colombia exhibits persistent coupling alongside an institutionally engineered, spatially unequal recovery capacity. Achieving SDG 12 therefore requires stratified policies that mandate consumption reduction in mature urban economies while subsidizing shared circular infrastructure for historically neglected rural jurisdictions.
The extensive use of high-resolution digital elevation data, along with continuous improvements in computing power and geographic information system (GIS) tools. Has driven the development of spatial data processing, management, and spatial interpolation methods. This study aims to construct a high-quality spatial distribution map of thermal comfort in densely populated areas of northwestern Jordan using climate data collected from six meteorological stations between 1991 and 2024, based on the indoor temperature index (IAT). To analyze the spatial variability of climate elements, a digital elevation model (DEM) with a spatial resolution of 30 m was used and resampled to a 0.5-km grid. Spatial interpolation employed inverse distance weighting (IDW), with each grid cell using data from the three nearest meteorological stations. The results showed that areas with higher temperatures inside the villas were clearly concentrated in the summer, especially in the lowlands near the Jordan Valley. Indicating that these areas are more susceptible to thermal stress. The model results also show that it performs well in predicting thermal comfort, with a coefficient of determination (R2) between 0.95 and 0.98 and mean squared error (MSE) between 0.35 and 0.50. which reflects the ability of these models to represent the relationship between climate variables and predict thermal comfort levels with a high degree of accuracy. The results indicate significant spatiotemporal differences in thermal comfort within the study area, with longer durations of heat stress in summer. This highlights the importance of combining geospatial methods with numerical simulations in studying the impacts of climate change and supporting urban planning and climate adaptation strategies.
The worsening jobs–housing imbalance in high-density urban areas has become increasingly prominent. However, existing studies predominantly focus on the city and regional scales, with limited attention to small-scale spatial units, failing to provide empirical evidence for refined regulation of transport and land use. As a typical compact development mode at the small scale, Transit-Oriented Development (TOD) offers an ideal spatial unit for examining micro-level jobs–housing relationships. However, its influence on jobs–housing balance remains unclear. This study takes 72 built subway station areas in Shenzhen’s highest-density built-up area as the research object. Employing quadratic polynomial regression and XGBoost-SHAP methods, it reveals the effects of transportation supply and land use on the employment–residential ratio, as well as threshold effects and indicator synergies. The main findings are as follows: (1) Transportation supply (Node), land use (Place), and TOD degree (TODness) all exhibit U-shaped effects on the employment–residential ratio, with inflection points at Node = 0.330, Place = 0.412, and TODness = 0.776, identifying the critical transition from balance to employment polarization. (2) Number of subway directions is the most critical indicator, together with betweenness centrality, floor area ratio, population size, and land use entropy, each exhibiting notable threshold effects. (3) A significant interaction effect exists between betweenness centrality and land use entropy, suggesting that traffic network connectivity and land use evenness can jointly regulate the employment–residential ratio in both directions. This study provides a quantitative basis for phased regulation of jobs–housing relationships in high-density TOD station areas, with findings transferable to other high-density cities.
Urban expansion generates habitat fragmentation, light pollution, and vegetation loss, driving biodiversity decline globally. Insectivorous bats are particularly suited to track these changes: their taxonomic and functional diversity, combined with their differential sensitivity to landscape configuration and roost availability, makes them reliable bioindicators of urbanization impacts. This study characterized bat acoustic diversity and community composition along an urban–rural gradient in Tacna, Peru, using passive acoustic monitoring. Twenty-three stations equipped with autonomous ultrasonic recorders were established between December 2024 and April 2025. Sonotype identification was validated through linear discriminant analysis (DFA), achieving 94.47% accuracy by Leave-One-Out Cross-Validation (LOOCV). Nine sonotypes were identified, seven Molossidae and two Vespertilionidae; Myotis atacamensis showed the highest detection frequency across all districts. Diversity indices were highest in the peri-urban zone of Pocollay (S = 8; H′ = 1.664) and lowest in Gregorio Albarracín (S = 4; H′ = 0.964). The high dissimilarity between Pachía and the urban core of Tacna (BC = 0.817) points to marked compositional differences linked to urbanization intensity. Records of Nyctinomops laticaudatus and N. macrotis constitute the first documentation for the urban area of Tacna. These results suggest urban and peri-urban sectors may function as ecological oases for bat sonotypes within a hyperarid matrix, providing a baseline for evaluating the role of green spaces, landscape connectivity, and artificial lighting in arid cities.
