基于多源大数据的郑州市可步行性与步行行为关系研究

Abstract Rapid urbanization at the expense of the environment led to a reduction in vegetation cover, and consequently aggravated land degradation, urban water logging, heat island effect and other effects. Revealing the driving mechanism behind urban land use change facilitates deeper insight into the human and biophysical effects in the urbanization process and thereby supports the sustainable urban development. This work proposed a margin-based measure of random forest for core driving factor identification of urban land use change, which mainly included urban vegetation change, constructed land, water bodies, etc., using multitemporal global land cover products and point-of-interest (POI) data. Taking the land use change in Wuhan from 2010 to 2020 as the case study, the proposed method was employed to measure and sort the driving forces of 24 biophysical and human factors. The results suggested that the margin-based method was more reliable and sensitive than the traditional importance measure of random forest when detecting the driving mechanism behind land use change. Meanwhile, both the importance values and the ranking orders of driving factors measured by the margin-based method were stable regardless of which similarity measure was chosen and applied. The findings also showed that topographic conditions persistently affected urban land use change, while transportation factors, instead of business services, gradually became the most important human driving factors in Wuhan in the last 10 years.

The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for capturing spatial dependencies and user movement patterns. While Large Language Models (LLMs) exhibit strong capabilities in semantic understanding and contextual reasoning, applying them to spatial tasks like next POI recommendation remains challenging. First, the infrequent nature of specific GPS coordinates makes it difficult for LLMs to model precise spatial contexts. Second, the lack of knowledge about POI transitions limits their ability to capture potential POI-POI relationships. To address these issues, we propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances LLMs with two specialized components. The Geographic Coordinate Injection Module (GCIM) transforms GPS coordinates into spatial representations using hierarchical and Fourier-based positional encoding, enabling the model to understand geographic features from multiple perspectives. The POI Alignment Module (PAM) incorporates POI transition relations into the LLM's semantic space, allowing it to infer global POI relationships and generalize to unseen POIs. Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.

Existing methods for learning urban space representations from Point-of-Interest (POI) data face several limitations, including issues with geographical delineation, inadequate spatial information modelling, underutilisation of POI semantic attributes, and computational inefficiencies. To address these issues, we propose CaLLiPer (Contrastive Language-Location Pre-training), a novel representation learning model that directly embeds continuous urban spaces into vector representations that can capture the spatial and semantic distribution of urban environment. This model leverages a multimodal contrastive learning objective, aligning location embeddings with textual POI descriptions, thereby bypassing the need for complex training corpus construction and negative sampling. We validate CaLLiPer's effectiveness by applying it to learning urban space representations in London, UK, where it demonstrates 5-15% improvement in predictive performance for land use classification and socioeconomic mapping tasks compared to state-of-the-art methods. Visualisations of the learned representations further illustrate our model's advantages in capturing spatial variations in urban semantics with high accuracy and fine resolution. Additionally, CaLLiPer achieves reduced training time, showcasing its efficiency and scalability. This work provides a promising pathway for scalable, semantically rich urban space representation learning that can support the development of geospatial foundation models. The implementation code is available at https://github.com/xlwang233/CaLLiPer.

This study leverages multi-source remote sensing data—Nighttime Light (NTL) imagery and POI (Point of Interest) datasets—to quantify the spatiotemporal interaction between urban spatial restructuring and logistics industry evolution in Zhengzhou, China. Using calibrated NPP/VIIRS NTL data (2012–2022) and fine-grained POI data, we (1) identified urban functional spaces through kernel density-based spatial grids weighted by public awareness parameters; (2) extracted built-up areas via the dynamic adaptive threshold segmentation of NTL gradients; (3) analyzed logistics agglomeration dynamics using emerging spatiotemporal hotspot analysis (ESTH) and space–time cube models. The results show that Zhengzhou’s urban form transitioned from a monocentric to a polycentric structure, with NTL trajectories revealing logistics hotspots expanding along air–rail multimodal corridors. POI-derived functional spaces shifted from single-dominant to composite patterns, while ESTH detected policy-driven clusters in Airport Economic Zones and market-driven suburban cold chain hubs. Bivariate LISA confirmed the spatial synergy between logistics growth and urban expansion, validating the “policy–space–industry” interaction framework. This research demonstrates how integrated NTL-POI remote sensing techniques can monitor policy impacts on urban systems, providing a replicable methodology for sustainable logistics planning.

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.

Semantic annotation for points of interest (POIs) is the process of annotating a POI with a category label, which facilitates many services related to POIs, such as POI search and recommendation. Most of the existing solutions extract features related to POIs from abundant user-generated content data (e.g., check-ins and user comments). However, such data are often difficult to obtain, especially for newly created POIs. In this paper, we aim to explore semantic annotation for POIs with limited information such as POI (place) names and geographic locations. Additionally, we have found that the street view images provide extensive visual clues about POI attributes and could be an essential supplement to limited information of POIs that enables semantic annotation. To this end, we propose a novel multimodal model for POI semantic annotation, namely M3PA, which achieves enhanced semantic annotation through fusing a POI’s textual and visual representations. Specifically, M3PA extracts visual features from street view images using a pre-trained image encoder and integrates these features to generate the visual representation of a targeted POI based on a geographic attention mechanism. Furthermore, M3PA utilizes the contextual information of neighboring POIs to extract textual features and captures their spatial relationships through geographical encoding to generate the textual representation of a targeted POI. Finally, the visual and textual representations of a POI are fused for semantic annotation. Extensive experiments with POI data from Amap validate the effectiveness of M3PA for POI semantic annotation, compared with several competitive baselines.

Urban functional zones (UFZ) identification with remote sensing imagery (RSI) is attracting increasing attention in urban planning and resource allocation in urban areas, etc. The UFZ is a comprehensive unit comprising geographical, how to effectively integrate the RSI and points of interest (POI) with different physical and socioeconomic characteristics is important and promising. However, there are two challenges for the UFZ identification. On one hand, the UFZ is closely related to buildings, and most current methods lack an in-depth understanding of building semantics. Therefore, an efficient integration of building footprint (FT) data deserves further investigation. On the other hand, these RSI, POI, and FT data are heterogeneous; how to effectively leverage complementary information among these highly heterogeneous modalities to enhance the comprehensive understanding of urban. To solve the above challenges, this article introduces an end-to-end deep learning-based multisource dynamic fusion network for UFZ identification on RSI, POI, and FT. In the proposed method, an adaptive weight interactive fusion module is designed to comprehensively integrate the complementary information among the heterogeneous RSI, POI, and FT data sources. In addition, a multiscale feature focus module is proposed to extract multiscale image features and emphasize critical characteristics. This method was applied to UFZ classification in Ningbo, Zhejiang Province, China, and the experimental results demonstrate the competitive performance.

Geospatial big data and remote sensing data are widely used in population spatialization studies. However, the relationship between them and population distribution has regional heterogeneity in different geographic contexts. It is necessary to improve data processing methods and spatialization models in areas with large geographical differences. We used land cover data to extract human activity, nighttime light and point-of-interest (POI) data to represent human activity intensity, and considered differences in geographical context to divide mainland China into northern, southern and western regions. We constructed random forest models to generate gridded population distribution datasets with a resolution of 500 m, and quantitatively evaluated the importance of auxiliary data in different geographical contexts. The street-level accuracy assessment showed that our population dataset is more accurate than WorldPop, with a higher R2 and smaller deviation. The improved datasets provided broad potential for exploring the spatial-temporal changes in grid-level population distribution in China from 2010 to 2020. The results indicated that the population density and settlement area have increased, and the overall pattern of population distribution has remained highly stable, but there are significant differences in population change patterns among cities with different urbanization processes. The importance of the ancillary data to the models varied significantly, with POI contributing the most to the southern region and the least to the western region. Moreover, POI had relatively less influence on model improvement in undeveloped areas. Our study could provide a reference for predicting social and economic spatialized data in different geographical context areas using POI and multisource satellite data.

ABSTRACT This study uses Shanghai to explore the effects of the COVID-19 pandemic on the spatial distribution patterns and driving factors of the urban catering industry. Through quantitative analysis of restaurant point-of-interest data and influencing factors in Shanghai’s main urban area from 2016 to 2022 using geographic information technology, machine learning, and spatial econometric models, this study predicts spatial changes in the catering industry. The number of restaurants in Shanghai’s main urban area continuously decreased from 2016 to 2022, with a significant pre-pandemic decline. The catering industry in this area has a significant spatial correlation in the spatial distribution, showing a pattern of “one core dominance with multiple cores coexisting,” with Jing’an and Huangpu Districts having the highest densities, which is expected to continue. Target markets remain a decisive factor in restaurant location selection post-COVID. Restaurant aggregation is most favorable when the population density is between 0 –50,000 people/km2, GDP is between 0–20 billion yuan/km2, housing prices are between 20,000 –130,000 yuan/m2, school density is between 0–20/km2, hospital density is between 0–10/km2, and attraction density is between 0–40/km2. Transportation conditions have no apparent threshold; the more developed the transportation, the more favorable the conditions for the catering industry. Post-COVID-19, areas with high housing prices and around tourist attractions are less likely to attract many restaurants. Areas around hospitals maintain restaurant aggregation, although this will decrease. Areas around schools will become popular for restaurants in the future. GRAPHICAL ABSTRACT

No abstract available

At present, data obtained from the Global Positioning System (GPS) is significantly valuable in mobility research. However, GPS‐based data lacks include trip purpose information. Consequently, many researchers have endeavoured to predict or impute these missing attributes. Existing studies have focused on constructing more features to improve prediction accuracy, but paid less attention to the model's applicability and transferability. In this study, five trip purposes are extracted, including education, recreation, personal, shopping, and transportation, from Chengdu Household Travel Survey (HTS) data. The individual and trip characteristics that are common and can be easily derived from GPS data are carefully selected and extracted. Point of Interest (POI) data of the trip destination are also collected to enhance input characteristics. To obtain more accurate results, an ensemble learning model, Gradient Boosting Decision Trees (GBDT), is employed to predict trip purposes. grid search and cross‐validation techniques are used to optimize the hyper‐parameters. Empirical results show that the proposed model achieves 0.788 accuracy, which is 22.17%, 14.53%, 10.36%, and 6.77% higher than Multinominal Logit (MNL), Artificial Neural Network (ANN), Random Forest (RF), and Deep Belief Network (DBN), respectively. It is also found that although increasing trip features improve the model's accuracy, it simultaneously impairs model's transferability and generalizability.

Urban vitality is a critical indicator of urban development quality and livability. However, existing studies often rely on single-source data or subjective evaluation methods, making it challenging to comprehensively and objectively capture the spatial-temporal characteristics of urban vitality. This study takes Baiyun District in Guangzhou as a case study, integrating multiple data sources—including Points of Interest (POI) data, streetscape elements, transportation networks, land use data, and Baidu heat maps—to construct an urban vitality index and explore its key influencing factors. The results reveal the spatial distribution patterns of urban vitality and the varying significance of different determinants, providing data-driven insights and policy implications for urban planning and development.

Urban diversity is essential for promoting urban vitality and achieving sustainable urban development. However, existing studies rely on static and non-visual data and seldom incorporate human perception aspects in the diversity estimation. Together with the modifiable areal unit problem (MAUP) in the traditional entropy-based approach, urban diversity is prone to be biased or underestimated. In this study, we use urban function (from POI) and visual semantics (from geo-tagged photos) to estimate what we call “perceived urban diversity”. More importantly, we propose to improve the traditional entropy-based diversity measures by addressing the MAUP issue using area- and accessibility-based extensions. Empirical analysis using Shenzhen, China, as a case study reveals that our “perceived diversity” indicators display stronger correlations to urban vitality. Furthermore, combining different data sources (e.g., geo-tagged photos) provides a more comprehensive portrayal of urban diversity. Finally, our results suggest that neighborhoods dominated by residential or commercial land uses would benefit the most from enhanced diversity. These findings are useful for a refined assessment of urban diversity and offer valuable insights for urban planning and community design.

“Where to go next” is the fundamental problem in sequential point-of-interest (POI) recommendation, which takes as input the individual check-in history, mines the dynamic preference and suggests the expected POI for the next step behavior. With the evolution of neural network architectures, sequential POI recommendation models have entered the well-established era of Transformer, where the core self-attention mechanism undertakes the sequential dependency modeling. However, due to the inherent computational complexity issue, i.e., quadratic scaling with the sequence length, Transformer-based methods might be unfeasible to handle long-term check-ins, which hinders the sufficient long-range dependency modeling. Recently, Mamba, as a selective structured state space model, lights on an efficient alternative, which excels at long sequence modeling tasks and achieves favorable performances in both text and vision communities. In this paper, we present GeoMamba, as an end-to-end sequential POI recommendation framework, to realize the fine-grained individual behavior pattern modeling with the assistance of geographical characteristics. Specifically, the framework is solely built upon the Mamba architecture, where the inherited linear scaling complexity facilitates the fast training and inference for long historical check-in sequences. To our best knowledge, it is the first attempt to explore the practicability of Mamba in sequential POI recommendation. Take a step further, we exploit a Mamba-based geography encoder to model the exact location of each POI. The encoder follows the hierarchical grid partition manner to encode GPS coordinates as sequences, and the proximity among generated representations in the latent space are consistent with the physical distributions across POIs. The empirical observation on 4 real-world location-based social network (LBSN) datasets elucidates that GeoMamba constantly attains superior recommendation performance against several state-of-the-art baselines, where the average improvement achieves up to 8.18%. Notably, compared to Transformer-based methods, GeoMamba speeds up the training and inference process by 3.26 and 1.76 times respectively when handling extremely long historical sequences.

Metropolitan areas, as pivotal hubs for global tourism and economic growth, necessitate sustainable spatial planning to balance development with ecological preservation. As critical geospatial datasets, nighttime light (NTL) and point of interest (POI) data enable the robust analysis of urban structural patterns. Building upon coupling coordination theory and polycentric spatial frameworks, this study investigates the spatial interdependencies between tourism POI and NTL data in China’s Changsha–Zhuzhou–Xiangtan Metropolitan Area (CZTMA). Key findings reveal high spatial coupling homogeneity, with three urban cores exhibiting radial value attenuation from city centers toward the tri-city intersection; concentric zonation patterns where NTL-dominant rings encircle high-coupling nuclei, contrasting with sporadic POI-intensive clusters in peri-urban towns; and sector-specific luminosity responses, where sightseeing infrastructure demonstrates the strongest localized NTL impacts through multiscale geographically weighted regression (MGWR). These findings establish a novel “data fusion-spatial coupling-governance” analytical framework and provide actionable insights for policymakers to harmonize tourism-driven urbanization with ecological resilience, contributing to United Nations Sustainable Development Goal (SDG) 11 (Sustainable Cities).

Urban growth models often prioritize environmental and accessibility factors while underestimating behavioral and functional dynamics. This study develops a POI-enhanced Cellular Automata (CA) framework to simulate urban expansion by incorporating three semantic indicators derived from Point-of-Interest (POI) data—density (PD), diversity (PDI), and functional centrality (FC). Taking Yan’an, China, as a case, the model integrates these indicators with terrain and infrastructure variables via logistic regression to estimate land-use transition probabilities. To ensure robustness, spatial block cross-validation was adopted to reduce spatial autocorrelation bias. Results show that the POI-based model outperforms the baseline in both Kappa and Figure of Merit metrics. High-density and mixed-function POI zones correspond with compact infill growth, while high-centrality zones predict decentralized expansion beyond administrative cores. These findings highlight how functional semantics sharpen spatial prediction and uncover latent behavioral demand. Policy implications include using POI-informed maps for adaptive zoning, ecological buffer protection, and growth hotspot management. The study contributes a transferable workflow for embedding behavioral logic into spatial simulation. However, limitations remain: the model relies on static POI data, omits vertical (3D) development, and lacks direct comparison with alternative models like Random Forest or SVM. Future research could explore dynamic POI trajectories, integrate 3D building forms, or adopt agent-based modeling for richer institutional representation. Overall, the approach enhances both the accuracy and interpretability of urban growth modeling, providing a flexible tool for planning in functionally evolving and ecologically constrained cities.

POI recommendation plays an important role in many applications, such as mobility prediction and location-based advertisements. Existing POI recommendation methods mainly capture the observed patterns in user visits for recommendations, without a comprehensive consideration of the underlying reasons behind the visits. Therefore, different causes of a visit, i.e., users’ interest and geographical context, are entangled. When the underlying causes change (e.g., when a user moves to a new place), the robustness of the recommendations cannot be guaranteed. To address the above challenges, we propose DUIG, a novel user interest and geographical influences disentanglement framework for POI recommendations. We first design a personalized disentanglement strategy to divide check-ins through geographical influence. Specifically, the colliding effect of causality is leveraged to the divide cause-specific check-ins, such that user interest and geographical influence can be properly disentangled in user and POI embeddings. Through this mechanism, even if the underlying reasons that affect a user’s preference change, intervention can be conducted upon the causes to make recommendations generalized to the new scenario. In addition, a geographical-aware negative sampling strategy is proposed to utilize hard negatives to regularize the embedding and disentanglement in the latent space, where a larger sampling probability is introduced for negative samples containing more geographic information. Extensive experiments on two real-world POI recommendation datasets demonstrate the superior performance of DUIG.

As urban spatial patterns are the prerequisite and foundation of urban planning, spatial pattern research will enable its improvement. The formation mechanism and definition of an urban “production–living–ecological” space is used here to construct a classification system for POI (points of interests) data, crawl POI data in Python, and DBSCAN (density-based spatial clustering of application with noise) to perform cluster analysis. This mechanism helps to determine the cluster density and to study the overall and component spatial patterns of the “production–living–ecological” space in the central urban area of Wuhan. The research results are as follows. (1) The spatial patterns of “production–living–ecological” space have significant spatial hierarchical characteristics. Among them, the spatial polarizations of “living” and “production” are significant, while the “ecological” spatial distribution is more balanced. (2) The “living” space and “production” space noise points account for a small proportion of the total and are locally clustered to easily become areas with development potential. The “ecological” space noise points account for a large proportion of the total. (3) The traffic accessibility has an important influence on the spatial patterns of “production–living–ecological” space. (4) The important spatial nodes of each element are consistent with the overall plan of Wuhan, but the distribution of the nodes for some elements is inconsistent. The research results show that the POI big data can accurately reveal the characteristics of urban spatial patterns, which is scientific and practical and provides a useful reference for the sustainable development of territorial and spatial planning.

Exploring the spatial distribution characteristics of tourist attractions and the influencing factors is of significant importance for destination development, yet little relevant research has been conducted. This study explores the spatial patterns and determinants of tourist attractions using Hubei Province of China as a case based on the POI (Points of Interest) data, combined with standard deviation ellipse, GeoDetector method and so on. The results show that: (1) The distribution of tourist attractions in Hubei Province is concentrated in Wuhan and Huanggang. (2) The overall spatial patterns of tourist attractions in Hubei Province show a trend of “overall dispersion, partial concentration”, with the direction of northwest-southeast. (3) The permanent population, passenger traffic volume, per capita GDP, and the added value of the tertiary industry are the primary factors influencing the spatial distribution of tourist attractions in Hubei Province. Additionally, topography and river systems factors also impact their distribution. This study provides critical information for theory and practice in terms of tourism resources optimization.

No abstract available

The rapid development of cities has led to increasingly problems in the road network structure of urban streets. Combining emerging big data technology with traditional street network analysis methods has become a new way to tackle it. Guilin is a famous international tourist city, and the “Two Rivers and Four Lakes” scenic area is an iconic symbol of Guilin’s scenery. Its streets connect various tourist spots. This study focused on the street’s layout of the “Two Rivers and Four Lakes” scenic area, and used a combination of spatial syntax and POI big data to analyse their spatial structure. The research results indicated that: (1) there was a positive correlation between the global integration value of the street and the POI value; (2) by combining functional density indicators with global integration analysis, streets that significantly deviate from the overall trend can be identified, and classified according to their characteristics to reveal the reasons for their contradictions; (3) we needed to propose three plans for optimizing the proportion of high street, enhancing street functions, and “improving street space” for different types of streets to ultimately realize the purpose of sustainable development of streets and cities.

Existing sequential Point of Interest (POI) recommendation methods overlook a fact that each city exhibits distinct characteristics and totally ignore the city signature. In this study, we claim that city matters in sequential POI recommendation and fully exploring city signature can highlight the characteristics of each city and facilitate cross-city complementary learning. To this end, we consider the two-city scenario and propose a Dual-Target Cross-City Sequential POI Recommendation model (DCSPR) to achieve the purpose of complementary learning across cities. On one hand, DCSPR respectively captures geographical and cultural characteristics for each city by mining intra-city regions and intra-city functions of POIs. On the other hand, DCSPR builds a transfer channel between cities based on intra-city functions, and adopts a novel transfer strategy to transfer useful cultural characteristics across cities by mining inter-city functions of POIs. Moreover, to utilize these captured characteristics for sequential POI recommendation, DCSPR involves a new region- and function-aware network for each city to learn transition patterns from multiple views. Extensive experiments conducted on two real-world datasets with four cities demonstrate the effectiveness of DCSPR.

The boundary delineation of the urban-rural fringe (URF) is the basic work of fine planning and governance of cities, which plays a positive role in promoting the process of global sustainable development and urban and rural integration. In the past, the delineation of URF had shortcomings such as a single selected data source, difficulty in obtaining data, and low spatial and temporal resolution. This study combines Point of Interest (POI) and Nighttime Light (NTL) data, proposes a new spatial recognition method of URF according to the characteristics of urban and rural spatial structure, and conducts empirical analysis with Wuhan as the research object, combining the information entropy of land use structure, NDVI, and population density data to verify and compare the delineation results and field verification was conducted for typical areas. The results show that (1) the fusion of POI and NTL can maximize the use of the characteristics of the differences in facility types, light intensity, and resolution between POI and NTL, compared with the urban-rural fringe boundary identified by POI, NTL or population density data alone, and it is more accurate and time-sensitive; (2) NPP and POI (fusion data of Suomi NPP-VIIRS and POI) can quantitatively identify potential central area and multi-layer structure of the city. It fluctuates between 0.2 and 0.6 in the urban core area of Wuhan and between 0.1 and 0.3 in the new town clusters, while in the URF and rural areas drops sharply to below 0.1; (3) the urban-rural fringe area of Wuhan covers a total area of 1482.35 km2, accounting for 17.30% of the total area of the city. Its land use types are mainly construction land, water area, and cultivated land, accounting for 40.75%, 30.03%, and 14.60% of the URF, respectively. Its NDVI and population density are at a medium level, with values of 1.630 and 2556.28 persons/km2, respectively; (4) the double mutation law of NPP and POI in urban and rural space confirms that the URF exists objectively as a regional entity generated in the process of urban expansion, provides empirical support for the theory of urban and rural ternary structure, and has a positive reference value for the allocation of global infrastructure, industrial division, ecological function division, and other researches.

Transitional streets serve as intermediary spaces between the Central Business Districts (CBDs) and surrounding residential areas, offering diverse functions and activities within urban interiors. However, a practical methodology for accurately classifying these streets has been lacking, due primarily to transitional areas’ spatial constraints and functional complexities. This study leverages Point of Interest (PoI) data from 2023 to develop an innovative methodological framework that addresses these challenges. This framework analyses transitional streets’ functional distribution and typology, employing PoI frequency density and functional type ratios to identify and classify functional zones. It generally delineates eight main types of transitional streets in the CBD of Chongqing, a prototypical hillside city. Utilising advanced data technology from internet maps, this research pioneers new approaches for identifying and analysing the functionality of transitional streets. The findings underscore the effectiveness of PoI data in precisely recognising the functional types of transitional streets, thereby providing a robust theoretical and practical foundation for the in-depth study of transitional streets. Moreover, the results enhance urban spatial planning in hillside cities of China, effectively demonstrating the advantages of PoI data in defining street typology compared to traditional methods. This approach provides a more detailed understanding of urban functional dynamics by allowing for a more nuanced data analysis of street functions.

The possibility of moving objects accessing different types of points of interest (POIs) at specific times is not always the same, so quantitative time geography research needs to consider the actual POI semantic information, including POI attributes and time information. Existing methods allocate probabilities to position points, including POIs, based on space–time position information, but ignore the semantic information of POIs. The accessing activities of moving objects in different POIs usually have obvious time characteristics, such as dinner usually taking place around 6 PM. In this paper, building upon existing probabilistic time geographic methods, we introduce POI attributes and their time preferences to propose a probabilistic time geographic model for assigning probabilities to POI accesses. This model provides a comprehensive measure of position probability with space–time uncertainty between known trajectory points, incorporating time, space, and semantic information, thereby avoiding data gaps caused by single-dimensional information. Experimental results demonstrate the effectiveness of the proposed method.

With the progress of urban development, the spatial quality of urban streets has become one of the key concerns. In view of the actual new needs of urban micro-renewal, this study takes the street accessibility, regional accessibility and other aspects, takes Zhengzhou West Street Area as a case study, extracts the elements of street space based on the street view image data, combines the artificial intelligence technology with the sDNA spatial accessibility grid analysis technology, and synthesises the classification and scientific analysis of the cutting-edge urban data such as the urban street walkability index system, and based on this, proposes the street vitality optimisation strategy for historical blocks. Based on this, we propose the optimisation strategy of street vitality in historic districts, and provide technical support for urban micro-renewal in terms of analysis and theory.

No abstract available

Assessing street walkability is a critical agenda in urban planning and multidisciplinary research, as it facilitates public health, community cohesion, and urban sustainability. Existing evaluation systems primarily focus on objective measurements, often neglecting subjective assessments and the diverse walking needs influenced by different urban spatial elements. This study addresses these gaps by constructing a comprehensive evaluation framework that integrates both subjective and objective dimensions, combining three neighbourhood indicators: Macro-Scale Index, Micro-Scale Index, and Street Walking Preferences Index. A normalization weighting method synthesizes these indicators into a comprehensive index. We applied this framework to assess the street environment within Beijing’s Fifth Ring Road. The empirical results demonstrate that: (1) The framework reliably reflects the distribution of walkability. (2) The three indicators show both similarities and differences, underscoring the need to consider the distinct roles of community and street-level elements and the interaction between subjective and objective dimensions. (3) In high-density cities with ring-road development patterns, the Macro-Scale Index closely aligns with the Comprehensive Index, demonstrating its accuracy in reflecting walkability. The proposed framework and findings offer new insights for street walkability research and theoretical support for developing more inclusive, sustainable and walkable cities.

Walkability reflects the well-being of a city, and its measurement is evolving rapidly due to advancements of big data and machine learning technologies. The study examines the transformative impact of these technological interventions on the evaluation of walkability trends over the period 2015 to 2022. We create a framework consisting of big data sources, machine learning methods, and research purposes, revealing research trajectories and associated challenges. Despite diverse data usage, image data dominates in walkability research. While street view and point of interest data were primarily used to depict the environment, social media and handheld/ wearable data were more commonly employed to represent user behaviours or perceptions. Leveraging machine learning in conjunction with big data assists researchers in three aspects of walkability studies. First, researchers utilise classification and clustering to predict street quality, walkability, and identify neighbourhoods with certain characteristics. Second, researchers unveil relationship between the built environment and pedestrian perceptions or behaviours through regression analysis. Third, researchers employ generative models to create streetscapes or urban structures, although their utilisation is limited. Meanwhile, challenges persist in data access, customisation of machine learning models for urban studies, and establishing standard criteria to guarantee data quality and model accuracy.

Green and blue spaces protect against heat exposure and provide opportunities for physical activity, social connection and psychological restoration. However, few studies combine green and blue space with walkability indices, potentially under-estimating levels of socioeconomic inequity. We aimed to create walkability indices that included green and blue walkability and compared these with conventional walkability methods. Walkability network buffers (400m, 800m, 1600m) around Australian Bureau of Statistics (ABS) mesh blocks were created for one large (Sydney) and two smaller cities (Newcastle, Wollongong). Estimates were made of network walkability areas using: 1) population density; 2) street connectivity; 3) destination availability; 4) street and park tree canopy percentage; and 5) blue space percentage (determined by a 10m buffer around coastlines/water bodies, and a 50m buffer around beaches). Z-scores for each of the five domains were calculated and used to create five walkability index models. Choropleth and bivariate maps were analysed for spatial patterns. Spearman's rank correlation coefficient was analysed between socioeconomic disadvantage and measures of walkability with and without green and blue space embellishment. For all three cities together at the 1600m scale, green + blue walkability was moderately associated with low socioeconomic disadvantage (Rho=0.5163, p-value<0.0001). In comparison, conventional walkability was negligibly associated with more disadvantaged areas (Rho=-0.0680, p-value<0.0001). For Sydney, conventional walkability and green + blue walkability were weakly but positively related at 1600m, but had a negligible relationship at 400m. Incorporating measures of green and blue space reveals greater levels of socioeconomic inequality in walkability than previously recognised. Spatially targeted investment in green and blue space is accordingly indicated to better balance socioeconomic inequalities in walkability. Incorporating blue and green infrastructure into walkability indices could also assist in global heat mitigation to reduce urban heat islands.

The determination of walkability for megacities is critically important, particularly in the context of fostering sustainable urban environments. This paper applies modified Delphi techniques to focus on identifying and prioritizing the key factors influencing urban walkability in large cities. The selected region of the Gulf Cooperation Council (GCC) served as the basis for this research, reflecting its unique socio-cultural and environmental challenges. A panel of local and international experts participated in the study, evaluating and ranking 111 walkability indicators categorized into five groups: cultural, functional, safety, aesthetic, and comfort. Two rounds of the Delphi survey were conducted, to obtain insights from professionals in urban planning, civil engineering, and related fields. The findings emphasize the critical role of sustainability in addressing the extreme nature of the GCC climate, highlighting the need for innovative and climate-responsive pedestrian infrastructure. Safety and environmental considerations were identified as essential for enhancing walkability and contributing to more sustainable and livable cities in the region. The study’s outcomes led to the development of a ‘walkability audit tool’ tailored to Gulf cities, which serves as a strategic guide for policymakers and urban planners to implement sustainable urbanization policies. By addressing the relationship between walkability and sustainability, this research contributes to creating resilient, inclusive, and walkable urban environments that are better equipped to meet the challenges of rapid urbanization and climate adaptation in the GCC region. The results obtained from this study provide actionable insights and practical tools for enhancing walkability and advancing sustainable urban development in the GCC and similar regions globally.

Evidence shows enhanced walking environment promotes overall physical activities and further alleviates the risk of chronic diseases and mental disorders. Current walkability research is limited by traditional GIS methods that fail to capture micro-level details and human perceptions. Additionally, existing image segmentation techniques return low accuracy when extracting complex street environment features. Therefore, we developed a hierarchical evaluation framework for urban walkability with high precision image segmentation techniques, and subjective measurements on four first-level indicators (greenness, openness, crowding, safety) and their corresponding second-level indicators. An entropy weight method was constructed to quantify the indicators based on questionnaires from 120 volunteers. Furthermore, we developed Detail-Strengthened High-Resolution Network (DS-HRNet), a deep learning model that demonstrates a 15% improvement in street scene segmentation performance compared to existing models. Using the newly developed deep learning model, we analyzed 113,900 street view images in central Wuhan City, China. Our walkability results revealed spatial heterogeneity across the city, characterized by substantial disparities between adjacent areas, particularly in commercial areas. Subsequent socioeconomic analysis demonstrated that better walkability exists in areas of higher socioeconomic status but lower proportion of non-local residents. This walkability inequality may further lead to health disparities through its influence on physical activity and social interaction.

Urban sustainability is closely linked to walkability, as it impacts social interaction, public health, and economic development. In megacities like Riyadh, where automobiles dominate mobility, the development of pedestrian infrastructure remains inadequate. An online survey was conducted through public sampling to evaluate walking conditions in central Riyadh’s commercial districts. The 302 participants evaluated 49 critical walkability indicators to determine their significance and satisfaction with the current conditions. Gap analysis and a displeasure measurement framework identified pedestrian challenges. Participants acknowledged the importance of walkability aspects but expressed strong dissatisfaction with existing conditions. Key barriers to pedestrian comfort included inadequate facilities, environmental discomfort, weak safety measures, and cultural driving preferences. The study highlighted crucial walkability issues requiring improvement, such as public toilets, shaded pathways, air quality, and pedestrian-friendly infrastructure. The findings emphasize the need for targeted policy interventions in Riyadh’s commercial districts to enhance pedestrian accessibility and comfort, to promote urban sustainability through improved walkability. This study offers a methodological advancement by combining Relative Importance Index, gap analysis, and a novel disgruntlement measurement framework to assess walkability. The use of 49 Delphi-derived indicators contextualized within a GCC megacity adds a unique perspective to urban sustainability research. The findings inform both local policy and global urban studies by demonstrating how culturally and climatically adaptive walkability metrics can guide equitable, data-driven interventions.

Jakarta has one of the world's lowest averages daily step counts due to a variety of factors, including a changing urban environment and a lack of pedestrian infrastructure. In response, the Jakarta government shifted its development strategy to prioritize mass transportation and pedestrians over private vehicles. However, tackling relatively complicated urban issues cannot be accomplished solely by strengthening the transport system; it must be followed by urban development oriented towards transit locations. Therefore, "transit-oriented development" (TOD) has been introduced to increase the neighbourhood's walkability. Walkability is described as a key indicator of active travel or as a parameter of how useful the built environment is for people who walk to various locations and for multiple purposes.The Dukuh Atas TOD, one of Jakarta's earliest TODs, is used as a case study – focusing on a 400-meter-radius core area – to analyze how TOD intervention impacts the walkability of an area.The study used descriptive qualitative methods, including the production of maps at two different time periods to compare conditions before and after the construction of the MRT station and the Dukuh Atas TOD, conducting field observationsto directly observe and understand, capture phenomena that arise, record them, and consider the relationships between aspects of these events, and gathering pedestrian and commuter perceptionsthrough questionnaires and interviews to analyze the variables of intermodal conflict on the pathway, maintenance and cleanliness, connectivity, amenities, and disability infrastructure. According to the variables used in this study to determine whether TOD intervention has affected walkability in Dukuh Atas area, despite some of sidewalks are still inaccessible to everyone, the results indicate that several government interventions implemented of Dukuh Atas TOD development have significantly improved walkability in several zones. This research is expected to contribute to the improvement of Dukuh Atas TOD in creating a walkable environment.

Abstract. Walkability, a core element of urban mobility, is indispensable for health, liveability, and sustainability. However, it continues to face challenges in the major cities of developing countries across the Global South. Adopting a case study of the metropolitan city of Kolkata, West Bengal, India, this study aims to assess the integrated visual walkability using Mapillary Street View Imagery combined with deep learning-based semantic segmentation techniques. Four factors have been considered to study the walkability: Greenery, Openness, Pavement and Crowdedness along the selected footpaths of the study area. The semantic labels are then used to quantify the selected indicators and the areas are mapped using GeoAI techniques to reveal the intra-city variations, on a normalised scale from 0- 1, where 0 indicates not walkable and 1 indicates highly walkable. The findings indicate significant discrepancies in pedestrian infrastructure, particularly within the central business districts (CBDs) of the city. These disparities are evident in inadequate footpath widths, unsafe walking spaces, and ignorance of inclusive design considerations. This shortfall in pedestrian-friendly infrastructure contributes to a less livable urban environment, impacting safety, accessibility, and overall enjoyment of the city for pedestrians. Further, the study acknowledges the potential of street view imageries and deep learning-based methods in studying urban mobility. The findings are intended to support data-driven, inclusive, and sustainable spatial planning for improved urban mobility.

Cities are complex systems that are constantly changing. This paper explores the capabilities of using crowd-sourced street-level imagery in observing city dynamics. Visual walkability is an example of such an index, where different results may be obtained depending on locational and temporal factors. This paper introduces a new index called Type of Visual Walkability (TVW) to characterize and classify street-level visual walkability in Inner London utilizing Mapillary images. The method is based on panoptic segmentation, where pixel-level segmentation and instance count are used in combination to generate more robust indicators of greenery, openness, crowdedness, and visual pavement. Following this, the TVW at street segment level is calculated and the spatiotemporal dynamics of TVW are explored. The results show significant seasonal variations. Specifically, many greenery-dominated streets become openness-dominated from autumn to winter and pavement-dominated streets become crowdedness-dominated in summer and autumn due to vegetation phenology and human activities. This case study showed that TVW provides a dynamic and explainable perspective in understanding urban design qualities for walkability. It facilitates the connection between assessment of the built environment and spatiotemporal analysis derived from street-level images and will inform urban planners and governments in building a walkable city and further promote active transport.

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The dynamic fluctuations in the real estate market significantly impact the development of the national economy. Investigating the spatiotemporal characteristics of housing prices can assist the government in formulating rational regulatory policies. Taking Zhengzhou City as the research subject, this study analyzed the spatiotemporal characteristics of housing prices based on housing price data and POI (Point of Interest) data from January 2022 to March 2024, utilizing a spatial scale of 500 m × 500 m grids. A hedonic price model and a geographically weighted regression (GWR) model were constructed to examine the mechanisms of 12 influencing factors on housing prices. The results indicate that housing prices in the eastern part of Zhengzhou are higher than those in the west, with an overall declining trend observed in Zhengzhou’s housing prices. Among the influencing factors, the age of the house exerts the greatest impact on housing prices, while finance has the least influence. The GWR model demonstrates superior fitting performance compared to the hedonic price model. The mechanisms of the influencing factors exhibit spatial heterogeneity. This study provides valuable insights for relevant government departments in Zhengzhou City, contributing to the optimization of urban planning and the regulation of the real estate market.

Abstract Street view imagery (SVI), an emerging geospatial dataset, is useful for evaluating active transportation infrastructure, but it faces potential biases from its vehicle-based capture method, diverging from pedestrians’ and cyclists’ perspectives. Existing literature lacks both an examination of these biases and a solution. This study identifies and quantifies these biases by comparing conventional SVI with views from the road shoulder/sidewalk. To mitigate such perspective biases, we introduce a novel framework with generative adversarial network (GAN)-based image generation models (Pix2Pix and CycleGAN), an image regression model (ResNet-50), and a tabular model (LightGBM). Experiments assessed model effectiveness in translating car-centric views to those from pedestrian and cyclist perspectives. Results show significant differences in semantic indicators (e.g. green view index) between road center and road shoulder/sidewalk SVI, with low Pearson’s correlation coefficients r (0.35–0.55 for road shoulders and 0.45–0.47 for sidewalks) indicating bias. The framework succeeded in creating realistic images and aligning pixel ratios between perspectives, achieving strong correlation coefficients (0.81 for road shoulders and 0.83 for sidewalks), thus reducing bias. This work contributes by providing a scalable and model-agnostic approach to produce accurate SVIs for urban planning and sustainability, setting a foundation for improving bikeability and walkability assessments and promoting active transportation.

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No abstract available

Data on pedestrian infrastructure is essential for improving the mobility environment and for planning efficiency. Although governmental agencies are responsible for capturing data on pedestrian infrastructure mostly by field audits, most have not completed such audits. In recent years, virtual auditing based on street view imagery (SVI), specifically through geo-crowdsourcing platforms, offers a more inclusive approach to pedestrian movement planning, but concerns about the quality and reliability of opensource geospatial data pose barriers to use by governments. Limited research has compared opensource data in relation to traditional government approaches. In this study, we compare pedestrian infrastructure data from an opensource virtual sidewalk audit platform (Project Sidewalk) with government data. We focus on neighborhoods with diverse walkability and income levels in the city of Seattle, Washington and in DuPage County, Illinois. Our analysis shows that Project Sidewalk data can be a reliable alternative to government data for most pedestrian infrastructure features. The agreement for different features ranges from 75% for pedestrian signals to complete agreement (100%) for missing sidewalks. However, variations in measuring the severity of barriers challenges dataset comparisons.

Improving the built environment to support walking is a popular strategy to increase urban sustainability and walkability. In the past decade alone, many US cities have implemented crosswalk visibility enhancement programs as part of road safety improvements and active transportation plans. However, there are no systematic ways of measuring and monitoring the presence of key built environment attributes that influence the safety and walkability of an area, such as marked crosswalks. Furthermore, little is known about how these attributes change over time at a national scale. In this paper, we introduce an innovative approach using a deep learning-based computer vision model on Street View images to identify changes in intersection-level marked crosswalks around more than 4,000 US transit stations over a 14-year period. We found an increase in the overall number of marked crosswalks at intersections. Furthermore, high-visibility crosswalks became more common, as they replaced existing parallel-line crosswalks. We further examine crosswalks around transit stations in New York City and San Francisco to illustrate geographic variations and compare associations with other characteristics of the built environment as reported in the Smart Location Database. Areas with increases in high-visibility crosswalks focused on high density residential areas and areas with a higher percent of zero-vehicle households. However, geographic variations exist. For example, in San Francisco, transit station areas outside downtown or major corridors (South and Southwest of the city) had the lower prevalence of marked crosswalks. This analysis confirms important gaps in crosswalk visibility that call for safety enhancements and opens the door for additional research involving these data. We conclude by discussing the limitations and future research opportunities using computer vision to automatically detect large-scale transportation infrastructure changes at a relatively low cost.

Estimated presence or absence of sidewalks and conditions of sidewalks in northeastern New Jersey. The map depicts an index of sidewalk walkability estimated from virtual street audits at 11,282 locations using Google Street View and spatial interpolation techniques. Levels of walkability ranged from low (no sidewalk or poor condition) to moderate (fair condition) to high (good condition). Precise measures of sidewalk conditions can help identify barriers to walking-based physical activity and key areas for intervention to maintain and modify sidewalk conditions.

Urban walkability is a cornerstone of public health, sustainability, and quality of life. Traditional walkability assessments rely on surveys and field audits, which are costly and difficult to scale. Recent studies have used satellite imagery, street view imagery, or population indicators to estimate walkability, but these single-source approaches capture only one dimension of the walking environment. Satellite data describe the built environment from above, but overlook the pedestrian perspective. Street view imagery captures conditions at the ground level, but lacks broader spatial context. Population dynamics reveal patterns of human activity but not the visual form of the environment. We introduce WalkCLIP, a multimodal framework that integrates these complementary viewpoints to predict urban walkability. WalkCLIP learns walkability-aware vision-language representations from GPT-4o generated image captions, refines these representations with a spatial aggregation module that incorporates neighborhood context, and fuses the resulting features with representations from a population dynamics foundation model. Evaluated at 4,660 locations throughout Minneapolis-Saint Paul, WalkCLIP outperforms unimodal and multimodal baselines in both predictive accuracy and spatial alignment. These results show that the integration of visual and behavioral signals yields reliable predictions of the walking environment.

Historic cities face a dual challenge of preserving cultural authenticity and adapting to modern urbanization, yet existing studies often overlook the multidimensional coupling mechanisms critical for sustainable urban renewal. This research has proposed a replicable framework to balance heritage conservation, ecological restoration, and pedestrian mobility. Focusing on the historic city of Shenyang, this study evaluated spatial dynamics via the Walkability Index (WI), Green View Index (GVI), and Cultural Heritage Index (CHI), and quantified their coupling coordination patterns. Multisource datasets including OpenStreetMap road networks, POIs, and Baidu street-view imagery were integrated. A Coupling Coordination Degree (CCD) model was developed to assess system interactions. Results revealed moderate overall walkability (WI = 42.66) with stark regional disparities, critically low greening (GVI = 10.14%), and polarized heritage distribution (CHI = 18.73) in Shenyang historic city. Tri-system coupling was moderate (CCD = 0.409–0.608), constrained by green-heritage disconnects in key districts. This work could contribute to interdisciplinary discourse by bridging computational modeling with human-centric urban design, providing scalable insights for global historic cities.

Semantic segmentation of crowdsourced street-level imagery plays a critical role in urban analytics by enabling pixel-wise understanding of urban scenes for applications such as walkability scoring, environmental comfort evaluation, and urban planning, where robustness to geometric transformations and projection-induced symmetry variations is essential. This study presents a comparative evaluation of two primary families of semantic segmentation models: transformer-based models (SegFormer and Mask2Former) and prompt-based models (CLIPSeg, LangSAM, and SAM+CLIP). The evaluation is conducted on images with varying geometric properties, including normal perspective, fisheye distortion, and panoramic format, representing different forms of projection symmetry and symmetry-breaking transformations, using data from Google Street View and Mapillary. Each model is evaluated on a unified benchmark with pixel-level annotations for key urban classes, including road, building, sky, vegetation, and additional elements grouped under the “Other” class. Segmentation performance is assessed through metric-based, statistical, and visual evaluations, with mean Intersection over Union (mIoU) and pixel accuracy serving as the primary metrics. Results show that LangSAM demonstrates strong robustness across different image formats, with mIoU scores of 64.48% on fisheye images, 85.78% on normal perspective images, and 96.07% on panoramic images, indicating strong semantic consistency under projection-induced symmetry variations. Among transformer-based models, SegFormer proves to be the most reliable, attains higher accuracy on fisheye and normal perspective images among all models, with mean IoU scores of 72.21%, 94.92%, and 75.13% on fisheye, normal, and panoramic imagery, respectively. LangSAM not only demonstrates robustness across different projection geometries but also delivers the lowest segmentation error, consistently identifying the correct class for corresponding objects. In contrast, CLIPSeg remains the weakest prompt-based model, with mIoU scores of 77.60% on normal images, 59.33% on panoramic images, and a substantial drop to 59.33% on fisheye imagery, reflecting sensitivity to projection-related symmetry distortions.

This study investigates the temporal dynamics of urban spatial quality in Pu'er City, China, utilizing multi-year street view imagery and artificial intelligence. We developed a framework integrating computer vision and visual language models to quantify changes in perceptual dimensions including aesthetics, safety, walkability, and maintenance quality from 2015 to 2023. Results reveal significant spatial-temporal patterns, with notable improvements in newly developed areas compared to stagnant conditions in older urban cores. The findings demonstrate the value of street-level visual intelligence for monitoring urban development and informing targeted interventions.

The commercial vitality of community-scale streets in Chinese cities is shaped by complex interactions between vehicular accessibility, environmental quality, and pedestrian perception. This study proposes an interpretable, image-based framework to examine how street-level features -- including parked vehicle density, greenery, cleanliness, and street width -- impact retail performance and user satisfaction in Harbin, China. Leveraging street view imagery and a multimodal large language model (VisualGLM-6B), we construct a Community Commercial Vitality Index (CCVI) from Meituan and Dianping data and analyze its relationship with spatial attributes extracted via GPT-4-based perception modeling. Our findings reveal that while moderate vehicle presence may enhance commercial access, excessive on-street parking -- especially in narrow streets -- erodes walkability and reduces both satisfaction and shop-level pricing. In contrast, streets with higher perceived greenery and cleanliness show significantly greater satisfaction scores but only weak associations with pricing. Street width moderates the effects of vehicle presence, underscoring the importance of spatial configuration. These results demonstrate the value of integrating AI-assisted perception with urban morphological analysis to capture non-linear and context-sensitive drivers of commercial success. This study advances both theoretical and methodological frontiers by highlighting the conditional role of vehicle activity in neighborhood commerce and demonstrating the feasibility of multimodal AI for perceptual urban diagnostics. The implications extend to urban design, parking management, and scalable planning tools for community revitalization.

Walkable urban environments are increasingly recognized as essential for sustainable mobility, public health, and social well-being. While macro-scale indicators of walkability are widely used, growing evidence highlights the importance of street-level physical conditions experienced at eye level. Advances in computer vision and street view imagery (SVI) offer new opportunities to quantify such streetscape characteristics, yet the applicability of existing semantic segmentation models in developing urban contexts remains underexplored. This study evaluates the suitability of five state-of-the-art semantic segmentation models for streetscape analysis using crowdsourced SVI from Phnom Penh, Cambodia. Through a comparative analysis, Oneformer was identified as the most suitable semantic segmentation model, uniquely successful in identifying street vendors through surrogate semantic class (base) and street furniture. A rigorous quantitative validation using manually annotated images confirmed the model’s reliability, achieving an mIoU of 65.7% within the complex urban fabric of Phnom Penh. This performance stems from OneFormer’s unified task-conditioned framework, which integrates semantic, instance, and panoptic information within a single query. Such an architecture ensures enhanced boundary stability and semantic coherence by consolidating visual noise into meaningful units, making it particularly robust for processing the irregular street elements typical of Southeast Asian cities. Applying the selected model revealed pronounced spatial variation in streetscape composition across three neighborhoods, reflecting distinct development stages and levels of informality. These findings suggest that carefully selected pretrained models can yield analytically useful representations of streetscape conditions in data-constrained settings, supporting more context-sensitive and inclusive urban analysis in rapidly developing cities.

Understanding micro-level perceptions of street scenes is highly concerned with residents’ behaviors and socioeconomic outcomes. While many studies rely on objective measures, such as physical features extracted from Street View Imagery (SVI) to proxy perceptions using derived formulas, others employ subjective measures from visual surveys to capture more subtle human perceptions. We argue that the two measurements can diverge significantly over the same perception concept, which might lead to opposite spatial implications in policy if not properly understood. Moreover, as perceptions are often examined individually, few studies have investigated their joint distribution patterns to reflect perceptions’ multi-dimensional nature. To fill the gaps, we collected five pairwise perceptions from SVIs (i.e., complexity, enclosure, greenness, imageability, and walkability) at the neighborhood level in Shanghai. Each perception consists of pairwise values subjectively measured using a GeoAI-based approach and objectively quantified using formulas. We statistically and spatially compared the coherence and divergence of the two measures, further examining the perceptual differences. Advanced techniques including cluster analysis and factor analysis were employed to jointly evaluate their spatial distribution discrepancy. Our results revealed more differences than similarities between the two measures statistically and spatially, confirming any spatial implications concluded from one approach can vary significantly from the other. The joint spatial pattern further corroborated our conclusions. Our study enriches the literature on micro-level street perception measures, uncovers their critical differences to guide future comparative studies, and offers new approaches for urban perception mapping.

Mapping pavement types, especially in sidewalks, is essential for urban planning and mobility studies. Identifying pavement materials is a key factor in assessing mobility, such as walkability and wheelchair usability. However, satellite imagery in this scenario is limited, and in situ mapping can be costly. A promising solution is to extract such geospatial features from street-level imagery. This study explores using open-vocabulary classification algorithms to segment and identify pavement types and surface materials in this scenario. Our approach uses large language models (LLMs) to improve the accuracy of classifying different pavement types. The methodology involves two experiments: the first uses free prompting with random street-view images, employing Grounding Dino and SAM algorithms to assess performance across categories. The second experiment evaluates standardized pavement classification using the Deep Pavements dataset and a fine-tuned CLIP algorithm optimized for detecting OSM-compliant pavement categories. The study presents open resources, such as the Deep Pavements dataset and a fine-tuned CLIP-based model, demonstrating a significant improvement in the true positive rate (TPR) from 56.04% to 93.5%. Our findings highlight both the potential and limitations of current open-vocabulary algorithms and emphasize the importance of diverse training datasets. This study advances urban feature mapping by offering a more intuitive and accurate approach to geospatial data extraction, enhancing urban accessibility and mobility mapping.

Street view imagery (SVI), with its rich visual information, is increasingly recognized as a valuable data source for urban research. Particularly, by leveraging computer vision techniques, SVI can be used to calculate various urban form indices (e.g., Green View Index, GVI), providing a new approach for large-scale quantitative assessments of urban environments. However, SVI data collected at the same location in different seasons can yield varying urban form indices due to phenological changes, even when the urban form remains constant. Numerous studies overlook this kind of seasonal bias. To address this gap, we propose a systematic analytical framework for quantifying and evaluating seasonal bias in SVI, drawing on more than 262,000 images from 40 cities worldwide. This framework encompasses three aspects: seasonal bias within urban areas, seasonal bias across cities on a global scale, and the impact of seasonal bias in practical applications. The results reveal that (1) seasonal bias is evident, with an average mean absolute percentage error (MAPE) of 54 % for GVI across all sampled cities, and it is particularly pronounced in areas with significant seasonal bias; (2) seasonal bias is strongly correlated with geographic location, with greater bias observed in cities with lower average rainfall and temperatures; and (3) in practical applications, ignoring seasonal bias may result in analytical errors (e.g., an ARI of 0.35 in clustering). By identifying and quantifying seasonal bias in SVI, this study contributes to improving the accuracy of urban environmental assessments based on street view data and provides new theoretical support for the broader application of such data on a global scale.

Since the reform and opening-up policy, the accelerated urbanization rate has triggered extensive construction of new towns, leading to architectural homogenization and environmental quality degradation. As urban development transitions toward a “quality improvement” paradigm, there is an urgent need to synergistically enhance the health performance of human settlements through the optimization of public space environments. The purpose of this study is to explore the impact of the built environment of urban streets on residents’ perceptions. In particular, in the context of rapid urbanization, how to improve the mental health and quality of life of residents by improving the street environment. Changbai Island Street in the Heping District of Shenyang City was selected for the study. Baidu Street View images combined with machine learning were employed to quantify physical characterizations like street plants and buildings. The ‘Place Pulse 2.0’ dataset was utilized to obtain data on residents’ perceptions of streets as beautiful, safe, boring, and lively. Correlation and regression analyses were used to reveal the relationship between physical characteristics such as green visual index, openness, and pedestrians. It was discovered that the green visual index had a positive effect on perceptions of it being beautiful and safe, while openness and building enclosure factors influenced perceptions of it being lively or boring. This study provides empirical data support for urban planning, emphasizing the need to focus on integrating environmental greenery, a sense of spatial enclosure, and traffic mobility in street design. Optimization strategies such as increasing green coverage, controlling building density, optimizing pedestrian space, and enhancing the sense of street enclosure were proposed. The results of the study not only help to understand the relationship between the built environment of streets and residents’ perceptions but also provide a theoretical basis and practical guidance for urban space design.

Amid the global challenges of rapid urbanization, understanding how micro-scale spatial features shape human perception is critical for advancing livable cities. This study pro-poses a data-driven framework that integrates street view imagery, deep learning-based semantic segmentation, and machine learning interpretation models including SHAP analysis to explore the relationship between urban spatial characteristics and subjective perceptions. A total of 12,604 street-level images from Xi’an, China, were analyzed to ex-tract seven spatial indicators. These indicators were then linked with perceptual data across six emotional dimensions derived from the Place Pulse 2.0 dataset. The analysis revealed that natural elements significantly enhance perceived comfort and aesthetics, while high-density built environments can suppress perceived safety and liveliness. Spatial clustering further identified three urban typologies—traditional, transitional, and modern—with distinct perceptual signatures. These findings offer scalable and transferable insights for perception-informed urban design and renewal, particularly in dense urban settings worldwide.

Street view imagery (SVI) has become a valuable geospatial data source for urban analysis, offering rich information about urban environments from a human-centric perspective. However, existing segmentation methods face significant challenges due to the inherent complexities of SVI, including scale variations, occlusions, and diverse semantic hierarchies. Drawing inspiration from the hierarchical nature of human visual cognition, this study proposes the hierarchical part-aware network (HPAN) to address these challenges in the fine-grained segmentation of SVI. The HPAN framework integrates four key components: (1) a hierarchical consistency learning module (HCLM), which ensures consistency across different levels of segmentation through novel loss functions; (2) a topology-aware graph matching module (TGMM), designed to model spatial relationships between object parts; (3) an edge-guided feature enhancement module (EFEM), which incorporates fine-grained edge information; and (4) a multilevel joint attention module (MLJAM), which adaptively fuses global scene semantics with local object details. Extensive experiments conducted on the cityscapes panoptic parts dataset demonstrate that HPAN outperforms existing methods across multiple panoptic quality metrics, particularly excelling in part-level segmentation tasks. Further evaluations on the mapillary vistas dataset and the cityscapes dataset validate HPAN's robust semantic segmentation performance across diverse street scenes. Generalization tests on different SVI sources, including challenging scenarios, such as low-light conditions and occluded environments, highlight the model's strong adaptability and effectiveness.

Urban livability and sustainability are increasingly studied at the neighborhood scale, where built, social, and governance conditions shape residents’ everyday experiences. Yet existing assessment frameworks often fail to integrate subjective perceptions with multi-dimensional environmental indicators in replicable and scalable ways. To address this gap, this study develops an Environmental, Social, and Governance (ESG)-informed framework for evaluating perceived environmental quality in urban communities. Using Baidu Street View imagery—selected due to its comprehensive coverage of Chinese urban areas—and Point of Interest (POI) data, we analyze seven communities in Shenyang, China, selected for their diversity in built form and demographic context. Kernel Density Analysis and Exploratory Factor Analysis (EFA) are applied to derive latent ESG-related spatial dimensions. These are then correlated with Place Pulse 2.0 perception scores using Spearman analysis to assess subjective livability. Results show that environmental and social factors—particularly greenery visibility—are strongly associated with favorable perceptions, while governance-related indicators display weaker or context-specific relationships. The findings highlight the differentiated influence of ESG components, with environmental openness and walkability emerging as key predictors of perceived livability. By integrating pixel-level spatial features with perception metrics, the proposed framework offers a scalable and transferable tool for human-centered neighborhood evaluation, with implications for planning strategies that align with how residents experience urban environments.

Street view imagery has become a vital tool for assessing urban street greenery, with the Green View Index (GVI) serving as the predominant metric. However, while GVI effectively quantifies overall greenery, it fails to capture the nuanced, human-scale experience of urban greenery. This study introduces the Front-Facing Green View Index (FFGVI), a metric designed to reflect the perspective of pedestrians traversing urban streets. The FFGVI computation involves three key steps: (1) calculating azimuths for road points, (2) retrieving front-facing street view images, and (3) applying semantic segmentation to identify green pixels in street view imagery. Building on this, this study proposes the Street Canyon Green View Index (SCGVI), a novel approach for identifying boulevards that evoke perceptions of comfort, spaciousness, and aesthetic quality akin to room-like streetscapes. Applying these indices to a case study in Nanjing, China, this study shows that (1) FFGVI exhibited a strong correlation with GVI (R = 0.88), whereas the association between SCGVI and GVI was marginally weaker (R = 0.78). GVI tends to overestimate perceived greenery due to the influence of lateral views dominated by side-facing vegetation; (2) FFGVI provides a more human-centered perspective, mitigating biases introduced by sampling point locations and obstructions such as large vehicles; and (3) SCGVI effectively identifies prominent boulevards that contribute to a positive urban experience. These findings suggest that FFGVI and SCGVI are valuable metrics for informing urban planning, enhancing urban tourism, and supporting greening strategies at the street level.

As a key indicator for measuring urban green visibility, the Green View Index (GVI) reflects actual visible greenery from a human perspective, playing a vital role in assessing urban greening levels and optimizing green space layouts. Existing studies predominantly rely on single-source remote sensing image analysis or traditional statistical regression methods such as Ordinary Least Squares and Geographically Weighted Regression. These approaches struggle to capture spatial variations in human-perceived greenery at the street level and fail to identify the non-stationary effects of different drivers within localized areas. This study focuses on the Luolong District in the central urban area of Luoyang City, China. Utilizing Baidu Street View imagery and semantic segmentation technology, an automated GVI extraction model was developed to reveal its spatial differentiation characteristics. Spearman correlation analysis and Multiscale Geographically Weighted Regression were employed to identify the dominant drivers of GVI across four dimensions: landscape pattern, vegetation cover, built environment, and accessibility. Field surveys were conducted to validate the findings. The Multiscale Geographically Weighted Regression method allows different variables to have distinct spatial scales of influence in parameter estimation. This approach overcomes the limitations of traditional models in revealing spatial non-stationarity, thereby more accurately characterizing the spatial response mechanism of the Global Vulnerability Index (GVI). Results indicate the following: (1) The study area’s average GVI is 15.24%, reflecting a low overall level with significant spatial variation, exhibiting a “polar core” distribution pattern. (2) Fractal dimension, normalized vegetation index (NDVI), enclosure index, road density, population density, and green space accessibility positively influence GVI, while connectivity index, Euclidean nearest neighbor distance, building density, residential density, and water body accessibility negatively affect it. Among these, NDVI and enclosure index are the most critical factors. (3) Spatial influence scales vary significantly across factors. Euclidean nearest neighbor distance, building density, population density, green space accessibility, and water body accessibility exert global effects on GVI, while fractal dimension, connectivity index, normalized vegetation index, enclosure index, road density, and residential density demonstrate regional dependence. Field survey results confirm that the analytical conclusions align closely with actual greening conditions and socioeconomic characteristics. This study provides data support and decision-making references for green space planning and human habitat optimization in Luoyang City while also offering methodological insights for evaluating urban street green view index and researching ecological spatial equity.

Amid accelerating climate change, intensifying urban heat island effects, and rising public demand for livable, walkable streets, there is an urgent practical need for interpretable and actionable evidence on streetscape quality. Yet, research on streetscape quality has often relied on single data sources and linear models, limiting insight into multidimensional perception; evidence from temperate monsoon cities remains scarce. Using Tianjin’s main urban area as a case study, we integrate street-view imagery with remote sensing imagery to characterize satellite-derived environmental indicators at the point scale and examine the following five perceptual outcomes: comfort, aesthetics, perceived greenness, summer heat perception, and willingness to linger. We develop a three-step interpretable assessment, as follows: Elastic Net logistic regression to establish directional and magnitude baselines; Generalized Additive Models with a logistic link to recover nonlinear patterns and threshold bands with Benjamini–Hochberg false discovery rate control and binned probability calibration; and Shapley additive explanations to provide parallel validation and global and local explanations. The results show that the Green View Index is consistently and positively associated with all five outcomes, whereas Spatial Balance is negative across the observed range. Sky View Factor and the Building Visibility Index display heterogeneous forms, including monotonic, U-shaped, and inverted-U patterns across outcomes; Normalized Difference Vegetation Index and Land Surface Temperature are likewise predominantly nonlinear with peak sensitivity in the midrange. In total, 54 of 55 smoothing terms remain significant after Benjamini–Hochberg false discovery rate correction. The summer heat perception outcome is highly imbalanced: 94.2% of samples are labeled positive. Overall calibration is good. On a standardized scale, we delineate optimal and risk intervals for key indicators and demonstrate the complementary explanatory value of street-view imagery and remote sensing imagery for people-centered perceptions. In Tianjin, a temperate monsoon megacity, the framework provides reproducible, actionable, design-relevant evidence to inform streetscape optimization and offers a template that can be adapted to other cities, subject to local calibration.

As digital tools increasingly shape spatial planning practices, understanding how different data sources reflect human experiences of urban environments is essential. Street View Imagery (SVI) and Public Participation GIS (PPGIS) represent two prominent approaches for capturing place-based perceptions that can support urban planning decisions, yet their comparability remains underexplored. This study investigates the alignment between SVI-based perceived attractiveness and residents'reported experiences gathered via a city-wide PPGIS survey in Helsinki, Finland. Using participant-rated SVI data and semantic image segmentation, we trained a machine learning model to predict perceived attractiveness based on visual features. We compared these predictions to PPGIS-identified locations marked as attractive or unattractive, calculating agreement using two sets of strict and moderate criteria. Our findings reveal only partial alignment between the two datasets. While agreement (with a moderate threshold) reached 67% for attractive and 77% for unattractive places, agreement (with a strict threshold) dropped to 27% and 29%, respectively. By analysing a range of contextual variables, including noise, traffic, population presence, and land use, we found that non-visual cues significantly contributed to mismatches. The model failed to account for experiential dimensions such as activity levels and environmental stressors that shape perceptions but are not visible in images. These results suggest that while SVI offers a scalable and visual proxy for urban perception, it cannot fully substitute the experiential richness captured through PPGIS. We argue that both methods are valuable but serve different purposes; therefore, a more integrated approach is needed to holistically capture how people perceive urban environments.

Street design quality and socio-economic factors jointly influence housing prices, but their intertwined effects and spatial variations remain under-quantified. Housing prices not only reflect residents’ neighborhood experiences but also stem from the spillover value of public streets perceived and used by different users. This study takes Tianjin as a case and views the street environment as an immediate experience proxy for through-travelers, combining street view images and crowdsourced perception data to extract both subjective and objective indicators of the street environment, and integrating neighborhood and location characteristics. We use Geographical-XGBoost to evaluate the relative contributions of multiple factors to housing prices and their spatial variations. The results show that incorporating both subjective and objective street information into the Hedonic Pricing Model (HPM) improves its explanatory power, while local modeling with G-XGBoost further reveals significant heterogeneity in the strength and direction of effects across different locations. The results indicate that incorporating both subjective and objective street information into the HPM enhances explanatory power, while local modeling with G-XGBoost reveals significant heterogeneity in the strength and direction of effects across different locations. Street greening, educational resources, and transportation accessibility are consistently associated with higher housing prices, but their strength varies by location. Core urban areas exhibit a “counterproductive effect” in terms of complexity and recognizability, while peripheral areas show a “barely acceptable effect,” which may increase cognitive load and uncertainty for through-travelers. In summary, street environments and socio-economic conditions jointly influence housing prices via a “corridor-side–community-side” dual-pathway: the former (enclosure, safety, recognizability) corresponds to immediate improvements for through-travelers, while the latter (education and public services) corresponds to long-term improvements for residents. Therefore, core urban areas should control design complexity and optimize human-scale safety cues, while peripheral areas should focus on enhancing public services and transportation, and meeting basic quality thresholds with green spaces and open areas. Urban renewal within a 15 min walking radius of residential areas is expected to collaboratively improve daily travel experiences and neighborhood quality for both residents and through-travelers, supporting differentiated housing policy development and enhancing overall quality of life.

Human perception is often considered a comprehensive evaluation of environmental quality. It is an important indicator of neighbourhood socioeconomic status and has a significant impact on various social outcomes. In response to the absence of locally trained models based on Chinese street view images and local annotators, we present two datasets. Dataset I, the perceived wealth and physical disorder scores annotation dataset, consists of 40,000 Chinese street view images that are annotated by local urban planners using the image comparison approach. Researchers can use Dataset I directly or further augment it to train their own artificial intelligence perception models in China. We use Dataset I to train image regression models, which are then employed to infer two perception scores for 36,262,700 street view images throughout urban China between 2013 and 2022. The resulting Dataset II, the perceived wealth and physical disorder scores prediction dataset, comprises three analytical units including image shooting points, 500m×500m grid cells, and 76,434 community administrative areas. Dataset II supports a variety of wide-coverage, fine-grained socio-spatial research projects in China, including studies on inequality, segregation, and gentrification. It can also serve as a critical input for examining other important socio-spatial phenomena, such as crime and physical activity patterns.

With the rapid urban aging trend in China, evaluating the age-friendliness of street environments is critical for inclusive urban planning. This study proposes the Age-Friendly Environment Assessment Tool (AFEAT) to assess street-level age-friendliness in high-density urban contexts, grounded in the World Health Organization’s (WHO) Global Age-Friendly Cities: A Guide and adapted to the spatial characteristics of Nanjing’s Qinhuai District. By integrating multi-source data such as street-view image segmentation, Point of Interest (POI)-based network accessibility, kernel density estimation, Analytic Hierarchy Process (AHP)-derived indicator weights, and Random Forest regression, the study develops a comprehensive and spatialized evaluation framework. The results reveal significant spatial disparities in age-friendliness across street segments, with Safe Mobility, Healthcare Services, and Walkable Environment identified as the most influential factors for older adults. High-performing areas are concentrated in the central urban core, while peripheral zones face challenges such as poor walkability, insufficient lighting, and a lack of facilities. The study recommends strengthening a walkability-based age-friendly safety and healthcare support system and optimizing the spatial distribution of recreational and medical facilities to address mismatches between supply and demand. These findings provide practical guidance for targeted, evidence-based interventions aimed at fostering equitable and resilient urban environments for aging populations.

Street View Imagery (SVI) has emerged as a valuable data form in urban studies, enabling new ways to map and sense urban environments. However, fundamental concerns regarding the representativeness, quality, and reliability of SVI remain underexplored, e.g. to what extent can cities be captured by such data and do data gaps result in bias. This research, positioned at the intersection of spatial data quality and urban analytics, addresses these concerns by proposing a novel and effective method to estimate SVI's element-level coverage in the urban environment. The method integrates the positional relationships between SVI and target elements, as well as the impact of physical obstructions. Expanding the domain of data quality to SVI, we introduce an indicator system that evaluates the extent of coverage, focusing on the completeness and frequency dimensions. Taking London as a case study, three experiments are conducted to identify potential biases in SVI's ability to cover and represent urban environmental elements, using building facades as an example. It is found that despite their high availability along urban road networks, Google Street View covers only 62.4 % of buildings in the case study area. The average facade coverage per building is 12.4 %. SVI tends to over-represent non-residential buildings, thus possibly resulting in biased analyses, and its coverage of environmental elements is position-dependent. The research also highlights the variability of SVI coverage under different data acquisition practices and proposes an optimal sampling interval range of 50-60 m for SVI collection. The findings suggest that while SVI offers valuable insights, it is no panacea - its application in urban research requires careful consideration of data coverage and element-level representativeness to ensure reliable results.

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Evaluating the walking suitability of urban streets is an important basis for creating a quality street environment. Based on the spatial properties of streets and human behavior perception, this study constructs a walking suitability evaluation method based on “dynamic-flow” and “static-frame” of streets, and measures it in five dimensions: connectivity of spatial structure, connectivity of transportation modes, integration of functional facilities, safety of street space and comfort of street environment. Taking Nanjing as an example, the results show that the overall walking suitability of streets in the main urban area is good, but there is still room for further improvement in the connectivity of transportation modes and the safety of street space. New neighborhoods and remote roads in the city are the areas where the street walking environment needs to be improved, which should be paid attention to in the future renovation. The evaluation results can be used as a reference for proposing targeted strategies in urban design.

To strike a trade-off between walking behavior and street resource constraint, extensive research tends to focus on how the urban environment affects walking behavior. However, most of the existing impact measurements focus on the cities in low-latitude temperate environments, which may not truly reflect the situation when assessing high-latitude cities. To address this drawback, in this paper, a general multi-factor framework is introduced to quantify the influence of street-level environmental factors on walking behavior. Specifically, a framework is constructed by comprehensively considering the subjective data and the objective data of Harbin, China, which is mainly composed of multivariate measurement indicators, a multi-source data analysis library, and four-dimensional evaluation paradigm. The results indicate that two main measures can promote the current situation of human-oriented living street environment planning, namely, increasing the distribution of green facilities and life service facilities in the old urban districts living street, and paying attention to the diversity of street greening and street landscape. The proposed framework is conducive to improve the planning status of human-centered street environments and guide the construction of pedestrian-friendly life and healthy streets.

In this paper, we argue for an explicit decoupling of “walkability” and “walking behavior” and for the advantages of a definition of walkability based on access. This provides impetus for a new approach to constructing and using walkability indices, combining accessibility theory with a goal of comprehensiveness and communicability. Diminishing returns-to-opportunities can be used to map the infinite origin-destination gravity potential space to a finite scale thus creating an easily communicable metric, or metrics. In addition, this method can be applied to any mode and applied to multiple destination types singly or combined. Application of this theoretical approach is demonstrated through the creation of a novel comprehensive open-source transport walking potential index, WalkTHERE. A 0-100 scale is used to represent the percentage of people’s total needs potentially accessible by walking. The index is applied to eight Australian and two European cities, and the specific data considerations and parameters chosen are described. Significant disparity is shown in walking access between different destinations within cities, and in walking access between cities. Walking access to recreational opportunities is highest, followed by education and shopping, with very little employment access for most residents. Avenues for expansion and further validation are discussed.

In order to structure an efficient and comfortable commercial district for pedestrians, we need to understand the interaction between pedestrian walking behavior and the complex elements of the built environment. Previous studies have focused on people’s activities in the context of the neighborhood rather than the commercial district. This study investigates the potential associations between multi-dimensional environmental factors and pedestrians under various temporal distributions in a densely populated commercial district. Multi-source urban data and semantic segmentation technics have been adopted to measure the built environmental quality from four classic dimensions of urban design, and combining the observations of pedestrian volumes of representative streets in the commercial district, we assess the relationship between the two at different times on the basis of a generalized linear model (GLM). The analytical results identify that the Morphology, Visual perception, Function, and Street configuration features of the commercial environment have a significant impact on walking activity, and temporal differences exist. The findings highlight the importance of built environment quality to pedestrians and street attractiveness, and inform designers, stakeholders, and municipalities on the revitalization of traditional commercial districts.

Social interaction, such as voluntary employment, can promote well-being and mental health for older people. Since walking and public transit are two major commuting modes for older adults, understanding the determinants of older employment behavior near metro stations is critical for the government and urban planners to encourage older employment. Using the mobile signaling data of 1,640,145 older employees and other multi-source spatiotemporal datasets in Shanghai, the Light Gradient Boosting Machine (LightGBM) is employed in this study to explore the nonlinear effects of the built environment on older employment near 333 metro stations. Results show that density, diversity, and design variables have a significant contribution on older employment, while distance to the city center, employment density among all age groups, and the number of older residents are the top three important variables. Partial dependence plots reveal that all independent variables have irregular nonlinear impacts on older employment. Each variable needs to reach an associated threshold to maximize older employment, and their nonlinear impacts are only effective when they are within certain ranges. Research findings can promote older employment and benefit mental health among older people by helping the government prioritize urban planning policies or interventions.

This study explores the mechanisms through which the street built environment (BE) influences mobile physical activity (MPA) using multi-source data and explainable machine learning methods. The research combines Geographically Weighted Regression (GWR) and Random Forest (RF) models to reveal the complex spatial heterogeneity between BE factors and MPA, and enhances the interpretability of results through the SHAP model, providing theoretical support for future targeted urban planning and MPA interventions. The study finds that the “density” dimension of BE plays a crucial role in MPA, particularly population density and building density. Additionally, accessibility and safety also significantly influence MPA, while design factors such as greening rates, water landscapes, and building façade design promote MPA. The study emphasizes that the influence of BE factors on MPA is nonlinear, with significant interaction effects between different variables, indicating that improving a single variable alone cannot fully explain changes in MPA. This research provides a new theoretical perspective for understanding the impact of BE factors on MPA and offers empirical evidence for precise interventions. In areas with low MPA participation, improving street design, enhancing traffic safety, and increasing green and water-friendly spaces can significantly promote residents’ MPA, thereby improving public health.

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The rapid expansion of ride-hailing services has profoundly impacted urban mobility and residents’ travel behavior. This study aims to precisely identify and quantify how the built environment and socioeconomic factors influence spatial variations in ride-hailing demand using multi-source data from Haikou, China. A multi-scale geographically weighted regression (MGWR) model is employed to address spatial scale heterogeneity. To more accurately capture environmental features around sampling points, the DeepLabv3+ model is used to segment street-level imagery, with extracted visual indicators integrated into the regression analysis. By combining multi-scale geospatial data and computer vision techniques, the study provides a refined understanding of the spatial dynamics between ride-hailing demand and urban form. The results indicate notable spatiotemporal imbalances in demand, with varying patterns across workdays and holidays. Key factors, such as distance to the city center, bus stop density, and street-level features like greenery and sidewalk proportions, exert significant but spatially varied impacts on demand. These findings offer actionable insights for urban transportation planning and the design of more adaptive mobility strategies in contemporary cities.

Purpose: Recognizing that walkability is a fundamental element of sustainable urban development, this study focuses on the variables affecting walkability in the living environment and aims to provide insights into land use planning strategies that can contribute to carbon emission reduction. The research specifically examines how to create a pedestrian-friendly street environment in existing urban areas, where employment and residential functions are mixed and co-exist. Methodology: By using multi-source data, this research evaluates streets based on two binary characteristics: the occurrence of walking activity and the walking experience. Findings: The results reveal a strong relationship between walking opportunities and street usage frequency, with the road network being identified as the most influential factor. Additionally, the impact of walking experience was found to be more significant than that of walking opportunity. Contributions: First, the study developed a matrix of dependent variables for street usage frequency and walking experience, and an independent variable matrix for street environment factors. Second, the research created an analytical framework to examine factors influencing street use and experience across different travel needs.

With the advancement of child-friendly urban planning initiatives, the significance of Active School Travel Spaces (ASTSs) in shaping urban development and promoting the physical and mental well-being of children has become increasingly apparent. This research focuses on 151 public primary schools in the central urban area of Lanzhou City. Utilizing the Amap pedestrian route planning API, we establish a walking route network, evaluate the paths using spatial syntax and street view recognition methods, and analyze their influencing factors using a Geographic Detector model. The results show the following: ① The overall friendliness of ASTSs in Lanzhou City is moderate, with 44% of school districts exhibiting low friendliness. ② The distribution of child friendliness in ASTS exhibits a “core-periphery” pattern. Anning District demonstrates higher friendliness compared to Chengguan District and Qilihe District, while Xigu District exhibits the lowest level of friendliness. ③ Different levels of friendliness have different tendencies for access, safety, and comfort. A high degree of friendliness favors comfort. Low friendliness has the lowest requirements for safety and comfort. ④ Population density and transportation convenience exert a significant positive impact on friendliness, while the size of the school district and the centrality of schools have a negative impact. The synergistic effects among these influencing factors notably enhance the explanatory power of friendliness.

Traditional road network evaluation approaches frequently fail to capture the inherent complexities of human mobility patterns, leading to suboptimal urban transportation planning outcomes. This study introduces a novel human-centric evaluation framework that leverages multi-source trajectory big data, including mobile signaling, GPS tracking, and internet map query data. Through advanced physical and semantic data integration techniques, our framework provides comprehensive assessment of three critical dimensions: commuting behavior patterns, daily activity range coverage, and transportation hub accessibility. Applied to a metropolitan case study, the framework demonstrates superior capability in identifying spatial disparities and peak-hour congestion patterns compared to conventional static indicators. Furthermore, we employ Gini coefficient analysis to quantify spatial equity, enabling evidence-based urban planning and policy formulation.

As China’s population ages rapidly, CITYWALK—a leisurely urban exploration method advocating slow-paced experiences—has increasingly become a vital pathway for the elderly to engage with historical spaces and cultural memories within cities. Historic districts, with their rich cultural heritage, emerge as preferred destinations for senior tourism and daily activities. However, their complex spatial environments and inadequate service configurations pose practical barriers to accessibility for older adults. This study examines Beijing’s Qianmen Dashilan district, constructing a three-dimensional evaluation framework—“spatial accessibility, service adaptability, and technological friendliness.” It employs multi-source data methods including field surveys, behavioral observations, in-depth interviews, and spatial coupling analysis of point data to systematically identify key constraints on elderly pedestrians’ routes. Findings reveal shortcomings including discontinuous accessibility infrastructure, uneven distribution of rest facilities, insufficient service node coverage, and generational barriers in smart navigation systems. Guided by the “micro-renewal” concept, this study proposes a synergistic optimization strategy of “micro-renovation + soft services + lightweight technology”: integrating community and commercial resources to strengthen emergency response and humanistic care; designing lightweight guidance systems to lower technological barriers. This research aims to provide an actionable theoretical framework and practical paradigm for age-friendly design in the context of historic district renewal, advancing the inclusivity and accessibility of urban public spaces.

The impact of objective and subjective environmental factors on health outcomes has been a topic of significant debate, with a growing body of research acknowledging the role of a physically active lifestyle in promoting health. However, consensus regarding their precise influence remains elusive. This study contributes to these discussions by exploring how individual health outcomes correlate with transport and leisure walking behaviours, set against both the objective and subjective aspects of environmental influences in the context of Wuhan, an inland Chinese megacity. Street view images, multi-source geospatial data and a questionnaire survey were employed to characterise the “5D + Greenery” objective and perceived characteristics of the neighbourhood environment. Multi-group structural equation modelling was utilised to unravel the complex relationship and gender heterogeneity among environmental factors, purpose-specific walking, and overweight. Our results suggest that both objective land use diversity and perceived convenience are significantly associated with overweight. The accessibility of local service facilities and visible greenery promote both transport and leisure walking. While perceived neighbourhood safety encourages transport walking, perceived walkability is positively correlated with leisure walking. Notably, leisure walking, usually considered beneficial, presents a positive association with overweight conditions, acting as a mediation. Gender disparities exist in pathways between the environment and purpose-specific walking, as well as weight. The findings lend support to the planning of an activity-supporting built environment as a crucial strategy for obesity prevention.

Rapid population aging calls for a shift from static facility configuration toward understanding how spatial structures coordinate with everyday behavior. This study develops a structure–behavior coordination framework to examine how the spatial embedding of community service centers and surrounding facilities aligns with older adults’ mobility and activity chains. Using Guangzhou as a case, three representative facility aggregation forms—clustered, linear, and patchy—were identified through POI-based spatial analysis. Behavioral mapping supported by Public Participation GIS (PPGIS) and social network analysis captured facility co-use and path continuity, while rank-based measures (Rank-QAP and Rank-Biased Overlap) evaluated correspondence between structural and behavioral centralities. Findings show form-sensitive rather than typological coordination: the clustered case (FY) exhibits compact, mixed-use integration; the linear case (DJ) requires ground-level access along main pedestrian corridors; and the patchy case (LG) relies on a few highly accessible dual-core nodes where improved connectivity strengthens cohesion. Everyday facilities such as markets, parks, and plazas act as behavioral anchors linking routine routes. The framework offers a transferable tool and comparable metrics for diagnosing alignment between built structure and everyday behavior, guiding adaptive, evidence-based planning for age-friendly community systems.

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ABSTRACT Walking is the most common mode of transportation in the world. The design standards may need to be adjusted to local conditions to ensure pedestrian safety in different parts of the world. The paper aim is to find the factors that influence the crossing speed of pedestrians and their behavior in the crosswalk, and thus provide better tools for the design of pedestrian facilities. Thirty crosswalks from four cities in Mexico were analyzed, obtaining a sample of 8700 pedestrian crossing speed records. The results show that land use, median design, pedestrian traffic signals, number of lanes, the day of the week, gender, age, carrying objects, pedestrian platoon size, population, and geographical area are significant factors in pedestrian crossing speed. In addition, it was observed that a significant number of pedestrians exhibit unsafe crossing behaviors. Male pedestrians were found to be more likely to exhibit these unsafe crossing behaviors.

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Abstract This study was undertaken to fill the information gap by exploring pedestrian behavior at footbridges in the Greater Accra and Kumasi Metropolitan areas of Ghana. Further, the study modelled the behavior of 69,840 pedestrians at the footbridges using Structural Equation Modeling (SEM). Pedestrians were observed as users and non-users of seven selected footbridges in the morning (7:00 am–9:00 am), afternoon (11:00 am–1:00 pm), and evening (3:00 pm–5:00 pm) periods for seven consecutive days (Monday to Sunday). Selected footbridges were characterized by traffic generators as schools, shopping malls, bus stops, office complexes, and restaurants in different matrices. The results showed that 30.7% of the observed pedestrians did not use the footbridges, males and young pedestrians were more likely not to use the footbridges as opposed to females and the elderly with more than half of observed pedestrians carrying luggage or load. Footbridge users were more likely to talk and hold phones than non-users and the elderly were more likely to run and ride compared to young pedestrians. Officials of the National Road Safety Authority should carry out effective public education on pedestrian safety targeting males and young pedestrians to encourage the use of pedestrian footbridges.

Objective observation of pedestrian behavior on the street has traditionally been difficult due to intensive commitment of time and resources with spatial analysis of pedestrian locations encountering additional problems. Recently, Unmanned Aerial Vehicles (UAVs) have gained popularity due to the significant improvements they offer over other conventional observation systems, such as their ability to cover larger surface areas in less time. This study tests the performance of UAV-based observation techniques in measuring pedestrian activity in two comparative settings in Santiago de Chile. The study develops an alternative technique adapting the behavioral mapping methodology that allows acquiring information about the people’s activities and the places where they are carried out. In this study a set of streets in the city of Santiago de Chile was selected as a case study, and the reliability of those observations was tested among raters in a population sample. Further, the use of a Geographic Information System (GIS) in the data coding process is detailed and exemplified using some of its spatial analysis tools. The results show high levels of inter-rater reliability in the different categories of recorded data. Finally, we discuss the advantages and limitations in observing pedestrian behavior using this technology and observation technique.

A number of pedestrian streets have been introduced to vitalize commercial areas in new towns, but there are many problems in their use behavior. A Multi-functional Administrative City (MAC) is the largest new city in Korea and has many pedestrian streets. The purpose of this study is to identify problems and derive improvement directions through analysis of user behavior according to the physical environment of pedestrian streets in commercial areas of the MAC. Two sites with different characteristics were selected and analyzed through field surveys and behavior mapping. First, smoking was the most frequent behavior, and pedestrian streets were designated as smoking spaces. It is necessary to limit smoking to a specific part of the street by installing a smoking booth. Second, despite being exclusively for pedestrians, there was frequent vehicle parking and motorcycle traffic. A plan is needed to curb the entry of vehicles and motorcycles. Third, a garbage collection box placed in the center spoiled the landscape and generated a bad smell. It is necessary to move it to the side of the street or manage it through a separate facility. Fourth, the management of facilities and stacked objects was insufficient, and street facilities that arouse the interest of pedestrians were insufficient. Facility maintenance should be improved, and landscaping or an event space should be prepared. Fifth, low traffic during the day was a main behavior, and there were few users. There is a need for a plan to utilize the space at various times

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With the rapid urbanization of geographical spaces worldwide, pedestrian safety is a major concern on urban roads. In developing economies like India, an unprecedented increase in accidents involving pedestrians has been observed at intersections. The present study focuses on pedestrian behavior, specifically, violation of red signals while crossing at signalized intersections. With the help of hazard-based duration models, the waiting duration of red-light violators has been analyzed. In addition, the response time of pedestrians during conflict has also been modeled with the help of a hazard-based duration approach. Four signalized intersections from Nagpur City in India were selected for the survival analysis. Kaplan–Meier survival curves have been plotted for both waiting time and response time. With the help of the semi-parametric Cox proportional hazard model, various factors have been identified to describe the survival function of the pedestrians’ crossing. However, the model results were found to be unsatisfactory since the explanatory variables failed in the proportional hazard assumption. Therefore, the parametric accelerated failure time model was utilized to determine the various covariates that affected the waiting time and the response time. The Weibull model was found to be the best fit for waiting duration analysis, while the log-logistic model was considered for the study of response time. The developed models can help understand the external factors and personal features of pedestrians in relation to the risk involved during violation crossings.

Understanding the movements of people is essential for the design and management of urban areas. This article presents a novel approach to understanding the asymmetry in route choice (i.e., the degree to which people choose different walking routes for their outbound and return journeys). The study utilizes a large volume of traces of individual routes, captured using a smartphone application. The routes are aggregated to a regular grid, and matrix statistics are developed to estimate the aggregate degree of route asymmetry for different types of route (shortest, longest, weekday, weekend, etc.). The results suggest that people change their route approximately 15% of the time. Although this varied little when observing trips made at the weekend or on a weekday, people taking journeys that deviated substantially from the shortest possible path were 6 percentage points less likely to change their routes than those taking journeys that were closest to the shortest path (14 and 20% asymmetry, respectively). The absolute length also impacted on the asymmetry of journeys, but not as substantially. This result is important because, for the first time, it reports a correlation between deviation from shortest route and aggregate pedestrian choice.

Point Of Interest (POI) categorization is to group POIs into several categories and make them easy-to-use in geospatial applications. Previous studies mainly used geospatial features, such as check-in sequences and satellite images, to group POIs into pre-defined rough categories. However, each POI has its own "atmosphere" beyond its geospatial features, which represents what kinds of people tend to visit it and how they spend their time there. This subtle atmosphere is important for users to decide whether to visit the POI, so considering it may be critical when providing commercial services, such as a property search service. In this paper, we propose a new POI categorization method that can capture the POI atmosphere by using user behavior on a web search engine. Our key observation is that the next queries of a search query about a POI tend to contain the user's purpose for visiting it. We harness this observation to train a neural encoder that maps POIs to continuous vectors (called embeddings) via next-query prediction with a deep structured semantic model (DSSM). Experimental results indicate that our method performs well for POI atmosphere categorization of parks as a case study. We believe that our method complements the existing POI categorization methods.

Walkability is a concept widely used in walking behaviour studies to describe how equipped an environment is to accommodate walking. The term has had widescale acceptance in Western academic literature. In many rapidly developing countries, however, the term is little recognised, and the contemporary value of walking is still fully recognised. Moreover, there is a dearth of data on this critical mode of transportation exists. This research investigates the notion of walkability in the context of one of the holiest places in the Islamic world. Many scholars have examined walkability at the micro-scale level of city centres; however, a Western-centric perspective dominates. Too few studies have explored walkability in regions with hot climates, and no specific examination has been made to study walkability issues associated with hot, arid weather during a religious event on the scale of Hajj and Umrah. To understand walkability, it is essential to examine the issue at the micro-scale of the street. This article examines the pedestrian perception of walking and the walking behaviour of pilgrims and residents in the central area of Makkah. The data of this study were collected using both qualitative and quantitative approaches. The current condition of the study area is examined through observations, interviews, and face-to-face questionnaires. The findings show ’comfort’ attributes, in particular, influence walking choices for the visitors where certain experiences and behaviours occur in response to the physical environment in the central Makkah area. Therefore, it seems that those attributes associated with comfort should dominate the concept of walkability in hot, arid climates, and this emphasis should guide urban planners in their decision-making.

Abstract Risky pedestrian behaviors, such as signal violations, crossing from undesignated points, walking on the main carriageway instead of footpaths, and waiting at undesignated locations for buses, contribute to a significant number of pedestrian-vehicular collisions at urban signalized junctions in Indian cities. Therefore, identifying the factors influencing risky pedestrian behavior is crucial in urban India. A total of 59,409 pedestrians’ road-using behavior was analyzed using video surveillance, complemented by on-site questionnaire responses from 3840 pedestrians regarding their risk perception, self-reported behaviors, and knowledge of traffic rules. Binary and ordered logit models were employed to assess the impact of the built environment, sociodemographic factors, and traffic enforcement on unsafe pedestrian actions. Results reveal a strong association between unsafe behavior and commercial zones, with young males more prone to signal violations and unsafe crossings. Further, poor lighting, inaccessible zebra crossings, on-street parking, lack of enforcement, and longer waiting times influence the likelihood of signal violations. A 1% increase in footpath encroachment by street vendors leads to an 18% rise in footpath underutilization. The lack of essential amenities and poor accessibility at bus stops discourages pedestrians from waiting at designated locations. Low educational levels and limited awareness of traffic rules exacerbate unsafe behaviors.

Pedestrian violations crossing behavior at intersections can easily lead to traffic disorder, accidents, injuries and deaths. This paper conducts a case study in Anning District of Lanzhou City to investigate the pedestrian violations by field observation, questionnaires and video recording. Out of 2852 identified valid pedestrian crossing samples at signalized intersections are randomly selected, of which 617 are involved as illegal violation samples. The factors affecting the violations are extracted and divided into internal and external factors. The internal ones are according to age and gender factors, while the external factors contain the crosswalk length, crossing time, headway, red light duration and number of partners. The results show that the rate of violation crossing among the elderly pedestrian is higher than that of other age categories and the rate of male is slightly higher than that of female. In the external factors, the crosswalk length, the crossing time and the headway of motor vehicle are positive correlation with the violation rate. For the signal light part, the longer red light duration means longer waiting time and the pedestrian violation rate will significantly increase. Furthermore the countdown signals are conducive to reducing the violation rate compared with no countdown display. Interestingly, the violation rate do not increase with the number of companions, however it’s more prone to violation for the individual pedestrians. Based on the above analysis, the corresponding solving strategies are proposed.

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The objective of this study is to analyze the pedestrian walking speed at crosswalk and non-crosswalk locations on urban roads. The investigation utilizes real-world data collected at various sites to examine walking speed in different types of crossings, including informal crossings, which occur at mid-block segments, and zebra crossings, which occur at designated crosswalks. The paper develops simulation scenarios in VISSIM to analyze the walking speed for the studied cases. The data analysis reveals that the speed influence level—defined as the influence of vehicle traffic flow on pedestrian walking speed—is lower at zebra crossings (24.2%) compared to informal crossings (32.3%). The simulation scenarios in VISSIM validate pedestrian walking behavior, implying the psychological stability of pedestrians when crossing at the designated crossings. The walking speed at zebra crossings demonstrated greater stability, exhibiting a mean change of 1.3%, in contrast to the less stable behavior observed at informal crossings, where the mean change was 17.7%. The findings of the research confirm the role of zebra crossings in improving traffic safety on urban roads.

Street vendors play an important role in urban economy of developing countries, including Nepal. Regardless their contribution to accessibility, affordability, and social vibrancy, they are often excluded from the spatial planning. Many researchers fail to catch the adaptive, informal and interactive nature of street vending, notably in relation to pedestrian movement and behavior. This study investigates the spatial and social dynamics between street vendors and pedestrian in dense urban setting of Lagankhel, Nepal, through computational tools. This study investigates the spatial arrangement and social interaction between street vendor and pedestrian through using computational tool. This research was conducted in the Lagankhel area of Lalitpur, a busy marketplace where both vendor activity and pedestrian flow intensify during the evening peak hours. Field observations were combined with parametric simulations: Isovist analysis was used to examine spatial visibility, while PedSim was simulated the pedestrian movement flow and stopping behavior. Data were collected across four urban zones to assess vendors positioning, visibility and interaction duration. Findings show that street vendors tend to occupy spaces with greater visual openness and spatial permeability, particularly near nodes of natural pedestrian slowdown or congregation. Pedestrians were observed to pause more frequently in areas with higher visibility catchment. These spatial behaviors highlight the adaptive strategies of vendors and the informal logic underlying their placement. Computational outputs showed that dense clustering of vendors reduced pedestrian flow efficiency, while clearer sightlines improved spatial legibility. These results support the development of inclusive planning frameworks that account for the dynamics of street vending without undermining pedestrian accessibility

The Integrated Choice and Latent Variable (ICLV) model has been widely applied in travel behavior studies, yet its use in understanding pedestrian route choice remains very limited. This paper seeks to address this gap by analyzing data from a series of controlled pedestrian route choice experiments. Four groups of experimental runs were designed, each involving two route options. The first three groups introduced specific controls: bottlenecks, distance constraints, and extra rewards, while the fourth group, without any imposed control, focused on the influence of route geometry (lengths and widths). For each group, we developed measurement and structural models, followed by three comparative models: a binary logit model using only measured variables (MV model), a model using only latent variables (LV model), and the ICLV model that integrates both. Across all the four scenarios, the adjusted R2 values have been improved from 0.286/0.135/0.108/0.035 (MV model) to 0.329/0.161/0.111/0.056 (ICLV model), and the ICLV model can provide interpretable results. These findings highlight the value of incorporating latent constructs based on Structural Equation Modelling (SEM), which enhances the explanatory power of pedestrian route choice models. Moreover, the differences in significant latent variables across various experimental settings offers further insights into the distinct mechanisms underlying pedestrian decision-making under varying conditions.

Walking is a sustainable transportation mode as it is the most affordable way to reduce the environmental and social effects of the transportation system. Walking can be used for transportation and recreational purposes. To encourage walking, it is necessary to improve walkability and walking behavior. The main aim of this paper is to assess the walkability of Al-Mutanabbi Street in Baghdad City, which is located in a cultural and historical area. The measure of effectiveness is the pedestrian level of service PLOS. In this paper, new indicators reflecting the perception of pedestrians have been included in the assessment method to overcome the limitation of the traditional method of PLOS determination. A questionnaire form was used to collect the data. The questions related to walkability are 21 questions grouped into seven groups. The responses of pedestrians were converted to scores to find the overall PLOS, which was C. However, the scores of some variables were low, reflecting the weakness in walking performance, such as the small spaces, low speed, and frequent conflicts with other pedestrians. The safety and security in the study area have the highest scores because no vehicles are allowed to pass the street. This PLOS method is more comprehensive than the traditional methods because it accounts for variables that other methodologies neglect.

Accurately measuring street dimensions is essential to evaluating how their design influences both travel behavior and safety. However, gathering street-level information at city-scale with precision is difficult given the quantity and complexity of urban intersections. To address this challenge in the context of pedestrian crossings — a crucial component of walkability — we introduce a scalable and accurate method for automatically measuring crossing distance at both marked and unmarked crosswalks, applied to America’s 100 largest cities. First, OpenStreetMap coordinates were used to retrieve satellite imagery of intersections throughout each city — totaling roughly three million images. Next, Meta’s Segment Anything Model was trained on a manually labelled subset of these images to differentiate drivable from non-drivable surfaces (i.e., roads vs. sidewalks). Third, all available crossing edges from OpenStreetMap were extracted. Finally, crossing edges were overlaid on the segmented intersection images, and a grow-cut algorithm was applied to connect each edge to its adjacent non-drivable surface (e.g., sidewalk, private property, etc.), thus enabling the calculation of crossing distance. This achieved 93% accuracy in measuring crossing distance, with a median absolute error of 2 feet 3 inches (0.69 meters), when compared to manually verified data for an entire city. Across the 100 largest U.S. cities, median crossing distances ranged from 32 feet to 78 feet (9.8 – 23.8m), with detectable regional patterns. Median crossing distance also displayed a positive relationship with the cities’ year of incorporation, illustrating in a novel way how American city planning increasingly emphasizes wider (and more car-centric) streets. These findings identified opportunities to improve pedestrian safety and increase walkability at multiple scales, from the individual block to the entire city.

Research on streets and public spaces in general has predominantly relied on systematic observation and behavioral mapping to investigate human behavior and its spatial patterns. This approach, however, often lacks the depth needed, especially in densely populated urban areas of Global South cities, to uncover the intricate social processes and politics that shape people’s behavior and patterns. In response to this limitation, our study takes a multifaceted approach, combining behavioral mapping (through systematic observation data) with rhythmanalysis (using general observation data and in-depth interviews) to study pedestrian streets with a case study in Hanoi, Vietnam. This research contributes to urban geography and urban studies methodology by analyzing the limitations and strengths of behavioral mapping and rhythmanalysis. We call for combining these methods in a way that allows them to complement each other. Such a combination should provide a nuanced and comprehensive understanding of the temporal and social forces that shape everyday life in public spaces.

Walking speed, a fundamental aspect of transportation, varies across individuals and is influenced by factors such as age, gender, and environmental conditions. This study focuses on pedestrian behavior, particularly walking speed, at bottlenecks in Larkin Sentral bus terminal in Johor Bahru, Malaysia. The country's lower average walking speed compared to other Asian nations underscores the need for efficient urban transportation planning. The objectives are to determine pedestrian walking speeds and explore the relationship between pedestrian attributes and walking speed. Data was collected using video recording at selected walkways during peak and non-peak hours. The analysis, conducted using Minitab software, reveals a mean walking speed of 0.80 m/s, influenced by factors such as age, gender, carrying bags, using phones, attire, and time. Correlation analysis indicates that time and phone usage significantly affect walking speed. This study contributes valuable insights for enhancing urban planning, emphasizing the impact of technology and time on pedestrian experiences at bus terminals. The implications for urban transportation planning are substantial, advocating for custom interventions in pedestrian-friendly designs at bus terminals. The study challenges assumptions, highlighting the need for nuanced understanding and further research to comprehend the complex dynamics shaping pedestrian behavior.