Computers Environment and Urban Systems最新文献

Despite the rapid advancement and extensive applications of online Volunteered Geographical Information (VGI) projects such as OpenStreetMap (OSM), the persistence of data inequality remains a significant challenge, compromising the global reliability of their data products. This study examines the influence of contributions made by organizations, which have notably risen within the OSM community, on data inequality. The Gini coefficient is utilized to quantify data inequality, while a suite of statistical methods, including spectral analysis and robust correlation analysis, is applied to evaluate the distribution and impact of organizational efforts across various nations. Our findings indicate that organizations predominantly allocate their resources to nations with less complete data and surpass collective efforts of average contributors in mitigating OSM data inequality. Furthermore, the phenomena appears to be particularly significant for NGOs or corporations with humanitarian visions.

Most studies of urban morphology rely on morphometrics, such as building area and street length. However, these methods often fall short in capturing visual patterns that carry abundant information about the configuration of urban elements and how they interact spatially. In this study, we introduce a novel method for learning morphology features based on figure-ground maps, which leverages recent developments in computer vision. Our method facilitates discovering and comparing urban form types in a fully unsupervised manner. Specifically, we examine building fabrics by 1 km patches. A visual representation learning model (SimCLR) casts each patch into a latent embedding space where similar patches are clustered while dissimilar patches are dispelled, thus generating morphology representations that entail the layout of building groups. The learned morphology features are tested in urban form typology clustering and comparison tasks in four diverse cities: Singapore, San Francisco, Barcelona, and Amsterdam, with data sourced from OpenStreetMap. Clustering results show effective identification of typical urban morphology types corresponding to urban functions and historical developments. Further analyses based on the representations reveal inner- and cross-city morphological homogeneity relating to socio-economic drivers. We conclude that this method is a promising alternative for effectively describing urban patterns in morphology analysis.

Bus-based mobile sensing has emerged as a cost-effective approach for collecting high spatio-temporal air quality data by leveraging the mobility of buses. However, when selecting an optimal subset of buses from a large fleet for deploying a limited number of sensors, existing studies have primarily focused on assessing the coverage of the study area by buses, disregarding the temporal gap between consecutive coverage at specific locations. It is worth noting that pollutant concentrations exhibit smooth variations over time, rendering data collected at very short intervals redundant. Therefore, this study first identified five key criteria for evaluating the air quality monitoring importance in various locations. Then two bus selection models that consider both the spatiotemporal coverage of the study area and the temporal gap between sensing data are proposed. Specifically, the maximal spatio-temporal coverage bus selection model (MaxCoverage) maximizes overall spatio-temporal coverage with a guaranteed time interval between consecutive sensor measurements, and the minimal fleet size model (MiniSize) selects the minimum number of buses based on based on specified requirements for monitoring time interval and counts. Experimental validation using a real-world bus trajectory dataset from Shenzhen, China demonstrates the effectiveness of the proposed models. The results show that the MaxCoverage_TC1 model has time intervals 2.7 timeslots longer than the baseline, and the MiniSize_TC1 model has an average time interval that is 1.4 timeslots longer.

Urban forests provide many benefits for municipalities and their residents, including air quality improvement, urban atmosphere cooling, and pluvial flooding reduction. Monitoring the trees is one of the tasks among the several urban forest assessment procedures. Trees with a risk of falling may threaten the locals and the infrastructure of the cities, thereby being an immediate concern for forestry managers. In general, a set of measures and aspects are collected from field survey analysis to estimate whether the trees represent a risk to the safety of the urban spaces. However, gathering the tree's physical measures in fieldwork campaigns is time-consuming and laborious considering the massive number of trees in the cities. Therefore, there is an urge for new computational-based methodologies, especially those related to the latest advances in artificial intelligence, to accelerate the assessment of trees in the municipality areas. In this sense, this work aims at using several machine learning-based methods in the context of tree condition inspection. Particularly, we present the prediction of the tree failure probability by using several aspects collected over time from fieldwork campaigns, with a special focus on external physical measures of the trees. Further, we provide the samples with their respective tree failure probability values as a new open dataset for further investigations on tree status monitoring. We also present a novel dataset composed of images of trees with bounding boxes delineations of the tree, trunk, and crown for automating the tree monitoring tasks. Regarding the tree failure probability estimation, we compared several regression algorithms for estimating the tree failure likelihood. Moreover, we propose a stacking generalization approach to enhance forecast accuracy and minimize prediction errors. The results showed the viability of the proposed method as an auxiliary tool in tree analysis tasks, which attained the lowest average Mean Absolute Error of 5.6901$\pm$1.1709 yielded by the stacking generalization model.

A comprehensive understanding of population distribution is critical for assessing socio-economic issues. However, the widely used dasymetric mapping method relies on models built at a coarse administrative scale and estimates population at a fine-gridded scale. This difference in scale between the training and estimating domains results in significant heterogeneity in data distribution. To address this issue, we proposed a scale heterogeneity-avoided method based on artificial neural networks that can take population density as an independent variable and gridded properties, including remote sensing images, digital terrain models, road networks, building footprints, and land use, as dependent variables. Our experiments in Hong Kong in 2016 and 2021 showed significant advantages of the proposed method. Compared to commonly used methods, our approach demonstrated a 19.4% improvement in the root mean square error. Furthermore, the advantages of our method became more apparent at larger census units, and the accuracy of the pre-trained model for directly estimating population in other temporal phases was satisfactory. Among the geospatial data variables, land use was the most significant in accurately estimating population. Replacing land use data with random numbers led to a decrease in accuracy by over 89.0%, while other properties only resulted in decreases of 2.7% to 13.9%. We further investigated spatiotemporal changes in population distribution from 2016 to 2021, finding that population growth mainly occurred in new built-up areas, while larger population decreases occurred in old towns. Throughout the study period, the population tended to concentrate more, as the average population density increased while the median population density decreased.

Storefront vacancy has been a widespread and worldwide phenomenon, raising concerns about the changing characteristic of the retail landscape, loss of community vitality, and hollowing out of cities. Although the causes leading to this phenomenon have been extensively debated, little granular data are available to evaluate the issue in a timely manner. Therefore, this study aims to develop a data-driven approach to capture the commercial structure of vacant storefronts on a store-by-store basis as well as to analyze their evolution patterns. First, street-level images were collected using mobile sensing in a low-cost, large-scale and efficient manner; then, a storefront vacancy estimation model was developed using computer vision techniques to infer the storefront location, operation status, business category, and vacancy rates. Three volunteers spent five days collecting street-level images from an urban area of 964 km² in the case city of Xining, China. As a result, 93,069 stores were identified in the city in March 2022, of which 25,488 were vacant. Moreover, the storefront vacancy rate increased significantly after the epidemic, from 21.8% in 2018 to 30.0% in 2022. Stores in shopping, catering, and life services had the maximum vacancies. The factors that had the greatest impact on storefront vacancy were, in order of importance, far from commercial zonings, low population density, and far from the urban center. However, these factors influenced the vacancy in diverse and complex ways, and in the future, urban planning strategies to address vacancy issues should be well considered and differentiated.

Accurately measuring the distribution of vitality in urban public spaces and evaluating the attractiveness index of landscape elements can enhance the precision of public space design and the rational planning of urban microupdate strategies. This study introduces a method based on the SLAM algorithm to create a 3D visual exposure analysis and applied in two squares in Macau to calculate the relationship between pathfinding decisions and visual exposure. The results demonstrate that both the extent of visual exposure (seen from the observer) and the area of isovist field (seen from the object) can statistically indicate the attractiveness index of a space. Consequently, an attractiveness map of the site can be constructed. This study effectively captures walking trajectories and visual data in real-world settings, integrating site-specific and pedestrian information into a digital twin system. This approach not only advances quantitative methodologies but also facilitates postoccupancy evaluations of public space usage and environmental behavior research, thereby expanding the potential for future investigations in this domain.

Considering that the number of wheelchair users is on the rise at the global level due to population aging, it is crucial to secure their rights to have adequate access to healthcare services. Spatial accessibility to healthcare services has been well recognized to influence people's health. However, research on healthcare accessibility of wheelchair users is scarce. This study proposes a barrier-free path planning method to estimate wheelchair users' travel time as the measurement of their accessibility. A study on Wuhan, China, is conducted to evaluate the spatial accessibility to healthcare services for wheelchair users and compare it with the general population. The results show that: (1) the levels of healthcare accessibility are unevenly distributed across the city center and the periphery of the study area for both wheelchair users and the general population, while wheelchair users have lower accessibility overall; (2) both similarities and differences in hospital and travel mode selection to access healthcare services co-exist in the study area between the two groups; (3) significant inequality in healthcare accessibility is observed in Hongshan and Qingshan districts. The research findings are beneficial for policymakers to further improve healthcare accessibility and its equality by optimizing the allocation of hospital resources and barrier-free public transport.

Bicycling can improve the sustainability and liveability of cities, many of which desperately require better active transport infrastructure. Urban and transport planners need to examine how improvements in infrastructure change bicyclists' behaviour. With this knowledge, investment in bicycling networks can be more efficient and encourage the use of bicycling for transportation. This study developed a simple Agent-Based Model (ABM) to simulate bicyclists' movements in response to the built environment and road network characteristics in the City of Penrith, in the Greater Sydney Area, Australia. In this case study, the GAMA platform was used to build the ABM and Strava and Riderlog data were used to calibrate and validate the model. The model outputs give insights into bicyclist movements through the road network. The incorporated built environment characteristics include the type of bicycling infrastructure, tree canopy, slope, land use mix, and vehicle traffic. These choice factors also allowed the computation of rider levels of comfort and safety on each trip. Potential refinements of the model include additional bicycling behaviour factors (such as aesthetic preferences), and bicyclists' interactions with each other and other modes of transport.

Road networks in the past are imperative for understanding evolution of transportation infrastructure, urban sprawl, and route planning, etc. Various approaches have been developed for road extraction from historical maps, among which deep learning techniques stand out as the most effective ones. However, little attention has been paid to investigating road vectorization and classification from historical maps. Moreover, road classification via machine learning methods usually requires large amounts of dedicated training data. To address these issues, this paper proposes a novel and comprehensive framework for road vectorization and classification on the basis of road segmentation from historical maps. First, deep learning is used to get pixel-wise raster road segmentation results, which are further skeletonized using morphological operations. Then, considering that each road class is represented with a certain symbol, a painting function is defined for each class able to paint the corresponding symbol. These painting functions are then used to draw road segments along the skeletons. Since the start and end points in each painting function are used to vectorise the segment, this method achieves vectorization and classification at the same time. Our method is validated on four Siegfried map sheets in Switzerland, and evaluated via both visual and quantitative assessments. The results indicate that the method is capable of classifying roads accurately. In particular, two evaluation metrics completeness and correctness achieve 90.69% and 72.71% respectively for road class 2 which accounts for the highest portion in the map. Moreover, the results of this method avoid the saw-toothed issue of vectorised road lines. This research is beneficial for creating complete vector road network datasets with class information to support decision-making in urban planning and transportation.