Computers Environment and Urban Systems最新文献

Casual encounters with diverse groups of people in urban spaces have been shown to foster social capital and trust, leading to higher quality of life, civic participation, and community resilience to hazards. To promote such diverse encounters and cultivate social ties, policymakers develop social infrastructure sites, such as community centers, parks, and plazas. However, their effects on the diversity of encounters, compared to baseline sites (e.g., grocery stores), have not been fully understood. In this study, we use a large-scale, privacy-enhanced mobility dataset of >120 K anonymized mobile phone users in the Boston area to evaluate the effects of social infrastructure sites on the observed frequencies of inter-income and inter-race encounters. Contrary to our intuition that all social infrastructure sites promote diverse encounters, we find the effects to be mixed and more nuanced. Overall, parks and social businesses promote more inter-income encounters, while community spaces promote more same-income encounters, but each produces opposite effects for inter-race encounters. Parks and community spaces located in low-income neighborhoods were shown to result in higher inter-income and inter-race encounters compared to ordinary sites, respectively, however, their associations were insignificant in high-income areas. These empirical results suggest that the type of social infrastructure and neighborhood traits may alter levels of diverse encounters.

Road networks play an important role in the sustainable development of human society. Conventionally, there are two sources of road data acquisition: road extraction from Remote Sensing (RS) imagery and GIS based map production. Each method has its limitations. The RS road extraction methods are primarily raster-based and the extracted roads are not directly usable in GIS due to their fragmented and noisy nature, while vector-based methods cannot utilize rich raster information. Further more, the vector and raster data can have discrepancies for various reasons. Efficient road data production requires an image-vector conflation process that can match and combine raster and vector-based road data automatically.

In this study, we propose a full image-vector conflation framework that directly integrates image and vector road data by appropriately transforming extracted roads from imagery and establishing a match relation between these roads and a credible target GIS road dataset. Based on analyzing these match relations, we propose new metrics for measuring the degree of agreement between the raster and vector road data. The proposed framework combines state-of-the-art deep learning methods for image segmentation and optimization-based models for object matching. We prepared a large-scale high-resolution road dataset covering two counties in Kansas, US. Using trained models from one of the two counties, we were able to extract road segments in the other county and match them to the TIGER/Line roads.

Our experiments show that conventional performance metrics for road extraction (e.g. IoU) are insufficient for measuring the degree of agreement between image and vector roads as they are pixel-based and are too sensitive to spatial displacement. Instead, the newly defined vector-based agreement metrics are needed for image-vector conflation purposes. Experiments show that, by the vector-based metrics, nearly 90% of GIS road lengths in the study area were extracted and over 90% of extracted roads matched the target GIS roads. The new framework streamlines raster-vector conflation of roads and can potentially expedite relevant geospatial analyses regarding change detection, disaster monitoring and GIS data production, among others.

Addressing climate change and urban energy problems is a great challenge. Building Integrated Photovoltaics (BIPV) plays a pivotal role in energy conservation and carbon emission reduction. However, traditional approaches to assessing solar radiation on buildings with physical models are computing-intensive and time-consuming. This study presents a hybrid approach by integrating physical model-based solar radiation calculation and machine learning (ML) for city-wide building solar radiation potential (SRP) analysis. By considering urban morphology, land cover, and meteorological characteristics, local climate zones (LCZs) are classified. The SRP of representative LCZs is precisely evaluated using computing-intensive physical models integrated with 3D building models. A ML model is then developed to effectively predict the SRP of building roofs and facades throughout the city. An experiment was conducted in Shenzhen, China to validate the presented approach. The results demonstrate that Shenzhen has a total annual building solar radiation of $3.28\ast {10}^{11}\mathrm{kwh}$. Luohu District exhibits the highest SRP density. The LCZ-based analysis highlights that compact low-rise LCZs offer greater SRP for roofs, while compact high-rise LCZs do so for facades. Moreover, BIPV could cut CO₂ emission by up to 41.85 million tons annually. Notably, solar PV installation only on rooftops in Shenzhen could meet 87.81% of the city's electricity department's carbon reduction goal. This study provides an alternative for city-wide SRP estimation by combining physical modeling and ML and offers valuable insights for data-driven and model-driven urban planning and management in low-carbon cities.

Promoting environmentally and socially sustainable urban mobility is crucial for cities, with urban greening emerging as a key strategy. Contact with nature during travel not only enhances well-being but also promotes sustainable behaviour. However, the availability of travel greenery varies, and only recently have new datasets and computational approaches made it possible to compare the conditions in the distribution of travel greenery within and between cities quantitatively. In this study of 43 large European cities, we undertook a comparative analysis of travel greenery availability by using high-resolution spatial data and daily school trips as a marker of a daily travel need. By recognising walking accessibility as the most sustainable and equally available mode of transportation, we first estimated the proportion of the population residing within walking distance to upper secondary schools. Second, we associated the detailed school routes with monthly green cover data and compared the spatial variation in travel greenery availability between European cities, taking seasonal variation into account. Lastly, we analysed spatial inequalities of travel greenery availability within the study cities using the Gini index, the Kolm-Pollak equally-distributed equivalent (EDE) index and Moran's I. Our findings reveal a consistent negative association between accessibility and green cover implying a trade-off between access and greenery. We found large variations between European cities in the walking accessibility of schools, ranging from 44% to 98% of the population being within 1600 m of their school. Moreover, our results show substantial within-city disparities in travel greenery availability in large European cities. We demonstrated methodologically the importance of considering seasonal variations when measuring greenery availability. Our study offers empirical evidence of urban greenery availability from a mobility-focused perspective. It provides a novel understanding with which to support researchers and planners in affording the benefits of nature to more people as they travel.

Urban surface radiometric temperatures, approximate to the surface kinetic temperatures, are predominantly retrieved using satellites or unmanned aerial vehicles (UAVs) and exhibit pronounced spatiotemporal variations. Despite numerous methods ranging from empirical to physical models for obtaining urban microscale surface radiometric temperatures via UAVs, challenges remain given the limited physical significance and substantial professional barriers to method application. Against this background, this study introduces a novel and straightforward approach for acquiring spatially distributed radiometric temperatures on sunny days without understanding the complex radiative transfer process as well as acquiring low-altitude atmospheric parameters. An automated machine learning was employed to train a model capable of efficiently estimating radiometric temperatures. Training and testing datasets were created based on the urban radiative transfer equation, incorporating three independent variables: UAV-measured surface brightness temperature, broadband emissivity, and sky view factor, which collectively represent the diverse thermal environments across different surface characteristics and urban layouts during sunny transitional and summer seasons. The model's accuracy was subsequently confirmed through direct comparisons with radiometric temperatures retrieved from UAV-collected multimodal images and kinetic temperatures synchronously collected on the ground across four periods. The results indicate that AutoGluon achieved high accuracy (MAE: 0.04 K; RMSE: 0.06 K; $R^2$: 0.99). Additional ground measurement validations further demonstrated the model's reliability, with absolute biases on sunlit surfaces maintained within 1.25 K. Given its capability for real-time, high-spatial-resolution mapping of radiometric temperatures (April test: 8.70 cm, July test: 6.89 cm) in urban microscales with considerable heterogeneity, such a method is envisioned to be an effective tool for the dynamic monitoring and management of thermal environments at the microscale level in urban settings.

Addressing urban inequalities has become a pressing concern on both the global sustainable development agenda and for local policy. Improving public transport services is seen as an important area where local governments can exert influence and potentially help reduce inequalities. Existing measures of accessibility used to inform decision-making for public transport infrastructure in London show spatial disparities, yet there is a gap in understanding how these disparities vary across demographic groups and how they evolve over time—whether they are improving or worsening. In this study, we investigate the distribution of public transport accessibility based on ethnicity and income deprivation in London over the past decade. We used data from the Census 2011 and 2021 for area-level ethnicity characteristics, English Indices of Deprivation for income deprivation in 2011 and 2019, and public transport accessibility metrics from Transport for London for 2010 and 2023, all at the small area level using lower super output areas (LSOAs) in Greater London. We found that, on average, public transport accessibility in London has increased over the past decade, with 78% of LSOAs experiencing improvements. Public transport accessibility in London showed an unequal distribution in cross-sectional analyses. Lower income neighbourhoods had poorer accessibility to public transportation in 2011 and 2023 after controlling for car-ownership and population density. These disparities were particularly pronounced for underground accessibility. Temporal analyses revealed that existing inequalities with respect to income deprivation and ethnicity are generally not improving. While wealthier groups benefited most from London Underground service improvements; lower income groups benefited more from bus service improvements. We also found that car ownership levels declined in areas with substantial increases to public transport accessibility and major housing developments, but not in those with moderate improvements.

In the evolving landscape of poverty research, especially in China, the focus has shifted from eliminating absolute poverty to relieving relative poverty. Although much of the existing studies have begun to use built environment big data, such as remote sensing and street view imagery, to measure poverty, peoples' consumption, an essential indicator of poverty receives less attention. This study delves into the relationship and spatial disparity between poverty measured by consumer big data and multidimensional poverty measured based on the census data. We investigated 1731 communities in Guangzhou as case study regions and combined their residents' mobile phone metadata and spatial cost of living data as the input consumer big data. Then, we constructed Index of Multiple Deprivation (IMD) levels based on the census data and built random forest classification model based on our consumer big data to predict IMD level at community level. The result shows that the predicted poverty of 81.11% communities were generally consistent with the IMD level, indicating that the consumer big data poverty mapping provided a viable poverty measurement from consumer behavior perspective. The SHapley Additive exPlanations' values result shows that Pinduoduo (a low-cost online shopping mobile application) contributes the most to predicted poverty from consumer behavior. For spatial disparities, poverty mapping based on consumer big data is more sensitive to the poverty in suburban developing neighborhoods and affordable housing communities compared with the IMD. The urban poverty mapping based on consumer big data offers a timely portray of communities' socio-economic challenges and consumption-related poverty, and provides support and evidence for accurate urban poverty alleviation strategies.

Resilient post-disaster recovery is crucial for the long-term sustainable development of modern cities, and in this regard, predicting the unusual flows of human mobility when disasters hit, could offer insights into how emergency responses could be managed to cope with such unexpected shocks more efficiently. For years, many studies have been dedicated to developing various models to predict human movement; however, abnormal human flows caused by large-scale urban disasters, such as urban floods, remain difficult to capture accurately using existing models. In this paper, we propose a spatiotemporal hybrid deep learning model based on a graph convolutional network and long short-term memory with a spatial heterogeneity component. Using 1.32 billion movement records from smartphone users, we applied the model to predict total hourly flows of human mobility in the “7.20” extreme urban flood in Zhengzhou, China. We found that the proposed model can significantly improve the prediction accuracy (i.e., $R^2$ from 0.887 to 0.951) for during-disaster flows while maintaining high accuracy for before- and after-disaster flows. We also show that our model outperforms selected mainstream machine learning models in every disaster stage in a set of sensitivity tests, which verifies not only its better performance for predicting both usual and unusual flows but also its robustness. The results underscore the effective role of spatial heterogeneity in predicting human mobility flow in a disaster context. This study offers a novel tool for better depicting human mobility under the impact of urban floods and provides useful insights for decision-makers managing how people move in large-scale disaster emergencies.

Zoning regulates land use and intensity of urban development at the county and municipal level in the United States, promoting economic growth, community health, and environmental preservation. However, limited availability of zoning data at scale hinders regional assessments of regulations and coordinated resilience planning efforts. In this study, we developed an open-source, replicable, and transferable framework to predict spatially complete zoning in areas where zoning information is publicly unavailable. We applied a Hierarchical Random Forest algorithm to predict multilevel zoning districts, including three core districts (residential, non-residential, mixed use) and 13 sub-districts. To mimic real-world data accessibility challenges, we evaluated two models: one filling gaps within a county (within-county) and the other extrapolating for counties with no available data (between-county). We tested our models statewide in North Carolina (NC), USA, and developed the State's first comprehensive zoning map. We found strong predictive performance for our within-county model (∼99% accuracy; macro averaged F1 score of ∼0.97) irrespective of district breakdown (i.e., core and sub). However, our between-county model performance was lower and varied depending on the training counties sampled and the district breakdown considered (19–90% accuracy; macro averaged F1 score of 0.105–0.451). Our framework provides spatially complete zoning maps for previously inaccessible locations, enabling researchers and planners to conduct large-scale comprehensive zoning assessments.

Cities around the world are grappling with multiple interconnected challenges, including population growth, shortage of affordable and decent housing, and the need for neighborhood improvements. Despite its critical importance for policy, our ability to effectively monitor and track urban change remains limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change, as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic pretrained embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions towards more liveable, equitable, and sustainable cities.