Computers Environment and Urban Systems最新文献

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.

Public events, such as concerts and sports games, can be major attractors for large crowds, leading to irregular surges in travel demand. Accurate human mobility prediction for public events is thus crucial for event planning as well as traffic or crowd management. While rich textual descriptions about public events are commonly available from online sources, it is challenging to encode such information in statistical or machine learning models. Existing methods are generally limited in incorporating textual information, handling data sparsity, or providing rationales for their predictions. To address these challenges, we introduce a framework for human mobility prediction under public events (LLM-MPE) based on Large Language Models (LLMs), leveraging their unprecedented ability to process textual data, learn from minimal examples, and generate human-readable explanations. Specifically, LLM-MPE first transforms raw, unstructured event descriptions from online sources into a standardized format, and then segments historical mobility data into regular and event-related components. A prompting strategy is designed to direct LLMs in making and rationalizing demand predictions considering historical mobility and event features. A case study is conducted for Barclays Center in New York City, based on publicly available event information and taxi trip data. Results show that LLM-MPE surpasses traditional models, particularly on event days, with textual data significantly enhancing its accuracy. Furthermore, LLM-MPE offers interpretable insights into its predictions. Despite the great potential of LLMs, we also identify key challenges including misinformation and high costs that remain barriers to their broader adoption in large-scale human mobility analysis.

Urban greenways enhance the social, environmental, and ecological facets of city life by offering accessible and engaging spaces for residents. Despite their significance, the route selection for these urban greenways often hinges on suitability analysis, which can be influenced by a planner's subjective judgment, thus potentially introducing bias. Spatial optimization is a potential solution for determining optimal urban greenway routes. However, urban greenway route planning poses a distinct spatial optimization challenge that is not addressed by existing models. While urban greenways are inherently linear features, there are generally no specific start or end points dictated in their planning, which contrasts with many existing line-based spatial optimization models. Moreover, the way that coverage for urban greenways is measured—by taking into account the area encompassed within a particular distance from the entire urban greenway—deviates from the method used in conventional coverage optimization models, which works through discrete point-based evaluations. To address these gaps, our study introduces the maximal covering location problem for lines (MCLP-Line) model, which is designed to determine the optimal single-line-shaped urban greenway route with maximum coverage of nearby residents. In this paper, we utilize a line graph data structure to transform the candidate road network into a graph where road segments become nodes and junctions are treated as edges. We delineate the mixed integer linear programming formulation for the MCLP-Line model and discuss approaches for eliminating subtours in the MCLP-Line model in detail. The study provides simulation tests using both randomly generated data and an empirical dataset from Lhasa to demonstrate the practicality and computational efficiency of the proposed model.

Fine-granular spatio-temporal trajectories, i.e., time-stamped sequences of locations, play a pivotal role in transport and urban analytics. However, sharing or publishing trajectory data of individuals raises concerns about location privacy given the potential for re-identification and unintentional dissemination of sensitive information. A key enabler for privacy breaches is precise temporal information. Thus, this study investigates the privacy-preserving capabilities of third-party free mechanisms protecting trajectories by exclusively targeting the temporal dimension. We compare a deterministic and a stochastic technique for shifting trajectories in time by adding an offset to each timestamp. The stochastic approach leverages a generalized version of differential privacy to render an individual's presence at any event plausibly deniable, obstructing re-identification attacks based on spatio-temporal side knowledge. Furthermore, we present a Markov chain-based speed perturbation technique that preserves dynamic patterns while obfuscating travel times and motion attributes. Using simulated re-identification attacks, we analyze privacy gains and contrast them with the utility loss. The results demonstrate a favorable utility-to-privacy ratio of the temporal techniques compared to established spatial and spatio-temporal approaches. This underlines the importance of accounting for temporal aspects in addition to spatial considerations in privacy-preserving trajectory publishing.