Computers Environment and Urban Systems最新文献

Integration of bike usage and transit services is an effective way to enhance accessibility for both transportation modes. Using high-resolution transit data, the study analyzes bike-transit multimodal accessibility and usage patterns through a social equity lens. Two types of accessibility increment are studied: bicycle increment to public transit – the benefits of using bicycle for transit riders, and transit increment to cycling – the merits of using transit for cyclists. Results show that bike-transit integration benefits both public transit riders and cyclists, expanding their accessible opportunities by up to 70% and enabling longer trips for cyclists while providing continuous benefits for public transit users. Meanwhile, better infrastructure significantly improves multimodal accessibility, resulting in more increment for public transit riders but less increment for cyclists. The paper also shows the spatiotemporal patterns of multimodal ridership. The research highlights disparities in bike-transit activities for Black communities due to inadequate local biking infrastructure. Black people majority neighborhoods enjoy less increment compared to other neighborhoods for shorter and very long trips; they also have disproportionately lower multimodal ridership despite much higher transit ridership and better transit access. Enhancing biking infrastructure in these areas can improve physical accessibility increment and promote social equity. The paper provides practical insights for transit planning, emphasizing the importance of connecting bike lanes and creating safer streets for cycling.

We introduce a spatially-explicit sensitivity framework to uncover potential biases in urban delineation approaches. Our starting point is that there is no broadly shared agreement on how to define or delineate urban areas, neither in terms of methods nor in terms of thresholds or criteria. Deciding on delineation criteria thus inevitably involves making certain assumptions that may unwittingly reproduce urban realities experienced by those expressing them, and have spatially unequally distributed implications. Understanding how specific criterion choices shape our understanding of ‘the urban’ and how, why, and – especially – where a definition leads to specific sensitivities is therefore key, especially when the definition is utilised beyond its intended application. Our framework to uncover these sensitivities is spatially explicit in the sense that it does not rely on aggregate statistics but instead focuses on the sensitivity of the ‘urban’ classification of individual spatial units at the finest spatial granularity. Applying the framework to the definition of the Degree of Urbanisation reveals that sensitivity is indeed not equally distributed across the world. Certain regions (e.g., areas around Dallas – Fort Worth) and specific types of urbanisation (e.g., desakota regions in Pacific Asia) exhibit higher sensitivity than others. We discuss how these sensitivities may embody certain implicit assumptions in the definition, and examine their broader theoretical implications.

Public transit offers urban populations physical accessibility to resources and opportunities. However, at the same time, transit trips often expose users to extreme environmental conditions, such as extreme heat and cold since transit journeys usually include out-of-vehicle trip segments including walking and waiting. Such exposure can be considered as environmental health costs because exposure to weather extremes can lead to adverse health outcomes. Even worse, climate change is increasing the intensity and frequency of extreme weather events. In this context, how can we make public transit accessibility measures ready for climate change? This paper attempts to answer this question by developing a generalized cost function approach combining travel time and environmental health costs into an integrated measure of dual accessibility: a measure of the travel costs of accessing a fixed number of destinations. We synthesize transport science, environmental health, remote sensing, and urban climatology to empower the proposed framework. To demonstrate the utility of the proposed method, we carry out an example study that incorporates transit passengers' extreme cold exposure into accessibility measures in the city of Winnipeg, Manitoba, Canada. Further, we perform a social equity analysis to investigate whether the increase in total integrated costs (i.e., decrease in accessibility) due to the inclusion of environmental health costs disproportionately affects socially disadvantaged population groups. The proposed method enables a more realistic and practical measurement of public transit accessibility under climate change; thereby, improving the readiness and resilience of our society and transport systems for future challenges.

Individual trajectories, capturing significant human-environment interactions across space and time, serve as vital inputs for geospatial foundation models (GeoFMs). However, existing attempts at learning trajectory representations often encoded trajectory spatial-temporal relationships implicitly, which poses challenges in learning and representing spatiotemporal patterns accurately. Therefore, this paper proposes a joint spatial-temporal graph representation learning method (ST-GraphRL) to formalize structurally-explicit while learnable spatial-temporal dependencies into trajectory representations. The proposed ST-GraphRL consists of three compositions: (i) a weighted directed spatial-temporal graph to explicitly construct mobility interactions over space and time dimensions; (ii) a two-stage joint encoder (i.e., decoupling and fusion), to learn entangled spatial-temporal dependencies by independently decomposing and jointly aggregating features in space and time; (iii) a decoder guides ST-GraphRL to learn mobility regularities and randomness by simulating the spatial-temporal joint distributions of trajectories. Tested on three real-world human mobility datasets, the proposed ST-GraphRL outperformed all the baseline models in predicting movements' spatial-temporal distributions and preserving trajectory similarity with high spatial-temporal correlations. Furthermore, analyzing spatial-temporal features in latent space, it affirms that the ST-GraphRL can effectively capture underlying mobility patterns. The results may also provide insights into representation learnings of other geospatial data to achieve general-purpose data representations, promoting the progress of GeoFMs.

Overlapping structures, often overlooked, are crucial in shaping comprehensive urban development and broader megaregional strategies. To address the gap, this study conducts the overlapping communities analysis in the Pearl River Delta (PRD), a megaregion in South China, using big geospatial data from 2018. A novel Overlapping Community Detection based on Density Peaks (OCDDP) is employed to generate multiple communities with diverse functions for different nodes in the commuting network of 60 sub-city divisions. We identify eight overlapping communities in PRD characterized by two categories of communities predominantly centered around Shenzhen and Guangzhou, revealing a bicentric spatial structure. Notably, central sub-cities are characterized by a low-overlap attribute, while peripheral sub-cities manifest a high-overlap tendency. Furthermore, the study investigates the driving forces behind these communities through ridge regression to analyze the impacts of various spatial flows, including policies, investment amount and times, branch funding and number, travel cost, and travel distance, co-patenting, and search index. This part found that four Shenzhen-centric communities are primarily driven by travel cost, co-patenting, branch funding, and number, while the four Guangzhou-centric communities are influenced by co-patenting, investment amount, and times. This study emphasizes differentiated functional linkages and the need for precise policy positioning and resource allocation, paving the way for a coordinated and holistic approach to megaregional development.

Mega city regions (MCRs) have emerged in many countries in the process of urbanisation. Understanding the spatio-temporal dynamics of MCRs is crucial for sustainable urban development. However, the spatial scales and boundaries of these MCRs remain poorly defined, and their temporal dynamics have received limited attention. To address these gaps, we propose a new framework and GSMA algorithm that considers inter-city flows to identify MCRs' central cities, ranges and hinterlands. By utilising comprehensive data of over 30 million inter-city flow records covering 369 cities from Amap and Tencent, calibrated with official data from the Ministry of Transport, we identify 10 MCRs and 16 central cities in China, providing a clearer understanding of the spatial ranges and core areas of MCRs. We find that MCR ranges show relative stability during routine activities and expansions during holiday periods. Compared with previous methods, the proposed framework and algorithm have two prominent advantages. First, our methodology incorporates the directional characteristics of flows into the identification of MCRs' central cities. Second, we strike a balance between enlarging regional influence and tightening the internal connections in MCR delineation. In addition, by incorporating temporal changes in inter-city flows, the study reveals the temporal dynamics of MCRs which reflects the intricate interplay between human activities and urban system dynamics.

Over the past 15 years, environmental criminologists have explored the application of agent-based models (ABMs) of crime events and various theoretical frameworks applied to understand them. Models have supported criminological theorising and, in some cases, been applied to make predictions about the impact of interventions devised to reduce crime. However, decision-making frameworks utilised in criminological ABMs have typically been implemented through traditional techniques such as condition-action rules. While these models have provided significant insights, they neglect a crucial component of theoretical accounts of offending, the notion that offenders are learning agents whose behavioural dynamics change over time and space. In response, this article presents an ABM of residential burglary in which offender agents utilise reinforcement learning (RL) to learn behaviours. This solution enables offender agents to learn from individual-level perceptions of the environment and, given these perceptions, develop behavioural responses that benefit themselves. The model includes conceptualisations of the Routine Activity Theory (RAT), Crime Pattern Theory (CPT) and a utility function, Target Attractiveness, which acts as a behavioural mould to nudge offender agents to learn behaviours in keeping with the Rational Choice Perspective (RCP). Trained behaviours are then tested by introducing crime prevention interventions into the model and examining the reactions of offender agents. In keeping with empirical studies of offending, experimental results demonstrate that offender agents utilising RL learn to offend at targets where rewards outweigh risks and effort, offend close to home, frequently victimise high-rewarding targets, and conversely learn to avoid offending in areas associated with high levels of risk and effort.

Agent-based models (ABMs) of human systems are often parameterized using real-world data. For some ABMs this is not possible because the reality upon which the models are based does not exist or is not generalizable from one setting to another. In this paper we implement an online decision game to parameterize an agent-based model of pedestrian and cyclist route choice decisions in a neighbourhood. Our conceptual framework is to use an experimental game to log decision-making behaviour, summarize this behaviour into a decision model, and then transfer this model to an ABM. The product of this framework is an ABM with agents informed by human decision making made within the game, rather than the real world. The results of our analysis suggest that the decision model is consistent with some general theory about decision making, but the ABM illustrates some unique and contextually specific patterns of flow. ABMs parameterized with game data may be useful for forecasting the effects of change on urban transportation infrastructure.

The field of urban morphology, crucial for understanding the evolutionary trajectories of cityscapes, has traditionally depended on manual classification methods. The surge in deep learning and computer vision technologies presents an opportunity to automate and enhance urban typo-morphology studies. This research addresses three critical shortcomings in the current body of work: the neglect of urban fabric's three-dimensional qualities, the homogeneity of spatial scales in dataset creation and the dependence on a single-perspective for urban fabric classification. A novel deep learning-based model, UrbanClassifier, is introduced, trained on a substantial dataset that encapsulates the three-dimensionality of urban fabric along with morphological types and development periods. Extensive experimentation across four European cities highlights the model's ability to incorporate diverse spatial scales and viewpoints in urban fabric analysis. The UrbanClassifier exemplifies a method integrating features from various scales and perspectives, thus laying the groundwork for scalable and accessible urban typo-morphology studies, aiding practitioners in discerning the spatio-temporal evolution of urban fabric.

Interpretations of the urban cellular automata (CA) model aim to ensure that its predictive behaviors are consistent with real-world processes. Current urban CA interpretations have revealed the impacts of driving factors on land development suitability, or neighborhood effects and random perturbation on simulation results. However, three limitations remain unresolved: (1) the interpretations of deep learning (DL)-based urban CA are seldom integrated with the prerequired feature selection, (2) the input features from different urban CA modules are still explained by separate approaches, and (3) the interpretation results are rarely derived at the cell level to uncover spatially varying urban land development patterns. This study proposes a SHapley Additive exPlanations (SHAP)-based urban CA interpretation framework to address these challenges and improve urban CA. This framework uses model-level SHAP importance to identify dominant features from different modules for constructing the final simulation model. Then, cell-level SHAP importance is used to uncover spatially varying driving forces of urban expansion. The framework's effectiveness is rigorously tested and confirmed using a convolution neural network CA (CNN-CA) model for Dongguan City. The experimental results demonstrate that (1) SHAP-based model interpretation improves feature selection for DL-based urban CA. The figure of merit for CNN-CA calibrated using SHAP-based important features improves by 3%, outperforming the tested baseline methods. (2) SHAP measures the impacts of each feature from different CA modules in a whole. In this case, physical factors are much more important at the model level than proximity and accessibility factors, while neighborhood effect is the second most crucial factor. (3) Cell-level SHAP interpretations uncover spatially different urban land development patterns. For example, due to the extensive industrial land development in the northern Songshan Lake Zone, in the CNN-CA model, proximity to major roads within this region is associated with positive SHAP-based contribution share on cell-level urban expansion.