Computers Environment and Urban Systems最新文献

The key component of designing sustainable, enriching, and inclusive cities is public participation. The soundscape is an integral part of an immersive environment in cities, and it should be considered as a resource that creates the acoustic image for an urban environment. For urban planning professionals, this requires an understanding of the constituents of citizens' emergent soundscape experience. The goal of this study is to present a systematic method for analyzing crowdsensed soundscape data with unsupervised machine learning methods. This study applies a crowdsensed sound- scape experience data collection method with low threshold for participation. The aim is to analyze the data using unsupervised machine learning methods to give insights into soundscape perception and quality.

For this purpose, qualitative and raw audio data were collected from 111 participants in Helsinki, Finland, and then clustered and further analyzed. We conclude that a machine learning analysis combined with accessible, mobile crowdsensing methods enable results that can be applied to track hidden experiential phenomena in the urban soundscape.

Urban function detection plays a significant role in urban complex system recognition and smart city construction. The location big data obtained from human activities, which is cohesive with urban functions, provides valuable insights into human mobility patterns. However, as urban functions become highly mixed, existing feature representation structures struggle to explicitly depict the latent human activity features, limiting their applicability for detecting mixed urban functions in a supervised manner. To close the gap, this study analogizes the latent human activity features to the shape, texture, and color semantics of images, with a contrastive learning framework being introduced to extract image-based crowd mobility features for detecting mixed urban functions. Firstly, by translating human activity features into image semantics, a novel feature representation structure termed the Trajectory Temporal Image (TTI) is proposed to explicitly represent human activity features. Secondly, the Vision Transformer (ViT) model is employed to extract image-based semantics in a self-supervised manner. Lastly, based on urban dynamics, a mathematical model is developed to represent mixed urban functions, and the decomposition of mixed urban functions is achieved using the theory of fuzzy sets. A case study is conducted using taxi trajectory data in three cities in China. Experimental results indicate the high discriminability of our proposed method, especially in areas with weak activity intensity, and reveal the relationship between the mixture index and the trip distance. The proposed method is promising to establish a solid scientific foundation for comprehending the urban complex system.

Fires and burning are the chief causes of particulate matter (PM2.5), a key measurement of air quality in communities and cities worldwide. This work develops a live fire tracking platform to show active reported fires from over twenty cities in the U.S., as well as predict their smoke paths and impacts on the air quality of regions within their range. Specifically, our close to real-time tracking and predictions culminates in a digital twin to protect public health and inform the public of fire and air quality risk. This tool tracks fire incidents in real-time, utilizes the 3D building footprints of Austin to simulate smoke outputs, and predicts fire incident smoke falloffs within the complex city environment. Results from this study include a complete fire and smoke digital twin model for Austin. We work in cooperation with the City of Austin Fire Department to ensure the accuracy of our forecast and also show that air quality sensor density within our cities cannot validate urban fire presence. We additionally release code and methodology to replicate these results for any city in the world. This work paves the path for similar digital twin models to be developed and deployed to better protect the health and safety of citizens.

CCS concepts

Computer systems organization → Embedded systems; Real- time systems; • Computing methodologies → Modeling and simu- lation; • Applied computing → Physical sciences and engineering.

The onset of the global Covid-19 pandemic in early 2020 brought many transportation systems in North America to a standstill. As life returned to normal, various modes of transportation exhibited differing rates of recovery, with disparities across regions. Limited research has delved into the regional variations in the recovery of these modes of transit over the past years. Such analysis is crucial for gaining insights into urban recovery and resilience, as well as understanding the factors influencing such recovery. In this work, we investigate the usage recovery of taxis, ride-hailing services, and subway ridership following the Covid-19 pandemic. We focus on New York City as our case study, employing clustering techniques to identify neighborhoods with similar recovery patterns. Furthermore, we examine the socio-economic, demographic, and built-environment factors contributing to regional variations in this recovery. Our research findings reveal that different modes of transportation responded differently to the pandemic, and these responses exhibited regional disparities. These findings hold significance for future health-related emergency response strategies and the regulation of existing transportation infrastructure.

Canadian cities have made significant investments in cycling infrastructure to support uptake in active transportation. Who has spatial access to supportive infrastructure is an important equity question: lack of access to safe infrastructure for cycling may limit who has an option to use a bicycle to meet their transportation needs (to access employment, educational, social, or other essential services) as well as who may achieve the physical and mental health benefits possible through physical activity. Our aim is to measure spatial access to cycling infrastructure in Canadian cities, and to provide a broad, national understanding of inequitable access to cycling infrastructure for equity-deserving populations (children, seniors, recent immigrants, visible minorities, and people with low incomes). Accordingly, we used a national dataset of cycling infrastructure (Can-BICS), which summarizes the quantity of cycling infrastructure for all dissemination areas in Canada, and 2016 Census data to estimate associations between area-level sociodemographic characteristics and access to cycling infrastructure. In unadjusted associations, equity-deserving groups (i.e., recent immigrants and people with low incomes) had better access to cycling infrastructure. Pearson coefficients highlighted variations in the equity of cycling infrastructure across cities. Overall, access was more equitable across equity-deserving groups in large cities, compared to mid-sized and small cities. After adjusting for covariates related to urban form and mode share, access to cycling infrastructure was higher in areas with more seniors, more recent immigrants, more visible minorities, and more people with low incomes, but lower in areas with more children. More importantly, there are still a substantial number of people from equity-deserving groups living in areas with very low levels of cycling infrastructure. For example, ∼ 1.5 million children under the age of 14 (31% of children), 1.5 million older adults (31%), 1.4 million visible minorities, and 0.5 million people with low income (20%) live in dissemination areas with the lowest level of cycling infrastructure. These results highlight the need to understand which populations stand to gain by cycling infrastructure investments and which populations are being left behind. This methodology represents a useful tool for information transport policy initiatives to advance bicycle equity at a national scale.

Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires - the largest and most deadly wildfires in California to date, respectively - as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society.

Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.

This study presents a methodology for creating a synthetic travel demand, encompassing households and individuals and their daily activities, to support agent-based modeling (ABM) in urban planning and travel analysis. Unlike previous studies, which often rely on proprietary data, our approach is entirely based on open data, ensuring replicability by the broader research community. The research is among the first to propose the entire framework for travel demand synthesis and ABM. Results are validated against ground truth from the Census and other public data sources. The ABM results are compared to an Information Minimization (IM) model, which is an aggregated model capturing commuting patterns by race. The study contributes to the field by offering a comprehensive and replicable data foundation for ABM, serving as a valuable resource for evaluating population and travel demand synthesis algorithms.

Accurately understanding the functions of buildings is crucial for urban monitoring, analysis of urban economic structures, and effectively allocating resources. Previous studies have investigated building function classification using single or dual data sources. However, the complexity of building functions cannot be fully reflected by a limited number of data sources. In addition, the functions of adjacent buildings often exhibit correlations, and contextual information between buildings has been ignored in previous studies. To address these problems, we propose a graph-based neural network (GNN) approach for building function classification that integrates multi-source data and mines contextual information between buildings. This approach initially extracts four types of features related to building functions: morphological features from vector-buildings, visual features from street-view images, spectral features from satellite images, and socio-economic features from points of interest. The buildings are then modeled as a graph, where the nodes and edges represent the buildings and their proximity. Descriptive features of the nodes were obtained by concatenating the aforementioned features. Finally, the constructed graph was fed into the GraphSAmple and aggreGatE (GraphSAGE) model, which is a typical GNN model for building function classification. The experimental results showed that our approach achieved an F1-score of 91.0%, which was 10.3–12.6% higher than that of the three comparison approaches. In addition, ablation experiments using different data sources revealed that the four data sources were complementary to each other and contributed to improving the building function classification. Our strategy provides an alternative and efficient solution for building function classification.

Analyses utilizing mobile positioning data rarely provide an exact method of data transformation to target spatial units. A common reason is likely the fact that researchers have already worked with spatially aggregated data prepared by the mobile operator or processing company. The article demonstrates the critical importance of employing an appropriate method to transform data from the mobile network into target spatial units, ensuring the precision and accuracy of the results. By evaluating ten different methods of data transformation from the mobile network topology to a population grid of 1 × 1 km, the optimal transformation has been sought. The most promising results were obtained through the methods using auxiliary information. While a dasymetric transformation utilizing building volume as the ancillary layer proved to be the most accurate, the utilization of free data from the Global Human Settlement Layer project also exhibits encouraging potential. Frequently used interpolation methods such as point-to-polygon (the user's location is considered to be the same as the base transceiver station's position.) or areal weighting are in fact the least appropriate methods of data transformation at a subregional level.