Computers Environment and Urban Systems最新文献

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.

Public transportation, especially in large cities, is critical for livability. Counting passengers as they travel between stations is crucial to establishing and maintaining effective transportation systems. Various information and communication technologies, such as GPS, Bluetooth, and Wi-Fi, have been used to measure people's movements automatically. Regarding public transportation applications, the automated fare collection (AFC) system has been widely adopted as a convenient method for measuring passengers, mainly because it is relatively easy to identify card owners uniquely and, as such, the movements of their card holders. However, there are serious concerns regarding privacy infringements when deploying such technologies, to the extent that Europe's General Data Protection Regulation has forbidden straightforward deployment for measuring pedestrian dynamics unless explicit consent has been provided. As a result, privacy-preservation techniques (e.g., anonymization) must be used when deploying such systems. Against this backdrop, we investigate to what extent a recently developed anonymization technique, known as detection k-anonymity, can be adapted to count public transportation travelers while preserving privacy. In the case study, we tested our methods with data from Beijing subway trips. Results show different scenarios when detection k-anonymity can be effectively applied and when it cannot. Due to the complicated relationship between the detection k-anonymity parameters, setting the proper parameter values can be difficult, leading to inaccurate results. Furthermore, through detection k-anonymity, it is possible to count travelers between two locations with high accuracy. However, counting travelers from more than two locations leads to more inaccurate results.

Active transport (AT)—physical activity (PA) during travel—can promote human health. Among built environment factors related to travel research, design refers to the street network features encouraging AT. The advent of street view images (SVIs) presents the potential to measure design during travel by capturing the eye-level built environments. Benefited by SVIs, this study innovatively introduces streetscape diversity—the interconnection of street view-derived built environment factors—during travel as the proxy to measure design from the street view perspective. Exposures to higher streetscape diversity could provide increased access to potential destinations and therapeutic landscapes, thereby promoting AT. Through integrating SVIs and young adults’ Global Positioning System (GPS) trajectories, this study utilized negative binomial regression models to assess the relationship between streetscape diversity and time spent in AT. Results indicate that the inclusion of streetscape diversity improves the model performance, and there is a positive relationship between streetscape diversity and AT. This finding indicates that increased access to travel routes with diverse streetscapes could increase the probability of participating in AT. Furthermore, integrating individual GPS data and SVIs allows more precise space-time measurement of individual environmental exposures.

Pluvial flash floods are fast-moving hazards and causes significant disruptions in urban areas. With the increase in heavy precipitations, the ability to proactively identify flash floods hotspots in cities is critical for flood nowcasting and predictive monitoring of risks. While rainfall runoff models and hydrologic models are useful models for flash flood prediction, these models are computationally expensive and effort intensive to be used for flood nowcasting. To address this challenge, this study presents interpretable machine learning models for predicting urban flash flood hotspots based on intertwined land and built environment features. The task of predicting flash flood hotspots is formulated as a binary classification problem, and three recent flash flood events in U.S. cities are selected for data collection and model validation. Various features related to land and built environment characteristics are constructed using diverse datasets, and the occurrences of flash floods are captured using crowdsource data from the events. Using these features and datasets, the flash flood hotspots of cities are predicted with two ensemble models based on decision trees. The results demonstrate that the models can achieve good accuracy (0.8) in identifying flooded/non-flooded locations. Especially, the models can achieve high true positive rate (0.83–0.89) and low missing rate, demonstrating the methods' practicability for accurately predicting flooded hotspots. The model interpretation results indicate that land features related to hydrological and topological features have greater impacts on flash flood risk, than built environment features. Further analysis reveals that the feature importance, model performance, and model transferability performance vary among cities and localized specifications of the models are needed for accurate prediction of flash flood for a particular city. The data-driven machine learning models presented in this study provide a useful tool for predicting flash flood hotspots based on the intertwined features of land and the built environment in cities to enable nowcasting and proactive monitoring of flash flood hotspots for emergency response and also inform integrated urban design and development towards flash flood risk reduction.

Urban outputs, from economy to innovation, are known to grow as a power of a city's population. But, since large cities tend to be central in transportation and communication networks, the effects attributed to city size may be confounded with those of intercity connectivity. Here, we map intercity networks for the world's two largest economies (the United States and China) to explore whether a city's position in the networks of communication, human mobility, and scientific collaboration explains variance in a city's patenting activity that is unaccounted for by its population. We find evidence that models incorporating intercity connectivity outperform population-based models and exhibit stronger predictive power for patenting activity, particularly for technologies of more recent vintage (which we expect to be more complex or sophisticated). The effects of intercity connectivity are more robust in China, even after controlling for population, GDP, and education, but not in the United States once adjusted for GDP and education. This divergence suggests distinct urban network dynamics driving innovation in these regions. In China, models with social media and mobility networks explain more heterogeneity in the scaling of innovation, whereas in the United States, scientific collaboration plays a more significant role. These findings support the significance of a city's position within the intercity network in shaping its success in innovative activities.

Walkability reflects the well-being of a city, and its measurement is evolving rapidly due to advancements of big data and machine learning technologies. The study examines the transformative impact of these technological interventions on the evaluation of walkability trends over the period 2015 to 2022. We create a framework consisting of big data sources, machine learning methods, and research purposes, revealing research trajectories and associated challenges. Despite diverse data usage, image data dominates in walkability research. While street view and point of interest data were primarily used to depict the environment, social media and handheld/ wearable data were more commonly employed to represent user behaviours or perceptions. Leveraging machine learning in conjunction with big data assists researchers in three aspects of walkability studies. First, researchers utilise classification and clustering to predict street quality, walkability, and identify neighbourhoods with certain characteristics. Second, researchers unveil relationship between the built environment and pedestrian perceptions or behaviours through regression analysis. Third, researchers employ generative models to create streetscapes or urban structures, although their utilisation is limited. Meanwhile, challenges persist in data access, customisation of machine learning models for urban studies, and establishing standard criteria to guarantee data quality and model accuracy.

This study assesses how the quality of place management (measured with user-generated ratings from Google Places) is related to crime occurrences at specific settings and whether specific crime types are related to specific types of places. In 50 randomly sampled neighborhoods in Ghent (Belgium) and London (United Kingdom), we analyzed Google Places data as a proxy measure for the quality of place management at the street segment level. We used hurdle models to examine the effects for both the prevalence and frequency of crime at micro places, and to deal with excess zeros in the data. User-generated ratings of places provide a useful place-level indicator for place management that are related to crime. However, contextual differences are found between Ghent and London. For London, the results suggest that higher quality of place management has a protective effect on crime occurrences at the street segment level. This study indicates the importance of exploring new and emerging data sources as unique measurement opportunities to enhance insight in crime prevention mechanisms, and also acknowledges its limitations. For the first time from a large-scale empirical perspective, this study suggest that improving place management at specific places might be an effective intervention to guard against crime.