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Large-scale disasters can disproportionately impact different population groups, causing prominent disparity and inequality, especially for the vulnerable and marginalized. Here, we investigate the resilience of human mobility under the disturbance of the unprecedented ‘720’ Zhengzhou flood in China in 2021 using records of 1.32 billion mobile phone signaling generated by 4.35 million people. We find that although pluvial floods can trigger mobility reductions, the overall structural dynamics of mobility networks remain relatively stable. We also find that the low levels of mobility resilience in female, adolescent and older adult groups are mainly due to their insufficient capabilities to maintain business-as-usual travel frequency during the flood. Most importantly, we reveal three types of counter-intuitive, yet widely existing, resilience patterns of human mobility (namely, ‘reverse bathtub’, ‘ever-increasing’ and ‘ever-decreasing’ patterns), and demonstrate a universal mechanism of disaster-avoidance response by further corroborating that those abnormal resilience patterns are not associated with people’s gender or age. In view of the common association between travel behaviors and travelers’ socio-demographic characteristics, our findings provide a caveat for scholars when disclosing disparities in human travel behaviors during flood-induced emergencies.

Industrial parks are functional urban areas that carry the capacity to support highly concentrated production activities. The robustness and anti-interference ability of these areas are of great importance to maintaining economic vitality of a country. Focusing on the rate of production recovery (RPR), this paper examines the recovery of 436 major industrial parks in mainland China during the first wave of COVID-19. Leveraging spatio-temporal big data, we measured 14 attributes pertaining to industrial parks, covering four categories, namely spatial location, central city, park development, and public service. We focused on the spatial association and heterogeneity of the recovery patterns and identified the factors that truly affected the recovery of industrial parks with quantitative evaluation of their effects. The results reveal that: (1) RPR of industrial parks are significantly spatially clustered, with an obvious “cold spot” in the early outbreak area of Hubei Province and a prominent “center-periphery” pattern in developed areas, which is highly correlated with the spread of the epidemic. (2) The mechanisms driving the resumption of industrial parks are complex and versatile. All four categories in the variable matrix are related to RPR, including up to eight effective influencing factors. The effect of influencing factors is spatially heterogeneous, and its intensity varies significantly across regions. What is more interesting is that some impact factors show positive effects in some industrial parks while inhibiting the recovery in others. On the basis of the discussion of those findings with practical experiences, the planning and construction strategies of industrial park are suggested to mitigate the impact of similar external shocks.

A street network box model has been designed to assess how connected road links interact and influence nitrogen dioxide (NO2) concentrations at the neighbourhood scale. The overall aim of developing this model is to investigate both theoretical and applied questions, including how social policy and neighbourhood-level action can improve air quality in cities.The computationally lightweight model presented here relies on component boxes connected at intersections and along road links to mix pollutant concentrations throughout the network. It is therefore similar in architecture and theory to the operational French network model, SIRANE. Mass is conserved so that the sum of vehicle emissions and pollutants advected into each box are equal to the sum of turbulent exchanges at the top of the box with overlying urban canopy background air and pollutants advected out of each box. Fast NOx-O3 reactions are fully integrated and simulated at every time step (1 second), which will enable high temporal resolution traffic emissions to be integrated in the future. The model, implemented in R, enables mixing between two boxes (simple road link), three boxes (‘t-junction’), and four boxes (cross roads), with the capability to automatically determine the direction of mixing based on the overlying wind direction. The network in turn consists of multiple boxes and connections, all of which can be oriented in any direction with respect to the wind direction.Low traffic neighbourhoods (LTNs) have become a popular tool for UK urban planners to attempt to reduce population exposure to NO2 and particulate matter, and encourage active travel. Inspired by Dutch city designs and Barcelona’s ‘superblocks’, LTNs typically take the form of blocking vehicle access to minor residential roads, while keeping them open for residents and active transport users. The model described above has been applied to an area of approximately 1 km2 in the Kings Heath area of Birmingham (UK) where an LTN has been proposed. The network consists of 29 boxes and each box represents a road link, including major road links surrounding the residential area. Background concentrations for NO, NO2 and O3 were obtained from a nearby AURN site maintained by Defra. Meteorological conditions were measured at Birmingham City airport and a mean wind speed for each 10-degree wind direction sector was determined. One model was run for each wind direction and corresponding speed, enabling the relative contribution of local emissions and transported pollutants for each box to be assessed. Subsequently, the frequency of occurrence was used to weight each model’s outputs to produce a simulated annual average concentration map.Initial results are promising; when compared to the output from traditional air quality modelling software, the spatial distribution of NO2 concentrations are in agreement. The impact of pollutant redistribution throughout the network under prevailing wind conditions and the potential impact of a LTN on NO2 concentrations inside and outside the designated area will also be presented.

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.

In most European cities, the key air pollutants driving adverse health outcomes are nitrogen dioxide (NO2) and fine particulate matter (PM2.5), with 64% of new paediatric asthma cases in urban centres attributed to elevated NO2 levels (Achakulwisut et al., 2019). In the complex landscape of a city, a synthesis of techniques to quantify air pollution is required to account for variations in traffic, meteorology, and urban geometry.Here, we present the results from a comparison study between measured air pollutant data collected at Marylebone Road, London and the output from a three-stage modelling chain. This site was chosen due to the availability of road-side air quality data collected within a street canyon (aspect ratio approximately equal to 1) and daily traffic flow in excess of 70,000 motor vehicles. The modelling chain consists of: 1) real-time traffic information of vehicle journey times, 2) speed-related emission calculations, and 3) air quality box-model to simulate the interaction of pollutants within the environment.While the transport sector accounts for much of the outdoor air pollution in UK cities, a limiting factor of current techniques is that traffic is approximated at coarse temporal and spatial resolutions. In this study, we present a novel technique that helps to ‘fill in’ the gaps in our traffic data by harnessing the power of real-time queries to Google Maps to obtain travel times between fixed locations, enabling the derivation of average vehicle speeds. This dataset can then be used to determine more accurate emission factors for NOx. Total emissions are then calculated with the aid of traffic flow data and vehicle fleet characteristics. The air quality box model simulates photochemical reactions that form NO2, the exchange of pollutants with the background air aloft, and advection of pollutants along the street.Hourly travel times and total vehicle flow data were collected between July and October 2019, totalling 905 observations and calculated emissions values. Meteorological data from Heathrow airport and background air quality from the Kensington AURN site were used as supporting inputs to the air quality box model. Each observation was treated as a starting point of the box model, and the simulation was run for 1 hour, with mixing due to advection occurring every 60 seconds. Results are promising; when using the full model chain modelled and measured NO2 concentrations are significantly correlated (r = 0.467, p < 0.000). In comparison, when a constant speed of 30 mph is used to calculate total emissions, therefore excluding the impact of congestion, the strength of the correlation decreases (r = 0.362, p < 0.000) and the model underestimates pollutant concentrations.The applications of this model chain are vast. For any street that is covered by a suitable mapping platform and has available data on vehicle numbers, it would be possible to provide a real-time estimation of pollutant concentrations at a high temporal resolution. This could be utilised in several ways, such as: assessing policy implementation, and providing a high resolution input for air quality modelling and health exposure studies.

Polycentric spatial strategy plays an important role in lessening the pressure on central cities, driving the development of suburban areas, and supporting the reform of urban structure and the optimization of urban space, which makes it a common choice of many megacities all around the world. Polycentric cities have always been the hot spot of academic research. The measurement based on the spatial form mainly recognizes the polycentricity of cities from the spatial distribution of population, economy, employment, and culture in the city. In planning practices, the polycentric spatial strategy for a city usually defines the main center and the sub-centers within a city. The main center, usually central business districts, is conceived as an economic powerhouse having a dominant influence on the other places of the city. The chapter proposes a composite index of centrality that combines both density-and diversity-based measures and take into account the spatial association in the driving indicators of centrality of a location.

In order to better understand the intrinsic mechanisms of urbanization, urban modeling has become a multidisciplinary effort, from disciplines such as geography, planning, regional science, urban and regional economics, and environmental science, which intends to create scientific models to account for functions and processes that generate urban spatial structures at either intra-urban or inter-urban scales. It is due to these intrinsic properties that urban models involve tremendous computational and data complexity and intensity. As the development of computational technologies such as high-performance computing, how to leverage high-performance computing to tackle the computational and data issues for urban modeling becomes an imperative objective. This chapter reviews and discusses the design and development of high-performance computing-enabled operational models for urban studies in several identified modeling application categories. To support our review and discussions, a case study of a general urban system model and its implementation within high-performance computing environments is presented to demonstrate the process of parallelization for urban models.

Agent-based models have been increasingly applied to the study of space-time dynamics in real-world systems driven by biophysical and social processes. For the sharing and communication of these models, code reusability and transparency play a pivotal role. In this chapter, we focus on code reusability and transparency of agent-based models from a cyberinfrastructure perspective. We identify challenges of code reusability and transparency in agent-based modeling and suggest how to overcome these challenges. As our findings reveal, while the understanding of and demands for code reuse and transparency are different in various domains, they are inherently related, and they contribute to each step of the agent-based modeling process. While the challenges to code development are daunting, continually evolving cyberinfrastructure-enabled computing technologies such as cloud computing, high-performance computing, and parallel computing tend to lower the computing-level learning curve and, more importantly, facilitate code reuse and transparency of agent-based models.

This study investigates the impact of extreme weather events on urban human flow disruptions using location-based service data obtained from Baidu Map. Utilizing the 2018 Typhoon Mangkhut as an example, the spatial and temporal variations of urban human flow patterns in Shenzhen are examined using GIS and spatial flow analysis. In addition, the variation of human flow by different urban functions (e.g. transport, recreational, institutional, commercial and residential related facilities) is also examined through an integration of flow data and point-of-interest (POI) data. The study reveals that urban flow patterns varied substantially before, during, and after the typhoon. Specifically, urban flows were found to have reduced by 39% during the disruption. Conversely, 56% of flows increased immediately after the disruption. In terms of functional variation, the assessment reveals that fundamental urban functions, such as industrial (work) and institutional - (education) related trips experienced less disruption, whereas the typhoon event appears to have a relatively larger negative influence on recreational related trips. Overall, the study provides implications for planners and policy makers to enhance urban resilience to disasters through a better understanding of the urban vulnerability to disruptive events., Highlights • The impact of extreme weather events on urban human flow change was analyzed using Typhoon Mangkhut as an example. • The disruptive impact of typhoon was analyzed using daily dynamics of human flow at a fine spatial scale (500m×500m). • The spatial and functional variations of human mobility flows were examined over time using a novel approach. • Implications for evaluating vulnerability and resilience of smart cities using location-based service data were provided.

Social media has become an emerging alternative to opinion polls for public opinion collection, while it is still posing many challenges as a passive data source, such as structurelessness, quantifiability, and representativeness. Social media data with geotags provide new opportunities to unveil the geographic locations of users expressing their opinions. This paper aims to answer two questions: 1) whether quantifiable measurement of public opinion can be obtained from social media and 2) whether it can produce better or complementary measures compared to opinion polls. This research proposes a novel approach to measure the relative opinion of Twitter users towards public issues in order to accommodate more complex opinion structures and take advantage of the geography pertaining to the public issues. To ensure that this new measure is technically feasible, a modeling framework is developed including building a training dataset by adopting a state-of-the-art approach and devising a new deep learning method called Opinion-Oriented Word Embedding. With a case study of the tweets selected for the 2016 U.S. presidential election, we demonstrate the predictive superiority of our relative opinion approach and we show how it can aid visual analytics and support opinion predictions. Although the relative opinion measure is proved to be more robust compared to polling, our study also suggests that the former can advantageously complement the later in opinion prediction.

One of the enduring issues of spatial origin-destination (OD) flow data analysis is the computational inefficiency or even the impossibility to handle large datasets. Despite the recent advancements in high performance computing (HPC) and the ready availability of powerful computing infrastructure, we argue that the best solutions are based on a thorough understanding of the fundamental properties of the data. This paper focuses on overcoming the computational challenge through data reduction that intelligently takes advantage of the heavy-tailed distributional property of most flow datasets. We specifically propose the classification technique of head/tail breaks to this end. We test this approach with representative algorithms from three common method families, namely flowAMOEBA from flow clustering, Louvain from network community detection, and PageRank from network centrality algorithms. A variety of flow datasets are adopted for the experiments, including inter-city travel flows, cellphone call flows, and synthetic flows. We propose a standard evaluation framework to evaluate the applicability of not only the selected three algorithms, but any given method in a systematic way. The results prove that head/tail breaks can significantly improve the computational capability and efficiency of flow data analyses while preserving result quality, on condition that the analysis emphasizes the &ldquo, head&rdquo, part of the dataset or the flows with high absolute values. We recommend considering this easy-to-implement data reduction technique before analyzing a large flow dataset.

Spatial land-use models over large geographic areas and at fine spatial resolutions face the challenges of spatial heterogeneity, model predictability, data quality, and of the ensuing uncertainty. We propose an improved neural network model, ART-Probability-Map (ART-P-MAP), tailored to address these issues in the context of spatial modeling of land-use change. First, it adaptively forms its own network structure to account for spatial heterogeneity. Second, it explicitly infers posterior probabilities of land conversion that facilitates the quantification of prediction uncertainty. Extensive calibration under various test settings is conducted on the proposed model to optimize its utility in seeking useful information within a spatially heterogeneous environment. The calibration strategy involves building a bagging ensemble for training and stratified sampling with varying category proportions for experimentation. Through a temporal validation approach, we examine models’ performance within a systematic assessment framework consisting of global metrics and cell-level uncertainty measurement. Compared with two baselines, ART-P-MAP achieves consistently good and stable performance across experiments and exhibits superior capability to handle the spatial heterogeneity and uncertainty involved in the land-use change problem. Finally, we conclude that, as a general probabilistic regression model, ART-P-MAP is applicable to a broad range of land-use change modeling approaches, which deserves future research.

A great potential exists for mitigating the computational costs of spatially explicit agent-based models (SE-ABMs) by taking advantage of parallel and high-performance computing. However, spatial dependency and heterogeneity of interactions between agents pose challenges for parallel SE-ABMs to achieve good scalability. This chapter summarizes an application of the principle of data locality to tackle these challenges by extending a theoretical approach to the representation of the spatial computational domain. We propose and formalize a graph-based locality-aware approach to scalable parallelization of SE-ABMs. To demonstrate the applicability of this approach, two sets of experimentation are laid out and a locality-aware algorithm is designed to facilitate the study of model scalability. The results of simulation experiments illustrate the advantage of our approach to scalable parallel agent-based models of spatial interaction.

Streets, as one type of land use, are generally treated as developed or impervious areas in most of the land-use/land-cover studies. This coarse classification substantially understates the value of streets as a type of public space with the most complexity. Street space, being an important arena for urban vitality, is valued by various dimensions, such as transportation, recreation, aesthetics, public health, and social interactions. Traditional remote sensing approaches taking a sky viewpoint cannot capture these dimensions not only due to the resolution issue but also the lack of a citizen viewpoint. The proliferation of street view images provides an unprecedented opportunity to characterize street spaces from a citizen perspective at the human scale for an entire city. This paper aims to characterize and classify street spaces based on features extracted from street view images by a deep learning model of computer vision. A rule-based clustering method is devised to support the empirically generated classification of street spaces. The proposed classification scheme of street spaces can serve as an indirect indicator of place-related functions if not a direct one, once its relationship with urban functions is empirically tested and established. This approach is empirically applied to Beijing city to demonstrate its validity.

The computational approach of agent-based models ABMs supports the representation of interactions among spatially situated individuals as a decentralized process giving rise to space–time complexity in geographic systems. To cope with the computational complexity of these models, this article proposes a parallel approach that leverages the power of multicore systems, as these architectures have quickly become ubiquitous in high-performance and desktop computing. An ABM of individual-level spatial interaction that simulates information exchange, spatial diffusion of opinion development, and consensus building among decision makers is proposed to demonstrate the advantages of the parallel approach against its sequential counterpart. This study focuses on two key spatial properties of the interaction system of interest, the extent and range of interaction, and examines their influence on the computing performance of the proposed parallel model and the performance scalability of the model as more computing resources are added. Significant influence from these two properties is found and can be attributed to three possible sources of effects, namely the model level, the parallelization level, and the platform level. It is suggested that these effects should be taken into consideration when leveraging multicore computing resources for the development of parallel ABMs.

Artificial neural networks have been widely applied to spatial modeling and knowledge discovery because of their high-level intelligence and flexibility. Their highly parallel and distributed structure makes them inherently suitable for parallel computing. As the technology of parallel and high-performance computing evolves and computing resources become more widely available, new opportunities exist for spatial neural network models to benefit from this advancement in terms of better handling computational and data intensity associated with spatial problems. In this study, we present a hybrid parallel ensemble neural network approach for modeling spatial land-use change. Our approach combines the shared-memory paradigm and the embarrassingly parallel method by leveraging the power of multicore computer clusters. The efficacy of this approach is demonstrated by the parallelization of Fuzzy ARTMAP neural network models, which have been extensively used in land-use modeling applications. We adopt an ensemble structure of neural networks to train multiple models in parallel and make use of the entire dataset simultaneously. We evaluate the proposed parallelization approach by examining performance variation of training datasets with alternative sizes. Experimental results reveal great potential of higher performance achievement when our hybrid parallel computing approach is applied to large spatial modeling problems.