Zhaoya Gong

• PhD
• Lecturer at University of Birmingham

Understanding the response of human mobility to disruptive weather events is beneficial for the development of urban risk mitigation and emergency response policies, thus enhancing urban resilience. Most human mobility studies relying on aggregate flow data inevitably neglect the heterogeneity of disaggregate travel patterns with distinctive spatio...

Although many studies have examined social inequalities related to urban parks, there is limited knowledge about the social inequalities of urban park use during crises. By integrating a large amount of mobile phone data and e-commerce user data, this study tracked 81,350 anonymized individuals’ urban park use behavior in Shenzhen, China, from 2019...

Numerous studies attempted to associate search engine data with travel behaviors. However, most existing studies focus on the destinations of search and travel, while ignoring the origins, which embed critical information of where the search requests were initiated and where the travelers came from. In this study, we explore the relationships betwe...

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...

This study focuses on a mesoscale perspective to examine the structural and spatial changes in the intercity mobility networks of China from three phases of before, during and after the Wuhan lockdown due to the outbreak of COVID-19. Taking advantages of mobility big data from Baidu Maps, we introduce the weighted stochastic block model to measure...

Node influence metrics have been applied to many applications, including ranking web pages on internet, or locations on spatial networks. PageRank is a popular and effective algorithm for estimating node influence. However, conventional PageRank method considers neither the heterogeneity of network structures nor additional network information, cau...

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...

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 sp...

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 var...

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 o...

General awareness of air quality has grown significantly in recent years, with 'Low Emission' and 'Clean Air' zones proposed for many cities across the UK. However, cities are a complex landscape and air pollutant concentrations can vary greatly from street to street. Therefore, a synthesis of techniques to quantify air pollution is required to acc...

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 th...

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 o...

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...

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,...

In this study, an exploratory flow clustering method, flowAMOEBA, is employed to study human activity based interactions between cities in Northeast China and in the rest of China at a short-term basis. This method allows us to discover new patterns of interaction from empirical trip data at the individual level. Our research is supported by big da...

The modelling of spatial choices is solidly grounded in the behavioural theory of discrete choices, which itself conceptualizes spatial choices as the result of a process consistent with random utility theory. Utility-based discrete choice models provide the primary framework of analysis of spatial choices. More recently computational models and ma...

地理空间科学是关于从复杂地理现象中揭示规律性的科学。本报告讨论地理空间科学中的两种主要的空间过程：空间选择和空间交互。以空间问题为导向，这里主要研究如何更好、更有效的完成从地理空间科学理论到时空数据，再从数据回归并提升理论的循环过程。这里着重提出地理计算的一些相关方法和技术，并强调它们在以上过程中起到的重要作用。本研究将主要涉及土地利用变化和人口迁移等方面的时空数据。

Big spatial data is characterized by not only large data volumes but also high velocity, which poses challenges for data processing systems. This study focuses on addressing the issue of deficiency in parallel processing of high-velocity spatial big data that features fast changing patterns of spatial dependency over time. We propose to use a many-...

This study adopts the vector field approach to tackle the problem posed by asymmetric distances during the reconstruction of a functional space from empirical spatial interaction data. An interaction field can be estimated by linking the vector concept with inverted doubly constrained spatial interaction models. It enables to infer a potential fiel...

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 a...

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 lan...

Spatially explicit agent-based models have a great potential to mitigate their computational costs by taking advantage of parallel and high-performance computing. However, the spatial dependency and heterogeneity of interactions pose challenges for parallel SE-ABMs to achieve good scalability. This paper applies the principle of data locality to ta...

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...

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...