Yao Yao

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China University of Geosciences · School of Geography and Information Engineering

Though global-coverage urban perception datasets have been recently created using machine learning, their efficacy in accurately assessing local urban perceptions for other countries and regions remains a problem. Here we describe a human-machine adversarial scoring framework using a methodology that incorporates deep learning and iterative feedbac...

Empirical data are limited to decipher where people live and work in large cities; however, neighborhood information, such as street view image, is rich and abundant. We construct a ResNet-50-based social detection model to explore the potential relationship between street view images and job-housing attributes. The method extracts street view imag...

The distribution of human perceptions in urban area was obtained. • This study first focuses on the spatial homogeneity of human perceptions. • A method is proposed to discover the homogeneous geographic domain of human perceptions. • This study explored the role of urban function in shaping human perceptions. Human perception of place refers to re...

Currently, raster-based landscape indices (LIs) that measures the landscape pattern of raster-format land-use data, can be easily computed by relevant software (e.g., Fragstats). Unfortunately, open-access software for vector-based LIs often implement a small variety of metrics, which cannot meet the growing demand of the GIS and landscape design r...

Background Residential green and blue spaces may be therapeutic for the mental health. However, solid evidence on the linkage between exposure to green and blue spaces and mental health among the elderly in non-Western countries is scarce and limited to exposure metrics based on remote sensing images (i.e., land cover and vegetation indices). Such...

Individual mobility prediction forecasts traveling activities of an individual traveler, and has wide applications in location-based services, public health, and transportation planning. Whereas, it remains challenging due to the complexity and uncertainty of human mobility. Existing methods mainly consider spatiotemporal contexts in current travel...

How to explore land spaces for future urban expansion under the ECL policy in China has been an essential issue. Previous methods or models just consider the land parcel's current land‐use condition, while ignoring its future urbanization trend. This study proposes a framework for identifying the suitable “transfer‐out” land parcels within the ECL...

Urban sensing has become increasingly important as cities evolve into the centers of human activities. Large language models (LLMs) offer new opportunities for urban sensing based on commonsense and worldview that emerged through their language-centric framework. This paper illustrates the transformative impact of LLMs, particularly in the potentia...

There is a lack of evidence regarding associations of eye-level greenness exposure with blood pressure among children. We aimed to investigate the associations between different types of eye-level greenness and pediatric blood pressure in China. From 2012 to 2013, we recruited 9354 children aged between 5 and 17 years in northeast China. Eye-level...

Understanding human mobility’s resilience during extreme rainfall is paramount for enhancing disaster response and urban resilience. Most studies, however, have overlooked the complexity of resilience patterns across scales, missing out on the varied spatial anomalies and their underlying causes. To bridge this gap, we propose a framework using mas...

The integration of urban rail transit and land use has been adopted as a crucial approach to fostering compact development in cities. Proximity to rail transit stations can increase the probability of land use changes, while few studies have analyzed the spatial heterogeneity of the impact of rail transit on land use changes. This study proposes a...

The HIV/AIDS epidemic in China is severe and complex. Comprehensive spatiotemporal analysis provides valuable insights for intervention policy formulation. Previous studies often overlooked local changes in time trends and regional disease development patterns. In this study, we propose a new spatiotemporal analysis method based on the Joinpoint Re...

Mining latent information from human trajectories for understanding our cities has been persistent endeavors in urban studies and spatial information science. Many previous studies relied on manually crafted features and followed a supervised learning pipeline for a particular task, e.g. land use classification. However, such methods often overlook...

The effective deployment of medical emergency equipment, such as automated external defibrillator (AED), is essential to myocardial infarction (MI) patients. However, there are shortcomings in current studies that simultaneously consider the risk of MI and the availability of medical resources when siting the AEDs. In this study, an AED site recomm...

A high-quality land-use dataset is crucial for constructing a high-performance land-use classification model. Due to the complexity and spatial heterogeneity of land-use, the dataset construction process is inefficient and costly. This challenge affects the quality of datasets, consequently impacting the model’s performance. The emerging field of D...

Objectives To examine (1) how visual green space quantity and quality affect depression among older adults; (2) whether and how the links may be mediated by perceived stress, physical activity, neighbourhood social cohesion, and air pollution (PM2.5); and (3) whether there are differences in the mediation across visual green space quantity and qual...

Vector cellular automata (VCA) models, which excel at representing spatiotemporal dynamics of irregularly shaped land parcels, have been widely employed in land use change simulations. However, current research faces the following issues: (1) most VCA models neglect the spatial heterogeneity of driving factors within each land parcel when evaluatin...

This report focuses on spatial data intelligent large models, delving into the principles, methods, and cutting-edge applications of these models. It provides an in-depth discussion on the definition, development history, current status, and trends of spatial data intelligent large models, as well as the challenges they face. The report systematica...

Background Little is known about the extent to which the age-friendliness of streetscape built environments may influence older adults’ active travel (AT) patterns. Moreover, with the exception of street greenery, the non-linear and threshold effects of other characteristics of streetscape built environments have not been examined. Methods This st...

Urban land use patterns can be more accurately mapped by fusing multimodal data. However, many studies only consider socioeconomic and physical attributes within land parcels, neglecting spatial interaction and uncertainty caused by multimodal data. To address these issues, we constructed a multimodal data fusion model (MDFNet) to extract natural p...

Vector cellular automata (VCA) are effective models for cadastral-scale land use change modeling, leveraging fine spatial granularity information from cadastral plot data. The temporal dimension has the potential to improve the performance of VCA further. However, it is challenging to precisely capture long sequence information of cadastral plot te...

Urbanization-induced land cover changes significantly impact ecological environments and socioeconomic growth. Vector-based cellular automata (VCA) models are an advanced cellular automata (CA) method that use irregular cells and perform well in simulating land use changes within urban areas. However, the applicability and parameter setting of VCA...

Predicting PM2.5 concentrations at an hourly temporal resolution in urban areas can provide key information for public health protection. The spatiotemporal dependency among monitoring stations and the spatiotemporal correlations between PM2.5 and relevant factors (e.g. meteorology and emissions) are both essential for such predictions. This study...

Missing person crimes can seriously affect the well-being of Chinese families, and missing person destination prediction can help to solve this problem. Using nongovernmental organization (NGO) data to predict the locations of missing persons by random forest (RF) model has made progress. However, studies using these data have ignored the mass of o...

It is important to measure uncoordinated regional urban economic development to help guide government policy. However, previous models have often struggled to capture the fine-grained spatiotemporal characteristics of economic development, thereby failing to provide insight into the fine-scale patterns. Sectoral structure and its evolution are stro...

Fast urbanization brings great challenges to sustainable development goals, such as excessive exploitation and population explosion. Classical cellular automata (CA) have been widely used to independently simulate the change of spatial features, i.e. land use, population, economic production, etc. However, most CA models rely on historical data as...

The practice of crime risk mapping, enabled by the utilization of geospatial big data such as street view images, has received significant research attention. However, in situations where available data is scarce, mapping models may suffer from underfitting and generate inaccurate spatial pattern estimations of crime risk. The covert nature of pick...

Rapid land-use change detection (LUCD) is pivotal for refined urban planning and management. In this paper, we investigate LUCD through learning embeddings of points of interest (POIs) from multiple temporalities. There are several prominent challenges: (1) the co-occurrence problem of multi-temporal POIs, (2) the heterogeneity of POI categorizatio...

A site selection framework for urban power substation at micro-scale using spatial optimization strategy and geospatial big data

Predicting the next location of human mobility and its semantic information can support recommendations for location-based services and trajectory mining, such as human mobility pattern recognition and sequential anomaly detection. Previous studies have ignored the implicit correlation between location and spatiotemporal information thereby limitin...

The world is facing more energy crises due to extreme weather and the rapidly growing demand for electricity. Siting new substations and optimizing the location of existing ones are necessary to address the energy crisis. The current site selection lacks consideration of spatial and temporal heterogeneity in urban power demand, which results in unr...

Access to subway stations is important for daily commuting, but scant attention has been given to green space exposure at subway station areas in people's residential neighbourhoods and workplace areas. This paper focuses on the association between street-level green space exposure around subway stations at residential and work locations and people...

Environmental inequalities generated by transit—oriented development (TOD) are of planning and policy relevance in developing countries. Existing literature has pointed out that TOD has the effect of ‘place making’, which means the newly developed transit systems may be able to change the environment and amenities of a certain area. While previous...

Background: Adolescents' daily active travel (AT) has positive health effects. However, previous studies had paid little attention on the differences between the streetscape characteristics-AT relationship on the weekdays and weekends, and most of them only focused on the environmental exposure around home and schools. Methods: This study used data...

Forecasting cities' carbon emissions is an essential support for peak carbon emissions. Previous studies have mainly focused on estimating carbon emissions at large regional scales. Higher spatial resolution mapping of carbon emissions and simulation of future scenarios are important to support locally appropriate policy guidance to reduce carbon e...

Social distancing policies and other restrictive measures have demonstrated efficacy in curbing the spread of the COVID-19 pandemic. However, these interventions have concurrently led to short- and long-term alterations in social connectedness. Comprehending the transformation in intracity social interactions is imperative for facilitating post-pan...

Urban logistics is vital to the development and operation of cities, and its optimization is highly beneficial to economic growth. The increasing customer needs and the complexity of urban systems are two challenges for current logistics optimization. However, little research considers both, failing to balance efficiency and cost. In this study, we...

Poverty threatens human development especially for developing countries, so ending poverty has become one of the most important United Nations Sustainable Development Goals (SDGs). This study aims to explore China's progress in poverty reduction from 2016 to 2019 through time-series multi-source geospatial data and a deep learning model. The povert...

Accurate and efficient assessment of large-scale urban renewal potential is an indispensable prerequisite for managing and facilitating projects. However, few studies consider the built environment when assessing urban renewal potential because it is difficult to measure. Street view images can show the physical setting of a place for humans to per...

we employed generalized structural equation models to examine 1) the relationships between objective and perceived streetscape characteristics and adolescents' active school travel and 2) the mediating roles of perceived safety, walkability, and air pollution. Results showed that street safety, vitality, greenery, and vehicle volume were positively...

Social segregation hinders the development of cities and has become a hot topic in urban research. Existing studies have focused on the difference in the distribution of crowd activities to measure segregation but have ignored the impact of the urban environment on crowd gathering and segregation. To study the impact and understand social segregati...

Awareness is growing that the uneven provision of street urban green space (UGS) may lead to environmental injustice. Most previous studies have focused on the overhead perspective of street UGS provision. However, only a few studies have evaluated the disparities in visible street UGS provision. While a plethora of studies have focused on a single...

Social media data are increasingly used to examine associations between environmental exposures and mental wellbeing. In particular, studies highlight that exposure to natural outdoor environments (NOEs) is associated with fewer negative emotions. Also, people in socioeconomically disadvantaged neighbourhoods tend to benefit more from NOEs than the...

Understanding urban functions help with government planning and resource allocation to promote economic development. Few studies have reflected the influence of population movement and migration on urban functions overtime on a large scale. This study adopted the time-series social media data and points of interest to analyse urban functions' distr...

Health inequalities are globally widespread due to regional socioeconomic inequalities. Myocardial infarction (MI) is a leading health problem causing deaths worldwide. Yet medical services for it are often inequitably distributed by region. Moreover, studies concerning MI's potential spatial risk factors generally suffer from difficulties in focus...

Many studies have disentangled the perceived benefits of vegetation on subjective well-being (SWB). Yet, scant attention has been paid to the joint effect of vegetation and building density on SWB. This study explores the relationship between streetscape vegetation (SV), building density and SWB in Beijing, China. Our analysis relies on rich measur...

Awareness is increasing that greenspace is beneficial for people’s heart health. While a plethora of studies have focused on the relationship between neighbourhood greenspace and cardiovascular diseases in the general population, scant attention has been given to ischaemic heart disease (IHD) emergency department visits for middle-aged and older ad...

The spatial structure of geochemical patterns is influenced by various geological processes, one of which may be mineralization. Thus, analysis of spatial geochemical patterns facilitates understanding of regional metallogenic mechanisms and recognition of geochemical anomalies related to mineralization. Convolutional neural networks (CNNs) used in...

Resource-based urban agglomerations often encounter greater challenges in the sustainable development of human settlements. The aim of this study is to propose an approach to the coordinated development of competitiveness by analyzing the interaction of human settlements competitiveness (HSC) in resource-based urban agglomerations. Through the comp...

We present a novel approach for estimating the proportional distributions of function types (i.e. functional distributions) in an urban area through learning semantics preserved embeddings of points-of-interest (POIs). Specifically, we represent POIs as low-dimensional vectors to capture (1) the spatial co-occurrence patterns of POIs and (2) the se...

Soil moisture is a fundamental ecological component for climate and hydrological studies. However, the distribution patterns of soil moisture are spatially heterogenous and influenced by multiple environmental factors. The knowledge is still limited in assessing the large-scale spatial heterogeneity of soil moisture in in situ data modelling, in si...

Accurate identification of urban land-use patterns is essential to rational optimization of urban structure. By combining the external physical characteristics of city parcels obtained from remote sensing images and the socioeconomic attributes revealed by social sensing data, land use can be better classified. However, most of the existing social...

Active travel to school is considered one of the channels for improving schoolchildren's daily physical activity level. The built environment is increasingly recognized as a factor likely to influence travel behavior. However, previous studies have primarily captured the macro-level built environment, usually assumed to be linearly associated with...

Human activity analysis based on sensor data plays a significant role in behavior sensing, human-machine interaction, health care, and so on. The current research focused on recognizing human activity and posture at the activity pattern level, neglecting the effective fusion of multi-sensor data and assessing different movement styles at the indivi...

The worldwide coronavirus disease (COVID-19) pandemic has seriously affected the physical health and mental wellbeing (i.e mental stress and suicide intention) of numerous urban inhabitants across the globe. While many studies have elucidated urban parkland enhances and mental wellbeing, the potential for parkland to mitigate mental health burden i...

Abstract: Promoting adolescents’ daily active travel to school (ATS) may be critical for their health and wellbeing. Based on survey data of 473 adolescents and street view images, we employed generalized structural equation models to examine 1) the relationship between objective and perceived streetscape characteristics and adolescents’ ATS and 2)...

The spatial distribution of buildings is one of the key factors influencing the local environment within a city. The quantitative measurement of building distribution can provide critical information for exploring local climate patterns in urban areas. Previous studies mainly focused on the two-dimensional spatial distribution of buildings and igno...

The severe outbreak of coronavirus disease 2019 ( COVID-19 ) demonstrates the importance of disease risk assessment. The existing risk assessment methods are limited by the real time and accuracy of data. Most of them take the administrative statistical unit as the analysis scale, which has modifiable areal unit problem (MAUP). First, based on a ra...

Researches on the urban development and urban planning have an urgent need for building geographic data. Traditional methods of extracting buildings from high-resolution remote sensing images need multi-view images, and have a high cost but a low degree of automation. Thus, these methods are not applied in many fields at large-scale. This study cou...

Unprecedented urbanization in China has directly resulted in residential vacancies, which has seriously stunted sustainable development, a part of China's new-type urbanization plan. Understanding the various types and mixes of residential vacancies is critical for the advancement of our knowledge of speculative urbanism and for devising vacancy-mi...

Although previous studies have assessed the relationships between visual space indicators and urban residents' psychological perceptions, systematic research on the relationship between the urban visual space and residents' psychological perceptions is still rare. The purpose of this study is to explore the correlation between the urban visual spac...

The spatial structural features and compositional relationships of multivariate geochemicals are influenced by complex geological processes (e.g., diagenesis and mineralization), and can help identify geochemical anomalies and provide key references for mineral resource exploration. However, previous machine-learning-based models often treat spatia...

Rapid urbanization has tremendously changed the global landscape with profound impacts on our society. Nighttime light (NTL) data can provide valuable information about human activities and socioeconomic conditions, thus has become an effective proxy to measure urban development. By using NTL-derived urban measures from 1992 to 2018, we analyzed th...

Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with high spatial and temporal resolutions using multisource images. Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-c...

Sensing urban greenness from street view data offers a new and alternative way of measuring the association between greenness exposure and subjective wellbeing in developing countries where traditional census data are poor. This paper focuses on the association between life satisfaction and street-level visible greenness exposure at residential and...

According to the point of interest (POI) data of cultural facilities in 2019, we firstly used a combination measurement of standard deviational ellipse analysis, nearest neighbor index and nuclear density estimation to measure the spatial agglomeration characteristics of cultural facilities in Xi’an. Then we analyzed the influential factors of this...

Vector-based cellular automata (CA) based on real land-parcel has become an important trend in current urban development simulation studies. Compared with raster-based and parcel-based CA models, vector CA models are difficult to be widely used because of their complex data structures and technical difficulties. The UrbanVCA, a brand-new vector CA-...

Open space and urban land are interactively attractive in the simulation. • Simulated OS is evaluated with walking accessibility and population coverage rate. • Different mean sizes and time-lags of OS are considered in the simulation. • OS-CA is an effective tool for assessing the policies for creating new OS. Open spaces (OSs) in urban areas play...