Environmental risks have become significant impediments to economic and social development. Increasing awareness of environmental risks can promote pro-environmental behaviors, thereby helping mitigate such risks. Currently, studies focusing on the extraction of environmental risk awareness are limited by several deficiencies, including the use of small data sets, brief analysis periods, and narrow research areas. To address these limitations, this study introduces a novel methodological framework that integrates Natural Language Processing (NLP) techniques with mass media and academic archives to extract environmental risk awareness over extended periods and large spatial scales. By applying geographic entity and focus extraction, coupled with environmental risk quantification, we examined nearly four decades of environmental risk awareness in China. The results reveal heightened awareness of natural disasters and climate change, while understanding of biological invasions and anthropogenic-related risks remains limited during the past four decades. Moreover, temporal, spatial, and thematic variations in the public’s and academics’ awareness are observed, with academia focusing more on systemic hazards and displaying a broader spatial distribution of awareness. National administrative centers significantly influence environmental risk awareness compared to provincial centers. Our research offers a useful tool for environmental risk awareness extraction, providing valuable insights for policy formulation and environmental conservation.

Estimating road traffic noise is essential for examining the quality of sounding environment and mitigating such a non-negligible pollutant in urban areas. However, existing estimated models often have limited applicability to specific traffic conditions, while the required parameters may not be readily available for city-wide collection. This paper proposes a data-driven approach for measuring road-level acoustic information of traffic with street view imagery. Specifically, we utilize portable vehicle-equipped hardware for in-situ noise acquisition and employ a deep learning model ResNet to learn high-level visual features from street view images that are closely associated with road traffic noise. The ResNet captures meaningful patterns from the input data, and the output probability vectors are then fed into a Random-Forest regression algorithm to quantitatively estimate the noise in decibels for different road segments. The MAE and RMSE of the DCNN-RF model are 2.01 and 2.71, respectively. Additionally, we employ a gradient-weighted Class Active Mapping approach to visually interpret our deep learning model and explore the significant elements in streetscapes that contribute to the model's estimations. Our proposed framework facilitates low-cost and fine-scale road traffic noise estimations and sheds light on how auditory information could be inferred from street imagery, which may benefit practices in geography and urban planning.

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.

Human emotion is an intrinsic psychological state that is influenced by human thoughts and behaviours. Human emotion distribution has been regarded as an important part of emotional geography research. However, it is difficult to form a global scaled map reflecting human emotions at the same sampling density because various emotional sampling data are usually positive occurrences without absence data. In this study, a methodological framework for mapping the global geographic distribution of human emotion is proposed and applied, combining a species distribution model with physical environment factors. State-of-the-art affective computing technology is used to extract human emotions from facial expressions in Flickr photos. Various human emotions are considered as different species to form their ‘habitats’ and predict the suitability, termed as ‘Emotional Habitat’. To our knowledge, this framework is the first method to predict emotional distribution from an ecological perspective. Different geographic distributions of seven dimensional emotions are explored and depicted, and emotional diversity and abnormality are detected at the global scale. These results confirm the effectiveness of our framework and offer new insights to understand the relationship between human emotions and the physical environment. Moreover, our method facilitates further rigorous exploration in emotional geography and enriches its content.

Regionalization attempts to group units into a few subsets to partition the entire area. The results represent the underlying spatial structure and facilitate decision-making. Massive amounts of trajectories produced in the urban space provide a new opportunity for regionalization from human mobility. This paper proposes and applies a novel regionalization method to cluster similar areal units and visualize the spatial structure by considering all trajectories in an area into a word embedding model. In this model, nodes in a trajectory are regarded as words in a sentence, and nodes can be clustered in the feature space. The result depicts the underlying socio-economic structure at multiple spatial scales. To our knowledge, this is the first regionalization method from trajectories with natural language processing technology. A case study of mobile phone trajectory data in Beijing is used to validate our method, and then we evaluate its performance by predicting the next location of an individual’s trajectory. The case study indicates that the method is fast, flexible and scalable to large trajectory datasets, and moreover, represents the structure of trajectory more effectively.

The advanced technologies in location-based services and telecom have yield large volumes of trajectory data. Understanding these data effectively requires intuitive yet accurate visual analysis. The visual analysis of massive trajectory data is challenged by the numerous interactions among different locations, which cause massive clutter. This paper presents a new methodology for visual analysis by integrating algebraic multigrid (AMG) method in data aggregation. The non-parametric method helps to build a multi-layer node representation from a graph which is extracted from trajectory data. The comparison with AMG and other methods shows that AMG method is more advanced in both the spatial representation and the importance of nodes. The new method is tested with real-world dataset of cell-phone signalling records in Beijing. The results show that our method is suitable for processing and creating abstraction of massive trajectory dataset, revealing inherent patterns and creating intuitive and vivid flow maps.