Journal of Geographical Systems最新文献

Spatial objects in a geographical information system are often vague. Fuzzy topology-based intersection methods were introduced to obtain the topological relation between such vague objects by considering the objects as fuzzy sets. The elements of the intersection matrices express the nature of the intersections between topological parts of the objects. In reality, it may only be possible to calculate some of the required intersections due to the unavailability of data. This paper introduces a method to construct a fuzzy 9-intersection matrix under incomplete observations. Further, it computes the possible fuzzy topological relation using a quantitative fuzzy 9-intersection method based on fuzzy similarity measure. Some special cases of the incomplete observations and fuzzy topological relation under such situations are discussed. Finally, few applications are presented, where the proposed method is used to obtain relation between two uncertain objects under the condition that the topological properties of the objects are partially known.

Abstract

Urbanization has caused increasingly severe heat island effect in cites, which also has worsened air quality in the urban areas due to a combination of factors. To reduce the impacts, several studies have proposed strategies based on different planning and design principles. Although some strategies were found to be useful for mitigating air pollution or urban heat island, the simultaneous effects were rarely discussed in the past studies. Therefore, this research tries to develop a framework that is able to evaluate the relative benefits of different mitigation methods on both urban heat island and air pollution, using a scenario-based computational fluid dynamics (CFD) modeling method. Six scenarios of different strategies were comprehensively evaluated and compared. It is found that pavement materials and rooftop greening showed more significant effects on reducing the heat island temperature and air pollutant concentration than that of changes in building volumes and water bodies in the study area. In addition, there are differences in mitigation effects on the two impacts, suggesting that careful comparative analysis should always be done before implementing the strategies. The proposed method could be very useful in the process of developing coping strategies for both heat island effect and air pollution in the urban areas.

Arthur Getis is a pioneer in spatial statistics. His collaboration with Keith Ord has inspired our long-standing collaboration between a geographer and a statistician. Getis often tackled real-world infectious disease problems using spatial statistics, which has motivated our work from time to time. In this paper, we report a 10-week spatial intervention for reducing COVID-19 vaccine hesitancy. In contrast to spatiotemporal modeling, we mapped and detected spatial patterning of vaccination each week in conjunction with the social vulnerability index (SVI). Between week one and week eight, we identified substantial spatial clustering effects of COVID-19 vaccine administrations. These effects were negatively associated with SVI, meaning that the more vulnerable populations were less likely to be vaccinated. This directional effect changed to positive suggesting significant progress from the intervention. Even though we observed some global spatial clustering in the early weeks, low-value clusters or cool spots for vaccine hesitance were no longer present after SVI was controlled. The use of spatial statistics and the SVI can help monitor targeted interventions to reduce vaccination disparities.

This paper develops a new method for analyzing the relationship between a set of points and another single point, the latter of which we call a reference point. This relationship has been discussed in various academic fields, such as geography, criminology, and epidemiology. Analytical methods, however, have not yet been fully developed, which has motivated this paper. Our method reveals how the number of points varies by the distance from a reference point and by direction. It visualizes the spatial pattern of points in relation to a reference point, describes the point pattern using mathematical models, and statistically evaluates the difference between two sets of points. We applied the proposed method to analyze the spatial pattern of the climbers of Mt. Azuma, Japan. The result gave us useful and interesting findings, indicating the method’s soundness.

The research of Art Getis has had many positive direct effects on the fields of regional science and spatial statistics. There already have been, and will continue to be, many indirect effects as well, as the repercussions of his work ripple through the field. With his contributions to spatial statistics, Art Getis laid out a rich research agenda for future work in the field. In this presentation, I describe several research problems inspired by his work. These include (a) the multiple testing problem, (b) the effects of global spatial autocorrelation on the power and use of local statistics, (c) the effects of weight misspecification on the power of local statistics, and (d) the statistical distribution of his local spatial heterogeneity (LOSH) statistic. In addition, there are a number of connections between the local statistics he developed and other statistics. These include the relationships between (a) the Getis-Ord statistic and the two-sample t statistic, (b) the Getis-Ord statistic and the maximum of Gaussian random fields, and (c) the LOSH statistic and the well-known Geary statistic.

Chinese toponym recognition is crucial in named entity recognition and has significant implications for improving geographic information systems. Based on the real-time nature of social media and rich geographical data contained in social media, it is important to identify Chinese toponyms, including compound toponyms, informal toponyms, and other forms of social media content, for automatic geospatial information extraction. However, the strong word-building ability, diverse features, and ambiguity of Chinese toponyms combined with the linguistic irregularities of social media pose significant challenges for accurately locating toponym boundaries and resolving ambiguities. Furthermore, existing Chinese toponym recognition methods often ignore the fusion of local and global features during feature extraction, resulting in semantic information loss. Therefore, we used the Chinese-roberta-wwm-ext pre-trained language model to encode input text and obtain character-level information. An improved SoftLexicon-based statistical method was employed to acquire word-level semantic information, which was then integrated with character-level semantic information. A two-channel neural network layer comprising a bi-directional long short-term memory and an inception-dilated convolutional neural network was utilized to extract global and local features from text. Additionally, a conditional random field was applied to establish label constraints. The proposed deep neural network model, called CHTopoNER, is designed to identify various forms of Chinese toponyms in irregular Chinese social media content. Its effectiveness was validated on four publicly available annotated toponym datasets and a custom social media dataset. CHTopoNER surpasses state-of-the-art Chinese toponym recognition models and achieves promising results for extracting various types of toponyms and spatial location terms.