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In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists' perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people's perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley's K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people's habitual cognition. [ABSTRACT FROM AUTHOR]

In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition.

In this letter, we investigate an overlay cognitive satellite terrestrial network (OCSTN), where the secondary terrestrial network assists the primary satellite communication in exchange for the opportunities of spectrum access. To achieve a win-win cooperation of the satellite and terrestrial networks in the OCSTN, we propose a cognitive region to determine the conditions that terrestrial nodes can work as effective relays to cooperate with the satellite network in a time-splitting spectrum sharing manner. Specifically, we first analyze the ergodic rates of the satellite and terrestrial networks. On this basis, we design a cognitive region seeking the feasible location set of the secondary relay and the corresponding range of time splitting ratios, which can improve the satellite communication performance while enabling the transmission of terrestrial network. [ABSTRACT FROM AUTHOR]

When deciding where to draw the boundaries for electoral districts, officials often strive to ensure that communities of interest are not split up but kept wholly within those boundaries. But what constitutes a community of interest is vague, with legal and academic sources describing either a thematic region with shared demographic and land-use traits, or a cognitive region that is meaningful to people and commonly agreed upon. This study, conducted in the city of Santa Barbara, California, seeks to identify communities of interest at the sub-city level as both thematic regions—by clustering Census tracts and land parcels according to classes of relevant variables—and cognitive regions—by surveying residents about the size and locational extent of their community and finding areas of agreement. We then assess the degree to which the two types of regions overlap as a way to evaluate how well the two meanings correspond. We also examine the amount of overlap between the two sets of regions and the city council electoral districts that were recently created in Santa Barbara. Our study finds that the two types of regions correspond relatively well to each other in this test city, but that the electoral districts correspond more to the thematic regions, understandable given that the district creation made no attempt to survey residents about their beliefs. [ABSTRACT FROM AUTHOR]

The significance of urban metro stations extends beyond their roles as transport nodes in a city. Their surroundings are usually well-developed and attract a lot of human activities, which make the metro station areas important cognitive places characterized by vague boundaries and rich semantics. Current studies mainly define metro station areas based on an estimation of walking distance to the stations (e.g., 700 m) and investigate these areas from the perspectives of transportation and land use instead of as cognitive places perceived by the crowd. To fill this gap, this study proposes a novel framework for extracting and understanding the cognitive regions of urban metro stations based on points of interest (POIs). First, we extract the cognitive regions of metro stations based on co-occurrence patterns of the stations and their surrounding POIs on web pages by proposing a cohesive approach combined of spatial clustering, web page extraction, knee-point detection, and polygon generation techniques. Second, we identify the semantics of metro stations based on POI types inside the regions using the term frequency-inverse document frequency (TF-IDF) method. In total 166 metro stations along with more than one million POIs in Shenzhen, China are utilized as data sources of the case study. The results indicate that our proposed framework can well detect the place characteristics of urban metro stations, which enriches the place-based GIS research and provides a human-centric perspective for urban planning and location-based-service (LBS) applications. • We made the first attempt to investigate urban metro stations as cognitive places in cities. • We proposed a novel approach for extracting the cognitive regions of metro stations based on their surrounding POIs. • We introduced a TF-IDF method to identify the semantics of metro station areas based on their inside POI types. • This study enriches place-based GIS research and provides a human-centric perspective for urban planning and LBS applications. [ABSTRACT FROM AUTHOR]