数字人文 文本挖掘与场所感知（空间感知）

Around the globe, Geographic Information Systems (GISs) are well established in the daily workflow of authorities, businesses and non-profit organisations. GIS can effectively handle spatial entities and offer sophisticated analysis and modelling functions to deal with space. Only a small fraction of the literature in Geographic Information Science—or GIScience in short—has advanced the development of place, addressing entities with an ambiguous boundary and relying more on the human or social attributes of a location rather than on crisp geographic boundaries. While the GIScience developments support the establishment of the digital humanities, GISs were never designed to handle subjective or vague data. We, an international group of authors, juxtapose place and space in English language and in several other languages and discuss potential consequences for Geoinformatics and GIScience. In particular, we address the question of whether linguistic and cultural settings play a role in the perception of place. We report on some facts revealed by this multi-language and multi-cultural dialogue, and what particular aspects of place we were able to discern regarding the few languages addressed.

The “spatial turn” in the humanities has led to increased exploration of spatial perspectives. This shift inspired the ALMA Digital Atlas of the Ancient Jewish World project, which aims to develop a comprehensive digital-analytical atlas. It is intended to serve as a tool for geographical and comparative research on ancient Jewish geography, spanning the Hellenistic and Byzantine periods. The atlas builds on two elemental entity types: place, which pertains to regions or settlements, and source, which addresses pertinent historical texts, archaeological finds, or both, allowing for the robust comparison of geographical information from various sources. This project seeks not only to address existing historical and geographical questions but also to raise new ones, offering fresh insights into geographical perception in antiquity.

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With the advent of information technology, numerous initiatives have been launched by cultural heritage, academic and commercial institutions aiming at digitization, organization, visualization and analysis of historical information of a given place. These projects usually utilize GIS (Geographic Information Systems) to represent and analyze a restricted range of spatial data, such as archaeological findings or landmarks from a single information source. To take the emerging field of spatial history to the next level—the spatial digital humanities—the traditional spatial data should be enriched with cultural and social data from heterogeneous resources, such as historical books, administrative documents, images, and multimedia objects, and allow for deeper analysis of the historical places’ cultural and social context. To this end, ontologies and modern semantic web technologies should be combined with GIS technology to enable easy data standardization and integration, uniform data modeling, open-access and cross-project data sharing and analysis. In this paper, we review this combined approach and its utilization attempts in recent spatial digital humanities projects for cities from all over the globe while discussing the field’s main common challenges and their possible solutions.

The constant development of internet technology in the digital age has changed the way people interact with space. To explore whether and what changes have occurred in the public’s spatial perception of the street during the shift from the traditional to the digital age, this paper compares the public’s spatial perception characteristics in the two contexts through a classical theory review and empirical coding analysis with the Xiaolouxiang historic district as an empirical case. The frameworks of public spatial perception characteristics are similar in both contexts, with a focus on the three dimensions of objects, buildings and spaces, and activities. However, compared with that in the traditional context, the public’s spatial perception of the street in the digital context of Xiaolouxiang has undergone new changes in three aspects: from focusing on the three-dimensional experience of space to focusing on the two-dimensional visual aesthetics of space, from emphasizing spatial totality to focusing on spatial details, and from focusing on the use of space to emphasizing the cultural characteristics of space. Research emphasizes that, within the context of digitalization, spatial design should place greater emphasis on visual aesthetics, detail processing, and cultural expression to better meet the needs and expectations of the public. This work identifies new features of the public’s perception of urban street space in the context of digitalization and accordingly provides suggestions for the future design of urban street space. The findings of this study not only enrich the foundational research content of spatial perception theory but also offer new perspectives for designers and planners in the practice of urban block renovation.

Abstract ArcGIS is an online tool which allows users to create and curate digital StoryMaps which contain a variety of geographic points, text, images, and web links. Instructors can turn to this software for a variety of uses in the humanities classroom, including incorporating a sense of place and movement to the study of various genres of texts. Through ArcGIS, students can create digital artifacts which bridge the gap between text and the world around them in alternative assignment sequences which include substantial writing and research components and offer more potential for creativity and collaboration than the standard argumentative essay.

The rise of digital platforms has transformed heritage interpretation from a single official narrative to multi-stakeholder participation. This study investigates how such platforms mediate the formation of a sense of place at the Kulangsu World Heritage Site (WHS). Data were collected from official narrative texts and user-generated content (UGC) on Dianping and Ctrip, and analyzed using high-frequency word statistics and semantic network analysis. The results reveal a clear divergence between official narratives, which emphasize Outstanding Universal Value (OUV), and tourist perceptions, which focus on visual landmarks and “check-in” practices shaped by the “digital gaze.” Moreover, the sense of place is shown to be a dynamic process, co-constructed through pre-visit expectations, on-site experiences, and post-visit reflections. The findings also highlight a transformation in tourists’ roles, shifting from passive cultural consumers to active participants in the co-construction of heritage values, with digital platforms serving as critical mediators. Theoretically, the study advances digital heritage scholarship by clarifying the mechanism of the digital gaze and the dynamic nature of sense of place. Practically, it underscores the importance of integrating official narratives with UGC to strengthen OUV communication, foster broader public engagement, and support the sustainable development of WHSs.

Spatial media bring out new forms of interaction with places, leading to the emergence of new ways of embodying the experience. The perception of place and its dynamics of change has been multiplied by the emergence of digital platforms, which create many and varied representations of place in spatial media. These representations are dependent on the digital platforms’ ecosystem, formed by platform-specific mechanisms of digital placemaking. The study applied text mining techniques and statistical methods to explore the role of user-generated content as a digital placemaking practice in shaping place experience. The online reviews were collected from Google Maps for 23 places from Poznań, Poland. The analysis showed that place experience is described by three dimensions: attributes, practices and atmosphere, or place practices that most closely reflect the specificity of a place. The place attributes blurred the boundaries between their digital images, whereas the atmosphere dimension reduces the diversity and uniqueness of the place. In conclusion, user-generated content (UGC) as an element of the process of digital placemaking increases place awareness and democratizes human participation in its creation, yet it affects its reduction to homogeneous information processed through mechanisms operating within a given digital platform.

Shamian Island is located in Guangzhou, China. Thanks to its unique history and official planning and protection, it has preserved a rich variety of cultural landscapes. On social media platforms such as Xiaohongshu (RedNote) and Douyin (Chinese version of TikTok), Shamian Island is listed as a popular check-in scenic spot in Guangzhou. This paper uses in-depth interviews and text analysis methods to explore how a digital sense of place is created on Shamian Island in China’s mediatized society, elucidating relationship construction and emotional connections between individuals and the island. The Shamian Island presented by the media is described as nice, fun, and attractive. People appreciate the charm of the island’s European-style buildings online, which triggers their yearning for exotic customs. During the offline check-in and tourism practices on Shamian Island, tourists have completed a series of actions to travel to actual places in order to participate in media narratives. Shamian Island is separated from the mainland by a stretch of water, and its elusive island nature brings about a particular experience. When interacting with the place, individuals continuously strengthen their own roles and participate in reproducing the local image. Media dissemination has constructed the perception of Shamian Island as a place and promoted emotional connections among tourists, local residents, and Shamian Island. Shamian Island is not only a geographical coordinate but also a social space that carries individual and collective memories.

The sustainable development of traditional villages faces a core challenge stemming from the disconnect between public perception and spatial planning. To address this issue, this study, taking Baishe Village—a national-level traditional village—as a case study, constructs and applies a “Digital Humanities + Spatial Analysis” research paradigm that integrates text mining, sentiment analysis, visual coding, and spatial analysis based on multimodal social media data (Sina Weibo and Xiaohongshu) from 2013 to 2023. It aims to conduct an in-depth analysis of tourists’ rural image perception structure, emotional tendencies, and their spatial differentiation characteristics, and subsequently propose spatial optimization pathways that promote the revitalization of its cultural landscape and sustainable land use. The main findings reveal the following: (1) In terms of cognitive structure, the rural image presents a ‘settlement-dominated’ four-dimensional structure, with settlement elements such as pit kilns (accounting for more than 70%) as the absolute core. (2) In terms of emotional tendencies, a cognitive tension is formed between the high recognition of architectural heritage value (positive sentiment: 57.44%) and significant dissatisfaction with service facilities. (3) In terms of spatial patterns, a “dual-core-driven” pattern of perceived hotspots emerges, with 83% of tourist activities concentrated in the central–southern main road area, revealing a “revitalization gap” in village spatial utilization. The contribution of this study lies in translating abstract public perceptions into quantifiable spatial insights, thereby constructing and validating a “Digital Humanities + Spatial Analysis” paradigm that fuses multimodal data and links abstract perception with concrete space. This provides a crucial theoretical basis and practical guidance for the living conservation of cultural landscapes, the enhancement of land use efficiency, and refined spatial governance.

Tang-Song poetry, a distinguished element of China’s traditional cultural heritage, is intricately linked with the historical and cultural development of Chinese cities. This paper uses Nanjing as a case study and applies digital humanities techniques to analyze and compare the spatiotemporal distribution of place names found in Tang-Song poetry with those in online travel narratives. The aim is to uncover key factors that have influenced the cultural continuity of these historical cities and their relevance today. Findings indicate that: (1) Locations mentioned in Tang and Song poetry show significant spatial differentiation, with urban areas displaying a clustered distribution and suburbs showing scattered hotspots. (2) The number of locations referenced in Song poetry increased significantly compared to Tang poetry, suggesting that Nanjing’s economic growth heightened the city’s appeal and inspired more literary output. (3) Song Dynasty poetry reflects a shift toward more neutral and negative emotions, with a marked decrease in positive expressions. This rise in negative sentiment can be traced to the decline in national strength from the Tang to the Song Dynasty, amplifying Nanjing’s role as a place of reflection and mourning. (4) Nanjing’s cultural hotspots, such as Xuanwu Lake and the Zhongshan Scenic Area, feature prominently in both Tang-Song poetry and modern travelogues. This study contributes to research in literary geography and literary tourism at the urban spatial level, offering fresh insights into the cultural legacy of historical cities.

This paper presents translation methods of innovative mapping approaches, from paper-based techniques from historical periods to the digital realm for 3 case studies: Bucharest, Lisbon and Rome. Such paper-based techniques are the 18th century’s city map of Rome (by Giambattista Nolli), the 1950s-60s psycho-geographical maps developed by situationists (Guy Debord) and novel visual layouts from architects and urban planners (Kevin Lynch, Saverio Muratori). A previous mapping developed by Bostenaru and Panagoupoulos (2014) of the 18th century Lisbon earthquake, based on a chronicle azulejos engraving of the event, is also considered as a base point for a more modern perception visualization of this location. Digital techniques include story maps and layered mapping. Several story maps types were investigated for walking or cycling tours. These kinds of tours are a method of investigation, more suitable in architecture than in geography, as reviewed for Rome. Photography is another such instrument and the path taken in the story map is suitable for this. The charted buildings span from 20th century heritage, to sites relevant for the effects of earthquakes, the latter for Bucharest and Lisbon. The Rapid Visual Screening method (RVS) focuses on the structural material in Bucharest, while heritage habitat approaches emphasize the role of green materials, for Lisbon and Rome. Innovations in the paper maps as well as scenario thinking (Helmuth Kahn) permit differentiating between emblematic and common buildings, which can be achieved digitally through story maps. As the example of Lisbon shows, a further step may be creating VR and 3D models or video content. In conclusion, these story map tools are useful to investigate layers of the city, for writing urban places.

Digital Humanities (DH) is a dynamic and developing field. In recent years, its evolution has been witnessed foremost in the growth of funded DH projects and through the willingness of scholars from diverse backgrounds to not only work in DH research, but also as ‘digital humanists’. One crucial component to DH research is that of spatial enquiry, the expansion of which has rapidly evolved from a small component often found buried in research objectives, to the research aim of a growing number of projects. Spatial humanities, while still a relatively new interdisciplinary field, is exhibiting continued advancement and focus from the academic community; however, working with digital data is rarely a straightforward pursuit, even for the most accomplished scholar. Primarily access to appropriate and reliable (spatial) datasets, the keystone of spatial humanities research, the sharing and openness of spatial methods, tools and data (SMTD), and education in the former, all remain a challenge. Witnessing the continued rise of spatial humanities research, this special issue brings together a selection of articles delivered at Spatial Humanities 2016, a conference held at Lancaster University (UK). The aim of this multi-disciplinary conference was to explore and demonstrate the contribution to knowledge that spatial technologies in humanities research may enable within and beyond the digital humanities. Here, this introductory text and associated articles present key research that embodies the growing relevance of the spatial humanities across a plethora of fields and demonstrates several of the prevailing and enduring struggles when working in digital and spatial research. These articles emphasise that, despite common obstacles, spatial humanists make up an imaginative and thriving community keen to share innovation and knowledge and provide stimulating new insights through research.

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Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era.

This Special Issue of the European Journal of Geography emerges from the engaging conversations and inspiring work shared at the Spatial Humanities 2024 Conference in Bamberg. It brings together nine original contributions spanning historical geography, urban geography, heritage studies, and geography and education. Through GIS, deep mapping, digital archives, and participatory methods, these studies reveal how spatial thinking and geospatial technologies enrich our understanding of places, histories, and human experiences. Together, they highlight the growing value of spatial humanities in bridging disciplines, telling new stories, and reshaping the ways we explore the world.

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This study examines the place attachment as conveyed through social media, specifically focusing on significant square spaces in Chiang Mai, Thailand. Analyzing social media posts related to the Three Kings Monument Square, Tha - Pare Gate Square and Chiang Mai Gate Square through Instagram application. — evaluating seven factors: demographics, frequency of visits, importance of place (IM), meaning of place (ME), functions and social activities (FU), knowledge of place, and insights from photographic analysis. The study utilized multiple - choice questionnaires, in - depth interviews, and Photo Elicitation Interviews (PEI). A total of seventy - five participants were recruited on - site, including residents of Chiang Mai Old City, aged over twenty, with a balanced representation of genders. The data collection involved interviews lasting over twenty minutes each, during w hich participants were prompted to share their perceptions and memories related to the photographs they had taken and gave caption s in a social media platform. Findings indicate that individuals tend to feel a stronger attachment to familiar places; however, there is a general lack of awareness regarding the historical importance of these sites. Instead, the attachment of places in socia l media appears to be more significantly affected by the practical uses of the spaces and the daily activities taking place there. Th e study concludes that social media is an insightful tool for understanding the changing identities of urban spaces, which are predominantly shaped by public perception and usage rather than historical context that interplay between social, cultural, a nd economic dimensions of place. The findings reveal that participants generally exhibit a neutral understanding of the places t hey feel attached to, with greater knowledge correlating to stronger connections, particularly among residents. However, limited historical awareness among short residency participants raises concerns about the potential loss of heritage value within the local community, the findings underscore the importance of functionality and memory in fostering place attachment in both physical and virtual contexts. This research contributes to understanding the dynamic relationship between urban spaces and digital platfo rms, offering insights for urban planning, heritage preservation, and the integration of social media in promoting place identity.

Rural locations often form a hinterland – geographically and culturally - for large conurbations that dominate a particular region. They are interconnected, sometimes interdependent, but also separated by the social and spatial perceptions of a place. For urban dwellers the hinterland is a place to ‘escape to the country’; for rural dwellers the town is the place of ‘bright lights’. The norms of either location often sit juxtaposed. This sense of place may be constructed from traditional, stereotypical ways of seeing and understanding communities. Digital technology has provided a platform to challenge these norms and provide new ways of representing the physical and cultural landscape of urban and rural spaces. In this conceptual paper we explore digital placemaking in the hinterland of the North-East of Scotland. In this region, the city (Aberdeen) dominates, but it is the rural hinterland that charms. Through an examination of co-created content of rural spaces in this region we consider the role of participatory digital placemaking. Drawing on an extensive body of previous research that has explored community heritage in the North-East of Scotland, we use case studies to consider the ways in which images, iconography and language shape and inform perceptions of the rural space in the digital environment. We argue that this bottom-up approach to placemaking in rural areas can help to shape the way that places are seen, understood, and valued by communities, visitors and wider online audiences. To conclude, this paper reflects on how rural participatory digital placemaking counteracts the urban norm and connects communities across town and country through a reimagined digital hinterland.

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Against the backdrop of digital media and policy-driven rural tourism, this study examines how Hakka homestays in the Heyuan region of Guangdong Province can transition from mere “space provision” to the construction of a poetry-narrative-based cultural IP, thereby fostering tourists’ sense of place identity. Grounded in the intersection of poetic narration, cultural heritage, and semiotic design, the research adopts a case study approach, focusing on: (1) how local symbols and emotions in poetry are transformed into narrative resources; (2) how these resources are narrativized in homestay spaces, experiences, and discourses; and (3) how such narrative-driven IP influences tourists’ place perception, emotional attachment, and sense of belonging. A three-stage model of “narrative resource extraction–narrative translation–identity generation” is proposed, showing that deep narrative integration is more effective than symbolic collage in building sustainable local identity. Theoretically, it extends poetic narration into a narratological framework for tourism and cultural IP construction. Practically, it proposes a “narrative-first” development model for homestays, offering strategic insights for sustainable cultural tourism.

This study examines evolving digital perception of Albania as a tourist destination using Reddit posts between 2013 and October 2024. Using sentiment analysis via VADER and dynamic topic modeling via BERTopic on a 4,276-post English-language dataset, the paper indicates that over 75 % of end-user posts are positive in sentiment—especially during peak travel months (May to September). Prominent themes addressed included accommodation logistics, public transportation, beach resorts, cultural identity, and general perceptions of Albania. Temporal observations show a surge in international interest and talk about Albania's tourism in 2023-2024, aligning with the post-pandemic recovery in international tourism. These findings confirm the validity of using Reddit as an in-real-time source of tourism information and affirm the place of Albania as a rising, affordable, and culturally rich Mediterranean destination. The study offers policy and tourism marketing insights to policymakers and marketers interested in knowing the priorities of travelers and guiding evidence-based development strategies.

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This study aims to evaluate the ecosystem services of Upo wetland, one of the best-known Ramsar sites in Korea, reflecting the characteristics of the ecological assets and local knowledges in the area. Application of spatial text-mining begins with collecting local perceptions and knowledge of residents on the 17 ecological assets of Upo site and surrounding area. Our results identified five important ecosystem services: flood control during heavy rainfall, water purification by aquatic plants, cultural and natural heritages, agricultural products and water provision for crop cultivation. GIS created a map where these ecosystem services were linked to the locations of 17 ecological assets. This map showed which ecosystem service is associated with particular ecological assets and their characteristics from residents’ perspectives. Mapping local knowledge using the spatial text-mining is able to identify multi-functional bases which provide various ecosystem services in the same location simultaneously. Identification of multi-functional bases can provide information for local government to design an effective and comprehensive management plan considering physical-cultural geography of ecosystem services.

ABSTRACT To find disaster relevant social media messages, current approaches utilize natural language processing methods or machine learning algorithms relying on text only, which have not been perfected due to the variability and uncertainty in the language used on social media and ignoring the geographic context of the messages when posted. Meanwhile, a disaster relevant social media message is highly sensitive to its posting location and time. However, limited studies exist to explore what spatial features and the extent of how temporal, and especially spatial features can aid text classification. This paper proposes a geographic context-aware text mining method to incorporate spatial and temporal information derived from social media and authoritative datasets, along with the text information, for classifying disaster relevant social media posts. This work designed and demonstrated how diverse types of spatial and temporal features can be derived from spatial data, and then used to enhance text mining. The deep learning-based method and commonly used machine learning algorithms, assessed the accuracy of the enhanced text-mining method. The performance results of different classification models generated by various combinations of textual, spatial, and temporal features indicate that additional spatial and temporal features help improve the overall accuracy of the classification.

This article provides an overview of the development of spatial humanities in Mexico, focusing specifically on the Bishopric of Michoacán. This ecclesiastical and jurisdictional unit, established during the Viceroyalty of New Spain, has recently been examined through the lens of spatial humanities. Our review identifies that this approach, when combined with traditional historical methods, can be highly effective in advancing research on territorial reconfiguration processes involving both Spanish and Indigenous actors. Furthermore, we present two examples of geographical text analysis (GTA) applied to the Relaciones Geográficas of Michoacán. Building on this review and the examples, we aim to encourage researchers interested in Mexico's past to enhance the potential of early historical sources by extracting valuable information intertwined with the spatial data contained in these documents.

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ABSTRACT Employing text mining and the Kano model, this study aimed to delineate and categorize the components of the coastal and marine tourism servicescape. The research utilized text mining technology to analyze 132,545 Korean language text entries from the Naver platform, identifying 80 key terms associated with the coastal and marine tourism servicescape. A network of these terms was then constructed, segmented into three clusters reflecting the servicescape dimensions: “ambient conditions”, “spatial layout and functionality”, and “signs, symbols, and artifacts”. These clusters were further distilled into 18 elements, establishing a comprehensive framework for analysis. Subsequently, the study utilized the Kano model to analyze 823 South Korean survey responses, categorizing the elements into four qualities: attractive, one-dimensional, must-be, and indifferent. The findings provided a nuanced understanding of the servicescape in coastal and marine tourism, offering theoretical contributions and practical implications for tourism management and policymaking.

Abstract Evaluating typical rural characteristics reveals certain advantages of rural revitalization and is crucial for understanding rural disparities and promoting development. Field research and statistical data can reflect the spatial distribution of local resources and development models. However, due to cost limitations and statistical constraints, it is impossible to effectively compare and evaluate the characteristics of rural development at the long time series, large scale and fine granularity required for sustainable regeneration. This study proposes a web-based method for the extraction and evaluation of rural revitalization characteristics (WERRC). The BERT-BiLSTM-Attention model categorizes rural web texts according to five themes: industrial prosperity, ecological livability, rural civilization, effective governance, and prosperous life. The Term Frequency-Inverse Document Frequency (TF-IDF) algorithm extracts rural characteristics, and the relative advantages of these features are compared among 100 Chinese villages. WERRC extracts the typical characteristics, obtains the spatial distribution and relative advantage, and then ranks them according to the five themes. The relationship between national policy guidance and rural development is explored. The results support further exploration of differentiated, high-quality development modes that incorporate rural advantages into policy, adjust industrial structure, and optimise revitalization strategies at the rural scale.

Research on coastal recreational activities has grown substantially, yet studies focusing on user perceptions of these spaces—critical for optimizing tourism experiences and management—remain fragmented and underdeveloped. This study addresses this gap by examining tourist sentiment in Xiamen, a renowned coastal city in China, using social media data. Text mining tools were utilized to process the Weibo contents through text segmentation, frequency analysis and cluster analysis. The Two-way Neural Network Fusion Model Based on the BERT (TNNFMB) deep learning approach was employed using transfer learning for sentiment analysis, while the Latent Dirichlet Allocation (LDA) model was used to uncover latent thematic patterns. Sentiment polarity analysis revealed that positive comments constituted 56.47%, negative comments only 16.3%, and neutral comments 27.2%, confirming a generally positive perception of visitors’ coastal experiences. Tourists’ social media posts primarily revolve around five core themes in coastal areas: coastal waters, waterfronts, adjacent environments, culture and creativity, and reputation and expectation. The spatial and temporal changes in sentiment scores were discovered. Areas emphasizing sea–land landscapes, cultural theme reinforcement, and open public activities generally achieved high and stable sentiment scores. Natural and natural–artificial mixed coastlines experienced significant seasonal variations in sentiment. The recommendations of this study, generated from a sentiment perspective, include shaping a harmonious coastal environment by improving coastal management and support services to enhance the comfort of the tourist experience. This study advances understanding of user-centric coastal tourism dynamics, providing evidence-based tools for managers to enhance tourist experiences and spatial quality.

The purpose of this study is to explore and analyse user perceptions of peri-rural landscapes in the Seoul metropolitan region, using Icheon City as a case study. While the multifunctionality of peri-rural areas—providing ecological, cultural, and socioeconomic benefits—is increasingly recognised, the perceptual and experiential dimensions remain underexplored in South Korea. To address this gap, 10,578 Naver Blog posts were collected and refined, resulting in 8078 valid entries. Methodologically, this study introduces an innovative approach by integrating centrality analysis with latent Dirichlet allocation (LDA) topic modeling of user-generated content, supported by a bespoke dictionary of 170 local landscape resources. This combined framework allows simultaneous examination of structural associations and thematic narratives within user perceptions. The results indicate that resources such as Seolbong Urban Park, Seolbong Mountain, and the Cornus Fruit (sansuyu) Villages function as symbolic hubs in the perceptual network, while thematic clusters capture multi-dimensional concerns spanning leisure, ecology, culture, suburbanization, and real estate. Synthesised together, these findings demonstrate that user perceptions construct peri-rural landscapes not as isolated sites, but as spatially cohesive and thematically interconnected systems that mediate between urban and rural domains. Overall, this study contributes to metropolitan planning discourse by highlighting perceptual dimensions alongside functional and ecological dimensions. It shows that users cognitively construct peri-rural landscapes as systems that are both spatially cohesive and thematically interconnected, and that function as spaces that link urban and rural areas. Crucially, this study provides a replicable framework for using user-generated content to inform the planning and management of peri-rural landscapes in metropolitan areas.

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A geospatial Knowledge Graph (KG) is a heterogeneous information network, capable of representing relationships between spatial entities in a machine-interpretable format, and has tremendous applications in logistics and social networks. Existing efforts to build a geospatial KG, have mainly used sparse spatial relationships, e.g., a district located inside a city, which provide only marginal benefits compared to a traditional database. In spite of the substantial advances in the tasks of link prediction and knowledge graph completion, identifying geospatial relationships remains challenging, particularly due to the fact that spatial entities are represented with single-point geometries, and textual attributes are frequently missing. In this study, we present GTMiner, a novel framework capable of jointly modeling Geospatial and Textual information to construct a knowledge graph, by mining three useful spatial relationships from a geospatial database, in an end-to-end fashion. The system is divided into three components: (1) a Candidate Selection module, to efficiently select a small number of candidate pairs; (2) a Relation Prediction component to predict spatial relationships between the entities; (3) a KG Refinement procedure, to improve both coverage and correctness of a geospatial knowledge graph. We carry out experiments on four cities' geospatial databases, from publicly-available sources and compare with existing algorithms for link prediction and geospatial data integration. Finally, we conduct an ablation study to motivate our design choices and an efficiency analysis to show that the time required by GTMiner for training and inference is comparable, or even shorter, than existing solutions.

The evaluation of regional characteristics can reveal the advantages of local industries that are significant to guide regional industrial restructuring and industrial layout. Detailed guidance requires fine‐grained evaluations of the regional characteristics, especially at the town level, since towns are the locations of industry. Existing evaluation methods, such as the location quotient and the Porter diamond model, depend on statistical data to compare the advantages of regional characteristics in different regions. Statistical data have fixed statistical items and spatial units that limit the content and granularity, respectively, of the evaluation. In existing methods, non‐covered industries and under‐counted units, especially at the town level and below, result in incomplete descriptions of the regional characteristics. In contrast, web text in the current internet era contains numerous descriptions of regional characteristics. Therefore, web text can potentially be used to evaluate these characteristics. This article proposes a novel web‐based method for the evaluation of regional characteristics (WERC). According to the features of the regional characteristics of the town in the web texts, the WERC method uses the term frequency method to extract the typical characteristics of the region by crawling text on websites and compares the relative advantage of the typical characteristics between different regions to determine the outstanding regional characteristics. WERC is used in a case study to evaluate the regional characteristics of 1,090 towns in Fujian Province, China, obtaining high precision (0.946) and recall (0.895). Unlike existing methods, the proposed method provides comprehensive quantitative town portraits to describe the regional characteristics. The town portrait not only shows the advantages of the typical characteristics of the town, but also quantifies the advantages using a ranking of the typical characteristics. The results can guide regional industrial restructuring and industrial layout and provide a novel approach for the evaluation of regional characteristics based on web text.

ABSTRACT We analyse silence and tranquillity in historical and contemporary corpora to understand ways landscapes were—and are—perceived in the Lake District National Park in England. Through macro and microreading we develop a taxonomy of aural experiences, and explore how changes to categories of silence from our taxonomy—for instance, the overall decline in mentions of absolute silence—provide clues to changes in the landscape and soundscape of the Lake District. Modern authors often contrast silence with anthropogenic sounds, while historical authors adhere to a cultural construction where the Lake District is presented as a tranquil area by ignoring industrial sounds. Using sentiment analysis we show that silence and tranquil sounds in our corpora are, as a whole, more positively associated than random text from the corpora, with this difference being especially marked in contemporary descriptions. Focusing closely on individual texts allows us to illustrate how this increased positivity can be related to the emergence of silence and tranquillity as valuable components of landscape. Mapping our corpora confirmed the influence of Wordsworth’s writing on descriptions of silence; and revealed the co-location of pockets of tranquillity near to transport arteries in contemporary descriptions.

Researchers have developed various approaches for exploring the spatial information, temporal patterns, and Twitter content in topics of interest in order to generate a better understanding of human behavior; however, few investigations have integrated these three dimensions simultaneously. This study analyzes the content of tweets in order to conduct a spatiotemporal exploration of the main topics of interest in Kuwait in order to provide a deeper understanding of the topics people think about, when they think about them, and where they tweet about them. To this end, we collect, process, and analyze tweets from nearly 120 areas in Kuwait over a 10-month period. The study’s results indicate that religion, emotions, education, and public policy are the most popular topics of interest in Kuwait. Regarding the spatiotemporal analysis, people post more tweets regarding religion on Fridays, a holy day for Muslims in Kuwait. Moreover, people are more likely to tweet about policy and education on weekdays rather than weekends. In contrast, people tweet about emotional expressions more often on weekends. From the spatial perspectives, spatial clustering in topics occurs across the days of the week. The findings are applicable to further topic analysis and similar research in other countries.

In the context of cultural tourism integration, tourists’ spatial perception intention is an important carrier of spatial evaluation. In historic cultural districts represented by Jinan Mingfu City, tourists’ perceptual depth remains underexplored, leading to a misalignment between cultural tourism development and spatial quality needs. Taking Jinan Mingfu City as a representative case of a historic cultural district, while the living heritage model has revitalized local economies, the absence of a tourist perspective has resulted in misalignment between cultural tourism development and spatial quality requirements. This study establishes a technical framework encompassing “data crawling-factor aggregation-human-machine collaborative optimization”. It integrates Python web crawlers, SnowNLP sentiment analysis, and TF-IDF text mining technologies to extract physical elements; constructs a three-dimensional evaluation framework of “visual perception-spatial comfort-cultural experience” through SPSS principal component analysis; and quantifies physical element indicators such as green vision rate and signboard clutter index through street view semantic segmentation (OneFormer framework). A synergistic mechanism of machine scoring and manual double-blind scoring is adopted for correlation analysis to determine the impact degree of indicators and optimization strategies. This study identified that indicators such as green vision rate, shading facility coverage, and street enclosure ratio significantly influence tourist evaluations, with a severe deficiency in cultural spaces. Accordingly, it proposes targeted strategies, including visual landscape optimization, facility layout adjustment, and cultural scenario implementation. By breaking away from traditional qualitative evaluation paradigms, this study provides data-based support for the spatial quality enhancement of historic districts, thereby enabling the transformation of these areas from experience-oriented protection to data-driven intelligent renewal and promoting the sustainable development of cultural tourism.

: [Objective] The integrated knowledge system of “environment – architecture – behaviour – perception” embedded in classical Chinese gardens has not been systematically recognized in the contemporary research system dominated by the study of material entities and combined with the contemporary design theory system, resulting in the disconnection between ontology research and design research of classical gardens, and the inefficiency of the transformation and application of research results. Aiming at the problem of excavating and characterizing multi-dimensional elements in classical gardens, this research reviews imperial poems in the Fuhai area of Yuanmingyuan Garden, summarizes the coupling relationship of key elements, and puts forward a suitable method of knowledge characterization, so as to expand the research path of “immaterial” gardens depicted in the poems. [Methods] The utilization of text mining, knowledge graph and associated technologies, in conjunction with the Forty Scenes of Yuanmingyuan and the Yangshi Lei Archives, along with other historical materials, facilitates the characterization of the imperial poetry texts regarding the Fuhai area in Yuanmingyuan during the Qianlong period. This characterization employs literary cartography and semantic network analysis to elucidate the linkage mechanism between the objective material elements in the ten scenes of the Fuhai area and the subjective behavioral perceptions. Initially, the aforesaid poems are subject to the steps of physical lexicon production, word division, word annotation, and data cleaning. The four types of elements, namely architecture, environment, behavior and perception, are then extracted. These elements are then combined with the 3D spatial model to create a layout map and to map the local semantic network corresponding to the physical object and the spatial layout. Secondly, the four types of elements are automatically recognized by software, supplemented by manual recognition and correction, to obtain the strength of semantic

Background: Research on spatial imagery as perceived by humans is an important frontier for deepening the theoretical understanding of Tourism Destination Image and promoting sustainable urban development. Significance: This study, from the perspective of tourists, explores the correlation mechanism between the cognitive image and affective image of urban space. This is of great significance for enhancing the overall spatial quality of cities, promoting the integration of the man–land relationship, and driving the sustainable development of tourism. Method: In this study, we took Harbin as the case site, collected 89,375 reviews and 23,561 review images of 488 scenic spots on the Mafengwo and Ctrip platforms, and constructed a multimodal dataset. We classified the image scenes with the help of the Places365-CNN model. We then extracted text emotional features by utilizing the SnowNLP deep learning algorithm. We constructed a map of the spatial influence mechanism acting on cognitive image and emotion through MGWR. Results: The experimental results showed that in the level of Pleasure, the five indicators NHS, HPA, RPA, PDS and WRV had significant spatial correlations with urban sentiment. In the level of Arousal, the three indicators PD, MaSD and WRV showed significant spatial characteristics. Conclusions: This study reveals the influence mechanism of urban spatial perception elements on tourists’ emotions. It not only deepens the understanding of the Tourism Destination Image theory, but also provides a practical path based on the optimization of perception scenarios for the improvement of urban space, which has important implications for regional sustainable development.

Spatial narratives help us to organize experiences and give them meaning. Previous approaches to understanding geographies in textual sources focus on geoparsing to automatically identify place names and allocate them to coordinates. Those are highly quantitative, and are limited to named places with coordinates, and have little concept of time. Narratives of journeys indicate that human experiences of geography are often subjective and more suited to qualitative representation. Geography is not limited to named places but incorporates the vague, imprecise, and ambiguous, e.g "the camp", or "the hills in the distance", and relative locations such as "near to", "on the left", "north of" or "a few hours' journey from". Places are organized worlds of meaning, characterized by experience, emotion, and memory as well as by geography. In this paper, we discuss our approach to gaining more insight from textual data beyond the toponyms and introduce an extensible framework for extracting, analyzing, and visualizing spatial elements that define the 'locale' as well as the 'sense of place' referenced in text using two test corpora --the Corpus of the Lake District Writing and Holocaust Survivors' Testimonies.

No abstract available

Against the background of tourism recovery following the COVID-19 pandemic, this study explores how destination personality shapes tourists’ emotions and their behavioral intentions at heritage sites. A framework is constructed to evaluate these three variables based on the Cognitive-Affective-Conative (CAC) theory. The methodology combines user-generated content (UGC), sentiment analysis, geographic information systems (GIS), and structural equation modeling (SEM). Online reviews serve as the data source for assessing both perceived destination personality and tourists’ emotional experiences. The statistical findings demonstrate that the personality of a place significantly impacts the emotions experienced by tourists. Among personality dimensions, prominence and responsibility most strongly affect tourists’ behavioral intentions. The research provides valuable insights for tourism managers and policymakers with guidance for fostering sustainable and responsible tourism development. Plain Language Summary A Study on Place Character, Tourists’ Emotions, and Intentions In the context of tourism recovery, the character of a place is an important factor in attracting tourists. However, previous research has ignored its relationship with visitors’ emotions and intentions. This research uses online reviews with place analysis and statistics to reveal how the destination influences visitors’ emotions and intentions. The data were collected from Google reviews to measure the places and the emotional experiences. The findings show that the character of a place has an impact on the emotions experienced by tourists. “Well-known” and “well-cared” are the important characters. Thus, heritage managers can use this low-cost method to see how to keep the sites popular.

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Understanding how urban environments shape public sentiment is crucial for urban planning. Traditional methods, such as surveys, often fail to capture evolving sentiment dynamics. This study leverages language and vision models to assess the influence of urban features on public emotions across spatial contexts and timeframes.A two-phase computational framework was developed. First, sentiment inference used a BERT-based model to extract sentiment from geotagged social media posts. Second, urban context inference applied PSPNet and Mask R-CNN to street view imagery to quantify urban design features, including visual enclosure, human scale, and streetscape complexity. The study integrates publicly available data and spatial simulation techniques to examine sentiment-urban form relationships over time.The analysis reveals that greenery and pedestrian-friendly infrastructure positively influence sentiment, while excessive openness and fenced-off areas correlate with negative sentiment. A hotspot analysis highlights shifting sentiment patterns, particularly during societal disruptions like the COVID-19 pandemic.Findings emphasize the need to incorporate public sentiment into urban simulations to create inclusive, safe, and resilient environments. The study provides data-driven insights for planners, supporting human-centered design interventions that enhance urban livability.

Location-based sentiment analysis has numerous applications, but suffers from both location uncertainty and lack of domain specificity. We propose an approach to automatically build domain-adaptive lexicons for region-specific sentiment analysis. For location estimation, we collect an initial lexicon using topic modeling on a collection of news articles about the target domain. For sentiment estimation we start with a preexisting lexicon. Both initial lexicons are then expanded recursively through employment of a word embedding trained on social media messages from the target area. The final location lexicon is used for location estimation, the final sentiment lexicon for automatically annotating data that is used to fine-tune a BERT transformer network on the task of sentiment estimation. We validate our approach by using the city of Amsterdam as a case study, and show that both the automatically expanded lexicons and the fine-tuned network outperform their respective baselines. This illustrates that with little manual input, our system improves through adapting to the domain.

Advances in digital sensors and Information flow have created an abundance of data generated by users under various emotional states in different situations. Although this opens up a new facet in spatial research, the large amount of data makes it difficult to analyze and obtain complete and comprehensive information leading to an increase in the demand for sentiment analysis. In this study, the Canadian National Research Council (NRC) of Sentiment and Emotion Lexicon (EmoLex) was used, based on data from the social network Twitter (now X), thus enabling the identification of the places in Lisbon where both positive and negative sentiment prevails. From the results obtained, the Portuguese are happy in spaces associated with leisure and consumption, such as museums, event venues, gardens, shopping centres, stores, and restaurants. The high score of words associated with negative sentiment have more bias, since the lexicon sometimes has difficulties to identify the context in which the word appears, ending up giving it a negative score (e.g., war, terminal).

Amid growing demands for heritage conservation and precision urban governance, this study proposes a multimodal framework to analyze tourist perception and behavior in Macau’s Historic Centre. We integrate geotagged social media images and text, ultra-wideband (UWB) pedestrian trajectories, and a LiDAR-derived 3D digital twin to examine the interplay among spatial configuration, movement, and affect. Visual content in tourist photos is classified with You Only Look Once (YOLOv8), and sentiment polarity in Weibo posts is estimated with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. UWB data provide fine-grained trajectories, and all modalities are georeferenced within the digital twin. Results indicate that iconic landmarks concentrate visual attention, pedestrian density, and positive sentiment, whereas peripheral sites show lower footfall yet strong emotional resonance. We further identify three coupling typologies that differentiate tourist experiences across spatial contexts. The study advances multimodal research on historic urban centers by delivering a reproducible framework that aligns image, text, and trajectory data to extract microscale patterns. Theoretically, it elucidates how spatial configuration, movement intensity, and affective expression co-produce experiential quality. Using Macau’s Historic Centre as an empirical testbed, the findings inform heritage revitalization, wayfinding, and crowd-management strategies.

ABSTRACT Previous studies have introduced various approaches for visualizing the spatial and temporal distributions of sentiments expressed on social media. However, many existing methods either overlook the evolving nature of sentiments or fail to account for the spatial distribution of sentiment trends related to specific topics. To gain a comprehensive understanding of how sentiments evolve in relation to topics and geographies, it is essential to capture the dynamic nature of sentiment through time series analysis and geovisualization. This article introduces a workflow that combines natural language processing, spatial time series analysis, and geovisualization techniques to identify and visualize the variations in sentiment trends on Twitter across different geographic regions and topics. By examining the 2016 presidential debates as a case study, we uncover distinct temporal patterns in sentiment distributions across various topics and states. Our findings also show that adjacent states do not always share similar sentiment trends, and that geographic clusters with similar sentiment trends also vary across topics. Failing to consider these variations may result in misunderstanding public discourse and sentiments since they are diverse and dynamic in nature.

This study examines the spatial and sentiment dynamics of 28 Michelin-starred restaurants in Busan, South Korea, to elucidate how geographic location and service attributes influence customer perceptions in the context of culinary tourism. A mixed-methods design, integrating Geographic Information System (GIS) spatial analysis and qualitative sentiment analysis, was employed. Michelin-starred restaurants in Busan were geocoded using QGIS. A total of 9,898 online reviews were processed through KH Coder to extract frequently occurring terms and co-occurrence relationships, and spatial heat maps were generated to visualize the clustering of positive sentiments across urban districts. The results revealed a significant concentration of highly rated restaurants and positive customer sentiment focusing specifically on the top two sentiments, "good" and "delicious" in Haeundae and Busanjin, where attributes such as food quality, ambiance, and service interact synergistically to enhance customer satisfaction. The integration of spatial and textual analyses provides empirical evidence that geographic concentration contributes to perceived restaurant value and reinforces district-level culinary reputation. The findings carry practical implications for urban planners, tourism policymakers, and restaurateurs, suggesting that strategic investment in culinary clusters and supporting infrastructure can strengthen Busan’s position as a premier gastronomic destination and promote sustainable urban development within the fine-dining sector.

Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.

No abstract available

Purpose Against the background of the popularity of social media and heritage tourism, this study aims to focus on world heritage sites, proposing a method to examine and compare the digital spatial footprints left by tourists using geographic information systems. Methodology By analyzing user-generated content from social media, this research explores how digital data shapes the destination image of WHS and the spatial relationships between the components of this destination image. Drawing on the cognitive-affective model (CAM), it investigates through an analysis of integrated data with more than 20,000 reviews and 2,000 photos. Innovation The creativity of this research lies in the creation of a comprehensive method that combines text and image analytics with machine learning and GIS to examine spatial relationships within the CAM framework in a visual manner. Results The results reveal tourists' perceptions, emotions, and attitudes towards George Town and Malacca in Malaysia, highlighting several key cognitive impressions, such as history, museums, churches, sea, and food, as well as the primary emotions expressed. Their distributions and relationships are also illustrated on maps. Implications Tourism practitioners, government officials, and residents can gain valuable insights from this study. The proposed methodology provides a valuable reference for future tourism studies and help to achieve a sustainable competitive advantage for other heritage destinations.

This study examined the fluctuation of customer sentiment regarding hotel experiences in Beppu City, Japan, as reflected in TripAdvisor reviews. A total of 4004 reviews in English and Japanese, contributed by both international and domestic customers, were collected from 233 hotels in Beppu between April 1st, 2015, and March 31st, 2023, for sentiment analysis. The Google Cloud Natural Language Processing API was utilized for sentiment extraction. This study employed spatio-temporal analysis to investigate the variation of sentiment over time and across different areas, as well as to explore differences in sentiment between international and domestic customers. The research results underscore a notable regional disparity in hotel satisfaction, particularly in the Kitahama and Cyuou Area of Beppu, following the COVID-19 pandemic.

Known as the 'city of Sassi', Matera underwent a renewal process involving all of the Basilicata regions in the last few years. The city's Tourism resources were almost unknown at a national and international level, although the Sassi were included in the UNESCO World Heritage List since 1993. The European Capital of Culture 2019 nomination triggered an intense regeneration, opening the city to global tourism and revealing a high resilience. Tourists' experiences and opinions have been valuable resources for designing tourism activities and creating a new symbolic identity for the city, especially in the Web 2.0 era. Here we propose to compute the reviews' polarity scores and use them with other characteristics (e.g., price, offered services and type of tourist facilities) to build spatial clusters according to the logic of Local Spatial Association Indicators (LISA). The geo-referenced semantic orientation of reviews concerning a particular activity or attraction represents a useful quantitative feature for further analyses and the production of territorial statistics. The proposal can be extended to other cases to monitor the change of sentiment towards specific areas of interest and plan possible intervention policies.

Using online reviews, this study explores the factors influencing visitor satisfaction in the context of ethnic-minority village tourism. Tourist satisfaction is a critical determinant of destination sustainability, particularly in rural ethnic minority villages that rely on tourism as a key driver of economic development and cultural preservation. Such villages offer unique cultural, environmental, and social experiences that distinguish them from mainstream destinations, yet they face challenges in meeting diverse tourist expectations while maintaining authenticity. To address this conundrum, online review data were analyzed using sentiment analysis, latent Dirichlet allocation topic modeling, and regression analysis. The findings identify several factors that impact upon tourist satisfaction: service quality, unique cultural experiences, geographically tailored marketing methods, and real-time monitoring and reaction capabilities. In addition, the results reveal a significant relationship between sentiment scores and tourist satisfaction, underlining the value of sentiment analysis in tourism evaluation. Key managerial implications include enhancing service quality, offering authentic cultural experiences, and developing regionally adaptive marketing strategies that incorporate real-time feedback mechanisms.

Tourism is a key driver of economic development in Indonesia, with historic sites such as Borobudur and Prambanan playing a significant role in attracting visitors. Understanding tourist movement patterns and sentiments is crucial for optimizing visitor experiences and enhancing tourism efficiency. This study integrates network science and sentiment analysis to examine tourist perceptions and movements in Central Java based on reviews from Google Maps and TripAdvisor. Using Natural Language Processing (NLP), sentiment analysis identifies that Borobudur and Prambanan receive overwhelmingly positive feedback, with key themes including “temple,” “complex,” and “sunrise.” The network analysis further identifies Pawon Temple as a central node in tourist movement, suggesting the need for improved infrastructure and strategic marketing. Quantitative findings reveal that 77.2 % of Borobudur reviews and 76.2 % of Prambanan reviews are positive, while Pawon Temple shows high degree centrality (1.63) and betweenness centrality (0.055) in the tourist network. These insights provide valuable recommendations for policymakers, destination managers, and digital tourism platforms such as Jasirah, enabling evidencebased strategies for the sustainable development of cultural and historical tourism in Central Java.

ABSTRACT This article explores the relationship between spatial factors, socioeconomic conditions, and Twitter (now called X) sentiment in New York City (NYC) during the COVID-19 pandemic. Using Twitter data, the study investigates how sentiment varied across different geographies. It examines whether sentiment scores, unemployment rates, and COVID-19 hospitalization rates in NYC zip codes revealed spatial associations. The research employs sentiment analysis, a natural language processing technique used to algorithmically determine the emotional tone of a text, on a database of geo-located tweets spanning January to December 2020. The findings reveal a shift towards more negative sentiment during the initial year of the pandemic. Moreover, the study uncovers variations in sentiment trends across boroughs and zip codes. Additionally, a zip code-level fixed-effects model demonstrates a statistically significant relationship between sentiment scores and unemployment rates. In summary, this article makes a two-fold contribution: firstly, it adds a spatial lens to the scholarly debate regarding the use of Twitter data as an indicator of publicly expressed sentiment; secondly, it provides empirical evidence on the spatial interconnectedness of sentiment, health (hospitalization), and socioeconomic factors (unemployment). Overall, this research sheds light on the nuanced relationship between sentiment and space during the COVID-19 pandemic in NYC.

Twitter (X) is one of the most frequently used social media platforms for people to freely express their opinions, including their perceptions of government performance during flood disasters. Among them, the handling of flood disasters in the Jabodetabek region is a highly discussed topic that causes widespread public reaction. This study aims to classify public sentiment using IndoBERT and a hybrid IndoBERT + CNN-LSTM model. A dataset of 3,894 Indonesian-language tweets was collected, pre-processed, and labelled. The sentiment classification was evaluated using 10-fold cross-validation with accuracy, precision, recall, and F1-score as performance metrics. IndoBERT achieved an accuracy of 91.76% and an F1-score of 90.66%, while the IndoBERT + CNN-LSTM model showed better performance with 94.92% accuracy and a 95.41% F1-score. Although this study used raw tweet locations without sentiment labels for geospatial mapping, the results show a significant improvement in sentiment classification from combining semantic and sequential modelling. For future research, the integration of sentiment data into spatial visualization is recommended to provide deeper insights into regional public opinion.

In the context of rapid digitalization and the post-pandemic recovery of the tourism sector, user-generated content (UGC) on digital platforms such as TripAdvisor has emerged as a critical source of information for understanding tourist behavior, satisfaction, and destination image formation. As tourists increasingly rely on peer reviews to plan their trips, analyzing this content offers valuable insights into how destinations are perceived globally. Chachapoyas, located in the Amazon region of northern Peru, represents an emerging tourist destination with notable cultural, archaeological, and natural attractions. Despite its growing visibility in national and international tourism circuits, empirical studies analyzing tourist perceptions of Chachapoyas through online content remain limited. This study aims to evaluate the destination image of Chachapoyas by examining the main attributes emphasized by tourists in their reviews posted on TripAdvisor. A total of 644 reviews published between 2018 and 2023 were systematically collected and analyzed using text mining and sentiment analysis techniques. The reviews were categorized into thematic dimensions such as lodging, restaurant services, transportation, major attractions, and overall visitor experience. The analysis focused on the frequency of key terms, the polarity of sentiments expressed, and the narrative structures used by tourists to describe their stay. The findings reveal that the most positively valued categories include “infrastructure”, “atmosphere of the place”, and especially “culture, history, and art”. Tourists consistently expressed positive sentiments regarding the preservation of cultural heritage, the authenticity of local traditions, and the welcoming attitude of residents. Conversely, negative comments were largely related to service inconsistencies, transportation challenges, and infrastructure limitations. The results emphasize the centrality of cultural heritage in shaping the visitor experience in Chachapoyas and highlight the increasing influence of online reviews in constructing and disseminating destination image. This research contributes methodologically by applying advanced content analysis tools and offers practical insights for tourism stakeholders in emerging destinations.

Sentiment analysis methods are rapidly being adopted by the field of Urban Design and Planning, for the crowdsourced evaluation of urban environments. However, most models used within this domain are able to identify positive or negative sentiment associated with a textual appraisal as a whole, without inferring information about specific urban aspects contained within it, or the sentiment associated with them. While Aspect Based Sentiment Analysis (ABSA) is becoming increasingly popular, most existing ABSA models are trained on non-urban themes such as restaurants, electronics, consumer goods and the like. This body of research develops an ABSA model capable of extracting urban aspects contained within geo-located textual urban appraisals, along with corresponding aspect sentiment classification. We annotate a dataset of 2500 crowdsourced reviews of public parks, and train a Bidirectional Encoder Representations from Transformers (BERT) model with Local Context Focus (LCF) on this data. Our model achieves significant improvement in prediction accuracy on urban reviews, for both Aspect Term Extraction (ATE) and Aspect Sentiment Classification (ASC) tasks. For demonstrative analysis, positive and negative urban aspects across Boston are spatially visualized. We hope that this model is useful for designers and planners for fine-grained urban sentiment evaluation.

In unraveling the profound connections between humans and place, the traditional concept of the sense of place takes on new dimensions in the digital era. This study contributes to a nuanced understanding by integrating digital and physical spaces within the context of information and communication technology (ICT). Beginning with a review of historical changes and debates surrounding the sense of place, the research establishes a foundation for understanding the evolving relationship with the place. Building on this, the study explores the intricate interplay between digital media and place, revealing how advancements in digital technology shape perceptions of the sense of place. Beyond analysis, the study introduces a three-dimensional framework for the sense of place (i.e., physical sense of place, digital sense of place, and hybrid sense of place), recognizing the dynamic relationship between individuals and their environment, incorporating the digital dimension. Firmly grounded in the perspective of relationships, this framework captures multifaceted connections individuals establish with both physical and digital spaces. Finally, the research explores practical applications of this reconceptualized sense of place. This research deepens the current understanding of the complex dynamics in constructing places in contemporary society, where digital and physical realms intertwine. This research serves as a crucial steppingstone for comprehending the evolving dynamics of the sense of place in the digital era, presenting a refined framework that captures the complex relationships between individuals, technology, and the places they inhabit.

This essay reflects on the role of place for humanities practices and contributes to emerging discussions on infrastructure for the humanities and socio-material conditions of scholarly knowledge production. I provide a theoretical framework for studying venues for humanities work drawing on the phenomenological approach to the concepts of place and space, the pedagogical perspective on learning spaces in higher education, and epistemological studies of scientific places. Next, I analyse the landscape for the reconfiguration of humanities venues and present arguments for engaging with space by referring to the functioning of digital humanities. This essay shows that place is an extremely important resource, seeing as it is endowed with the power to drive new practices, institutionalize a community, and consolidate a discipline. Therefore, humanists should reflect critically on the ‘architecture of the humanities’ and engage in making their own spaces that determine practices, communication, and well-being.

Tibetan Buddhism, as an indigenous religion, has a significant and far-reaching influence in the Tibetan areas of China. This study, focusing on Lhasa, explores the integration of Tibetan Buddhist spiritual perceptions within urban spaces. Employing a novel approach that combines street view data and deep learning technology, the research aims to identify and map the spatial distribution of Tibetan Buddhist spiritual sites against the backdrop of the urban landscape. Our analysis reveals a notable concentration of these spiritual places near urban architectural and cultural heritage areas, highlighting the profound connection between residents’ cultural life and spiritual practices. Despite challenges posed by modern urbanisation, these spiritual sites demonstrate resilience and adaptability, continuing to serve as cultural and spiritual pillars of the Tibetan Buddhist community. This study contributes to the fields of urban planning, religious studies, and digital humanities by demonstrating the potential of technology in examining the impact of urban development on cultural and religious landscapes. The research underscores the importance of protecting and integrating spaces of spiritual perception in urban development planning. It shows that safeguarding these spaces is crucial not only for cultural heritage preservation but also for achieving sustainable urban development and social harmony. This study opens new avenues for interdisciplinary research, advocating for a deeper understanding of the dynamic relationship between urban development and spiritual spaces from psychological, sociological, and environmental science perspectives. As urban landscapes evolve, the study emphasises the need to maintain a balance between material sustainability and cultural and spiritual richness in urban planning.

This paper systematically reviews the theoretical evolution and research progress of "sense of place"from the perspective of cultural geography, exploring its core connotations, multidimensional structure, and practical value. As a product of human-environment interaction, sense of place encompasses three core dimensions: place identity (cognition of distinctiveness), place attachment (emotional bond), and place dependence (functional connection). Its dynamic and polymorphic nature is highlighted by differences in disciplinary perspectives and among various groups. International research, with an earlier start, features a mature theoretical framework, emphasizes interdisciplinary integration and mixed methodologies (combining qualitative and quantitative approaches), and focuses on the differentiated experiences of groups such as immigrants, residents, and students. It has also expanded into areas like risk perception and historical heritage. Domestic research in China is primarily led by tourism geography, centering on case studies of scenic areas like Jiuzhaigou and Suzhou Gardens. It examines the influence of natural landscapes and place identity on tourism support but exhibits limitations such as insufficient theoretical originality, a narrow range of research subjects, and over-reliance on quantitative methods. Future research needs to strengthen interdisciplinary dialogue, integrating theories from sociology and anthropology to deepen the analysis of cultural embeddedness; innovate methodologies (e.g., big data, ethnography) to capture the impact of virtual spaces in the digital era; extend studies to emerging groups like transnational migrants and urban floating populations; and, based on China's urban-rural dynamics and traditional cultural context, construct localized theoretical frameworks to promote harmonious human-environment relationships and sustainable development.