多源数据融合下的澳门特种兵旅游空间热点识别

建设现代旅游业体系是促进旅游业高质量发展行稳致远、推进旅游强国建设的重要支撑。基于公平性、安全性、创新性、持续性和融合性等维度，构建现代旅游业体系发展水平的测度指标体系，采用空间自相关、马尔科夫链、面板回归模型等分析方法，揭示2013—2022年中国现代旅游业体系发展水平的时空分异特征、演化规律和影响因素，并提出建设和完善中国现代旅游业体系的推进路径。结果表明：（1）2013—2022年中国现代旅游业体系整体发展水平得到较大提升，总体表现为东部、中部、东北、西部依次递减的水平差异。（2）中国现代旅游业体系发展水平存在空间异质性，呈现从东南沿西北依次递减的分布格局；全局经历了不断增强的空间集聚过程，在局部表现为明显的“高值与高值、低值与低值”的集聚特征；现代旅游业体系发展水平等级的空间转移受到领域类型的影响，中低水平和较高水平、高水平的省（自治区、直辖市）均表现出“遇强趋强、遇弱仍强”的演进规律。（3）经济发展实力、交通基础设施、政府政策调控、数字技术水平、对外开放程度是中国现代旅游业体系发展水平的重要影响因素。（4）建设中国现代旅游业体系需要通过发挥发达城市的辐射作用、优化快进慢游的交通网络、强化关键领域的政策支持、推进数字技术的创新应用、扩大对外开放的合作交流等措施，助力旅游业更好地成为新兴战略性支柱产业和具有显著时代特征的民生产业、幸福产业。

旅游资源空间分布的研究可以为地区旅游资源的优化升级提供实践的价值依据.文章运用ArcGIS空间分析核密度估计法、最邻近指数、局部自相关方法、SPSS相关性分析了新疆62处红色旅游资源空间分布特征及影响因素.研究结果显示：新疆红色旅游资源空间结构在整体上呈现出“小集聚，大分散”的形态，具有明显的“三核三带”的特征，在空间上呈点状集聚分布特征，呈现出“东北—西南”的冷热点区分布格局.该格局的形成主要受历史文化活动、交通条件、社会因素和经济发展水平的影响.未来，在文旅融合发展的浪潮下，应进一步推动新疆旅游高质量、快速度发展，积极践行“旅游兴疆”，兵地联手发挥红色旅游资源优势，进而推动新疆全域旅游发展.

川滇黔长征国家文化公园作为红军长征途中极具里程碑意义的标志性空间载体，兼具高辨识度文旅品牌价值与红色文化传承功能，是创新红旅融合发展机制的重要范例。在虚实交互场景下，亟需借助旅游空间错位深挖供需关系内核，破解区域发展失衡的困境，助推实现文化赓续传承、产业提质增效与跨区域协同发展。采用2013—2022年川滇黔长征国家文化公园A级景区和网络关注度数据，运用景区丰度指数和空间错位模型，探究市（自治州）—县（区、市）双元尺度下旅游空间错位的时空演化规律及驱动机制。研究表明：（1）在时空分布上，旅游景区丰度与网络关注度均呈“S”型上升，景区分布东密西疏，由多中心向网络化发展，形成黔东南—安顺—贵阳、遵义—泸州—宜宾两大核心；高网络关注度由离散转向集聚，形成甘孜藏族自治州、阿坝藏族羌族自治州和遵义市等集聚区。（2）在错位关系上，市（自治州）尺度以中错位为主，呈西北—东南的负—正—负分异；县（区、市）尺度以低错位、正错位为主。（3）不同尺度空间错位具有相对性与尺度敏感性，69%的市（自治州）与县（区、市）错位方向一致，31%的市（自治州）与县（区、市）则存在尺度分异。州正县负区域需提升资源整合度与形象识别度，打造红农旅融合品牌；州负县正区域则要资源联动、客源互通、区域协同，推动红色文旅与乡村振兴高质量发展。

文章选取甘孜、淄博和哈尔滨等30个网红旅游地，借助互联网多源数据，深入探究其生命周期演化特征与规律，进而提出了网红旅游地从“爆红”到“长红”的系统性调控策略。研究发现：①中国网红旅游地热度存在明显的层级差异，头部效应和季节性明显，空间上总体呈东南高、西北低的分布格局。②网红旅游地生命周期可以划分为稳定型、爆发型、山谷型、山峰型和特殊型5种类型，且不同类型的网红旅游地生命周期演化特征存在明显差异。据此，研究提出“树立服务型管理思维、打造地方特色文旅IP、创新多媒介形象传播、加强数字化城市建设”4个方面的网红旅游地系统性调控策略，以期为网红旅游地避免步入衰退期或走向断崖式下跌，实现健康可持续发展提供决策参考。

本文以开封市为案例，基于抖音用户评论，分析游客对“网红城市”的目的地感知及“长红”路径。研究发现，开封旅游的核心竞争力在于高性价比与沉浸式文化体验，通过整合全域资源形成文化IP集群效应，但交通基础设施滞后与景区承载能力不足仍是制约其发展的关键短板。开封市应在保持文化创新优势的同时，提升治理能力，实现从“网红”到“长红”的可持续发展。本文为同类型城市破解“网红效应”短暂性困境、实现文旅产业高质量发展提供了理论与实践参考。

在探讨文化和旅游融合发展理论框架的基础上，以全国31个省市区2013—2017年文化产业和旅游产业发展的相关面板数据为基础，运用耦合协调度模型，分析中国文化和旅游融合发展的耦合协调度，并探讨其空间分异规律，基于地理探测器数据分析，结果显示：①2012—2017年我国文化与旅游耦合协调度呈现出“波动上升”的时序特征；②文化与旅游协调发展是公共资本、社会资本投入、科技手段使用、社会消费支撑等因素共同驱动的结果。

城市旅游休闲热点区是城市发展和转型升级的新增长极。本研究以中国旅游休闲城市武汉市为例，依托POI数据和GIS空间分析技术，分析武汉市旅游休闲热点区的空间特征和业态集聚特征，进而探讨其驱动机制。研究发现：①武汉市旅游休闲热点区呈现出由中心城区集聚向远城区蔓延、单核主导向多核引领的空间演变规律。②武汉市旅游休闲热点区规模与业态密度显著提升，业态结构由单一业态引领向多业态均衡发展演变。③武汉市旅游休闲热点区的形成和演变是人地关系相互作用的结果，区位条件、旅游资源空间整合、旧城改造政策落地、旅游重点项目的打造、旅游发展政策、城市发展导向与产业转型升级以及交通功能完善等动力均合力推动了热点区的形成和演变。

基于小红书“打卡”笔记数据，将“网红打卡地”划分为商业购物、餐饮美食、旅游名片、休闲娱乐、特色商铺、文艺场馆和历史建筑7大类，运用平均最邻近、核密度法和冷热点分析等ArcGIS空间分析法探究乌鲁木齐市“网红打卡地”空间特征，并结合地理探测器探索其影响因素。结果表明：（1） 乌鲁木齐市“网红打卡地”总体呈“南密北疏，内密外疏”的空间结构特征；空间分布类型为集聚型；空间密度呈“单核多点”分布形态。（2） 乌鲁木齐市“网红打卡地”热点区的空间分布特征与城市发展格局大致相符，包含中山路热点区、红山路热点区、友好路热点区、铁路局热点区、红光山热点区和喀什路热点区6个主要热点区域。（3） 乌鲁木齐市“网红打卡地”受经济规模、消费需求和交通布局因素作用力较强，各指标对“网红打卡地”空间分布的解释力差异明显。

以粤港澳大湾区为研究区域，选取2007和2018年为时间断面，运用引力模型，对粤港澳大湾区铁路交通可达性和旅游经济联系强度进行了测算和分析，结论如下：①粤港澳大湾区交通可达性呈现出以东莞、深圳、香港为中心，向东西两侧逐渐减低圈层式空间分布格局，空间差异特征显著。②粤港澳大湾区旅游经济联系强度空间网络化趋势显著，从以广州—佛山、珠海—澳门、深圳—香港为核心的点轴空间形态，到扇形网络化特征，中心辐射作用加强，边缘化减弱。珠江两岸城市的旅游经济联系强度不断增强，但与同一岸邻近城市的联系强度要远高于与对岸城市的联系强度。③粤港澳大湾区旅游经济联系总量呈现出中间强四周弱的形态，核心边缘特征、空间对称特征、极化特征较为显著。④交通可达性与旅游经济联系整体耦合协调发展水平一般，高铁交通的改善对推动旅游经济的发展的作用还未完全释放出来。⑤类型划分上，深圳和香港为可达性最好，且旅游经济联系最高的地区；而江门、惠州、肇庆三市为交通可达性较差，旅游经济联系强度较弱的地区。

中国澳门地区作为高密度旅游城市，公共如厕设施布局的合理性对居民生活便利、游客出行体验及城市公共服务品质具有重要影响。本文基于中国澳门地区独立式公共厕所、道路网络、景点、公园、公交站点及居民商业活动等多源空间数据，运用GIS空间分析方法，从空间分布、服务覆盖、步行可达性和重点区域供需匹配等方面对公共如厕设施布局进行评价。研究依据《城市环境卫生设施规划标准》及相关步行可达性研究，结合中国澳门地区高密度建成环境特征，设定不同尺度的服务半径与时间阈值。结果表明中国澳门地区公共如厕设施总体呈半岛集聚、离岛薄弱的分布特征，核心旅游区和高强度活动区局部仍存在服务盲区、供需错位和配置不均衡等问题。借鉴同类高密度城市公共卫生设施配置经验，并结合中国澳门地区实际空间需求，本文从设施增补、布局均衡、分类配置及运营管理等方面提出优化路径，以期为高密度旅游城市公共如厕设施布局优化提供参考。

This study employs the theory of the “six elements” of tourism and utilizes spatial analysis methods, including nearest neighbor index, kernel density analysis, bivariate spatial autocorrelation, and Ripley’s K -function, to examine the spatial distribution and correlation characteristics of point of interest data related to tourism elements in the urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang of China based on data collected in April 2024. In addition, we explore the influencing factors using a geographical detector. The results show the following. (1) The spatial distribution characteristics of each tourism element exhibit significant concentration, with the degree of spatial agglomeration ranking from high to low as follows: “food”>“shopping”>“accommodation”>“transportation”>“entertainment”>“tourism”. (2) Each tourism element demonstrates weak spatial continuity, resulting in a distribution pattern characterized by “one core, one axis, and multiple centers”. At the county level, the spatial correlation among tourism elements is generally weak; however, a strong correlation exists between the “transportation” element and other elements, whereas the “tourism” element exhibits weak correlations, indicating a need for optimization in the spatial distribution of tourism elements. (3) The characteristic value of the overall spatial agglomeration scale of the “six elements” of tourism is 33.83 km. Among the different elements, the “tourism” factor shows the largest spatial agglomeration scale eigenvalue (42.95 km), whereas the “accommodation” factor has the smallest (18.48 km). (4) The influence of the interaction between each factor on the spatial pattern of tourism elements is significantly greater than that of any single factor. This research highlights the effects of multi-dimensional factors, including economic development level, infrastructure, and population on the spatial pattern of tourism elements, with GDP, night light index, number of A-level scenic spots, population density, and the proportion of the tertiary industry having the most significant effects.

Urban recreation space is an important organic part of a city,and its development is a reflection of not only the urban socioeconomic culture,but also the evolution of urban spatial structure.Based on Baidu POI data,using Ripley’s K function,spatial autocorrelation,and the nearest neighbor hierarchical clustering analysis methods,this paper studied the spatial characteristics of public recreational facilities in Urumqi City,Xinjiang,China,summarized the patterns of urban recreation space,and explored the influencing factors of urban recreation space.The results show as follows: (1) The spatial distribution of the leisure facilities in Urumqi shows a marked centrality and agglomeration. (2) The hot spots of the six kinds of recreational facilities show different tendency of agglomeration.The entertainment facilities,shopping facilities and sport facilities tend to cluster near important transport hubs and commercial centers.The hotspots of catering facilities show obvious population orientation.The hot spots of tourism facilities are all located near hot scenic spots.The hot spots of cultural facilities are located in the mixed ethnic areas. (3) The recreation space pattern in Urumqi is displaying a “layer+ sector + group” mode.The city center is the planar comprehensive leisure center,and the periphery of the city center is the fanshaped leisure facilities area with low density,grouped multifunctional leisure area and grouped catering leisure area.The suburbs have few leisure facilities but are grouped tourism leisure areas. (4) It is proved that the recreation space of Urumqi is positively correlated with the spatial distribution of population.

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Historic cities facing overtourism require evidence-based visitor dispersal to balance tourism growth with sustainable destination development. Focusing on Kyoto City, Japan, this study proposes an integrated analytical framework that combines objective tourism supply (POIs) and tourism demand (GPS trajectories) with visitor subjective perceptions from online tourist reviews to identify Alternative Attractions for Visitor Dispersal and evaluate their Composite Attractiveness. We (i) map supply–demand patterns to distinguish Hotspot Attractions versus Alternative Attractions (high-supply/low-demand); (ii) quantify Subjective Perceptions via an Aspect-Based Sentiment Analysis pipeline (ABSA) across landscape, experience, service, and transportation; and (iii) embed these sentiments into an improved Two-Step Floating Catchment Area (2SFCA) method that reframes accessibility from “reaching places” to “attaining high-quality experiences.” Kyoto exhibits a marked supply–demand mismatch, with Alternative Attractions concentrated around Fushimi, Sakyo (Nanzen-ji area), and outer Arashiyama. Negative perceptions (e.g., crowding) diminish the attractiveness of central Hotspot Attractions, whereas positive perceptions (e.g., pleasant atmosphere) enhance the attractiveness of peripheral Alternative Attractions, offsetting locational disadvantages. This framework offers not only data-driven support for Kyoto but also a replicable, experience-oriented model for sustainable tourism spatial management in other similarly challenged destinations.

According to the Japan Tourism Agency, in 2024, visitor numbers are estimated to exceed previous records, reaching 35 million people. The issues of overtourism and overcrowding in popular tourist areas pose challenges to the sustainable development of the industry. This paper takes Kyoto City in Japan as a case study, using POI (Points of Interest) data and GPS data as the data sources. It combines the G1 sequential relationship analysis method with the entropy weight method to determine the influence weights of various types of POIs on tourism activities. By employing kernel density estimation and weighted overlay analysis methods, the study integrates tourism-related resources and existing tourism demand to identify tourism hotspots and potential areas. The research findings indicate that traditional popular areas in Kyoto, such as the Gion and Kiyomizu-dera Temple areas, have high tourist density. Additionally, new areas with development potential, such as the Momoyama and Daigo-ji Temple areas, have been identified. These research results can provide data support and a basis for future tourism promotion and planning.

With the rapid development of the tourism industry, the temporal and spatial distribution of tourism activities is uneven. Traditional methods for identifying tourist hotspots suffer from delayed data acquisition and low recognition accuracy. A framework for identifying tourist hotspots based on spatiotemporal big data is constructed, integrating multi-source data; an improved density clustering algorithm is proposed; a warning model based on hotspot features is designed. The improved algorithm misjudges only 1 location, with 92% accuracy and a 20% speed increase. Its timeliness and accuracy outperform traditional methods. This study provides effective decision support for tourism management departments.

Selecting Yuntai Mountain Scenic Area as the case study, this research integrates multi-source heterogeneous data, including social media texts and tourist GNSS trajectories. By comprehensively applying the Analytic Hierarchy Process (AHP) and Direction-Constrained DBSCAN (DC-DBSCAN), it analyzes the characteristics of tourists' online attention and physical spatiotemporal behaviors, constructing a virtual-real coupling response mechanism for the scenic area. The results show: (1) The online attention of Yuntai Mountain Scenic Area exhibits an alternating "stable-pulse" fluctuation pattern, significantly influenced by statutory holidays, with tourists' focus concentrated on core attractions and considerable negative emotional feedback regarding physical exertion. (2) The physical flow of tourists demonstrates a temporal rhythm of "early entry, late exit, and single-peak aggregation." Spatially, a core skeleton road network primarily consisting of Fenghuang Ridge and Hongshi Gorge is formed. The duration of stay presents a bimodal clustering characteristic of short-term check-ins and in-depth experiences. (3) There is a significant spatiotemporal divergence between online attention and offline physical flow. Temporally, the rise in online heat precedes the peak of physical tourist flow by 1-2 weeks; spatially, Hongshi Gorge shows a virtual-real balance, Zhuyu Peak exhibits online overheating, while experiential attractions like Fenghuang Ridge and Xiaozhai Valley present significant offline overheating.

Understanding the tourism resource network attention is crucial for promoting sustainable tourism development. This study utilized multi-source data to assess tourism resource network attention in Western Hunan, with GIS spatial analysis and the Geodetector method applied to identify spatial patterns and influencing factors. The results indicate a distinct “dual-core” spatial clustering in network attention, with natural landscape resources centralized in Zhangjiajie and cultural landscape resources in Xiangxi Prefecture. Recreational tourism resources exhibit a similar clustering pattern around these primary and secondary centers. The factors and intensities influencing network attention differ by tourism resource type. For overall tourism resources, natural landscapes, and cultural landscapes, tourist attractions rating (X11) and attraction clustering degree (X12) are the primary drivers, with the strongest impact on natural landscapes (q = 0.648, 0.373), followed by overall resources (q = 0.361, 0.216) and cultural landscapes (q = 0.311, 0.206). In contrast, recreational resources are most influenced by nearby attractions and tourism service capacity (q(X12) = 0.743, q(X15) = 0.620), alongside notable effects from regional factors related to economic development, industrial structure, and tourism development (X1–X9). The interaction between inherent tourism resource characteristics (X10–X15) and regional environmental factors (X1–X9) enhances the driving effect on tourism resource network attention. These findings inform differentiated, resource-specific tourism planning strategies for sustainable development in Western Hunan, promoting balanced regional growth and optimized resource management through a data-driven approach.

ABSTRACT Urban recreationists interact at various locations at various times, resulting in potential conflicts. For sustainable urban tourism, this study extends the resident–tourist comparison into that of local, domestic, and overseas tourists and applies social network analysis to compare the spatiotemporal patterns of these three types of tourists. Results show that locals are accustomed to visiting natural parks and hutongs on weekends, while out-of-town tourists might tour popular sites in the city center during holidays, where domestic and overseas tourists may still encounter each other in hot spots. Only the flow of overseas tourists contradicts the distance attenuation effect.

Discovering the Representative places (RPs) of a city will benefit the understanding of local culture and help to improve life experiences. Previous studies have been limited in regard to the large-scale spatial identification of RPs due to the vagueness of boundaries and the lack of appropriate data sources and efficient tools. Furthermore, human perception of these places remains unclear. To address this gap, this research adopts a novel approach to identify and evaluate the RPs of a city from the perspective of human perception. Our methodology involves the utilization of deep learning systems, text semantic analysis, and other techniques to integrate multi-source data, including points of interest (POIs), street view images, and social media data. Taking Nanjing, China, as a case, we identified 192 RPs and their perceptual ranges (PRRPs). The results show the following: (1) Comparing RPs to non-RPs, RPs show higher average scores across four perceptual dimensions (positive indicators): Beautiful (7.11% higher), Lively (34.23% higher), Safety (28.42% higher), and Wealthy (28.26% higher). Conversely, RPs exhibit lower average scores in two perceptual dimensions (negative indicators): Boring (79.04% lower) and Depressing (20.35% lower). (2) Across various perceptual dimensions, RPs have utilized 15.13% of the land area to effectively cover approximately 50% of human perceptual hotspots and cold spots. (3) The RPs exhibit significant variations across different types, levels, and human preferences. These results demonstrate the positive perceived effects that RPs have, providing valuable insights to support urban management, the transformation of the built environment, and the promotion of sustainable urban development, and provide guidance for urban planners and designers to make improvements in urban design and planning to make these sites more attractive.

The main technology for realizing digitalization in smart tourism is intelligent detection technology, and its advantages are mainly reflected in two aspects: tourism services and management models. Through the three-tier technical architecture of "multi-source data acquisition - edge computing processing - intelligent decision-making application", this paper uses UAV tilt photography system to achieve centimeter level 3D modeling, and combines algorithms and sensor networks to build a "spatial integration" monitoring system, which provides important theoretical support and practical path for building a new intelligent tourism paradigm of "global perception - intelligent decision-making - immersive experience".

… tourists' perceived emotions to refine the built environment elements in traditional villages[19… the region[20], and predicting the tourists' tourism demand for the space, the Detecting …

Spatial characteristics of leisure tourism resources are essential for human life, the urban economy, and tourism planning. This article presents a novel framework to explore these characteristics based on multisource data, such as points of interest, OpenStreetMap roads, Sentinel-2 multispectral instrument images, and other data, and proposes a new tourism area identification method by integrating the attractiveness of attractions with term frequency–inverse document frequency. The roles of the influencing factors were measured by using the geodetector and related statistical analyses. The results showed that the resources were centered on Jiaozhou Bay, and their axial direction was “northeast to southwest.” The distribution of the overall resources was characterized by “one cluster with multiple core points,” and different types of resources had different aggregation distributions. The recreational recreation and cultural leisure zones were more likely to be distributed in and near the center of each district, and their numbers were high, while the shopping leisure and natural recreation (NR) zones were the opposite. The distribution of each type of resource was the result of a combination of factors working together, except for NR resources, which were mainly influenced by natural factors, while others were mainly affected by socioeconomic factors. The study findings are instructive for tourism planning.