Hasil untuk "Geography. Anthropology. Recreation"

Dongyi Lv, Qiuyu Ding, Heng-Da Xu et al.

Generative recommendation with large language models (LLMs) reframes prediction as sequence generation, yet existing LLM-based recommenders remain limited in leveraging geographic signals that are crucial in mobility and local-services scenarios. Here, we present Reasoning Over Space (ROS), a framework that utilizes geography as a vital decision variable within the reasoning process. ROS introduces a Hierarchical Spatial Semantic ID (SID) that discretizes coarse-to-fine locality and POI semantics into compositional tokens, and endows LLM with a three-stage Mobility Chain-of-Thought (CoT) paradigm that models user personality, constructs an intent-aligned candidate space, and performs locality informed pruning. We further align the model with real world geography via spatial-guided Reinforcement Learning (RL). Experiments on three widely used location-based social network (LBSN) datasets show that ROS achieves over 10% relative gains in hit rate over strongest LLM-based baselines and improves cross-city transfer, despite using a smaller backbone model.

Shuyao Wu, Delong Li, Zhonghao Zhang

Urban parks play a vital role in delivering various essential ecosystem services that significantly contribute to the well-being of urban populations. However, there is quite a limited understanding of how people value these ecosystem services differently. Here, we investigated the relationships among nine ecosystem service demands in urban parks across China using a large-scale survey with 20,075 responses and a point-allotment experiment. We found particularly high preferences for air purification and recreation services at the expense of other services among urban residents in China. These preferences were further reflected in three distinct demand bundles: air purification-dominated, recreation-dominated, and balanced demands. Each bundle delineated a typical group of people with different representative characteristics. Socio-economic and environmental factors, such as environmental interest and vegetation coverage, were found to significantly influence the trade-off intensity among service demands. These results underscore the necessity for tailored urban park designs that address diverse service demands with the aim of enhancing the quality of urban life in China and beyond sustainably.

Pietro Capovilla

We present an explicit construction of closed oriented aspherical smooth 4-manifolds with $χ= σ= n$ for every positive integer $n$. This proves a conjecture of Edmonds by providing a closed oriented aspherical 4-manifold with Euler characteristic 1, and it shows that the real analogue of the Bogomolov-Miyaoka-Yau inequality fails for aspherical 4-manifolds. By the Hitchin-Thorpe inequality, these manifolds do not admit Einstein metrics. As a further consequence of our construction, we show that every closed aspherical 3-manifold with amenable fundamental group is virtually the $π_1$-injective boundary of an aspherical 4-manifold with vanishing Euler characteristic and vanishing simplicial volume, thereby answering questions of Edmonds and of Löh-Moraschini-Raptis up to finite covers.

Jon Wang, Meng Lu

The use of geospatially dependent information, which has been stipulated as a law in geography, to model geographic patterns forms the cornerstone of geostatistics, and has been inherited in many data science based techniques as well, such as statistical learning algorithms. Still, we observe hesitations in interpreting geographic dependency scientifically as a property in geography, since interpretations of such dependency are subject to model choice with different hypotheses of trends and stationarity. Rather than questioning what can be considered as trends or why it is non-stationary, in this work, we share and consolidate a view that the properties of geographic dependency, being it trending or stationary, are essentially variations can be explained further by unobserved or unknown predictors, and not intrinsic to geographic space. Particularly, geoinformation dependency properties are in fact a projection of high dimensional feature space formed by all potential predictors into the lower dimension of geographic space, where geographic coordinates are equivalent to other predictors for modelling geographic patterns. This work brings together different aspects of geographic dependency, including similarity and heterogeneity, under a coherent framework, and aligns with the understanding of modelling in high dimensional feature space with different modelling concept including the classical geostatistics, Gaussian Process Regression and popular data science based spatial modelling techniques.

Nadia Boutaleb

Kensuke Ohtake

This paper studies spatial patterns formed by proximate population migration driven by real wage gradients and other idiosyncratic factors. The model consists of a tractable core-periphery model incorporating a quasi-linear log utility function and an advection-diffusion equation that expresses population migration. It is found that diffusion stabilizes a homogeneous stationary solution when transport costs are sufficiently low, and it also inhibits the monotonic facilitation of agglomeration caused by lower transport costs in some cases. When the homogeneous stationary solution is unstable, numerical simulations show spatial patterns with multiple urban areas. Insights into the relation between agglomeration and control parameters (transport costs and preference for variety of consumers) gained from the large-time behavior of solutions confirm the validity of the analysis of linearized equations.

Bruno Buongiorno Nardelli, Daniele Iudicone

Revealing the ongoing changes in ocean dynamics and their impact on marine ecosystems requires the joint analysis of multiple variables. Yet, global observational records only cover a few decades, posing a challenge in the separation of climatic trends from internal dynamical modes. Here, we apply an empirical stochastic model to identify the emergent patterns of trends in six fundamental components of upper ocean physics. We analyze a data-driven reconstruction of the ocean state covering the 1993-2018 period. We found that including temporal derivatives into the state vector enhances the description of the ocean's dynamical system. Once Pacific oscillations are properly accounted for, averaged surface warming appears >60% faster, and a deep reshaping of the seascape is revealed. A clustering of the trend patterns identifies the main factors that drive observed trends in chlorophyll-a concentration. This data-driven approach opens new perspectives in empirical climate modelling.

Luwei Zeng, T. Donna Chen, John S. Miller et al.

Due to accelerated sea level rise and climate change, the transportation system is increasingly affected by recurrent flooding coastal regions, yet the cumulative travel disruption effects are not well understood. In Part 1 of this study, the accessibility impacts of recurrent flooding on the auto mode were examined. In this paper (Part 2 of the study), the impact of recurrent flooding on transit service accessibility was quantified with the aid of spatially and temporally disaggregated crowdsourced flood incident data from WAZE. A fixed route transit network is built for five time of day periods for 710 traffic analysis zones (TAZs), to capture the spatial and temporal variation of transit accessibility reduction due to recurrent flooding. Results show that the greatest transit accessibility reduction occurs during the morning peak hour, with individual TAZ transit accessibility reduction ranging from 0 to 88.2% for work trips (with an average of 6.4%) and ranging from 0 to 99.9% for non-work trips (with an average of 3.7%). Furthermore, social vulnerability analysis indicates that TAZs with a greater share of people with higher vulnerability in transportation and socioeconomic status are more likely to experience recurrent flooding-induced transit accessibility reduction. Results from this study reinforce the notion that transportation impacts under recurrent flooding are not uniformly experienced throughout a region, and this spatial and temporal variation translates to different impacts borne by various population groups. Disaggregate impact analysis like this study can support transportation engineers and planners to prioritize resources to ensure equitable transit accessibility under increasing climate disruptions.

Cheng-Hong Yang, Chih-Hsien Wu, Kuei-Hau Luo et al.

Air pollution has become a major global threat to human health. Urbanization and industrialization over the past few decades have increased the air pollution. Plausible connections have been made between air pollutants and dementia. This study used machine learning algorithms (k-nearest neighbors, random forest, gradient-boosted decision trees, eXtreme gradient boosting, and CatBoost) to investigate the association between cognitive impairment and air pollution. Data from the Taiwan Biobank and 75 air-pollution-monitoring stations in Taiwan were analyzed to determine individual levels of exposure to air pollutants. The pollutants examined were particulate matter with a diameter of ≤ 2.5 μm (PM2.5), nitrogen dioxide, nitric oxide, carbon monoxide, and ozone. The results revealed that the most strongly correlated with cognitive impairment were ozone, PM2.5, and carbon monoxide levels with adjustment of educational level, age, and household income. The model based on these factors achieved accuracy as high as 0.97 for detecting cognitive impairment, indicating a positive association between air pollutions and cognitive impairment.

Marcos Plischuk, Rocío García Mancuso, Bárbara Desántolo

mohammed nazhan mahdi

Shevelev Sergey, Mikhaylov Pavel, Vorobeva Irina et al.

The work is based on materials from 15 sample plots laid out in mixed spruce stands of the Yenisei Ridge, with a relative completeness in the range of 1.62-1.27, belonging to the green moss group of forest types. Based on the data obtained, the features of changes in spruce species heights and their interdependence with other taxation characteristics were established. The results of data processing indicate the need to develop a set of taxation standards for the Yenisei Ridge region.

Mingming Cheng, D. Edwards, S. Darcy et al.

Joshua W Morse, T. Gladkikh, Diana M. Hackenburg et al.

The COVID-19 pandemic has rapidly modified Earth’s social-ecological systems in many ways; here we study its impacts on human-nature interactions. We conducted an online survey focused on peoples’ relationships with the non-human world during the pandemic and received valid responses from 3,204 adult residents of the state of Vermont (U.S.A.). We analyzed reported changes in outdoor activities and the values associated with human-nature relationships across geographic areas and demographic characteristics. We find that participation increased on average for some activities (foraging, gardening, hiking, jogging, photography and other art, relaxing alone, walking, and watching wildlife), and decreased for others (camping, relaxing with others). The values respondents ranked as more important during the pandemic factored into two groups, which we label as “Nurture and Recreation values” and “Inspiration and Nourishment values.” Using multinomial logistic regression, we found that respondents’ preferences for changes in activity engagement and value factors are statistically associated with some demographic characteristics, including geography, gender, income, and employment status during the pandemic. Our results suggest that nature may play an important role in coping during times of crisis, but that the specific interactions and associated values that people perceive as most important may vary between populations. Our findings emphasize for both emergency and natural resources planning the importance of understanding variation in how and why people interact with and benefit from nature during crises.

Yunseon Choe, W. Lee, K. Sim

Understanding visitors’ movements is crucial to achieving the goals of sustainable destination management while dealing with the environmental, social, and cultural impact of tourism. This study examines the movement patterns of visitors within Taeanhaean National Park (TNP) by adopting a destination measurement approach from a longitudinal perspective. The spatial distribution of the visitors’ activities and movement patterns was obtained by using mobile positioning data (MPD) and we applied the theoretical concept of tourism destinations, which considers geographical, temporal, and compositional dimensions. Given the destination attributes and conservation values, the MPD analysis proved suitable as an aid for park managers to allocate resources efficiently and define the characteristics of park and recreation facilities. This analysis has extended our knowledge of visitors’ patterns at a large marine national park by increasing consistency and high resolution of real-time spatio-temporal data for longer periods and better representing the study population. The study results will allow park managers to implement destination management planning focused on influencing spatial distributions of visitors’ movements in a specific environment.

Mihail Arabadji, R. Inanc Baykur

We prove that any finitely presented group can be realized as the fundamental group of a spin Lefschetz fibration over the 2-sphere. We moreover show that any admissible lattice point in the symplectic geography plane below the Noether line can be realized by a simply-connected spin Lefschetz fibration.

Isabella Smith, Eleanor Velasquez, P. Norman et al.

Although the popularity of protected areas for recreation has been increasing, short term changes in visitation occurred during the COVID-19 pandemic. To examine how volunteer geographic information data can be used to monitor such often rapid changes in visitation across multiple locations, data from online fitness platforms for mountain biking (Trailforks) and remote area hiking (Wikiloc) were analysed before (2019) and during (2020–2021) the COVID-19 pandemic for 40 protected areas in Queensland, Australia. Mountain biking was popular with a total of 93,311 routes on Trailforks, with 26,936 routes in 2019, increasing to 37,406 in 2020, and then decreasing to 28,969 in 2021. Approximately 66% of all the routes were from just three urban protected areas out of the 12 with route data. There were 4367 routes for remote area hiking on Wikiloc across 36 protected areas, which increased slightly from 1081 in 2019, to 1421 in 2020 and to 1865 in 2021. Across 18 factors, distance from urban areas and networks of mountain biking trails best predicted popularity for mountain biking based on Generalised Linear Models. In contrast, average slope and large networks of hiking trails best predicted hiking, with similar results for each year. The two sources of online data were correlated with trail counter data, although not consistently. The results highlight how external factors affect visitation, but also how the same types of protected areas remained popular, and that the impacts of COVID-19 pandemic on visitation in South-East Queensland protected areas was less dramatic than for other regions. This study further highlights how volunteered geographic information can be used to assess the popularity of protected areas, including in rapidly changing conditions. Management implications Rapid changes in visitation can be challenging to monitor and manage, as occurred with the COVID-19 pandemic. The impacts of the COVID-19 pandemic on mountain biking and hiking and factors predicting protected area popularity were examined across different parks. Visitation increased at different stages of the pandemic, with mountain bikers’ preferring urban parks with networks of mountain bike trails while some hikers preferred more remote large parks. Managers can expand on traditional methods of visitor monitoring by using volunteered geographic information to monitor rapid and longer-term trends of visitation to protected areas.

L. Fischer, J. Honold, Alexandra Botzat et al.

I. Altman, M. M. Chemers

Jinfeng Wang, Robert Haining, Tonglin Zhang et al.

Spatial statistics is dominated by spatial autocorrelation (SAC) based Kriging and BHM, and spatial local heterogeneity based hotspots and geographical regression methods, appraised as the first and second laws of Geography (Tobler 1970; Goodchild 2004), respectively. Spatial stratified heterogeneity (SSH), the phenomena of a partition that within strata is more similar than between strata, examples are climate zones and landuse classes and remote sensing classification, is prevalent in geography and understood since ancient Greek, is surprisingly neglected in Spatial Statistics, probably due to the existence of hundreds of classification algorithms. In this article, we go beyond the classifications and disclose that SSH is the sources of sample bias, statistic bias, modelling confounding and misleading CI, and recommend robust solutions to overcome the negativity. In the meantime, we elaborate four benefits from SSH: creating identical PDF or equivalent to random sampling in stratum; the spatial pattern in strata, the borders between strata as a specific information for nonlinear causation; and general interaction by overlaying two spatial patterns. We developed the equation of SSH and discuss its context. The comprehensive investigation formulates the statistics for SSH, presenting a new principle and toolbox in spatial statistics.