Points Of Interest Research Articles

Numerical prediction of ground-borne vibrations due to continuous vibratory driving of circular closed-ended piles

Various types of pile driving work may induce high-intensity ground-borne vibrations. Predicting vibration intensity before adopting mitigation measures is vital for minimizing the impact of vibration on nearby structures and occupants. Vibratory pile driving is a commonly applied foundation construction method. However, numerical simulation models for ground vibrations during a complete process of vibratory driving have rarely been studied. This study introduces an axisymmetric finite element model that utilizes the arbitrary Lagrangian-Eulerian technique to simulate the continuous vibratory driving of a circular closed-ended pile penetrating from the ground surface to a target depth. The model validity was confirmed by assessing the calculated ground vibrations against the findings documented in earlier research. The results showed that the critical penetration depth of piles, at which the maximum peak particle velocity (PPV) occurs, varied with the radial distance and depth of points of interest, contradicting a common preconception. Moreover, the maximum PPV did not always occur on the ground surface across all radial distances. Parametric analysis revealed that an increase in the soil cohesion strength, pile diameter, or soil-pile friction, or a decrease in the driving frequency or soil damping ratio would increase ground vibrations due to vibratory pile driving.

User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks

With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users’ geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches.

HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalized POI recommendation

The presence of user-generated ratings has dramatically facilitated the development of recommendation systems to aid users in discovering relevant and personalized points of interest (POI). It is worth mentioning that users’ choices and preferences are not static but rather dynamic, reflecting the ever-changing nature of human experiences and influences. Furthermore, the utilization of social influence and geographical proximity of users is still insufficient to capture the homophily effect within networks. In this paper, an interesting Hybrid Gate-based Graph Convolutional Network (HyGate-GCN) combining with feature vectors embedding and interaction, where a modified gated-GCN is proposed for personalized recommendations by adequately employing the behavior of users’ check-ins, temporal properties of users’ decisions, social properties of users, as well as the user/POI profile information data. Specifically, a novel POI graph reflecting the geographical proximity is first established to describe the behavior of users’ check-ins and, at the same time, an improved overlap ratio about POIs is employed to effectively describe temporal properties of users’ decisions. Then, an attention mechanism is developed to encode feature vectors of both the users and POIs, with the objective of assigning higher importance to features that are deemed relevant. Furthermore, a temporal Kalman filter dynamically estimating ratings is developed to exploit the information about the evolving preferences of users over time. Finally, a modified gated-GCN model with merging and refining gates is constructed to effectively acquire the homophily phenomenon in both trust network graphs and spatial adjacency matrix graphs of users and POIs respectively. Experimental results provide evidence of the effectiveness of our approach in improving accuracy and personalization.

Spatiotemporal context transition model based on graph convolutional network and its implementation

PurposeThe purpose of this paper is to present a method that addresses the data sparsity problem in points of interest (POI) recommendation by introducing spatiotemporal context features based on location-based social network (LBSN) data. The objective is to improve the accuracy and effectiveness of POI recommendations by considering both spatial and temporal aspects.Design/methodology/approachTo achieve this, the paper introduces a model that integrates the spatiotemporal context of POI records and spatiotemporal transition learning. The model uses graph convolutional embedding to embed spatiotemporal context information into feature vectors. Additionally, a recurrent neural network is used to represent the transitions of spatiotemporal context, effectively capturing the user’s spatiotemporal context and its changing trends. The proposed method combines long-term user preferences modeling with spatiotemporal context modeling to achieve POI recommendations based on a joint representation and transition of spatiotemporal context.FindingsExperimental results demonstrate that the proposed method outperforms existing methods. By incorporating spatiotemporal context features, the approach addresses the issue of incomplete modeling of spatiotemporal context features in POI recommendations. This leads to improved recommendation accuracy and alleviation of the data sparsity problem.Practical implicationsThe research has practical implications for enhancing the recommendation systems used in various location-based applications. By incorporating spatiotemporal context, the proposed method can provide more relevant and personalized recommendations, improving the user experience and satisfaction.Originality/valueThe paper’s contribution lies in the incorporation of spatiotemporal context features into POI records, considering the joint representation and transition of spatiotemporal context. This novel approach fills the gap left by existing methods that typically separate spatial and temporal modeling. The research provides valuable insights into improving the effectiveness of POI recommendation systems by leveraging spatiotemporal information.

Understanding urban traffic flows in response to COVID-19 pandemic with emerging urban big data in Glasgow

Urban traffic analysis has played an important role in urban development, providing insights for urban planning, traffic management, and resource allocation. Meanwhile, the global pandemic of COVID-19 has significantly changed people's travel behaviour in urban areas. This research uses the spatial Durbin model to understand the relationship between traffic flows, urban infrastructure, and socio-demographic indicators before, during, and after pandemic periods. We include factors such as road characteristics, socio-demographics, surrounding built environments (land use and nearby points of interest), and the emerging urban big data source of Google Street View images to understand their influences on time series traffic flows. Taking the city of Glasgow as the case study, we have found that areas with more young and white dwellers are associated with more traffic flows, while natural green spaces are associated with fewer traffic flows. Major roads between cities and towns also show heavier traffic flows. Besides, the application of Google Street View images in this research has revealed the heterogeneous effects of green space on urban traffic flows, as the magnitudes of their effects vary by distance. We also detect that the spatial dependence between adjacent neighbourhoods among the traffic flows and associated urban parameters is variable during the four COVID-19 periods. With the influence of COVID-19, there has been a significant decrease in long-distance travel. The noticeable change in travel behaviour presents a valuable opportunity to encourage active travel in the near future.

Assistance in Picking Up and Delivering Objects for Individuals with Reduced Mobility Using the TIAGo Robot

Individuals with reduced mobility, including the growing elderly demographic and those with spinal cord injuries, often face significant challenges in daily activities, leading to a dependence on assistance. To enhance their independence, we propose a robotic system that facilitates greater autonomy. Our approach involves a functional assistive robotic implementation for picking, placing, and delivering containers using the TIAGo mobile manipulator robot. We developed software and routines for detecting containers marked with an ArUco code and manipulating them using the MoveIt library. Subsequently, the robot navigates to specific points of interest within a room to deliver the container to the user or another designated location. This assistance task is commanded through a user interface based on a web application that can be accessed from the personal phones of patients. The functionality of the system was validated through testing. Additionally, a series of user trials were conducted, yielding positive feedback on the performance and the demonstration. Insights gained from user feedback will be incorporated into future improvements to the system.

Examining the spatial dependency between shared accommodations and points of interest

This study proposes a three-step analytical framework to examine the proximity between Airbnb listings and points of interest (POIs), and to evaluate the spatial dependence between them across dimensions. Grounded in Tobler's first law (TFL) of geography, this framework integrates standard deviational ellipse, average nearest neighbor, and geographically weighted regression methods. The framework is validated using data from 14 U.S. cities of varying sizes, encompassing 122,005 Airbnb listings and 113,169 POIs. Although the results reveal a notable proximity of Airbnb listings to POIs, the degree of spatial dependency varies across cities and property types. Notably, several frequently overlooked POIs, such as schools, post boxes, bakeries, and bicycle rental stations, demonstrate significant relationships with Airbnb listings. This research advances the literature on shared accommodations by elucidating the spatial relationship between Airbnb and POIs, introducing new location factors, and providing a robust analytical framework. The findings provide valuable practical implications for enhancing business and management strategies for Airbnb stakeholders, while balancing the growth of the shared accommodation sector against its effects on local communities.

Carsharing adoption dynamics considering service type and area expansions with insights from a Montreal case study

Carsharing operators (CSOs) are adapting their service over time to meet changing demands and grow their market share. Service areas are enlarged and, in some cities, “dual-mode settings” evolve, incorporating free-floating carsharing (FFcs) as a new service alongside existing station-based carsharing (SBcs). This paper proposes a methodology to discuss adoption dynamics in such a context, specifically focusing on the impact of existing services and service extensions on the adoption of the new service. We propose a framework, comprising of two parts: a potential market assessment and an adoption model. The potential market assessment focuses on establishing the relationships between the local population, carsharing memberships and Points of Interest (POIs) within the given service area. The adoption model then describes the likelihood of consumers adopting the FFcs service. By combining these two models, the effects of service extensions can be assessed. We evaluate the framework using a nearly six year dataset from Communauto, Montreal. The first 35 months of data are set as training data, while the subsequent 33 months are used for validation of predictive performance. Results demonstrate that the proposed model accurately predicts adoption dynamics. Prior experience of SBcs and initial information spread are found to be key parameters for demand prediction determining early adoption peaks and, due to follower effects, also impact long-term demand. Additionally, we quantify the importance of covering residential areas and points of interests in the service area, highlighting the synergy effects of service area expansions.

Scientific plurality and amyotrophic lateral sclerosis (ALS): A philosophical and historical perspective on Charcot’s texts

ABSTRACT The history of amyotrophic lateral sclerosis (ALS)—also known as Charcot’s disease, Lou Gehrig’s disease, and motor neuron disease (MND)—freezes the texts of the scientist and physician Jean-Martin Charcot in a hagiographic narrative describing a brilliant discovery, based on the anatomo-clinical method. This narrative is often used by biologists and physicians as a reference point. This article shows that the use of the hagiographic register faces limitations. In particular, it obscures points of interest from Charcot’s texts on ALS, such as the epistemological and ontological implications of scientific plurality in medicine. Although Charcot recognized the importance of scientific plurality in medicine, he prioritized the approaches and conferred the most important epistemic authority on clinical and pathological observations. In his view, animal modeling remains secondary to the understanding of disease. The concept of ALS and its diagnostic operability are the result of symptoms and lesions. By studying the past, we can highlight the specific features of the present. Today, although the ALS concept retains its diagnostic and clinical relevance, it is increasingly called into question in etiological and mechanistic research. Despite these differences, Charcot’s reflections are a reminder of the importance of theoretical thinking on scientific plurality, all the more so today in the context of ALS research, in which combining different approaches is increasingly valued to understand the phenotypic and genetic heterogeneity of ALS.

POI recommendation by deep neural matrix factorization integrated attention-aware meta-paths

With the continuous accumulation of massive amounts of mobile data, point-of-interest (POI) recommendation has become a vital task for location-based social networks. Deep neural networks or matrix factorization (MF) alone are challenging to effectively learn user–POI interaction functions. Moreover, the user–POI interaction matrix is sparse, and the heterogeneous characteristics of auxiliary information are underused. Therefore, we propose an innovative POI recommendation method that integrates attention-aware meta-paths based on deep neural matrix factorization (DNMF-AM). First, we develop a multi-relational heterogeneous information network of “user–POI–geographic region–POI category.” Multiple-weighted isomorphic information networks based on meta-paths are employed to obtain node-embedding vectors across different relationships. Attention networks are employed to aggregate node vectors across various relationships and serve as auxiliary information to mitigate the challenges of data sparsity. Subsequently, the internal embedding vectors of the users and POIs are extracted using feature embedding based on the user–POI interaction matrix. Second, these vectors are integrated with the embedding vectors obtained by aggregating the attention networks. Third, deep neural matrix factorization is used to learn linear and nonlinear user–POI interactions to mitigate the implicit feedback problem. This outcome is achieved using generalized matrix factorization and convolution-constrained multi-head self-attention mechanism deep neural networks. Extensive experiments conducted on two real-world datasets demonstrate that the DNMF-AM outperforms the optimal baseline NeuMF-CAA by 4.24% and 5.04% in terms of HR@10 and NDCG@10, respectively.

How do driving factors affect the diurnal variation of land surface temperature across different urban functional blocks? A case study of Xi'an, China

A comprehensive and in-depth understanding of the formation mechanisms of the urban thermal environment is the basis for thermal environment regulation, however, there is insufficient knowledge regarding how driving factors influence daytime and nighttime land surface temperature (LST) within urban functional blocks (UFBs). We selected Xi'an, China as a case study, integrating remote sensing data including ECOSTRESS, Landsat-8, and Gaofen-1, along with geographic data including road network, areas of interest, points of interest, building footprint, and mobile phone signaling. It divided 10 types of UFBs, inverted daytime and nighttime LST, calculated 5 types of driving factors, and finally analyzed the contribution and marginal effects of driving factors on day-night LST using boosted regression tree. The results showed that LST and its driving factors differed significantly in different times and UFBs. Industrial blocks and urban villages had higher LST in the daytime, while residential blocks, commercial blocks, and public service blocks had higher LST in nighttime. Industrial blocks were the dominant blocks that drove the overall LST up during the day, while residential blocks were the dominant blocks at night. Location distance (-) and population density (+) affected LST in all UFBs during day and night, NDVI (-), building density (+), and floor area ratio (-) were key factors for most UFBs during daytime, and NDVI (+), surface albedo (-), and point density of interest (+) were key factors during nighttime. Most of the driving factors had significant influence thresholds, but there were small differences across UFBs. This paper aims to elucidate the mechanisms by which driving factors influence urban LST during day and night across different UFBs, thereby providing new support for more targeted thermal environment regulation and diurnal trade-offs at the block scale.

Optimizing Spatial Distribution of Retail Shops against Neighborhood Tree Canopy Shade Using Big Data Extracted from Streetscape

The visibility of retail frontages is critical for earning profits from spontaneous traffic visits to retail shops located along a street. The urban tree canopy plays a crucial role in enhancing the street-side environment, yet more is not always better when considering the placement of retail shops behind trees with big canopies. Related evidence in the literature is rarely provided, and an unclear relationship has been reported to exist between the number of shops for a specific retail type and the quantified ratio of the canopy shade in a street view. In this study, both big data crawling and deep learning were employed to unravel this relationship for retail shops in Changchun, Northeast China. The entire study area was divided into 6037 grid cells with a side length of ~0.6 km, wherein the number of shops of five retail types (food and beverage, shopping, life services, entertainment, and hotel) were quantified by computer counting their points of interest (POIs). The canopy shade was evaluated using the green view index (GVI) quantified through the ratio of canopy pixels divided by all the pixels in a street view image obtained through an online map API. A neighboring road network was categorized into four classes: class I road density mainly reduced the number of retail shops, and the road densities of classes III and IV accounted for more retail shops. The relationship between the number of retail shops and the GVI could be fitted with positive skewness curves for class II roads, where the critical peak of the GVI was estimated to be about 3.27%. The optimization scheme indicated that more retail shops should be placed along class I and II roads. In conclusion, more retail shops for food and beverage, shopping, and life services should be placed in the landscape neighboring big canopies.

Predicting long-term air pollutant concentrations through deep learning-based integration of heterogeneous urban data

Accurate prediction of air pollutant concentrations, specifically concerning inhalable particulate matter such as PM2.5, is crucial for proactive measures to safeguard the well-being of urban residents. This paper focuses on addressing the perceptible latency effect for long-term PM2.5 predictions produced by existing statistical models. We emphasize the importance of numerical computations in capturing substantial changes, and enhance prediction accuracy by integrating them with high-dimensional, diverse urban data. Specifically, our approach collects data from a global-to-meso-scale atmospheric dispersion model named System for Integrated modeLling of Atmospheric coMposition (SILAM), along with numerical weather forecasts, traffic congestion measurement, meteorological factors and static sources (road network and points of interest). We find that existing deep learning models are prone to overfitting when applied to complex datasets, primarily due to their uniform treatment of diverse data types as time series without adapting to the specific characteristics of each data type. To counter this, we propose a simple yet transferable deep learning architecture, focusing on the proper use of various data types. Additionally, our comparative analysis, through a case study in Shenzhen, China, shows our model not only enhances SILAM dispersion accuracy for 24h-ahead PM2.5 forecasts by a significant 30.3%, but also mitigates the noticeable latency effect of existing models by 19.5%. Finally, an ablation study further validates the importance of each data source and module of our approach.

Introducing PLEMS: the application of a low-cost, portable monitoring system in environmental walks.

The application of innovative systems using low-cost microcontrollers in human biometeorology studies is a promising alternative to conventional monitoring devices, which are usually cost-intensive and provide measurements at specific points, as in stationary meteorological stations. A Portable Low-cost Environmental Monitoring System (PLEMS) aimed at the pedestrian scale is introduced. The backpack-type equipment consists of a microcontroller with attached sensors that assess environmental conditions in a broad sense, integrating measurements of air quality, lighting and noise levels alongside variables typically measured at meteorological stations. The application of the system took place in altogether 12 environmental walks carried out with questionnaire-surveys with concurrent environmental monitoring with the PLEMS in Curitiba, Brazil, a subtropical location characterized by a Cfb climate type. Results allowed us to test the equipment and method of data gathering within a limited period (approximately 50 min) and for a short walking circuit of 800 m. The equipment was successfully able to capture even slightest differences in environmental conditions among points of interest, whereas subjective responses (n= 3843 responses to a total of 11 questions) showed consistency with measured data. From a multi-domain perspective, relevant insights could be obtained for the measured variables.

Economic Criticism

Abstract This year’s review of work in economic criticism focuses on three points of interest that reflect the year’s work in economic theory, providing an overview of some of the broader critical enquiries preoccupying the field of economic writing in 2023 and early 2024. The first is the intersection of animal studies and Marxism, exemplified by Leigh Claire La Berge’s Marx for Cats: A Radical Bestiary. The second key aspect of economic writing this year is the overlapping concerns of women, money, and property, represented here in Lana L. Dalley’s edited collection, Women’s Economic Writing in the Nineteenth Century and Jill Rappoport’s Imagining Women’s Property in Victorian Fiction. The third focus lands on speculative time and futurity within the domain of American literature, capitalism, and finance, as Paul Crosthwaite’s Speculative Time: American Literature in an Age of Crisis astutely captures.

Design of an orchid conservation trail in Ixtaczoquitlán, Veracruz, Mexico

Objective: To raise awareness about the ecological importance of orchids and to promote the species commercialized by a company focused on their production through the design of a trail. Design/Methodology/Approach: A diagnosis of the plot was carried out to identify its characteristics and points of interest. A trail was designed to connect the most outstanding points. Native trees, which are the habitat of orchids, were subsequently planted to establish the outdoor collections. Results: The appeal of the place and the established phorophytes were taken into consideration to design the trail. Likewise, recommendations were made on what species of orchids should be planted in each station along the trail. Study Limitations/Implications: Evaluating visitor experience is pending. The trail lacks infrastructure for people with disabilities. The trail is located in an area subjected to anthropization and it will take several years before orchids can be established on the trees planted in July 2023. Findings/Conclusions: Trails are a useful tool to promote the conservation of native orchids; however, under anthropization conditions, it should be subjected to a systematized implementation to provide the climate and phorophyte conditions necessary for the survival of orchids.

Accessible Comics for Blind and Low Vision Readers: An Emerging Journey

In Spring 2021, there was an interdisciplinary collaboration comprising two international events. Scholars and artists in comic studies, disability studies, and accessibility services met to explore the intersection of comics making and nonvisual information accessibility. The article showcases lessons shared by event participants and offers ongoing directions for interdisciplinary research and practice. The goal is to promote new approaches to information engagement for publishers, designers, and consumers alike. Points of interest on the journey towards creating accessible comics include considerations for community engagement, equity in design thinking, and seeding collaborations with key stakeholders.

Uncovering driving factors and spatiotemporal patterns of urban passenger car CO2 emissions: A case study in Hangzhou, China

While passenger cars offer the convenience of door-to-door travel, they are responsible for a substantial share of urban traffic-related carbon emissions, accounting for 44% of the global transport sector's emissions in 2022. Existing studies have not adequately identified the driving factors and spatiotemporal patterns of Passenger car CO2 Emissions (PCEs). To fill this gap, a comprehensive analytical framework for analysing PCE, encompassing several key components, has been developed. First, the PCE is estimated at a fine-grained level by utilising million-scale GPS trajectory data. Secondly, road network and urban functional features are extracted based on multi-source data, examining their nonlinear impacts on PCE. The driving factors of PCE during peak and off-peak periods are identified based on interpretable factor analysis. Finally, latent space clustering techniques are applied to reveal the spatiotemporal patterns of PCE, verified by significance tests. The effectiveness of this framework is demonstrated via a case study in Hangzhou, China. Notably, during peak hours, the driving factors of PCE are the road network's closeness centrality, service-related Points of Interest (POI) density, and trunk road density. In addition, five distinctive PCE patterns are identified. These findings facilitate the development of targeted and differentiated passenger car traffic management strategies, promoting carbon emission reduction in the transport sector.

Unveiling the daily rhythm of urban space: Exploring the influence of built environment on spatiotemporal mobility patterns

Urban mobility research is crucial due to the complex and dynamic nature of cities. Traditional zoning methods often overlook the temporal dynamics of urban space usage. This paper aims to investigate the daily rhythm of urban space usage and how built environmental features affect spatiotemporal mobility. The methodology integrates various urban data sources, including street and building vectors and points of interest (POI). In Ankara, Türkiye, temporal usage types were identified using principle component analysis and k-means algorithms via Google Maps location-based service data. Multinomial logistic regression model was used to examine the impact of built-environment factors on daily mobility patterns. Results reveal distinct daily usage rhythms, categorizing busy areas into morning, noon, early evening, late evening, diurnal, and nocturnal zones. The study finds: (1) temporal usage is closely associated with spatial characteristics and resident income; (2) work-related activities drive morning mobility, while nighttime mobility relates to residence, income, and accessibility; and (3) population density alone does not guarantee continuous activities; diverse POIs alongside high density are crucial. The identified relationship between mobility measures and urban built environment indicators provides a comprehensive understanding of spatiotemporal variations, offering insights for critical policy evaluations and proposals for the future of urban areas.

Procedural content generation in pervasive games: state of affairs, mistakes, and successes

PurposeIn ubiquitous games, sensor data and the player’s dynamic profile personalize the experience, adapting scenarios and difficulty to the player’s context. However, manually creating custom virtual environments becomes impractical due to the scalability required in the real world, where gameplay depends on the player’s environment and various points of interest. Procedural content generation (PCG) naturally emerges as an automated solution. This study aims to review the state of the art among pervasive games that use procedural generation techniques.Design/methodology/approachThe authors performed a systematic review of the literature on PCG techniques in ubiquitous games. The research methodology included initial database searches, forward snowballing and backward snowballing.FindingsThe authors selected 40 articles published from 2008 to 2022 after analyzing 1,017 PCG articles. Many of these paper use dynamic difficulty generation methods that affect game customization, balance and playability. The majority of titles are in the area of education and require rigorous evaluation. Some articles mention the use of PCG but provide limited details of algorithms or processes.Research limitations/implicationsThe low presence of recent articles stands out, which can lead to two conclusions: poor use of indexing of articles in the area or low amount of research over these years. One of the search bases did not enter due to the number of keywords used.Practical implicationsThe paper is a guide for researchers in the area of pervasive games who are interested in using PCG techniques in their games.Originality/valueThis approach revealed articles combining PCG and ubiquitous games, with no previous systematic reviews at this intersection.