Points Of Interest Research Articles

An Ecological and Distributional Resume of the Liverworts and Mosses of India

A general account of the ecology and distribution of Indian Liverworts atd Mosses is given. Points of interest as to the species which have originated from India, species which are in common with the various Continents and Insular areas etc. are given.

Distributional, Resume of the Maritime Strand Flora of India

A distributional resume of the maritime strand flora on a regional basis conforming to coastal physiographic sub-divisions of India is given. Points of interest on regional distribution, growth forms and other relevant observation of phytogeographical interest are given.

Optimizing the Layout of Service Facilities for Older People Based on POI Data and Machine Learning: Guangzhou City as an Example

Population aging is a global issue. China is facing the same challenge, especially in its megacities, with more than 10 million permanent urban residents. These densely populated cities urgently need the scientific planning and optimization of the layout of service facilities for older people. Taking Guangzhou, a megacity in China, as an example, this study uses point-of-interest (POI) data and the ID3 machine learning decision tree algorithm to train a site selection model for service facilities for older people. The model can help to select appropriate locations for new service facilities for older people more scientifically and accurately, and it can provide targeted suggestions to optimize the layout of the service facilities for older people in Guangzhou. First, Guangzhou city is divided into 29,793 grids of 500 m × 500 m based on the range of activities of older people, and 985 grids are found to contain service facilities for older people. Then, the POI data of the grid are fed into the ID3 algorithm for training to obtain a prediction model for the selection of sites for service facilities for older people. The effective prediction rate of the model reaches 87.54%. Then, we apply the site selection model to predict the whole city of Guangzhou, and 4534 grids are suitable for service facilities for older people. In addition, considering the degree of concentration of the elderly population in each street, we further filter out 1066 priority grids as the final site selection. Finally, taking into account the situation of the streets in different districts, we propose several strategies to optimize the layout of the construction of service facilities for older people.

Housing prices and points of interest in three Polish cities

AbstractThis paper shows the usefulness of open source points-of-interest (POI) data for understanding the causes of volatility in residential property prices. Points of interest are unique locations or features cartographically mapped in space and explicitly connected to various aspects of human life. This point number, and density, primarily reflect a city’s spatial structure. They could therefore indicate a higher quality of life in a given urban zone, resulting in higher demand for housing in the area and, consequently, higher housing prices. This study was conducted in three Polish cities: Warsaw, Poznań and Olsztyn. Our research also attempted to establish which POI categories constitute stimulants and which are destimulants in the housing market. To determine the significance of POI in the price formation process, we used the quantile regression model and its spatial version, apart from classical regression models. The results of our research show the importance of POIs for the formation of housing prices. Individual POI categories are of minor importance, while the simultaneous presence of many different POIs definitely has a positive impact on housing prices.

The Island of Cyprus through the Eyes of Eighteenth Century Travelers: A Deep Map

AbstractTraveling and recording the adventures lived abroad in travelogues was a common practice among European aristocrats in the eighteenth century. The travel journals are filled with descriptions of the places they visited, but also with characterizations of their impressions and emotions evoked by historical landmarks. This paper aims to explore the sensations of northern and central European travelers to investigate the identity and sense of place of Cyprus, as experienced by the travelers, through deep mapping. The exploration of impressions focuses on seven categories: places, points of interest, people, hospitality, accessibility, landscape, and food/drinks. The impressions expressed by words in the travelogues are coded in a five-point scale to be able to obtain statistics from the geodatabase created in GIS. The result is a bunch of maps that show the diverse level of pleasantness according to the various places and historical landmarks visited. Moreover, this paper demonstrates the importance of combining quantitative and qualitative data through digital tools in archaeological and historical research.

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

The urban agglomeration on the north slope of the Tianshan Mountains is a pivotal place in Western China; it is essential for the economic growth of Xinjiang and acts as a critical bridge between China’s interior and the Asia–Europe continent. Due to unique natural conditions, the local population distribution exhibits distinct regional characteristics. This study employs the spatial lag model (SLM) from conventional spatial analysis and the random forest model (RFM) from contemporary machine learning techniques. It integrates traditional geographic data, including land cover data and nighttime light data, with geographical big data, such as POI (points of interest) and OSM (OpenStreetMap), to build a comprehensive indicator database. Subsequently, it simulates the spatial population distribution within the urban agglomeration on the northern slopes of the Tianshan Mountains in 2020. The accuracy of the results is then compared and assessed against the accuracy of other available population raster datasets, and the spatial distribution pattern in 2020 is analyzed. The findings reveal the following: (1) The result of SLM, combined with multi-source data, predicts the population distribution as a relatively uniform and nearly circular structure, with minimal spatial differentiation. (2) The result of RFM, employing multi-source data, better captures the spatial population distribution, resulting in irregular boundaries that are indicative of strong spatial heterogeneity. (3) Both models demonstrate superior accuracy in simulating population distribution. The spatial lag model’s accuracy surpasses that of the GHS and GPW datasets, albeit still trailing behind WorldPop and LandScan. Meanwhile, the random forest model significantly outperforms the four aforementioned population raster datasets. (4) The population spatial pattern in the urban agglomeration on the north slope of the Tianshan Mountains predominantly consists of four distinct circles, illustrating a “one axis, one center, and multiple focal points” distribution characteristic. Combining the random forest model with geographic big data for spatialized population simulation offers robust scientific validity and practicality. It holds potential for broader application within the urban agglomeration on the Tianshan Mountains and across Xinjiang. This study can offer insights for studies on regional population spatial distributions and inform sustainable development strategies for cities and their populations.

MvStHgL: Multi-view Hypergraph Learning with Spatial-temporal Periodic Interests for Next POI Recommendation

Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia, and it remains challenging due to highly dynamic and personalized interactions in user movements. Currently, state-of-the-art works develop various graph- and sequential-based learning methods to model user-POI interactions and transition regularities. However, there are still two significant shortcomings in these works: (1) Ignoring personalized spatial- and temporal-aspect interactive characteristics capable of exhibiting periodic interests of users; (2) Insufficiently leveraging the sequential patterns of interactions for beyond-pairwise high-order collaborative signals among users’ sequences. To jointly address these challenges, we propose a novel multi-view hypergraph learning with spatial-temporal periodic interests for next POI recommendation (MvStHgL). In the local view, we attempt to learn the POI representation of each interaction via jointing periodic characteristics of spatial and temporal aspects. In the global view, we design a hypergraph by regarding interactive sequences as hyperedges to capture high-order collaborative signals across users, for further POI representations. More specifically, the output of POI representations in the local view is used for the initialized embedding, and the aggregation and propagation in the hypergraph are performed by a novel Node-to-Hypergraph-to-Node scheme. Furthermore, the captured POI embeddings are applied to achieve sequential dependency modeling for next POI prediction. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art models.

Deducing neighborhoods of classes from a fitted model

AbstractIn this article, a new kind of interpretable machine learning method is presented, which can help to understand the partition of the feature space into predicted classes in a classification model using quantile shifts, and this way make the underlying statistical or machine learning model more trustworthy. Basically, real data points (or specific points of interest) are used and the changes of the prediction after slightly raising or decreasing specific features are observed. By comparing the predictions before and after the shifts, under certain conditions the observed changes in the predictions can be interpreted as neighborhoods of the classes with regard to the shifted features. Chord diagrams are used to visualize the observed changes. For illustration, this quantile shift method (QSM) is applied to an artificial example with medical labels and a real data example.

Campus Navigation using Augmented Reality and Virtual Reality

With the rapid advancement of Augmented Reality (AR) and Virtual Reality (VR) technologies, there has been a growing interest in leveraging these immersive technologies for enhancing campus navigation experiences. This paper presents a research study focused on designing and implementing a campus navigation system for the Maharashtra Institute of Technology World Peace University (MIT WPU) using AR/VR technologies. The proposed system aims to provide students, faculty, and visitors with an intuitive and immersive wayfinding experience, facilitating navigation across the expansive campus grounds. The core features of the proposed campus navigation system include real-time location tracking, interactive campus maps, personalized route planning, and integration with campus services and facilities information. The AR component overlays digital navigation cues and points of interest onto the user's physical surroundings, enhancing spatial awareness and providing contextual information. The VR component offers an immersive 3D visualization of the campus environment, allowing users to explore virtual replicas of buildings, landmarks, and amenities. The research methodology involves a multi-stage process, beginning with requirements gathering through stakeholder interviews and user surveys to understand the navigation challenges faced by campus users. Based on the gathered requirements, a prototype AR/VR campus navigation application is developed, leveraging state-of-the-art AR/VR development tools and technologies

Identifying building function using multisource data: A case study of China's three major urban agglomerations

As crucial geographical components shaping urban landscapes, defining the functions of buildings plays one of the pivotal roles in planning urban development and facilitating socio-economic human activities. Existing studies in large-scale mapping mainly ignore the environment around buildings, leading to insufficient meeting the demands of large-scale building-level mapping in certain disciplines. To abridge this gap, this article calculates the geometric characteristics of building footprint, the distances from buildings to adjacent objects, and the kernel densities of Point-of-Interest (POIs) to identify building main functions in three Chinese urban agglomerations (Beijing-Tianjin-Hebei Urban Agglomeration, Yangtze River Delta Urban Agglomeration, and Pearl River Delta Urban Agglomeration). Leveraging XGBoost, our models attained accuracies of 0.936, 0.934, and 0.940 in the three urban agglomerations, accompanied by kappa coefficients of 0.883, 0.868, and 0.891 respectively, suggesting their capability of identifying building functions in large-scale mapping. Furthermore, the conducted experiments on varying feature combinations and transferability highlight the significance of the built environment in enhancing classification accuracy. Additionally, the analysis of the classification results of buildings in the three urban agglomerations shows harmonized development strategies for Yangtze River Delta Urban Agglomeration and Pearl River Delta Urban Agglomeration, while cities within the Beijing-Tianjin-Hebei Urban Agglomeration do not show the same development strategies. All results demonstrate the feasibility of employing multi-source accessible data to efficiently categorize building functions over large areas in a cost-effective manner. This methodology can be replicated in China and even on a larger scale to offer insights into complex urban spaces and to help urban planners provide policy support such as old city renovations and new city planning.

Understanding the opportunity-centric accessibility for public charging infrastructure

In this study, we utilize data from over 28,000 public charging stations (PCSs) and 5.5 million points of interest across twenty U.S. metropolitan areas to underscore the importance of considering the availability of daily activity opportunities when assessing accessibility to PCSs, rather than relying solely on spatial proximity. Specifically, we conduct comprehensive comparisons of opportunity-centric accessibility measures with distance-based measures and perform counterfactual analyses under various PCS deployment strategies. Our findings reveal significant inequalities in PCS access across different neighborhoods under distance-based and opportunity-centric measures. However, a greater disparity exists when considering opportunities, with high-income communities having significantly better access to PCSs. Counterfactual analyses suggest that equitable deployment based on distance measures results in the least equitable outcomes when considering opportunities, primarily due to the existing disparity in opportunity distributions in our cities. Our insights highlight the complexity of locating PCSs and can guide nationwide PCS deployment for long-term societal benefits.

An Android Smart City Traveller App (NaviCity)

Discover the cutting-edge and adaptable "Smart City Traveler App" that elevates the journey of individuals discovering unfamiliar cities. Through the utilization of state-of-the-art technologies, this mobile application expertly evaluates user preferences, time limitations, and curiosities, supplying individuals with custom travel plans and suggestions. With the ultimate purpose of enhancing sightseeing for both new and experienced travelers, the app commences with registration where users are prompted to input crucial information for tailoring their exploration. By leveraging the comprehensive location data collected through the Google Places API, the app guarantees a wealth of information on countless destinations for users to access. The Smart City Traveler App, constructed with a sleek combination of Flutter and Firebase, takes the hassle out of trip planning by aggregating top FourSquare rankings. With an intuitive interface, users can easily peruse and utilize this wealth of information to make informed choices. Not only does the app boast detailed maps pinpointing points of interest, but it also offers up various routes to maximize time efficiency. In the case of longer stays, the app even suggests exciting adventure activities or refreshing water parks, based on highly rated reviews. Incorporating Google Maps, the Smart City Traveler App puts the power in users' hands to confidently explore the city. Transforming city exploration into an unforgettable journey, this app is a must-have for adventurous travelers. Seamlessly blending cutting-edge technology with the thrill of discovery, the Smart City Traveler App is a testament to the endless possibilities of merging travel and technology. With personalized features and efficient optimization, it empowers users to fully indulge in the urban landscape, tailored to their own preferences and constraints.

Saliency-Guided Point Cloud Compression for 3D Live Reconstruction

3D modeling and reconstruction are critical to creating immersive XR experiences, providing realistic virtual environments, objects, and interactions that increase user engagement and enable new forms of content manipulation. Today, 3D data can be easily captured using off-the-shelf, specialized headsets; very often, these tools provide real-time, albeit low-resolution, integration of continuously captured depth maps. This approach is generally suitable for basic AR and MR applications, where users can easily direct their attention to points of interest and benefit from a fully user-centric perspective. However, it proves to be less effective in more complex scenarios such as multi-user telepresence or telerobotics, where real-time transmission of local surroundings to remote users is essential. Two primary questions emerge: (i) what strategies are available for achieving real-time 3D reconstruction in such systems? and (ii) how can the effectiveness of real-time 3D reconstruction methods be assessed? This paper explores various approaches to the challenge of live 3D reconstruction from typical point cloud data. It first introduces some common data flow patterns that characterize virtual reality applications and shows that achieving high-speed data transmission and efficient data compression is critical to maintaining visual continuity and ensuring a satisfactory user experience. The paper thus introduces the concept of saliency-driven compression/reconstruction and compares it with alternative state-of-the-art approaches.

Latent Representation Learning for Geospatial Entities

Representation learning has been instrumental in the success of machine learning, offering compact and performant data representations for diverse downstream tasks. In the spatial domain, it has been pivotal in extracting latent patterns from various data types, including points, polylines, polygons, and networked structures. However, existing approaches often fall short of explicitly capturing both semantic and spatial information, relying on proxies and synthetic features. This paper presents GeoNN, a novel graph neural network-based model designed to learn spatially-aware embeddings for geospatial entities. GeoNN leverages edge features generated from geodesic functions, dynamically selecting relevant features based on relative locations. It introduces both transductive (GeoNN-T) and inductive (GeoNN-I) models, ensuring effective encoding of geospatial features and scalability with entity changes. Extensive experiments demonstrate GeoNN’s effectiveness in location-sensitive superpixel-based graphs and real-world points of interest, outperforming baselines across various evaluation measures.

Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data

This study proposes a data-driven framework for understanding the space-time patterns of exposure and contribution of different activities to traffic congestion in urban road networks by using GPS trajectory and Point-of-Interest (POI) big datasets. Taking taxi trips related to traffic congestion in Wuhan, China as a case study, we first infer the types of individual activities from GPS trajectories and POIs and identify traffic congestion on each road. Then we develop two indicators to measure the congestion exposure of different activity types. Further, we reveal the space-time patterns of activity-related congestion through spatiotemporal analysis of the indicators of traffic congestion associated with different activities. The findings of this study shed light on how different types of activities contribute to the space-time heterogeneity of traffic congestion, and highlight the significance of considering the space-time patterns of congestion related with different activity types in urban transportation management.

New challenges for sustainable urban mobility

Micromobility provide intelligent, fast, and sustainable trips for tourist from accommodation facilities to attraction locations. However, some points of interest may be located away from the accommodation i.e. in a different city of a metropolitan area. In this case, cycling to the attractions may not be possible and multimodal bike and public transport trips can be considered for more sustainable tourism. Therefore, it is essential to identify how adequate a city transport infrastructures and services are for these types of trips. In this paper, we propose a cycling indicator for tourism accessibility with the aim of defining the level of multimodal bike-public transport friendliness through numerical values related to a specific zone in the main city of a metropolitan area. The indicator was applied to the metropolitan area of Bari (Italy) and results showed that the presence of bike lanes and bike-sharing plays an important role.

The metaphor of kitsch in Caragiale's work. A Stormy Night & Company at "Tudor Vianu" Theatre Giurgiu

Kitsch, this aesthetic category developed in the second half of the 20th century, was one of Caragiale's points of interest, describing through it the shabby and ambitious world of a state that wanted at all costs to align itself with its Western European models. Surprised in various forms, kitsch can be found in the substratum of Caragiale's literature, underlined scenically by the performance of A Stormy Night & Company at the ”Tudor Vianu” Theatre in Giurgiu, which serves as an argument. Moreover, kitsch can become an epithet attributed to man, easily found in the theatre proposed for analysis.

A graph-based approach for traffic prediction using similarity and causal relations between nodes

Accurate traffic prediction is crucial for the development of intelligent transportation systems (ITS) and various smart applications. To achieve this, effectively capturing the complex interplay between spatial and temporal dependencies across traffic nodes is essential. However, existing research often focuses solely on fixed spatial dependencies between neighboring nodes, neglecting the potential influence of distant nodes. This paper addresses these limitations by exploring two key types of relationships among traffic nodes. First, we examine indirect relationships based on similarity, where nodes exhibit similar traffic patterns irrespective of their physical proximity. Second, we investigate direct causal relationships, where traffic conditions at one node are directly influenced by other nodes. Based on these findings, we propose a novel approach named TPSC (Traffic Prediction using nodes Similarity and Causal relations). TPSC incorporates a classifier that utilizes the K-Prototype algorithm to group nodes based on their traffic similarity and nearby points of interest (POIs). Separate models are then trained for each cluster. Our model leverages a spatial module that employs graph convolutional network representation learning alongside transfer entropy to capture causal relationships and dynamic spatial dependencies. Additionally, a temporal module is introduced to capture periodic temporal dependencies across three components: recent patterns, daily patterns, and weekly patterns. Extensive experiments are conducted to evaluate proposed model on four large-scale traffic datasets for multiple traffic characteristics including speed, flow, and travel time. The experimental results demonstrate that TPSC surpasses the performance of the compared baselines, highlighting its superior predictive capabilities in traffic prediction.

Investigation of user-product interaction by determining the focal points of visual interest in different types of kitchen furniture: An eye-tracking study

Investigation of user-product interaction by determining the focal points of visual interest in different types of kitchen furniture: An eye-tracking study

Spot scanning imaging calibration method based on deviation model for wafer inspection

The spot scanning defect detection system has good detection performance for wafer defects. However, due to the existence of system assembly errors, the wafer defect inspection system could not scan following the planned trace, which resulted in distortion of the reconstructed image and had a greater impact on defect feature extraction and accurate defect location. The system deviation calibration and adjustment can further facilitate the accuracy of the system position and image reconstruction. To address this issue, the causes and shapes of imaging distortion were analyzed and simulated, and a simple and effective calibration method based on a scanning deviation model for the spot scanning defect detection system was proposed. First, a scanning deviation model was established following the characteristics of the spot scanning system, and different degrees of reconstructed image distortion were simulated and analyzed as the system deviation changes. Then, an 8-inch wafer with a grid was used as the calibrator and scanned using the developed spot scanning system. Serveral grid corners in the reconstructed image are selected and used as the calibration feature points. The constraint function is constructed using the equidistant features between adjacent grid points, and then the quasi-Newton method is used to optimize the parameters of the function to obtain the best deviation estimation of the spot scanning system. To prove the validity of the estimated deviation parameters, another patterned wafer was scanned and the distortion image was used for reconstruction. The experiment demonstrated that an accurate and distortion-free wafer image can be reconstructed using the calibrated deviation parameters, and the root mean square error of the measured distance between two points of interest was only 0.61 pixels, which proved the effectiveness of the proposed calibration method.