Relevant Points Of Interest Research Articles

A Novel Address-Matching Framework Based on Region Proposal

Geocoding is a fundamental component of geographic information science that plays a crucial role in various geographical studies and applications involving text data. Current mainstream geocoding methods fall into two categories: geodesic-grid prediction and address matching. However, the geodesic-grid-prediction method’s localization accuracy is hindered by the density of grid partitioning, struggling to strike a balance between prediction accuracy and grid density. Address-matching methods mainly focus on the semantics of query text. However, they tend to ignore keyword information that can be used to distinguish candidates and introduce potential interference, which reduces matching accuracy. Inspired by the human map-usage process, we propose a two-stage address-matching approach that integrates geodesic-grid prediction and text-matching models. Initially, a multi-level text-classification model is used to generate a retrieval region proposal for an input query text. Subsequently, we search for the most relevant point of interest (POI) within the region-proposal area using a semantics-based text-retrieval model. We evaluated the proposed method using POI data from the Beijing Chaoyang District. The experimental results indicate that the proposed method provides high address-matching accuracy, increasing Recall@1 by 0.55 to 1.56 percentage points and MRR@5 by 0.54 to 1.68 percentage points.

Experiments with RouteNav, A Wayfinding App for Blind Travelers in a Transit Hub.

RouteNav is an iOS app designed to support wayfinding for blind travelers in an indoor/outdoor transit hub. It doesn't rely on external infrastructure (such as BLE beacons); instead, localization is obtained by fusing spatial information from inertial dead reckoning and GPS (when available) via particle filtering. Routes are expressed as sequences of "tiles", where each tile may contain relevant points of interest. Redundant modalities are used to guide users to switching goalposts within tiles. In this paper, we describe the different components of RouteNav, and report on a user study with seven blind participants, who traversed three challenging routes in a transit hub while receiving input from the app.

Locational error in the estimation of regional discrete choice models using distance as a regressor

In many microeconometric studies distance from a relevant point of interest (such as a hospital) is often used as a predictor in a regression framework. Confidentiality rules, often, require to geo-mask spatial micro-data, reducing the quality of such relevant information and distorting inference on models’ parameters. This paper extends previous literature, extending the classical results on the measurement error in a linear regression model to the case of hospital choice, showing that in a discrete choice model the higher is the distortion produced by the geo-masking, the higher will be the downward bias in absolute value toward zero of the coefficient associated to the distance in the models. Monte Carlo simulations allow us to provide evidence of theoretical hypothesis. Results can be used by the data producers to choose the optimal value of the parameters of geo-masking preserving confidentiality, not destroying the statistical information.

The matching process and the implications of the competitive market system

Abstract In the broad field of economic research, the process of resource allocation and the degree of manifestation of the freedom of the competitive market are relevant points of interest in order to achieve in terms of microeconomic performance. Through this work, we have proposed to investigate the extent to which competitive markets tend towards that form of perfection and what are the main challenges and implications for the market freedom. The novelty of this study counts in bringing to the forefront the match-making process, implicitly the study of the match-making market, introduced by the American economist Alvin E. Roth in 2015. In particular, it analyzes a new valence of the competitive market, the operating mechanism being aimed at highlighting a specific terminology, such as linguistic decryption of markets. This paper has extended the concept of matching, by analyzing the ethical issues involved, as well as the way in which we are witnessing a crescendo of concrete examples of the functioning of these markets. At the same time, we used the use of methodological tools specific to the analysis of statistical data on measuring the degree of competitive freedom in the main key sectors in Romania in 2015 and 2019, by applying The Aggregate Index of Competitive Pressure (AICP). The results showed the degree of openness to competition of the main sectors in Romania, as well as the fact that the matching market assumes the effective operation and manifestation of the competitive mechanism.

Cold-start Point-of-interest Recommendation through Crowdsourcing

Recommender system is a popular tool that aims to provide personalized suggestions to user about items, products, services, and so on. Recommender system has effectively been used in online social networks, especially the location-based social networks for providing suggestions for interesting places known as POIs (points-of-interest). Popular recommender systems explore historical data to learn users’ preferences and, subsequently, they recommend locations to an active user. This strategy faces a major problem when a new POI or business evolves in a city. New business has no historical user experience data. Thus, a recommender system fails to gather enough knowledge about the new businesses, resulting in ignoring them during recommendations. This scenario is popularly known as a cold-start POI problem. Users never get recommendations of the new businesses in a city even though they can be relevant to a user. Also, from a business owner’s perspective, such a recommendation strategy does not help its reachability among users. Therefore, it is important for a recommender system to remain updated with new businesses in a city and ensure that all relevant POIs are recommended to a user irrespective of their lifetime. A POI recommendation approach is proposed in this work that can effectively handle the new businesses, or the cold-start POI problem, in a city. We crowdsource descriptions of cold-start POIs from various online social networks. The reviews of users are exploited here to learn the inherent features at the existing POIs and the new crowdsourced POIs. Finally, the proposed approach recommends top- K POIs consisting of the existing and new POIs. We perform experiments on the real-world Yelp dataset, which is one of the largest available data resources containing details on a wide range of businesses, users, and reviews. The proposed approach is compared with four existing POI recommendation approaches. The obtained results show that our approach outperforms others in handling cold-start POIs.

Nearest and farthest spatial skyline queries under multiplicative weighted Euclidean distances

Consider two point sets in the plane, a set of points of interest and a set of query points that is used to establish distance restrictions with respect to the set of points of interest. A nearest/farthest spatial skyline query retrieves the subset of desirable or relevant points of interest, called skyline points, such that no other point of interest is simultaneously closer to/farther from all the query points. The nearest/farthest top-k spatial skylines, are the best k nearest/farthest spatial skylines among the existent ones. All these queries find applications in decision-making support systems, facility location, crisis management and in trips or events planning. To take into account that each point of interest has a different importance, a weight is assigned to each of them and multiplicative weighted Euclidean distances are used. In this paper, we study, for the first time, the nearest and farthest spatial skyline queries when multiplicative weighted Euclidean distances are considered. We prove that most of the properties of the traditional non weighted nearest and farthest spatial skyline queries are no longer true under the weighted Euclidean distance and, consequently, the strategies used for solving non weighted spatial skyline queries are not usable in the weighted case. We present a sequential and a parallel algorithm, to be run on the CPU and on a Graphics Processing Unit, respectively, for solving nearest/farthest weighted spatial skyline queries and to extract the nearest/farthest top-k spatial skylines. We provide the time and space complexity analysis of both algorithms together with their theoretical comparison. We also have developed a simple interface to deal with weighted spatial skyline queries which allows to visualize and store in a file the obtained spatial skylines. Finally, we present and discuss experimental results obtained with the implementation of the proposed sequential and parallel algorithms.

Measurement Errors Arising When Using Distances in Microeconometric Modelling and the Individuals’ Position Is Geo-Masked for Confidentiality

In many microeconometric models we use distances. For instance, in modelling the individual behavior in labor economics or in health studies, the distance from a relevant point of interest (such as a hospital or a workplace) is often used as a predictor in a regression framework. However, in order to preserve confidentiality, spatial micro-data are often geo-masked, thus reducing their quality and dramatically distorting the inferential conclusions. In particular in this case, a measurement error is introduced in the independent variable which negatively affects the properties of the estimators. This paper studies these negative effects, discusses their consequences, and suggests possible interpretations and directions to data producers, end users, and practitioners.

Analysis of errors in histology by root cause analysis: a pilot study.

The study objective is to evaluate critical points in the process of pre-analytical histology in an Anatomic Pathology laboratory. Errors are an integral part of human systems, including the complex system of Anatomic Pathology. Previous studies focused on errors committed in diagnosis and did not consider the issues related to the histology preparation of routine processes. Root Cause Analysis was applied to the process of histology preparation in order to identify the root cause of each previously identified problem. The analysis started by defining an 'a priori' list of errors that could occur in the histology preparation processes. During a three-month period, a trained technician tracked the errors encountered during the process and reported them on a form. 'Fishbone' diagram and 'Five whys' methods were then applied 8,346 histological cases were reviewed, for which 19,774 samples were made and from which 29,956 histologies were prepared. 132 errors were identified. Errors were detected in each phase: accessioning (6.5%), gross dissecting (28%), processing (1.5%), embedding (4.5%), tissue cutting and slide mounting (23%), coloring, (1.5%), labeling and releasing (35%). Root cause analysis is effective and easy to use in clinical risk management. It is an important step for the identification and prevention of errors, that are frequently due to multiple causes. Developing operators' awareness of their central role in the risk management process is possible by targeted training. Furthermore, by highlighting the most relevant points of interest, it is possible to improve both the methodology and the procedural safety.

Investigation of a BeO-based optically stimulated luminescence dosemeter

The optical sensitivity of BeO-based luminophors has been well-known for many years. The optical stimulation of BeO with blue light is most effective. Then the dosemeters emit luminescent light in the ultraviolet-range around 325 nm. Matched on these facts a simple optically stimulated luminescence (OSL) treatment has been developed. Intense blue light-emitting diodes are used for cw-stimulation. A Hamamatsu solar blind photomultiplier detects the OSL-light. Good separation of both spectral ranges by optical filters is very important. The dosemeter has a linear dose response between approximately 20 muGy and >10 Gy. It was suggested, that a modification of stimulation conditions would allow measurements down to 1 muGy. Fading, photon energy dependence and reproducibility of OSL-signal correspond well with requirements to clinical and personal dosemeters. In addition, basic questions of the OSL-process in BeO have been investigated. A relevant point of interest was the dependency of the OSL-signal on stimulation power.