Conflating point of interest (POI) data: A systematic review of matching methods

• URL: http://arxiv.org/abs/2310.15320v1
• Date: Mon, 23 Oct 2023 19:38:31 GMT
• Title: Conflating point of interest (POI) data: A systematic review of matching methods
• Authors: Kai Sun, Yingjie Hu, Yue Ma, Ryan Zhenqi Zhou, Yunqiang Zhu
• Abstract summary: Point of interest (POI) data provide digital representations of places in the real world. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. Researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas.
• Score: 5.439489511940086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point of interest (POI) data provide digital representations of places in the real world, and have been increasingly used to understand human-place interactions, support urban management, and build smart cities. Many POI datasets have been developed, which often have different geographic coverages, attribute focuses, and data quality. From time to time, researchers may need to conflate two or more POI datasets in order to build a better representation of the places in the study areas. While various POI conflation methods have been developed, there lacks a systematic review, and consequently, it is difficult for researchers new to POI conflation to quickly grasp and use these existing methods. This paper fills such a gap. Following the protocol of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we conduct a systematic review by searching through three bibliographic databases using reproducible syntax to identify related studies. We then focus on a main step of POI conflation, i.e., POI matching, and systematically summarize and categorize the identified methods. Current limitations and future opportunities are discussed afterwards. We hope that this review can provide some guidance for researchers interested in conflating POI datasets for their research.

Related papers

• SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers [43.18330795060871]
  SPIQA is a dataset specifically designed to interpret complex figures and tables within the context of scientific research articles. We employ automatic and manual curation to create the dataset. SPIQA comprises 270K questions divided into training, validation, and three different evaluation splits.
  arXiv  Detail & Related papers  (2024-07-12T16:37:59Z)
• A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
  Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
  arXiv  Detail & Related papers  (2024-05-30T04:46:40Z)
• Using Graph Neural Networks to Predict Local Culture [2.5870115809699783]
  This study proposes a graph neural network (GNN) approach that permits combining and evaluating multiple sources of information about internal characteristics of neighbourhoods. By exploring a public large-scale dataset from Yelp, we show the potential of our approach for considering structural connectedness in predicting neighbourhood attributes.
  arXiv  Detail & Related papers  (2024-02-27T21:43:14Z)
• A Survey on Data Selection for Language Models [148.300726396877]
  Data selection methods aim to determine which data points to include in a training dataset. Deep learning is mostly driven by empirical evidence and experimentation on large-scale data is expensive. Few organizations have the resources for extensive data selection research.
  arXiv  Detail & Related papers  (2024-02-26T18:54:35Z)
• MGeo: Multi-Modal Geographic Pre-Training Method [49.78466122982627]
  We propose a novel query-POI matching method Multi-modal Geographic language model (MGeo) MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs.
  arXiv  Detail & Related papers  (2023-01-11T03:05:12Z)
• Self-supervised Graph-based Point-of-interest Recommendation [66.58064122520747]
  Next Point-of-Interest (POI) recommendation has become a prominent component in location-based e-commerce. We propose a Self-supervised Graph-enhanced POI Recommender (S2GRec) for next POI recommendation. In particular, we devise a novel Graph-enhanced Self-attentive layer to incorporate the collaborative signals from both global transition graph and local trajectory graphs.
  arXiv  Detail & Related papers  (2022-10-22T17:29:34Z)
• Research Trends and Applications of Data Augmentation Algorithms [77.34726150561087]
  We identify the main areas of application of data augmentation algorithms, the types of algorithms used, significant research trends, their progression over time and research gaps in data augmentation literature. We expect readers to understand the potential of data augmentation, as well as identify future research directions and open questions within data augmentation research.
  arXiv  Detail & Related papers  (2022-07-18T11:38:32Z)
• DeepShovel: An Online Collaborative Platform for Data Extraction in Geoscience Literature with AI Assistance [48.55345030503826]
  Geoscientists need to read a huge amount of literature to locate, extract, and aggregate relevant results and data. DeepShovel is a publicly-available AI-assisted data extraction system to support their needs. A follow-up user evaluation with 14 researchers suggested DeepShovel improved users' efficiency of data extraction for building scientific databases.
  arXiv  Detail & Related papers  (2022-02-21T12:18:08Z)
• An End-to-end Point of Interest (POI) Conflation Framework [0.966840768820136]
  Point of interest (POI) data serves as a valuable source of semantic information for places of interest. This study proposes a novel end-to-end POI conflation framework consisting of six steps.
  arXiv  Detail & Related papers  (2021-09-13T15:50:48Z)
• Evaluating the state-of-the-art in mapping research spaces: a Brazilian case study [0.0]
  Two recent works propose methods for creating research maps from scientists' publication records. We evaluate these models' ability to predict whether a given entity will enter a new field. We conduct a case study to showcase how these models can be used to characterize science dynamics in the context of Brazil.
  arXiv  Detail & Related papers  (2021-04-07T18:14:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.