Area Modeling using Stay Information for Large-Scale Users and Analysis for Influence of COVID-19

• URL: http://arxiv.org/abs/2401.10648v1
• Date: Fri, 19 Jan 2024 11:48:52 GMT
• Title: Area Modeling using Stay Information for Large-Scale Users and Analysis for Influence of COVID-19
• Authors: Kazuyuki Shoji, Shunsuke Aoki, Takuro Yonezawa, Nobuo Kawaguchi
• Abstract summary: Area usage is subject to change over time due to various events including seasonal shifts and pandemics. There are many existing studies on area modeling, which characterize an area with some kind of information. We propose a novel area modeling method named Area2Vec, inspired by Word2Vec, which models areas based on people's location data.
• Score: 2.6044668705725287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding how people use area in a city can be a valuable information in a wide range of fields, from marketing to urban planning. Area usage is subject to change over time due to various events including seasonal shifts and pandemics. Before the spread of smartphones, this data had been collected through questionnaire survey. However, this is not a sustainable approach in terms of time to results and cost. There are many existing studies on area modeling, which characterize an area with some kind of information, using Point of Interest (POI) or inter-area movement data. However, since POI is data that is statically tied to space, and inter-area movement data ignores the behavior of people within an area, existing methods are not sufficient in terms of capturing area usage changes. In this paper, we propose a novel area modeling method named Area2Vec, inspired by Word2Vec, which models areas based on people's location data. This method is based on the discovery that it is possible to characterize an area based on its usage by using people's stay information in the area. And it is a novel method that can reflect the dynamically changing people's behavior in an area in the modeling results. We validated Area2vec by performing a functional classification of areas in a district of Japan. The results show that Area2Vec can be usable in general area analysis. We also investigated area usage changes due to COVID-19 in two districts in Japan. We could find that COVID-19 made people refrain from unnecessary going out, such as visiting entertainment areas.

Related papers

• FUSU: A Multi-temporal-source Land Use Change Segmentation Dataset for Fine-grained Urban Semantic Understanding [6.833536116934201]
  We introduce FUSU, the first fine-grained land use change segmentation dataset for Fine-grained Urban Semantic Understanding. FUSU features the most detailed land use classification system to date, with 17 classes and 30 billion pixels of annotations. It includes bi-temporal high-resolution satellite images with 0.2-0.5 m ground sample distance and monthly optical and radar satellite time series, covering 847 km2 across five urban areas in the southern and northern of China.
  arXiv  Detail & Related papers  (2024-05-29T12:56:11Z)
• Regions are Who Walk Them: a Large Pre-trained Spatiotemporal Model Based on Human Mobility for Ubiquitous Urban Sensing [24.48869607589127]
  We propose a largetemporal model based on trajectories (RAW) to tap into the rich information within human mobility data. Our proposed method, relying solely on human mobility data without additional features, exhibits certain level of relevance in user profiling and region analysis.
  arXiv  Detail & Related papers  (2023-11-17T11:55:11Z)
• A Visual Analytics System for Profiling Urban Land Use Evolution [5.053505466956614]
  Urban Chronicles is an open-source web-based visual analytics system that enables interactive exploration of changes in land use patterns. We show the capabilities of the system by exploring the data over several years at different scales.
  arXiv  Detail & Related papers  (2021-12-12T02:36:54Z)
• Learning Continuous Environment Fields via Implicit Functions [144.4913852552954]
  We propose a novel scene representation that encodes reaching distance -- the distance between any position in the scene to a goal along a feasible trajectory. We demonstrate that this environment field representation can directly guide the dynamic behaviors of agents in 2D mazes or 3D indoor scenes.
  arXiv  Detail & Related papers  (2021-11-27T22:36:58Z)
• PANet: Perspective-Aware Network with Dynamic Receptive Fields and Self-Distilling Supervision for Crowd Counting [63.84828478688975]
  We propose a novel perspective-aware approach called PANet to address the perspective problem. Based on the observation that the size of the objects varies greatly in one image due to the perspective effect, we propose the dynamic receptive fields (DRF) framework. The framework is able to adjust the receptive field by the dilated convolution parameters according to the input image, which helps the model to extract more discriminative features for each local region.
  arXiv  Detail & Related papers  (2021-10-31T04:43:05Z)
• Learning Large-scale Location Embedding From Human Mobility Trajectories with Graphs [0.0]
  This study learns vector representations for locations using the large-scale LBS data. This model embeds context information in human mobility and spatial information. GCN-L2V can be applied in a complementary manner to other place embedding methods and down-streaming Geo-aware applications.
  arXiv  Detail & Related papers  (2021-02-23T09:11:33Z)
• Discovering Underground Maps from Fashion [80.02941583103612]
  We propose a method to automatically create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method finds neighborhoods with a similar fashion sense and segments the map without supervision.
  arXiv  Detail & Related papers  (2020-12-04T23:40:59Z)
• Toward Accurate Person-level Action Recognition in Videos of Crowded Scenes [131.9067467127761]
  We focus on improving the action recognition by fully-utilizing the information of scenes and collecting new data. Specifically, we adopt a strong human detector to detect spatial location of each frame. We then apply action recognition models to learn thetemporal information from video frames on both the HIE dataset and new data with diverse scenes from the internet.
  arXiv  Detail & Related papers  (2020-10-16T13:08:50Z)
• Dataset Cartography: Mapping and Diagnosing Datasets with Training Dynamics [118.75207687144817]
  We introduce Data Maps, a model-based tool to characterize and diagnose datasets. We leverage a largely ignored source of information: the behavior of the model on individual instances during training. Our results indicate that a shift in focus from quantity to quality of data could lead to robust models and improved out-of-distribution generalization.
  arXiv  Detail & Related papers  (2020-09-22T20:19:41Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• FADACS: A Few-shot Adversarial Domain Adaptation Architecture for Context-Aware Parking Availability Sensing [5.160087162892865]
  We design an end-to-end transfer learning framework for parking availability sensing to predict parking occupancy in areas in which the parking data is insufficient to feed into data-hungry models. This framework overcomes two challenges: 1) many real-world cases cannot provide enough data for most existing data-driven models, and 2) it is difficult to merge sensor data and heterogeneous contextual information.
  arXiv  Detail & Related papers  (2020-07-13T08:25:26Z)

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.