Modelling mobility and visualizing people's flow patterns in rural areas for future infrastructure development as a good transnational land-governance practice

• URL: http://arxiv.org/abs/2103.01777v1
• Date: Sat, 20 Feb 2021 09:52:29 GMT
• Title: Modelling mobility and visualizing people's flow patterns in rural areas for future infrastructure development as a good transnational land-governance practice
• Authors: Paula Botella, Pawe{\l} Gora, Martyna Sosnowska, Izabela Karsznia, Sara Carvajal Querol
• Abstract summary: This paper summarizes a cross-border mobility study, origin-destination mobility modelling and visualization. It was conducted in support of the infrastructure development efforts of local authorities and NGOs on the area over the Kayanga-Geba River, at the border between Senegal and Guinea Bissau.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper summarizes a cross-border mobility study, origin-destination mobility modelling and visualization, conducted in support of the infrastructure development efforts of local authorities and NGOs on the area over the Kayanga-Geba River, at the border between Senegal and Guinea Bissau. It builds on the data collected through participatory mapping for the elaboration of the Cross-Border Land Management and Development Plans (Plans PAGET) aiming to harmonize the different national territorial management tools into a unique transnational tool through the consideration of border areas as a territorial unity. Despite a small amount of available mobility data, we were able to build a mobility model for the considered area, and implemented it in the Traffic Simulation Framework, which was later used to calculate origin-destination matrices for the studied regions in two cases: with and without a cross-border mobility. We analyzed the differences in the mobility patterns and visualized the mobility flows, deliberating on what may be the potential impacts of building a bridge in the study area. Our methodology is general and can be applied in similar studies on different areas. However, the quality of results may depend on the available data.

Related papers

• Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis [11.90100976089832]
  We develop a novel generative deep learning approach for human mobility modeling and synthesis. It incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to accurately represent mobility patterns across diverse regions.
  arXiv  Detail & Related papers  (2024-05-24T02:04:10Z)
• Multi-Region Transfer Learning for Segmentation of Crop Field Boundaries in Satellite Images with Limited Labels [6.79949280366368]
  We present an approach for segmentation of crop field boundaries in satellite images in regions lacking labeled data. We show that our approach outperforms existing methods and that multi-region transfer learning substantially boosts performance for multiple model architectures.
  arXiv  Detail & Related papers  (2024-03-29T22:24:12Z)
• Latent Traversals in Generative Models as Potential Flows [113.4232528843775]
  We propose to model latent structures with a learned dynamic potential landscape. Inspired by physics, optimal transport, and neuroscience, these potential landscapes are learned as physically realistic partial differential equations. Our method achieves both more qualitatively and quantitatively disentangled trajectories than state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-04-25T15:53:45Z)
• Continuous Trajectory Generation Based on Two-Stage GAN [50.55181727145379]
  We propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network. Specifically, we build the generator under the human mobility hypothesis of the A* algorithm to learn the human mobility behavior. For the discriminator, we combine the sequential reward with the mobility yaw reward to enhance the effectiveness of the generator.
  arXiv  Detail & Related papers  (2023-01-16T09:54:02Z)
• BEVBert: Multimodal Map Pre-training for Language-guided Navigation [75.23388288113817]
  We propose a new map-based pre-training paradigm that is spatial-aware for use in vision-and-language navigation (VLN) We build a local metric map to explicitly aggregate incomplete observations and remove duplicates, while modeling navigation dependency in a global topological map. Based on the hybrid map, we devise a pre-training framework to learn a multimodal map representation, which enhances spatial-aware cross-modal reasoning thereby facilitating the language-guided navigation goal.
  arXiv  Detail & Related papers  (2022-12-08T16:27:54Z)
• Adaptive Trajectory Prediction via Transferable GNN [74.09424229172781]
  We propose a novel Transferable Graph Neural Network (T-GNN) framework, which jointly conducts trajectory prediction as well as domain alignment in a unified framework. Specifically, a domain invariant GNN is proposed to explore the structural motion knowledge where the domain specific knowledge is reduced. An attention-based adaptive knowledge learning module is further proposed to explore fine-grained individual-level feature representation for knowledge transfer.
  arXiv  Detail & Related papers  (2022-03-09T21:08:47Z)
• Multi-Graph Fusion Networks for Urban Region Embedding [40.97361959702485]
  Learning embeddings for urban regions from human mobility data can reveal the functionality of regions, and then enables correlated but distinct tasks such as crime prediction. We propose multi-graph fusion networks (MGFN) to enable the cross domain prediction tasks. Experimental results demonstrate that the proposed MGFN outperforms the state-of-the-art methods by up to 12.35%.
  arXiv  Detail & Related papers  (2022-01-24T15:48:50Z)
• Methodological Foundation of a Numerical Taxonomy of Urban Form [62.997667081978825]
  We present a method for numerical taxonomy of urban form derived from biological systematics. We derive homogeneous urban tissue types and, by determining overall morphological similarity between them, generate a hierarchical classification of urban form. After framing and presenting the method, we test it on two cities - Prague and Amsterdam.
  arXiv  Detail & Related papers  (2021-04-30T12:47:52Z)
• Dual-Teacher++: Exploiting Intra-domain and Inter-domain Knowledge with Reliable Transfer for Cardiac Segmentation [69.09432302497116]
  We propose a cutting-edge semi-supervised domain adaptation framework, namely Dual-Teacher++. We design novel dual teacher models, including an inter-domain teacher model to explore cross-modality priors from source domain (e.g., MR) and an intra-domain teacher model to investigate the knowledge beneath unlabeled target domain. In this way, the student model can obtain reliable dual-domain knowledge and yield improved performance on target domain data.
  arXiv  Detail & Related papers  (2021-01-07T05:17:38Z)
• Road Mapping in Low Data Environments with OpenStreetMap [0.3162999570707049]
  A comprehensive, up-to-date mapping of the geographical distribution of roads has the potential to act as an indicator for broader economic development. This work investigates the viability of high resolution satellite imagery and crowd-sourced resources like OpenStreetMap in the construction of such a mapping.
  arXiv  Detail & Related papers  (2020-06-14T19:39:57Z)
• Learning Geo-Contextual Embeddings for Commuting Flow Prediction [20.600183945696863]
  Predicting commuting flows based on infrastructure and land-use information is critical for urban planning and public policy development. Conventional models, such as gravity model, are mainly derived from physics principles and limited by their predictive power in real-world scenarios. We propose Geo-contextual Multitask Embedding Learner (GMEL), a model that captures the spatial correlations from geographic contextual information for commuting flow prediction.
  arXiv  Detail & Related papers  (2020-05-04T17:45:18Z)

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.