Housing Potential Common Data Model and City Digital Twin

• URL: http://arxiv.org/abs/2605.05535v1
• Date: Thu, 07 May 2026 00:27:47 GMT
• Title: Housing Potential Common Data Model and City Digital Twin
• Abstract summary: This research introduces the Housing Potential Common Data Model (HPCDM) to overcome existing data silos.<n>The report details the evaluation of the model along with the creation of a City Digital Twin for housing and a pilot dashboard application to demonstrate a practical implementation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The evaluation of housing potential requires consideration of a location from multiple perspectives, ranging from zoning and land use to population characteristics and access to services. This research introduces the Housing Potential Common Data Model (HPCDM) to overcome existing data silos, serving as a standard to support integration and interoperability across the diverse range of datasets that are required for housing potential analysis. This report details the evaluation of the model along with the creation of a City Digital Twin for housing and a pilot dashboard application to demonstrate a practical implementation. Beyond the technical framework, this work identifies critical barriers to adoption and provides actionable mitigation strategies for urban planners and stakeholders.

Related papers

• An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark [55.73054722370559]
  Cross-city point-of-interest (POI) recommendation is crucial for navigating unfamiliar urban environments.<n>We re-examine whether conclusions drawn on small prior benchmarks still hold under worldwide coverage, low home-destination region overlap, and large, semantically rich POI inventories.
  arXiv  Detail & Related papers  (2026-08-28T02:16:33Z)
• Can Urban Blight Be Accessed with Vision-language Models: A Case Study in Detroit [0.5747738795689892]
  This study introduced a scalable framework for estimating residential blight using open-source large vision-language models on multiple views.<n>We compared professional human annotations of these features across several models, including an ensemble stacking approach based on XGBoost and a weighted scoring system.<n>Results showed that (i) multiple street views can contribute to the improvement of accuracy, (ii) large vision-language models have different strengths of inference, (iii) the ensemble learner outperforms individual base models, enhancing robustness across all residential conditions and blight assessment.
  arXiv  Detail & Related papers  (2026-08-03T06:21:05Z)
• A Joint Synthetic Housing-Household Inventory [1.338174941551702]
  High-fidelity datasets that jointly capture detailed housing structures and demographic characteristics at the household level do not currently exist.<n>This paper presents a framework for constructing a joint housing-household inventory that explicitly links individuals and households to compatible housing units.<n>The resulting inventory supports a broad range of applications, including disaster resilience planning, housing and affordability analysis, energy-use assessment, and public health research.
  arXiv  Detail & Related papers  (2026-05-16T14:58:44Z)
• VIEW2SPACE: Studying Multi-View Visual Reasoning from Sparse Observations [47.94531550391802]
  Multi-view visual reasoning is essential for intelligent systems that must understand complex environments from sparse and discrete viewpoints.<n>In real-world scenarios, reasoning across views requires integrating partial observations without explicit guidance.<n>We leverage physically grounded simulation to construct diverse, high-fidelity 3D scenes with precise per-view metadata.
  arXiv  Detail & Related papers  (2026-03-17T13:36:30Z)
• One Size Fits None: A Personalized Framework for Urban Accessibility Using Exponential Decay [0.0]
  This study develops a personalized accessibility framework that integrates exponential decay functions with user-customizable weighting systems.<n>The framework enables real-time, personalized urban evaluation based on individual priorities and lifestyle requirements.
  arXiv  Detail & Related papers  (2025-10-28T19:13:05Z)
• A Survey on Efficient Vision-Language-Action Models [153.11669266922993]
  Vision-Language-Action models (VLAs) represent a significant frontier in embodied intelligence, aiming to bridge digital knowledge with physical-world interaction.<n>Motivated by the urgent need to address these challenges, this survey presents the first comprehensive review of Efficient Vision-Language-Action models.
  arXiv  Detail & Related papers  (2025-10-27T17:57:33Z)
• Forecasting Residential Heating and Electricity Demand with Scalable, High-Resolution, Open-Source Models [0.0]
  We present a novel framework for high-resolution forecasting of residential heating and electricity demand using probabilistic deep learning models.<n>We focus specifically on providing hourly building-level electricity and heating demand forecasts for the residential sector.
  arXiv  Detail & Related papers  (2025-05-28T21:16:27Z)
• Collaborative Imputation of Urban Time Series through Cross-city Meta-learning [54.438991949772145]
  We propose a novel collaborative imputation paradigm leveraging meta-learned implicit neural representations (INRs)<n>We then introduce a cross-city collaborative learning scheme through model-agnostic meta learning.<n>Experiments on a diverse urban dataset from 20 global cities demonstrate our model's superior imputation performance and generalizability.
  arXiv  Detail & Related papers  (2025-01-20T07:12:40Z)
• Identifying every building's function in large-scale urban areas with multi-modality remote-sensing data [5.18540804614798]
  This study proposes a semi-supervised framework to identify every building's function in large-scale urban areas. optical images, building height, and nighttime-light data are collected to describe the morphological attributes of buildings. Results are evaluated by 20,000 validation points and statistical survey reports from the government.
  arXiv  Detail & Related papers  (2024-05-08T15:32:20Z)
• A Deep Learning Representation of Spatial Interaction Model for Resilient Spatial Planning of Community Business Clusters [4.8051028509814575]
  We propose a SIM-GAT model to predict visitation flows between community business clusters and their trade areas. A graph-based deep learning model, i.e., Graph AttenTion network (GAT), is used to capture the clusters and interdependencies of business.
  arXiv  Detail & Related papers  (2024-01-09T23:42:21Z)
• Unified Data Management and Comprehensive Performance Evaluation for Urban Spatial-Temporal Prediction [Experiment, Analysis & Benchmark] [78.05103666987655]
  This work addresses challenges in accessing and utilizing diverse urban spatial-temporal datasets. We introduceatomic files, a unified storage format designed for urban spatial-temporal big data, and validate its effectiveness on 40 diverse datasets. We conduct extensive experiments using diverse models and datasets, establishing a performance leaderboard and identifying promising research directions.
  arXiv  Detail & Related papers  (2023-08-24T16:20:00Z)
• Dynamic Urban Planning: an Agent-Based Model Coupling Mobility Mode and Housing Choice. Use case Kendall Square [5.620109882646823]
  This paper presents an agent-based model that characterizes citizens' behavioural patterns with respect to housing and mobility choice. The realistic identification and representation of the criteria that affect this decision-making process will help understand and evaluate the impacts of potential housing incentives.
  arXiv  Detail & Related papers  (2021-06-28T10:54:44Z)
• 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)

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.