Population synthesis for urban resident modeling using deep generative models

• URL: http://arxiv.org/abs/2011.06851v1
• Date: Fri, 13 Nov 2020 10:48:19 GMT
• Title: Population synthesis for urban resident modeling using deep generative models
• Authors: Martin Johnsen, Oliver Brandt, Sergio Garrido, Francisco C. Pereira
• Abstract summary: This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings. We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam, where we study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents.
• Score: 6.037276428689637
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The impacts of new real estate developments are strongly associated to its population distribution (types and compositions of households, incomes, social demographics) conditioned on aspects such as dwelling typology, price, location, and floor level. This paper presents a Machine Learning based method to model the population distribution of upcoming developments of new buildings within larger neighborhood/condo settings. We use a real data set from Ecopark Township, a real estate development project in Hanoi, Vietnam, where we study two machine learning algorithms from the deep generative models literature to create a population of synthetic agents: Conditional Variational Auto-Encoder (CVAE) and Conditional Generative Adversarial Networks (CGAN). A large experimental study was performed, showing that the CVAE outperforms both the empirical distribution, a non-trivial baseline model, and the CGAN in estimating the population distribution of new real estate development projects.

Related papers

• Targeted Maximum Likelihood Estimation for Integral Projection Models in Population Ecology [4.209594014085132]
  We develop robust and efficient estimators for properties derived from a fitted Integral projection model. We conduct extensive simulations using real data from both long term studies of Idaho steppe plant communities and experimental Rotifer populations.
  arXiv  Detail & Related papers  (2024-11-12T19:56:05Z)
• SPARTAN: A Sparse Transformer Learning Local Causation [63.29645501232935]
  Causal structures play a central role in world models that flexibly adapt to changes in the environment. We present the SPARse TrANsformer World model (SPARTAN), a Transformer-based world model that learns local causal structures between entities in a scene. By applying sparsity regularisation on the attention pattern between object-factored tokens, SPARTAN identifies sparse local causal models that accurately predict future object states.
  arXiv  Detail & Related papers  (2024-11-11T11:42:48Z)
• Synthetic location trajectory generation using categorical diffusion models [50.809683239937584]
  Diffusion models (DPMs) have rapidly evolved to be one of the predominant generative models for the simulation of synthetic data. We propose using DPMs for the generation of synthetic individual location trajectories (ILTs) which are sequences of variables representing physical locations visited by individuals.
  arXiv  Detail & Related papers  (2024-02-19T15:57:39Z)
• On the Stability of Iterative Retraining of Generative Models on their own Data [56.153542044045224]
  We study the impact of training generative models on mixed datasets. We first prove the stability of iterative training under the condition that the initial generative models approximate the data distribution well enough. We empirically validate our theory on both synthetic and natural images by iteratively training normalizing flows and state-of-the-art diffusion models.
  arXiv  Detail & Related papers  (2023-09-30T16:41:04Z)
• Improving Heterogeneous Model Reuse by Density Estimation [105.97036205113258]
  This paper studies multiparty learning, aiming to learn a model using the private data of different participants. Model reuse is a promising solution for multiparty learning, assuming that a local model has been trained for each party.
  arXiv  Detail & Related papers  (2023-05-23T09:46:54Z)
• Copula-based transferable models for synthetic population generation [1.370096215615823]
  Population synthesis involves generating synthetic yet realistic representations of a target population of micro-agents. Traditional methods, often reliant on target population samples, face limitations due to high costs and small sample sizes. We propose a novel framework based on copulas to generate synthetic data for target populations where only empirical marginal distributions are known.
  arXiv  Detail & Related papers  (2023-02-17T23:58:14Z)
• Latent Diffusion Energy-Based Model for Interpretable Text Modeling [104.85356157724372]
  We introduce a novel symbiosis between the diffusion models and latent space EBMs in a variational learning framework. We develop a geometric clustering-based regularization jointly with the information bottleneck to further improve the quality of the learned latent space.
  arXiv  Detail & Related papers  (2022-06-13T03:41:31Z)
• Generating unrepresented proportions of geological facies using Generative Adversarial Networks [0.0]
  We investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset. Specifically, we design a conditional GANs model that can drive the generated facies toward new proportions not found in the training set. The presented numerical experiments on images of binary and multiple facies showed good geological consistency as well as strong correlation with the target conditions.
  arXiv  Detail & Related papers  (2022-03-17T22:38:45Z)
• 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)
• UrbanVCA: a vector-based cellular automata framework to simulate the urban land-use change at the land-parcel level [4.12627107272774]
  The UrbanVCA is a brand-new vector CA-based urban development simulation framework. Using Shunde, Guangdong as the study area, the UrbanVCA simulates multiple types of urban land-use changes at the land-parcel level. The simulation results in 2030 show that the eco-protection scenario can promote urban agglomeration and reduce ecological aggression and loss of arable land by at least 60%.
  arXiv  Detail & Related papers  (2021-03-15T17:03:22Z)
• Deep Evolutionary Learning for Molecular Design [1.8047694351309207]
  We propose a deep evolutionary learning process that integrates fragment-based deep generative model and multi-objective evolutionary computation for molecular design. Our approach enables (1) evolutionary operations in the latent space of the generative model, rather than the structural space, to generate novel promising molecular structures for the next evolutionary generation.
  arXiv  Detail & Related papers  (2020-12-28T03:15:46Z)

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.