Spatial sensitivity analysis for urban land use prediction with physics-constrained conditional generative adversarial networks

by   Adrian Albert, et al.

Accurately forecasting urban development and its environmental and climate impacts critically depends on realistic models of the spatial structure of the built environment, and of its dependence on key factors such as population and economic development. Scenario simulation and sensitivity analysis, i.e., predicting how changes in underlying factors at a given location affect urbanization outcomes at other locations, is currently not achievable at a large scale with traditional urban growth models, which are either too simplistic, or depend on detailed locally-collected socioeconomic data that is not available in most places. Here we develop a framework to estimate, purely from globally-available remote-sensing data and without parametric assumptions, the spatial sensitivity of the (static) rate of change of urban sprawl to key macroeconomic development indicators. We formulate this spatial regression problem as an image-to-image translation task using conditional generative adversarial networks (GANs), where the gradients necessary for comparative static analysis are provided by the backpropagation algorithm used to train the model. This framework allows to naturally incorporate physical constraints, e.g., the inability to build over water bodies. To validate the spatial structure of model-generated built environment distributions, we use spatial statistics commonly used in urban form analysis. We apply our method to a novel dataset comprising of layers on the built environment, nightlighs measurements (a proxy for economic development and energy use), and population density for the world's most populous 15,000 cities.


page 4

page 5

page 6

page 8


Modeling urbanization patterns with generative adversarial networks

In this study we propose a new method to simulate hyper-realistic urban ...

MetroGAN: Simulating Urban Morphology with Generative Adversarial Network

Simulating urban morphology with location attributes is a challenging ta...

A scala library for spatial sensitivity analysis

The sensitivity analysis and validation of simulation models require spe...

GANmapper: geographical content filling

We present a new method to create spatial data using a generative advers...

Modelling and simulating spatial extremes by combining extreme value theory with generative adversarial networks

Modelling dependencies between climate extremes is important for climate...

Generating Realistic Geology Conditioned on Physical Measurements with Generative Adversarial Networks

An important problem in geostatistics is to build models of the subsurfa...

Spatial Modeling of Trends in Crime over Time in Philadelphia

Understanding the relationship between change in crime over time and the...

Please sign up or login with your details

Forgot password? Click here to reset