As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the "15-minutes city" have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relationship. Here, we use mobile phone, census, and volunteered geographical data to measure geographic variations in the relationship between origin-destination flows and local urban accessibility in Barcelona. Using a Negative Binomial Geographically Weighted Regression model, we show that, globally, people tend to visit neighborhoods with better access to education and retail. Locally, these and other features change in sign and magnitude through the different neighborhoods of the city in ways that are not explained by administrative boundaries, and that provide deeper insights regarding urban characteristics such as rental prices. In conclusion, our work suggests that the qualities of a 15-minutes city can be measured at scale, delivering actionable insights on the polycentric structure of cities, and how people use and access this structure.

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A city of cities: Measuring how 15-minutes urban accessibility shapes human mobility in Barcelona.

Hybrid parallel framework for multiple-point geostatistics on Tianhe-2: A robust solution for large-scale simulation

Automatic reconstruction method of 3D geological models based on deep convolutional generative adversarial networks

Characterization of complex subsurface structures is challenging due to the demand to preserve geological realism of the training images in earth and environmental sciences. In this work, we propose a novel method to reconstruct complex hydrological structures by using deep convolutional generative adversarial networks (DCGAN) in the Monte-Carlo simulation process, named MC-GAN. Network architectures for reconstructing both two-dimensional (2D) and three-dimensional (3D) complex spatial structures are provided in this method. We first exploit the robust DCGAN to reproduce abundant and various spatial pattern blocks. Then, we combine the various heterogeneous patterns to reconstruct a complex hydrological structure by using the Monte-Carlo stochastic simulation process. The method is able to represent multiple-scale spatial structures under the premise of using the same generative adversarial network architecture. It not only ensures the simulation efficiency, but also makes the heterogeneous patterns in the realizations more diverse. Three sets of training images were used to test the capability of the proposed method. The experiment results demonstrate that our method can accurately characterize complex heterogeneous spatial structures. At the same time, the trained deep learning model can be reused effectively to generate multiple-scale spatial structures. 

Deep convolutional generative adversarial networks for modeling complex hydrological structures in Monte-Carlo simulation

A parametric 3D geological modeling method considering stratigraphic interface topology optimization and coding expert knowledge

An empirical analysis of spatial and temporal heterogeneity of meteorological factors affecting air quality in china

Research on the spatiotemporal heterogeneity of influenza and its main driving factors —— an empirical analysis based on stwr model

Semantics of fair geoscience data: a key factor to facilitate the data science workflow

The open data movement has brought revolutionary changes to the field of mineralogy. With a growing number of datasets made available through community efforts, researchers are now able to explore new scientific topics such as mineral ecology, mineral evolution and new classification systems. The recent results have shown that the necessary open data coupled with data science skills and expertise in mineralogy will lead to impressive new scientific discoveries. Yet, feedback from researchers also reflects the needs for better FAIRness of open data, that is, findable, accessible, interoperable and reusable for both humans and machines. In this paper, we present our recent work on building the open data service of Mindat, one of the largest mineral databases in the world. In the past years, Mindat has supported numerous scientific studies but a machine interface for data access has never been established. Through the OpenMindat project we have achieved solid progress on two activities: (1) cleanse data and improve data quality, and (2) build a data sharing platform and establish a machine interface for data query and access. We hope OpenMindat will help address the increasing data needs from researchers in mineralogy for an internationally recognized authoritative database that is fully compliant with the FAIR guiding principles and helps accelerate scientific discoveries. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/gdj3.204

Xiang Que

Papers

An empirical analysis of spatial and temporal heterogeneity of meteorological factors affecting air quality in china

Research on the spatiotemporal heterogeneity of influenza and its main driving factors —— an empirical analysis based on stwr model

Semantics of fair geoscience data: a key factor to facilitate the data science workflow

OpenMindat: Open and FAIR mineralogy data from the Mindat database

Spatiotemporal Heterogeneity of the Influence from Main Meteorological Factors on Air Quality Index in China Based on Stwr