Xiang Que

Bio: Xiang Que is an academic researcher from Fujian Agriculture and Forestry University. The author has contributed to research in topics: Computer science & Air quality index. The author has an hindex of 2, co-authored 3 publications receiving 6 citations. Previous affiliations of Xiang Que include University of Idaho.

Multiple-point geostatistical simulation based on conditional conduction probability

Abstract: Multiple-point geostatistical (MPS) simulation can enhance extraction and synthesis of various information in earth and environmental sciences. In particular, it is able to characterize the complex spatial structures of heterogeneous phenomena more accurately. In this paper, we propose a new MPS simulation method based on conditional conduction probability, namely the CCPSIM algorithm, to mitigate the uncertainty of MPS realizations. In CCPSIM, the simulated nodes will be treated differently from the original samples. The probability distributions of the simulated nodes will be used as prior conditions to calculate the probability distributions of the following nodes, and the prior conditions will be conducted during the whole simulation process. 2D and 3D synthetic tests are used to verify the applicability and advantages of CCPSIM. The results confirm that CCPSIM is able to reproduce spatial patterns of heterogeneous structures presented in categorical training images, and it reduces the uncertainty of the MPS realizations caused by the undistinguished using of the original known samples and the simulated uncertain values.

A spatiotemporal weighted regression model (STWR v1.0) for analyzing local nonstationarity in space and time

Abstract: . Local spatiotemporal non-stationarity occurs in various natural and socioeconomic processes. Many studies have attempted to introduce time as a new dimension into the geographically weighted regression model (GWR), but the actual results are sometimes not satisfied or even worse than the original GWR model. The core issue here is a mechanism for weighting effects of both temporal variation and spatial variation. In many geographical and temporal weighted regression models (GTWR), the concept of time distance has been inappropriately treated as time interval. Consequently, the combined effect of temporal and spatial variation is often inaccurate in the resulting spatiotemporal kernel function. This limitation restricts the configuration and performance of spatiotemporal weights in many existing GTWR models. To address this issue, we propose a new spatiotemporal weighted regression (STWR) model and the calibration method for it. A highlight of STWR is a new temporal kernel function, in which the method for temporal weighting is based on the degree of impact from each observed point to a regression point. The degree of impact, in turn, is based on the rate of value variation of the nearby observed point during the time interval. The updated spatiotemporal kernel function is based on a weighted combination of the temporal kernel with a commonly used spatial kernel (Gaussian or bi-square) by specifying a linear function of spatial bandwidth versus time. Three simulated datasets of spatiotemporal processes were used to test the performance of GWR, GTWR and STWR. Results show that STWR significantly improves the quality of fit and accuracy. Similar results were obtained by using real-world data for the precipitation hydrogen isotopes (δ2H) in Northeastern United States. The Leave-one-out cross-validation (LOOCV) test demonstrates that, comparing with GWR, the total prediction error of STWR is reduced by using recent observed points. Prediction surfaces of models in this case study show that STWR is more localized than GWR. Our research validates the ability of STWR to take full advantage of all the value variation of past observed points. We hope STWR can bring fresh ideas and new capabilities for analyzing and interpreting local spatiotemporal non-stationarity in many disciplines.

Toward Trust-Based Recommender Systems for Open Data: A Literature Review

Abstract: In recent years, the concept of “open data” has received increasing attention among data providers and publishers. For some data portals in public sectors, such as data.gov, the openness enables public oversight of governmental proceedings. For many other data portals, especially those in academia, open data has shown its potential for driving new scientific discoveries and creating opportunities for multidisciplinary collaboration. While the number of open data portals and the volume of shared data have increased significantly, most open data portals still use keywords and faceted models as their primary methods for data search and discovery. There should be opportunities to incorporate more intelligent functions to facilitate the data flow between data portals and end-users. To find more theoretical and empirical evidence for that proposition, in this paper, we conduct a systematic literature review of open data, social trust, and recommender systems to explain the fundamental concepts and illustrate the potential of using trust-based recommender systems for open data portals. We hope this literature review can benefit practitioners in the field of open data and facilitate the discussion of future work.

Land Cover Impacts on Surface Temperatures: Evaluation and Application of a Novel Spatiotemporal Weighted Regression Approach

Abstract: The urban heat island (UHI) effect is an important topic for many cities across the globe. Previous studies, however, have mostly focused on UHI changes along either the spatial or temporal dimension. A simultaneous evaluation of the spatial and temporal variations is essential for understanding the long-term impacts of land cover on the UHI. This study presents the first evaluation and application of a newly developed spatiotemporal weighted regression framework (STWR), the performance of which was tested against conventional models including the ordinary least squares (OLS) and the geographically weighted regression (GWR) models. We conducted a series of simulation tests followed by an empirical study over central Phoenix, AZ. The results show that the STWR model achieves better parameter estimation and response prediction results with significantly smaller errors than the OLS and GWR models. This finding holds true when the regression coefficients are constant, spatially heterogeneous, and spatiotemporally heterogeneous. The empirical study reveals that the STWR model provides better model fit than the OLS and GWR models. The LST has a negative relationship with GNDVI and LNDVI and a positive relationship with GNDBI for the three years studied. Over the last 20 years, the cooling effect from green vegetation has weakened and the warming effect from built-up features has intensified. We suggest the wide adoption of the STWR model for spatiotemporal studies, as it uses past observations to reduce uncertainty and improve estimation and prediction results.

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

Abstract: 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.

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

Abstract: 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.