Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced cloud computing facilities and the open Landsat data archives available, NDUI has the potential for global studies at the 30 m scale.

COVID-19 outbreaks in China in late December 2019, then in the United States (US) in early 2020. In the initial wave of diffusion, the virus respectively took 14 and 33 days to spread across the provinces/states in the Chinese mainland and the coterminous US, during which there are 43% and 70% zero entries in the space-time series for China and US respectively, indicating a zero-inflated count process. A logistic growth curve as a function of the number of days since the first case appeared in each of these countries accurately portrays the national aggregate per capita rates of infection for both. This paper presents two space-time model specifications, one based upon the generalized linear mixed model, and the other upon Moran eigenvector space-time filtering, to describe the spread of COVID-19 in the initial 19 and 58 days across the Chinese mainland and the coterminous US, respectively. Results from these case studies show both models shed new light on the role of spatial structures in COVID-19 diffusion, models that can forecast new cases in subsequent days. A principal finding is that describing the spatio-temporal diffusion of COVID-19 benefits from including a hierarchical structural component to supplement the commonly employed contagion component.

Scatterplots are essential tools for data exploration. However, this tool poorly scales with data-size, with overplotting and excessive delay being the main problems. Generalization methods in the attribute domain focus on visual manipulations, but do not take into account the inherent nature of information redundancy in most geographic data. These methods may also result in alterations of statistical properties of data. Recent developments in spatial statistics, particularly the formulation of effective sample size and the fast approximation of the eigenvalues of a spatial weights matrix, make it possible to assess the information content of a georeferenced data-set, which can serve as the basis for resampling such data. Experiments with both simulated data and actual remotely sensed data show that an equivalent scatterplot consisting of point clouds and fitted lines can be produced from a small subset extracted from a parent georeferenced data-set through spatial resampling. The spatially simplified data subset also maintains key statistical properties as well as the geographic coverage of the original data.

This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM_2.5 concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM_2.5 concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM_2.5 concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM_2.5 analysis and prediction.

Geoinformatic Tupu, or Geoinformatic graph spectrum, is a theoretical as well as a technical framework for generalizing geographic knowledge and solving real world problems. Geoinformatic Tupu is a promising platform for capitalizing on the technical advances of Geographic Information Systems, and to integrate the Chinese traditional way of thinking with modern information technology. It has been one of the major research topics in the Chinese GIScience community in recent decades, with an evolving epistemological development. A core objective of Geoinformatic Tupu is to recover and represent geographic principles with the Tupu approach, which is adopted in this paper to formulate the First Law of Geography (FLG) [i.e. the law of spatial autocorrelation] as the Moran Spectrum – a combination of sequential diagrams, graphs, and numeric components. Using the Moran Spectrum as a conduit, we present the theory of Moran Eigenvector Spatial Filtering (MESF), a distinct branch of spatial statistics that has demonstrable advantages in statistical modelling and machine learning, but has yet to be widely disseminated due to its conceptual and computational complexity. This paper demonstrates the effectiveness of the Tupu approach in enriching the representation of the FLG as well as deepening its applications. It also suggests inclusion of the Moran Spectrum as a core component in Geoinformatic Tupu.