Complex Network-Based Research on the Resilience of Rural Settlements in Sanshui Watershed

In the context of farmland afforestation and urbanization, it is necessary for the small watershed rural settlements in the hilly–gully Loess Plateau to coordinate spatiotemporal changes and take the path of resilience development. In the case of the Sanshui Watershed, this paper investigates the rural settlement systems based on complex networks, and develops a research framework of “spatial simulation–resilience evaluation–spatial planning”. The results include the evolution trends of settlement space from present to future, as well as its spatial resilience in static and dynamic states. In this study, a total of six central villages and six types of rural development are finalized, and the study area possesses a prolonged spatiotemporal resilience when 29 villages remain, thus forming an ideal spatial pattern of “rural corridor zones + characteristic towns”. The findings of this study can represent guidance for resilience development in small watershed villages and provide a basis for guiding the regional urban–rural integration, village layout, as well as resource allocation and construction.

Spatial simulation of COVID-19 new cases development

The time dependent SIR model is extended to simulate infection across spatial boundaries. We used New Jersey data as an example to test the extended SIR model. Infection from neighboring counties are modelled by connectivity matrix where each pair of neighboring counties has an element in the connectivity matrix. The magnitude of this matrix element represents the degree to which the infected from one county can affect the susceptible in one of its neighboring counties. Simulated result from the extended spatial SIR model is compared with observed new COVID-19 cases measured in the 21 counties in New Jersey. The extended model has to solve 84 simulated functions simultaneously and the large number of parameters involved in the spatial SIR model are auto tuned using genetic algorithm.

A Novel Spatial Simulation Method for Mapping the Urban Forest Carbon Density in Southern China by the Google Earth Engine

Urban forest is an important component of terrestrial ecosystems and is highly related to global climate change. However, because of complex city landscapes, deriving the spatial distribution of urban forest carbon density and conducting accuracy assessments are difficult. This study proposes a novel spatial simulation method, optimized geographically weighted logarithm regression (OGWLR), using Landsat 8 data acquired by the Google Earth Engine (GEE) and field survey data to map the forest carbon density of Shenzhen city in southern China. To verify the effectiveness of the novel method, multiple linear regression (MLR), k-nearest neighbors (kNN), random forest (RF) and geographically weighted regression (GWR) models were established for comparison. The results showed that OGWLR achieved the highest coefficient of determination (R2 = 0.54) and the lowest root mean square error (RMSE = 13.28 Mg/ha) among all estimation models. In addition, OGWLR achieved a more consistent spatial distribution of carbon density with the actual situation. The carbon density of the forests in the study area was large in the central and western regions and coastal areas and small in the building and road areas. Therefore, this method can provide a new reference for urban forest carbon density estimation and mapping.

High-Order Data-Driven Spatial Simulation of Categorical Variables

Modern approaches for the spatial simulation of categorical variables are largely based on multi-point statistical methods, where a training image is used to derive complex spatial relationships using relevant patterns. In these approaches, simulated realizations are driven by the training image utilized, while the spatial statistics of the actual sample data are ignored. This paper presents a data-driven, high-order simulation approach based on the approximation of high-order spatial indicator moments. The high-order spatial statistics are expressed as functions of spatial distances that are similar to variogram models for two-point methods, while higher-order statistics are connected with lower-orders via boundary conditions. Using an advanced recursive B-spline approximation algorithm, the high-order statistics are reconstructed from the available data and are subsequently used for the construction of conditional distributions using Bayes’ rule. Random values are subsequently simulated for all unsampled grid nodes. The main advantages of the proposed technique are its ability to (a) simulate without a training image to reproduce the high-order statistics of the data, and (b) adapt the model’s complexity to the information available in the data. The practical intricacies and effectiveness of the proposed approach are demonstrated through applications at two copper deposits.