Modeling spatial autocorrelation is widely done in fields like geology, population biology,
and social economy. A commonly used statistics is Moran's I, where we distinguish between …

Nowadays, global public health crises are occurring more frequently, and accurate
prediction of these diseases can reduce the burden on the healthcare system. Taking …

One factor that goes into the construction of a space–time model, in this case, the
Generalized Space–Time Autoregressive (GSTAR) model, is the weight matrix, which acts …

The recent COVID-19 pandemic once again showed the value of harnessing reliable and
timely data in fighting the disease. Obtained from multiple sources via different collection …

The sudden changes in human mobility, the immense increase in demand for logistics and
delivery systems, governmental restrictions, and uncertainty of the spread dynamics have …

The release rate of CO2 gas can be influenced by peatlands' physical properties, such as
water level and soil moisture, and rainfall. To anticipate the unstable condition which is …

The degree of industrial agglomeration in China has contributed to the reduction of nitrogen
dioxide pollution because of financial support, the allocation of environmental governance …

Urbanisation is an important aspect of environmental management and requires special
attention, as half of the world's population lives in urban areas. As the urban population …

The weight matrix is one of the most important things in Generalized Space–Time
Autoregressive (GSTAR) modeling. Commonly, the weight matrix is built based on the …

Modelling non-negative discrete valued for time series analysis was introduced by previous
researchers, that is Integer-Valued Autoregressive (INAR) model. The model involves both …