Irregular geographic spatio-temporal-field data have been rapidly accumulating; however, data organizations and operations for different irregular types are often segregated, leading to systematic drawbacks, such as interface expansion difficulty and high coupling codes in GIS implementations. The paper proposes a unified approach to organizing and operating irregular geographic spatio-temporal-field data. The proposed approach has two components, namely ‘concepts and definitions’, and ‘logical model’. The first component introduces the concept of primitive elements, which are formal sets of data points, to serve as the smallest building blocks in the data organization. We define the corresponding primitive elements for three prevalent irregularity types (including sparse, imbalanced, and heterogeneous). The second component utilizes object-oriented programming to support the implementation of various operators. Additionally, we develop the layered architecture to decouple data organization, operation, and visualization to assure low coupling among layers. For demonstrations, we conduct case studies to show the effectiveness of our approach. Additionally, we conduct experiments to new irregularity types and illustrate the flexibility and scalability of our approach. Comparisons with classic tensor methods and spatio-temporal analysis methods show that our approach has more comprehensive supports for different data types.
At small granularity (e.g., 10-minutes to hourly), expressway traffic volumes rely heavily on drivers' driving habits heterogeneity and decision randomness, making it challenging for accurate modeling. In this paper, we propose a small granularity simulation model named Small-Granularity Expressway Traffic Volumes with Quantum Walks (SGETV-QW). The proposed model adopts quantum walks to generate probability patterns of the exiting time of drivers from the expressway. Then, we refine and map the generated probability patterns to empirical traffic-volume data via a stepwise regression and quantify the modeling accuracy in both the time and frequency domain. We validate SGETV-QW for traffic volume data from seven stations along the Nanjing-Changzhou Expressway in China and compare it with Autoregressive Integrated Moving Average Model (ARIMA) and Long and Short-Term Memory (LSTM) networks. The results show that SGETV-QW improves the simulation accuracy at small granularity. In addition, traffic volumes simulated by SGETV-QW have almost the same frequency spectrum as observed traffic volumes. Finally, we conduct a sensibility analysis and show that SGETV-QW can adapt its parameters to model traffic volumes at different granularities.
Lossy compression has been applied to the data compression of large-scale Earth system model data (ESMD) due to its advantages of a high compression ratio. However, few lossy compression methods consider both global and local multidimensional coupling correlations, which could lead to information loss in data approximation of lossy compression. Here, an adaptive lossy compression method, adaptive hierarchical geospatial field data representation (Adaptive-HGFDR), is developed based on the foundation of a stream compression method for geospatial data called blocked hierarchical geospatial field data representation (Blocked-HGFDR). In addition, the original Blocked-HGFDR method is also improved from the following perspectives. Firstly, the original data are divided into a series of data blocks of a more balanced size to reduce the effect of the dimensional unbalance of ESMD. Following this, based on the mathematical relationship between the compression parameter and compression error in Blocked-HGFDR, the control mechanism is developed to determine the optimal compression parameter for the given compression error. By assigning each data block an independent compression parameter, Adaptive-HGFDR can capture the local variation of multidimensional coupling correlations to improve the approximation accuracy. Experiments are carried out based on the Community Earth System Model (CESM) data. The results show that our method has higher compression ratio and more uniform error distributions compared with ZFP and Blocked-HGFDR. For the compression results among 22 climate variables, Adaptive-HGFDR can achieve good compression performances for most flux variables with significant spatiotemporal heterogeneity and fast changing rate. This study provides a new potential method for the lossy compression of the large-scale Earth system model data.
Due to the increasing complexity of GIS data and service modes, there is an urgent need for the next generation of GIS with new representation and computation methods. A number of spatiotemporal models, analytical and visualization methods, as well as system architectures have been proposed. However, previous studies failed to integrate basic geographical theories with latest computing technologies. Without a well-defined body of underlying theories, new models and methods are limited in scope and not able to meet the ultimate requirements of the next-generation GIS, which demands multidimensional, highly dynamic and semantic-rich representations and computational power. Geometric algebra (GA) provides an ideal tool for the expression and calculation of multidimensional geometric objects, and has proved to be effective for GIS representation and computation applications in our previous studies. We propose to use GA as the basic mathematical language for the establishment of the next-generation GIS. We present the framework of a GA-based next-generation GIS and describe the representation space, data structure, and computational models in this paper. A few issues that have not been sufficiently addressed by previous studies are discussed in detail with potential solutions proposed. These include multi-scale representations, modelling of geographic processes, simulation of geographic interactions, and multi-element modelling. The GA-based next-generation GIS uses an integrated structure consisting of a theoretical architecture, model for information expression, and computational methods. Implementation of the approach aims to improve GIS capacities in applications such as global spatiotemporal modelling and analysis, regional geographic modelling and simulation, smart city applications, and many others.