Integration Development of Urban Agglomeration in Central Liaoning, China, by Trajectory Gravity Model

Integration development of urban agglomeration is important for regional economic research and management. In this paper, a method was proposed to study the integration development of urban agglomeration by trajectory gravity model. It can analyze the gravitational strength of the core city to other cities and characterize the spatial trajectory of its gravitational direction, expansion, etc. quantitatively. The main idea is to do the fitting analysis between the urban axes and the gravitational lines. The correlation coefficients retrieved from the fitting analysis can reflect the correlation of two indices. For the different cities in the same year, a higher value means a stronger relationship. There is a clear gravitational force between the cities when the value above 0.75. For the most cities in different years, the gravitational force between the core city with itself is increasing by years. At the same time, the direction of growth of the urban axes tends to increase in the direction of the gravitational force between cities. There is a clear tendency for the trajectories of the cities to move closer together. The proposed model was applied to the integration development of China Liaoning central urban agglomeration from 2008 to 2016. The results show that cities are constantly attracted to each other through urban gravity.

Study on the Impact of the COVID-19 Pandemic on the Spatial Behavior of Urban Tourists Based on Commentary Big Data: A Case Study of Nanjing, China

The global outbreak of the COVID-19 epidemic has caused a considerable impact on humans, which expresses the urgency and importance of studying its impacts. Previous studies either frequently use aggregated research methods of statistic data or stay during COVID-19. The afterward impacts of COVID-19 on human behaviors need to be explored further. This article carries out a non-aggregated study methodology in human geography based on big data from social media comments and takes Nanjing, China, as the research case to explore the afterward impact of the COVID-19 epidemic on the spatial behavior of urban tourists. Precisely, we propose the methodology covers two main aspects regarding travel contact trajectory and spatial trajectory. In contact trajectory, we explore three indicators—Connection Strength, Degree Centrality, and Betweenness Centrality—of the collected attractions. Then, in spatial trajectory, we input the results from contact trajectory into ArcGIS by using the Orientation–Destination Model and Standard Deviation Ellipse to explore the influences on the spatial pattern. By setting up comparative groups for the three periods of before, during, and after the COVID-19 in Nanjing, this study found that, in the post-epidemic era, (1) the spatial behavior of urban tourists showed a state of overall contraction; (2) the objects of contraction changed from urban architectural attractions to urban natural attractions; (3) the form of contraction presents concentric circles with the central city (Old City of Nanjing) as the core; (4) the direction of contraction heads to the large-scale natural landscape in the central city, which highlights the importance of green open spaces in the post-epidemic era.

Modeling Trajectories with Neural Ordinary Differential Equations

Recent advances in location-acquisition techniques have generated massive spatial trajectory data. Recurrent Neural Networks (RNNs) are modern tools for modeling such trajectory data. After revisiting RNN-based methods for trajectory modeling, we expose two common critical drawbacks in the existing uses. First, RNNs are discrete-time models that only update the hidden states upon the arrival of new observations, which makes them an awkward fit for learning real-world trajectories with continuous-time dynamics. Second, real-world trajectories are never perfectly accurate due to unexpected sensor noise. Most RNN-based approaches are deterministic and thereby vulnerable to such noise. To tackle these challenges, we devise a novel method entitled TrajODE for more natural modeling of trajectories. It combines the continuous-time characteristic of Neural Ordinary Differential Equations (ODE) with the robustness of stochastic latent spaces. Extensive experiments on the task of trajectory classification demonstrate the superiority of our framework against the RNN counterparts.

Spatial transcriptome sequencing revealed spatial trajectory in the Non-Small Cell Lung Carcinoma

Deepening understanding in the heterogeneity of tumors is critical for clinical treatment. Here we investigate tissue-wide gene expression heterogeneity throughout a multifocal lung cancer using the spatial transcriptomics (ST) technology. We identified gene expression gradients in stroma adjacent to tumor regions that allow for re-understanding of the tumor micro-environment. The establishment of these profiles was the first step towards an unbiased view of lung cancer and can serve as a dictionary for future studies. Tumor subclones were detected by ST technology in our research, while we contrast the EMT ability among in subclones which inferred the possible trajectory of tumor metastasis and invasion, which was confirmed by constructing the pseudo-time model of spatial transition within subclones. Together, these results uncovered lung cancer spatial heterogeneity, highlight potential tumor micro-environment differences and spatial evolution trajectory, and served as a resource for further investigation of tumor microenvironment.

Determination of the Parameters of the Trajectory of Movement of a Standard Tool in Multi-Coordinate Machining of Spatially Modified Teeth of Spur Wheels with the Copying Technique

The article presents a methodology for determining the parameters of the spatial trajectory of movement of a standard cutting tool processing ring gears with spatially modified teeth working by the copying technique. The implementation of the methodology makes it possible to determine the reference points coordinates of the trajectory of movement of the cutting tool in the process of multiple-axis processing of spatially modified teeth of spur wheels on CNC machines. The results obtained in the development of the methodology for determining the trajectory of the cutting tool movement can be further used in the process of performing the technological preparation for the production of spatially modified ring gears, including large-scale ones, in the conditions of single and small-scale production, as well as with choosing a technological equipment, the appointment of cutting modes, as well as in the process of writing control programs for CNC machines.

The Intensity Analysis of Production Living Ecological Land in Shandong Province, China

Due to the limitedness of land, the coordinated development of production, living, and ecological (PLE) land is essential for sustainable development. A clear understanding of PLE land change is necessary given the increased human activities, especially in developing regions. This study first reclassified remotely sensed landuse maps in Shandong province into PLE land. Then the spatiotemporal change of PLE land between 2000 and 2015 was analyzed using spatial trajectory and intensity analysis methods. The results show that the rate of PLE land change in the interval of 2000–2005 was the highest, and it kept decreasing during 2005–2010 and 2010–2015. The overall quantity component accounts for more difference than the exchange and shift components for all intervals. At the category level, the largest quantity component of change was PE land loss, followed by LP land gain. LP land gain targeted PE land intensively in each interval. The loss of E land was mainly to PE land in terms of size, and to PL land in terms of intensity. The findings of this study contribute to a deeper understanding of the spatiotemporal transitions of PLE land in Shandong province, which could help policy making for PLE land regulation.

Indigeneity and political economy: Class and ethnicity of the Guarani-Kaiowa

Ontological and identitary questions affecting indigenous peoples are discussed through an assessment of the socio-spatial trajectory of the Guarani-Kaiowa of South America, employing an analytical framework centred around land, labour and ethnicity. These enhanced politico-economic categories provide important entry points for understanding the violence and exploitation perpetrated against indigenous groups, as well as their capacity to reclaim ancestral territory lost to development. Evidence indicates that ethnicity is integral to class-based processes, given that the advance of capitalist relations both presumes and produces difference and subordination. The case study in the Brazilian state of Mato Grosso do Sul demonstrates that the Guarani-Kaiowa became refugees in their own land due to ethnic differences, but at the same time their labour has underpinned the regional economy to a considerable extent through interrelated mechanisms of peasantification and proleterianisation. Trends of exploitation and alienation have intensified in recent decades due to racism and socio-spatial segregation, but the action/reaction of subordinate groups has also been reinforced through references to their ethnicity.