Simulating urban land-use change based on the multi-hierarchal planning–environment interaction process

Urban land-use change is the result of coupling interaction between planning and environment systems. The aim of our study was to construct an effective model to show how the urban land-use changes under the planning–environment interaction system with multi-hierarchy and major function oriented zoning. Combining the Cellular automata (CA) model with logistic regression model, the proposed multi-hierarchal vector CA model (MH-VCA3) was constructed by mining multi-hierarchal land-use transition rules under the planning–environment interaction system. Taking Jiangyin City (China) as an example, we compared the simulated result of the proposed model to those of the well-accepted Logistic CA and traditional multi-level CA models to demonstrate the effectiveness of the consideration of top-down decomposition constraint and bottom-up updating. Furthermore, by simulating the land-use changes under different population regionalization scenarios, we found that in order to form the spatial pattern of “agglomeration in the north and ecology in the south,” the planned population growth at the global hierarchal level should be allocated to the district units according to the law of Central district > Chengxi district > Chengdong district > Chengnan district > Chengdongnan district. The proposed model is expected to provide scientific support for the formulation of urban planning schemes in the future.     

Revealing the spatiotemporal characteristics of the general public's panic levels during the pandemic crisis in China

The existing crisis management research mostly reveals the patterns of the public's panic levels from the perspectives of public management, sociology, and psychology, only a few studies have revealed the spatiotemporal characteristics. Therefore, this study investigates the spatial distribution and temporal patterns and influencing factors on the general public's panic levels using the Baidu Index data from a geographic perspective. The results show that: (1) The public's panic levels were significantly correlated with the spatial distance between the epicenter and the region of investigation, and with the number of confirmed cases in different regions when the pandemic began to spread. (2) Based on the spatial distance between the epicenter and the region, the public's panic levels in different regions could be divided into three segments: core segment (0–500 km), buffer segment (500–1300 km), and peripheral segment (>1300 km). The panic levels of different people in the three segments were consistent with the Psychological Typhoon Eye Effect and the Ripple Effect can be detected in the buffer segment. (3) The public's panic levels were strongly correlated with whether the spread of the infectious disease crisis occurred and how long it lasted. It is suggested that crisis information management in the future needs to pay more attention to the spatial division of control measures. The type of crisis information released to the general public should depend on the spatial relationship associated with the place where the crisis breaks out.     

Complex adaptive blended learning for higher GIS education: A theory-driven pedagogy moving forward after the pandemic

None of the prior studies implementing blended learning (BL) for GIS education has explored pedagogical theories to support BL design, and none coupled blended GIS learning practice with theories. Based on the theoretical framework of complex adaptive blended learning system (CABLS), this article presents a holistic and systematic view of BL for higher GIS education (termed as CABLS-GIS), where its key components are tightly integrated as an integral whole. The framework was implemented for an introductory-level GIS course to empirically evaluate the effectiveness of a BL design from students' perspectives. Most students involved in the implementation agreed that the BL enhanced their learning outcomes. They were mostly positive about the BL and motivated to use the BL materials. Incorporating more BL materials, especially practical ones, and active learning activities were suggested by them. A few negative comments about the BL suggested a thorough consideration of individual learners' characteristics.     

矿山巷道三维建模数据组织与算法研究

巷道作为矿山的重要组成部分,在矿山开发建设过程中发挥着重要作用,基于虚拟现实技术的巷道三维模拟能够为采矿工程、安全生产、施工、人员及设备管理等提供直观的数据展示和决策支持,其精确程度直接影响着矿山的工程质量与采选能力。本文主要探讨巷道三维建模数据组织方法,直壁拱形断面巷道三维建模的关键算法,并基于实验数据,利用OGRE三维引擎技术完成巷道三维自动建模,验证了数据组织和算法的有效性和优越性。     

三维激光扫描仪在复杂环境中的应用

随着扫描仪性能的不断提升和对环境适应性的增强,在许多常规测量无法完成的情况下,借助其非接触、远距离测量的特点,可以在特殊环境下完成许多常规测量无法完成的任务。     

抵偿高程面任意带高斯投影坐标系统的变形分析及应用

对投影变形问题进行了分析,结合实例,探讨了抵偿高程面任意带高斯投影对控制投影变形的实用性。     

磁异常探测中磁场矢量导数快速计算方法

目的 针对计算磁位高阶导数常用的频率域快速傅立叶变换方法在实际应用中存在边界效应的问题,提出了分别在x和y方向上移相π/2二维余弦变换定义及其时域微分定理,推导出了磁场矢量分量的导数计算公式,并将其应用于方向解析信号的磁异常边界识别方法。通过棱柱体源磁场理论数据对比分析,验证了基于余弦变换计算磁位高阶导数的方法在不需要扩边或滤波处理、未增加额外处理数据和算法复杂度的前提下,可避免或减小边界效应,算法稳定性更好,计算结果精度更高。     

顾及用户体验的三维城市模型自适应组织方法

传统基于“图层付象”的组织方法,没有考虑三维城市模型的不同内容以及不同细节层次的粒度差异,导致在网络环境下的传输效率低,难以满足多用户并发访问的流畅可视化。深入分析了大范围漫游与小范围聚焦的用户体验特点,通过元数据统筹管理和对象离散化,即时响应用户请求,减少无效数据传输,保证了多用户并发环境下的高效调度和浏览。针对模型LoI)和分解的对象,设计了结构统一的对象ID,隐式存储关联关系并支持分布式模型存储管理。以分布式数据库MongoDB为平台进行实验,验证了本文方法的可行性和有效性。     

地面三维激光扫描点云数据精度影响因素及控制措施

地面三维激光扫描技术虽然具有分辨率高、准确度高、效率高等特点,但在应用过程中存在一系列影响三维激光扫描精度的因素。文中详细分析扫描距离、物体表面材质、控制网、标靶测量精度、光斑大小、扫描点间距、点云拼接精度、全反射物质和外界环境等因素对三维激光扫描精度的影响,并提出对以上影响因素的控制方法。     

Singular value decomposition and cluster analysis as regression diagnostics tools for geodetic applications

It is well known that high-leverage observations significantly affect the estimation of parameters. In geodetic literature, mainly redundancy numbers are used for the detection of single high-leverage observations or of single redundant observations. In this paper a further objective method for the detection of groups of important and less important (and thus redundant) observations is developed. In addition, the parameters which are predominantly affected by these groups of observations are identified. This method thus complements other diagnostics tools, such as, e.g., multiple row diagnostics methods as described in statistical literature (see, e.g., Belsley et al. in Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New York, 1980). The method proposed in this paper is based on geometric aspects of adjustment theory and uses the singular value decomposition of the design matrix of an adjustment problem together with cluster analysis methods for regression diagnostics. It can be applied to any geodetic adjustment problem and can be used for the detection of (groups of) observations that significantly affect the estimated parameters or that are of negligible impact. One of the advantages of the proposed method is the improvement of the reliability of observation plans and thus the reduction of the impact of individual observations (and outliers) on the estimated parameters. This is of particular importance for the very long baseline interferometry technique which serves as an application example of the regression diagnostics tool.