Explore the vast range of titles available.

Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements, but modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Principles and Applications, Second Edition maintains a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This second edition expands to new topics that satisfy a growing need in the GIS, professional surveyor, machine control, and Big Data communities while continuing to embrace the earth center fixed coordinate system as the fundamental point of origin of one, two, and three-dimensional data sets. Ideal for both beginner and advanced levels, this book also provides guidance and insight on how to link to the data collected and stored in legacy systems. -- Provided by publisher.

Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements, but modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Principles and Applications, Second Edition maintains a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This second edition expands to new topics that satisfy a growing need in the GIS, professional surveyor, machine control, and Big Data communities while continuing to embrace the earth center fixed coordinate system as the fundamental point of origin of one, two, and three-dimensional data sets. Ideal for both beginner and advanced levels, this book also provides guidance and insight on how to link to the data collected and stored in legacy systems.

This paper investigates the implementation of a Multi-Objective Optimization technique for improving public transportation route planning in the setting of smart cities. Recognizing the difficulties of urban mobility, our technique incorporates a variety of criteria, including traffic patterns, cost-effectiveness, and environmental impact, to create an efficient route design system. The research applies complex algorithms to overcome the issues present in existing route planning procedures, using real-world data sources such as GPS data and traffic reports. We illustrate the efficacy of our strategy in boosting time efficiency, lowering costs, and decreasing environmental footprints via extensive case studies. The assessment measures used emphasise the suggested system’s advantages over current techniques. The debate digs into the larger implications for smart city development, recognising limits and providing possibilities for further study. This study adds vital insights and practical answers to the developing subject of smart city transportation, providing a solid basis for the continuing growth of urban mobility.

Relevant research has investigated how predictive modeling algorithms, deep-learning-based sensing technologies, and big urban data configure immersive hyperconnected virtual spaces in digital twin cities: digital twin modeling tools, monitoring and sensing technologies, and Internet-of-Things-based decision support systems articulate big-data-driven urban geopolitics. This systematic review aims to inspect the recently published literature on digital twin simulation tools, spatial cognition algorithms, and multi-sensor fusion technology in sustainable urban governance networks. We integrate research developing on how blockchain-based digital twins, smart infrastructure sensors, and real-time Internet of Things data assist urban computing technologies. The research problems are whether: data-driven smart sustainable urbanism requires visual recognition tools, monitoring and sensing technologies, and simulation-based digital twins; deep-learning-based sensing technologies, spatial cognition algorithms, and environment perception mechanisms configure digital twin cities; and digital twin simulation modeling, deep-learning-based sensing technologies, and urban data fusion optimize Internet-of-Things-based smart city environments. Our analyses particularly prove that virtual navigation tools, geospatial mapping technologies, and Internet of Things connected sensors enable smart urban governance. Digital twin simulation, data visualization tools, and ambient sound recognition software configure sustainable urban governance networks. Virtual simulation algorithms, deep learning neural network architectures, and cyber-physical cognitive systems articulate networked smart cities. Throughout January and March 2023, a quantitative literature review was carried out across the ProQuest, Scopus, and Web of Science databases, with search terms comprising “sustainable urban governance networks” + “digital twin simulation tools”, “spatial cognition algorithms”, and “multi-sensor fusion technology”. A Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) flow diagram was generated using a Shiny App. AXIS (Appraisal tool for Cross-Sectional Studies), Dedoose, MMAT (Mixed Methods Appraisal Tool), and the Systematic Review Data Repository (SRDR) were used to assess the quality of the identified scholarly sources. Dimensions and VOSviewer were employed for bibliometric mapping through spatial and data layout algorithms. The findings gathered from our analyses clarify that Internet-of-Things-based smart city environments integrate 3D virtual simulation technology, intelligent sensing devices, and digital twin modeling.

The generation of ever-bigger data sets pertaining to the distribution of activities in cities is paralleled by massive increases in computer power and memory that are enabling very large-scale urban models to be constructed. Here we present an effort to extend traditional land use–transport interaction (LUTI) models to extensive spatial systems so that they are able to track increasingly wide repercussions on the location of population, employment and related distributions of spatial interactions. The prototype model framework we propose and implement called QUANT is available anywhere, at any time, at any place, and is open to any user. It is characterised as a set of web-based services within which simulation, visualisation and scenario generation are configured. We begin by presenting the core spatial interaction model built around the journey to work, and extend this to deal with many sectors. We detail the computational environment, with a focus on the size of the problem which is an application to a 8436 zone system comprising England, Scotland and Wales generating matrices of around 71 million cells. We detail the data and spatial system, showing how we extend the model to visualise spatial interactions as vector fields and accessibility indicators. We briefly demonstrate the implementation of the model and outline how we can generate the impact of changes in employment and changes in travel costs that enable transport modes to compete for travellers. We conclude by indicating that the power of the new framework consists of running hundreds of ‘what if?’ scenarios which let the user immediately evaluate their impacts and then evolve new and better ones. 与城市活动分布相关的越来越大的数据集的产生，再加上计算机能力和存储器的巨大提升，使得我们能构建非常大规模的城市模型。在本文中，我们试图将传统的土地利用-交通交互（LUTI)模型扩展到广泛的空间系统，使之能追踪对人口、就业和空间相互作用的相关分布的日益广泛的影响。我们提出并实施的原型模型框架名为“QUANT”，它可以在任何地方、任何时间、任何地点使用，并且对任何用户开放。它的特征是一组基于网络的服务，其中配置了模拟、可视化和场景生成。我们从展示围绕工作通勤构建的核心空间交互模型开始，并将其扩展以处理众多部门。我们详细介绍了计算环境，重点是问题的规模，这是一个由英格兰、苏格兰和威尔士组成的8,436区域系统的应用，生成大约7,100万个单元的矩阵。我们详细介绍了数据和空间系统，展示了我们如何扩展模型，将空间交互可视化为矢量场和可访问性指标。我们简要展示了模型的实施，并概述了我们如何生成就业变化和出行成本变化的影响（这些使得各种交通模式对出行者而言具有竞争力）。我们的结论是，新框架的优势包括运行数百个“如果...则...”情形，让用户立即评估其影响，然后发展出新的、更好的方案。

Construction of the Huaihe River ecological-economic belt—an important component of the “One Belt, One Road” initiative—is essential for the development of central China. Urban competitiveness can reflect the level of urban development and comprehensive strength that, in turn, determine the trend of urban development. To evaluate urban competitiveness in the Huaihe River eco-economic belt, a comprehensive model is established and the dynamic factor analysis method is used for urban panel data. The results show that the economic development of a city has the greatest impact on its competitiveness while the impact of quality of life is small. In general, the spatial distribution of static scores of urban competitiveness in the Huaihe River eco-economic belt is unbalanced and the variation trend of dynamic scores mainly manifests as M or W shapes with regularity in time and space. The spatial distribution of the comprehensive scores of urban competitiveness varies dramatically, ranging from strong in eastern coastal areas to weak in central and western regions. In the construction of the Huaihe River eco-economic belt, urban development should rely on the comparative advantages of central cities to drive the common development of surrounding cities, helping in the overall development of the eco-economic belt and promoting the coordinated development of eastern and western regions.

The traditional development model of high consumption and low efficiency approaches the threshold of resource and environmental carrying capacity, bringing a series of severe social and ecological environmental problems, and it is urgent to realize green development transformation. Based on a multidimensional perspective, this paper constructed a comprehensive evaluation system for urban green development along the Yellow River Basin (YRB). Entropy method, exploratory spatial data analysis (ESDA) model, and trend analysis were used to measure and characterize the spatiotemporal evolution of urban green development index (UGDI) along the YRB in 2008, 2013, and 2018, and geographically weighted regression (GWR) model was used to explore the influencing factors of urban green development. The results are as follows: (1) the UGDI along the YRB showed a slow upward trend, but the absolute value was relatively low, mainly concentrated in 0.2699–0.3799. (2) The UGDI had obvious regional differences, and cities such as Baotou and Zibo showed a “high-high” agglomeration, while most cities in the midstream showed a “low-low” agglomeration, and in terms of space, they were “high in the east and west, low in the middle” and “high in the north and low in the south.” (3) Influencing factors had different degrees of impact on the UGDI. Economic, industrial, urbanization, and green infrastructure factors played a positive role in promoting the urban green development, while the relationship between governance, technological factors, and green development varies from city to city.

Rainfall data series with adequate quality and length are often incomplete or nonexistent. Thus, filling in rainfall gaps becomes necessary to complete databases. This article proposes the use of satellite products (TRMM—Tropical Rainfall Measuring Mission, CHIRPS—Climate Hazards Group InfraRed Precipitation with Stations and CMORPH—CPC Morphing Technique) to fill gaps in the rainfall historical series. The simple regression method, using satellite rainfall estimates, was tested to fill the missing data from 164 rainfall gauge stations in the Amazon region. Large dispersions were observed between rainfall data, with R2 ranging from 0.383 to 0.844, the best results were found in areas with less rainfall. As well, the greatest performance of the products was verified in the dry period, with r and d higher than 0.899 and 0.950, respectively. The product with the best representation in the region was CHIRPS, which had the lowest monthly values of mean absolute error (0.979 mm) and root mean square error (3.656 mm). The results confirm that the satellite estimates satisfactorily represent the seasonal variation of rainfall in the region, despite presenting cases of overestimation and underestimation of data. The higher performance of CHIRPS can be explained by the higher spatial resolution (0.05°), allowing for more accurate weather forecasts. In fact, CHIRPS has the CHPclim model, which adds other factors to the good product performance. These characteristics justify the better performance of the CHIRPS product for filling gaps in daily rainfall data in the Amazon region, favoring the best monthly rainfall estimates for each region state analyzed. Recommendations for Resource Managers Satellite products have been increasingly used for estimating rainfall data in regions with a low number of installed rainfall gauge stations. Thus, the assessment and selection of these products needs to be elaborated for the best decision making of water resource managers. Rainfall data are important to recognize the occurrence patterns for prediction of the climatic behavior of a region. Sectors such as agriculture and disaster prevention (droughts, floods, erosion of watersheds, and river silting) need knowledge of rainfall for planning, management, and mitigation. Knowledge of rainfall behavior is very important in the Amazon region. In this case, the dry season and temperatures have been increasing due to global climate change. These changes establish conditions for more intense fires, which increases the deforestation of the region.

Examining the factors that influence population distribution enables us to gain insights into the patterns and evolutionary trends of distribution over time. Based on the Spatial Durbin Model (SDM) with satellite data of nighttime light, net primary productivity (NPP), and the digital evaluation model (DEM), this study examines the population distribution of 303 prefecture-level cities in China between 2007 and 2017 in terms of three dimensions—economic development, ecological environment, and topography. The empirical results reveal that, firstly, the above-mentioned multiple factors have caused the current population distribution in Chinese cities. Economic development emerges as a potent force driving population concentration within local regions while simultaneously exerting a draining influence on surrounding urban centers. Secondly, the environment has a significant agglomeration effect on local regions and surrounding areas, while the average altitude can inhibit population aggregation. It is worth noting, however, that in eastern China, average altitude surprisingly contributes to population concentration within the local area.

In the work, based on the analysis of the countries-leaders of competitiveness, it was revealed that all of them among the most developed sectors of the economy had components of the competitiveness index related to innovative development and infrastructure. This suggests that it is the sectors of innovation and infrastructure that can be considered strategically important and make efforts for their development. Therefore, it makes sense to build models related to these areas. The paper presents two macroeconomic models for assessing the influence of factors on competitiveness, one of which will be innovation-oriented, and the second - infrastructure-oriented. These models use spatial data reflecting the assessments of 111 countries for a number of indicators produced by international organizations such as the World Economic Forum, the World Bank, and the World Intellectual Property Organization. Data for the Russian Federation are not included in the sample, since they will be used later to verify the results of the models.