It's All Analytics – Part II

Spatial indexing technology is widely used in Geographic Information Systems (GIS) and spatial databases. As a data retrieval technology, spatial indexing is becoming increasingly important in the big-data age. The purpose of this study is to propose a unified indexing strategy for the mixed data of a future marine GIS. First, data organisation of the system is described. Second, the display condition of each type of data is introduced. These conditions are the basis for the construction of a unified indexing structure. Third, a unified indexing structure for mixed data is presented. The construction process and the search method of the indexing structure are described. Finally, we implement the indexing strategy in our system “Automotive Intelligent Chart Three-dimensional Electronic Chart Display and Information Systems” (AIC 3D ECDIS). Our strategy can provide fast and integrated data retrieval. The spatial indexing strategy we propose breaks through the limitation of data types in our system. It can also be applied in other GIS systems. With the advent of the big-data age, mixed data indexing will become more and more important.

Qom is the eighth most populated city in center of Iran, and its population growth rate is among the highest in this country. Th presence of a Great Salt Lake, petroleum potential and tourism attractions in this city sheds light on the importance of how solid waste landfill locations should be disposed, located and managed as an environmental issue. Considering the key parameters in landfill site selection, in this study a series of location analysis have been conducted to locate optimum regions for municipal solid waste disposal, using analytical hierarchy process (AHP) and geographical information system (GIS). The main factors in selecting the suitable location for waste disposal include geomorphology–hydrography, environmental–social factors and design criteria, each of which are subdivided into several categories. Criteria are selected according to the regional condition; therefore, important factors such as distance from sea and forested areas were not considered. In the next step, digital layers are weighted and classified according to the available standards and expert judgment. Then, analytical multi-criteria decision-making algorithms as AHP and weighted linear combination are applied upon existing layers in GIS. The results show that by implementing the AHP method in this region only 7% of the study area has a very good and appropriate condition for landfill location and the field observation confirms them. Finally, considering the environmental effects of landfill, appropriate locations are suggested.

How can we maximize the value of accumulated RDF data? Whereas the RDF data can be queried using the SPARQL language, even the SPARQL-based operation has a limitation in implementing traversal or analytical algorithms. Recently, a variety of database implementations dedicated to analyses on the property graph (PG) model have emerged. Importing RDF datasets into these graph analysis engines provides access to the accumulated datasets through various application interfaces. However, the RDF model and the PG model are not interoperable. Here, we developed a framework based on the Graph to Graph Mapping Language (G2GML) for mapping RDF graphs to PGs to make the most of accumulated RDF data. Using this framework, accumulated graph data described in the RDF model can be converted to the PG model, which can then be loaded to graph database engines for further analysis. For supporting different graph database implementations, we redefined the PG model and proposed its exchangeable serialization formats. We demonstrate several use cases, where publicly available RDF data are extracted and converted to PGs. This study bridges RDF and PGs and contributes to interoperable management of knowledge graphs, thereby expanding the use cases of accumulated RDF data.

In a time when safety is still of the highest importance, especially for vulnerable groups like women, this chapter analyzes the powerful intersection of crime mapping, hotspot analysis, and geographic information systems (GIS) to strengthen and increase protection. This chapter examines how GIS may be a flexible tool to understand and prevent crimes against women. The chapter begins by laying the framework for understanding women's many safety challenges, stressing the need for a comprehensive strategy. It then deepens the reader into crime mapping while highlighting GIS's significant role in displaying and interpreting spatial data. It uses illustrative examples to show how mapping may be used to uncover the spatial and temporal patterns of crime that affect women. This chapter focuses on locating and identifying areas where women may be at a higher risk of becoming victims of crime. By analyzing the complex analytical methodologies and algorithms utilized for hotspot detection, this chapter offers insights into predictive modeling for preventive safety measures.

This study aims to evaluate the implementation of virtual Industrial Studies (SI) including (l) context, (2) input, (3) process, and (4) output, carried out by students of the Family Welfare Vocational Education Study Program (PVKK) Universitas Sarjanawiyata Tamansiswa (UST). This evaluation research uses the CIPP model with quantitative descriptive methods. The research instrument used a questionnaire that was collected online. The results of the evaluation research show that: (1) the context of industrial studies on policies, objectives, guidelines, and guidance shows a very good category, while socialization is in a good category; 2) Network input and search engine are in a good category, and equipment, IT ability and motivation are in the very high category, 3) all process components are in the very good category, 4) all output components are in the very good category. Industrial studies virtually provide meaningful experiences, improve insight, data literacy, digital, and managerial systems in the industry more broadly to students.

Application of remote sensing and geographic information systems to the delineation and analysis of riparian buffer zones

Introduction to Numerical Ordinary and Partial Differential Equations Using MATLAB® teaches readers how to numerically solve both ordinary and partial differential equations with ease. This innovative publication brings together a skillful treatment of MATLAB and programming alongside theory and modeling. By presenting these topics in tandem, the author enables and encourages readers to perform their own computer experiments, leading them to a more profound understanding of differential equations.The text consists of three parts: Introduction to MATLAB and Numerical Preliminaries, which introduces readers to the software and its graphical capabilities and shows how to use it to write programs Ordinary Differential Equations Partial Differential Equations All the tools needed to master using MATLAB to solve differential equations are provided and include: Exercises for the Reader that range from routine computations to more advanced conceptual and theoretical questions (solutions appendix included) Illustrative examples, provided throughout the text, that demonstrate MATLAB's powerful ability to solve differential equations Explanations that are rigorous, yet written in a very accessible, user-friendly style Access to an FTP site that includes downloadable files of all the programs developed in the text This textbook can be tailored for courses in numerical differential equations and numerical analysis as well as traditional courses in ordinary and/or partial differential equations. All the material has been classroom-tested over the course of many years, with the result that any self-learner with an understanding of basic single-variable calculus can master this topic. Systematic use is made of MATLAB's superb graphical capabilities to display and analyze results. An extensive chapter on the finite element method covers enough practical aspects (including mesh generation) to enable the reader to numerically solve general elliptic boundary value problems. With its thorough coverage of analytic concepts, geometric concepts, programs and algorithms, and applications, this is an unsurpassed pedagogical tool.

Archaeological research can not be separated from the three dimensions of archeology: form, space and time. Spatial dimension is essential in archeology that covers all phases of archaeological research (theory, method, practice). Collection, analysis, interpretation and presentation of archaeological data should be actively and creatively covered in the dimension of space, was realized in the form of a map. In the development of the map are integrated with other graphical data and databases, known as Geographic Information System. Study of spatial Archaeology in Karangagung Tengah conducted to determine the distribution pattern of the site and what factors influence the formation of the pattern. Archaeological sites scattered in the edges of tidal rivers with access to the Lalan River and Sembilang River which empties into the Strait of Bangka. Distribution of these sites mapped to the benefit of further research and reference to delineate the Karangagung Tengah zoning for conservation purposes.

Interoperable cross-domain semantic and geospatial framework for automatic change detection

Hydrological soil group is essential to soil information for several fields of modeling and applications. This information can affect suitable environmental, agricultural, and hydrological development. Laboratory analysis for soil sampling cannot efficiently provide the needed information because these analyses are commonly costly, time-consuming, and limited in retrieving the temporal and spatial variability. In this context, remote sensing is now solid to offer meaningful spatial data for studying soil characteristics on various spatial scales utilizing the different spectral reflectance. For this study, the integration of Geographic Information System (GIS) remote sensing data and survey data with the Artificial Neural Network (ANN) were used to generate a hydrological soil group map and to infer spatial patterns of soils across complete area converges for Alghadaf Wadi in the Western Desert of Iraq. The generated soil information was tested based on the sand, silt, and clay content. The testing result indicated that the differences between actual and predicted values to determine soil classes are agreed well. Therefore, this method is vital for mapping and monitoring soil texture by providing timely, fast repetitive data and relatively cheap.

Sustainable development in the energy industry is rapidly expanding beyond the conceptual stage. Policies addressing the three principles of Sustainable Development (economic growth, environmental protection, and social progress) are being established and strategies to execute these policies are being developed and implemented in the field. Export pipeline projects provide a wide variety of applications for the three elements of sustainable development. Properly designed, installed and operated pipeline systems enable the energy industry to deliver hydrocarbon products to the market place in a way that delivers economic rewards while preserving the integrity of the environment and surrounding communities and their ways of life. Conoco is developing a strong corporate culture around sustainable development; and, pipeline systems play a vital role in delivering the triple bottom line results for our stakeholders. This paper will present some of the key focal points used by Conoco Inc. in pipeline project development. It proposes GIS technology to make pipeline projects a contributor to sustainable growth success.

The built environment in the Industry 4.0 is digitizing rapidly and producing great amounts of data. These data are of different kinds and follow specific structures and logics, depending on what they are used for and where they come from. This includes vendor-neutral and exchangeable files, such as the Industry Foundation Classes (IFC), that capture building and infrastructure data in neutral and exchangeable files; knowledge graphs and linked data that store building data in web-based graph databases; time-series databases that store sensor data values (big data; data lakes); diverse standard file formats to represent 2D and 3D geometry; and property servers as well as product databases. This chapter will particularly investigate knowledge graphs and linked data. These data technologies promise to represent any data on the web and link them all together. This chapter will explain the most basic parts of the technology, while omitting technical detail. It gives an overview of how these technologies are used in the AEC and smart buildings domains. The chapter ends with a discussion on applicability and limitations of the documented technologies and an outlook into the future.KeywordsKnowledge graphsLinked dataSemantic webSmart buildingsIndustry4.0Geometry

Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues.

For centuries, mills with beam and weight presses have been the production units used in the town of Écija to obtain the oil. The intensification of olive tree cultivation in the 18th century gave rise to the proliferation of these constructions throughout the town, which at one point was home to no less than 286 mills of this type. However, by the mid-20th century events surrounding the local olive groves caused many of them to fall into disuse. Nowadays, the mills present an advanced state of decay, to the extent that many of them have disappeared partly or completely. In view of the functional nature of these production units and the short space of time in which they were built, we decided to conduct a typological study aimed at identifying any common patterns in their design. The geometric and proportional relationships between their constituent parts obtained using digital information models (Geographic Information System (GIS), 3D point clouds and databases) enabled us to determine standard structures based on ranges of deduced values. The repetition of these patterns suggested that it would be useful to create a graphical database using a parameterised HBIM (Historic Building Information Modelling), which in turn facilitates the introduction of attributes associated with these mills from a dynamic database, therefore favouring interoperability in heritage management as a response to the critical situation of the mills today. At the same time, the correspondence in the relationships of proportionality between the mills analysed typologically and the model of a 16th-century mill, suggests that 18th-century mills were adapted to patterns developed in older presses.Highlights:- The typological study of the Écija (Seville) mills with beam and weight presses reveals common patterns in these preindustrial buildings.- The proportional relationships between their constituent parts obtained using digital information models enabled us to determine standard structures. - These patterns have similarly enabled us to generate a parameterised HBIM model as a standard graphical base for the historical mills in Écija.

The application of advanced analytics and machine learning (ML) models for disaster risk reduction. By leveraging supervised and unsupervised learning algorithms, the study integrates multi-source datasets—including satellite imagery, sensor networks, and geospatial data—into predictive frameworks for early warning and hazard assessment. Techniques such as deep learning, random forests, and support vector machines are evaluated for forecasting events like floods, earthquakes, and wildfires. The research presents end-to-end workflows: from data pre-processing and feature engineering to model training, real-time inference, and deployment in Geographic Information Systems (GIS). Results demonstrate that ML-enhanced analytics significantly improve the accuracy and timeliness of disaster prediction, enabling authorities to optimize response strategies and minimize socio-economic impacts in vulnerable regions.