Spatio-temporal data access for information-based decision making

In recent years, applications aimed at exploring and analyzing spatial data have emerged, powered by the increasing need of software that integrates Geographic Information Systems (GIS) and On-Line Analytical Processing (OLAP). These applications have been called SOLAP (Spatial OLAP). In previous work, the authors have introduced Piet, a system based on a formal data model that integrates in a single framework GIS, OLAP (On-Line Analytical Processing), and Moving Object data. Real-world problems are inherently spatio-temporal. Thus, in this paper we present a data model that extends Piet, allowing tracking the history of spatial data in the GIS layers. We present a formal study of the two typical ways of introducing time into Piet: timestamping the thematic layers in the GIS, and timestamping the spatial objects in each layer. We denote these strategies snapshot-based and timestamp-based representations, respectively, following well-known terminology borrowed from temporal databases. We present and discuss the formal model for both alternatives. Based on the timestamp-based representation, we introduce a formal First-Order spatio-temporal query language, which we denote $\mathcal{L}_t,$ able to express spatio-temporal queries over GIS, OLAP, and trajectory data. Finally, we discuss implementation issues, the update operators that must be supported by the model, and sketch a temporal extension to Piet-QL, the SQL-like query language that supports Piet.

While the use of three-dimensional (3D) geographical information system (GIS) is becoming in rapid development and being used in various fields such as urban and regional planning, disaster management and planning, mobile navigation and etc., commercial and open source GIS software packages tend to offer 3D-GIS functionalities for their products. On the basis, GIS analysis functions are to provide information with respect to geographical location and by having 3D spatial data as an input, it will give advantages in providing horizontal position information. However, to analyze moving objects (temporal) in 3D seems not an easy task and not fully supported by current GIS platform packages. Previously in two-dimensional (2D) GIS practice, main issue addressed by researchers in managing temporal spatial objects is GIS packages were designed based on hardware and software constraints whereby it should be based on the temporal spatial objects ontology. Nowadays, the trend of managing temporal 3D data is via 3D spatial simulation or animation. This approach will not in assistance for GIS users in conducting spatial queries. Without having a suitable ontology and valid topological data structure for temporal 3D data, it will cause repetitive of temporal data (redundancy) and complications in executing spatial analysis in 3D environment. Therefore this paper focuses on the ontology for managing moving 3D spatial objects (i.e. air pollution, flood). The characteristics of moving objects were reviewed thoroughly by categorizing it based on its different appearances. Moreover, existing methods in managing temporal database were addressed and discussed for its practicalities. Another important aspect in managing temporal 3D objects is the implementation of topological data structures for 3D spatial objects were reviewed. In the last section of this paper it summarized the issues and further ideas towards implementing and managing temporal 3D spatial objects in GIS based on the Geoinformation Ontology (GeO).

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models.

A bi-level object-oriented data model together with a user query language called OFQL is designed which can support applications like GIS (geographic information systems). The data model is divided into two layers, the higher-level data model and the lower-level data model. The higher-level data model or the geographic object data model primarily consists of the geographic objects and a set of semantic spatial functions through which the topological relationships of the geographic objects are defined or derived. The lower-level data model or the geometric object data model has geometric objects which are the actual spatial representations of the geographic objects in the higher-level data model. It also has a set of geometric functions that retrieve, manipulate, and compute for geometric objects. The general architecture of a GIS system using this data modeling approach consists of two modules: the query processor and the function implementor. With the OFQL user-interface, the user is able to pose queries of a geographic nature without knowing the details of the spatial representation and computation of the geographic objects.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Data acquired from site surveys provide an essential resource to various exploration and production activities, such as field development planning, well positioning, and subsea equipment positioning. For many years, site survey data has been delivered as a Computer-aided Design (CAD) product. As operators move into a Geographic Information Systems (GIS) environment, shortfalls from CAD-based workflows have become obvious. This paper presents a method of managing site survey data based on geo-information management principles and practices. The methodology presented in this paper defines a GIS data model for seabed survey data interpretation that satisfies GIS query, presentation, and data exchange purposes. This paper also defines a standard for GIS seabed survey data deliverables from contractors to operators, including methods and processes to move the model to/from CAD-based applications. The implementation of the data model has proven to be a relative success and allows for a better integration of subsurface applications internally. The results have helped define an efficient contractor-operator data exchange process by eliminating data ambiguity and enabling seamless GIS data transfer. Results have also produced a more intelligent seabed survey GIS dataset with rules and behaviors capability offered by an ArcGIS Geodatabase embedded into the data model. Use of a standard GIS data model and a standard list of GIS data deliverables required from contractors to operators will allow project teams to fully embrace the power of GIS data integration and allow nearly seamless and lossless movement of CAD/GIS representations of the model. Introduction In order to reach maximum potential benefit of data provided from seabed site surveys throughout the E &amp; P lifecylcle (Figure 1), there is a need to ensure that data delivered from survey contractors to operators is done so in a structured, efficient, and consistent manner.

This paper discusses modifications to the U.S. Geological Survey modular, three-dimensional, finite-difference, ground-water flow model, commonly called MODFLOW, so that it can read and write files used by a geographic information system (GIS). The modified model program is called MODFLOWARC. Simulation programs such as MODFLOW generally require large amounts of input data and produce large amounts of output data. Viewing data graphically, generating head contours, and creating or editing model data arrays such as hydraulic conductivity are examples of tasks that currently are performed either by the use of independent software packages or by tedious manual editing, manipulating, and transferring data. Programs such as GIS programs are commonly used to facilitate preparation of the model input data and analyze model output data. Auxiliary programs are frequently required to translate data between programs, when different programs use different data formats. Thus, the user might use GIS techniques to create model input data, run a translation program to convert input data into a format compatible with the groundwater flow model, run the model, run a translation program to convert the model output into the correct format for GIS, and use GIS to display and analyze this output. MODFLOWARC, avoids the two translation steps and transfers data directly to and from the ground-water-flow model. This paper describes the design and use of MODFLOWARC and includes instructions for data input/output of the Basic, Blockcentered flow, River, Recharge, Well, Drain, Evapotranspiration, General-head boundary, and Streamflow-routing packages. The modification to MODFLOW and the Streamflow-Routing package was minimized. KEY TERMS: Ground-water; flow model; GIS _________ 1U.S. Geological Survey, Water Resources Division, 10615 S.E. Cherry blossom Drive, Portland, Oregon, 97215. 2U.S. Geological Survey, Water Resources Division, Room 406 Federal Building, Lincoln, Nebraska, 68508.

A geographic information system (GIS) is a relatively new software tool for geotechnical engineers. Its capabilities range from conventional data storage to complex spatial analysis and graphical presentation. This paper demonstrates the potential for a GIS to become a powerful, but routine, office tool. It highlights the importance of the geotechnical engineer in both the development and application of the technology. The impact of a GIS within a geotechnical design office is discussed with reference to a number of case histories. These include an archaeological survey, a landscaping assessment, a contaminated land remediation and a construction planning project. Successful application of the technology required a combination of geotechnical knowledge and the GIS-based skills. This was achieved, in practice, by training the geotechnical engineers to be proficient in using the software as well as understanding the GIS concepts. In terms of technical and financial performance, the multi-tasking functionality of the GIS was found to be well-suited to projects in which large quantities of data were analysed. The GIS enabled automation of repetitive and search procedures and quickly became the tool of choice. The GIS opened up opportunities for viewing and analysing data and enhanced the capabilities of the project engineer.

As the development of Geographic Information Systems (GIS) proceeds to advanced scientific and societal applications, there is an emerging need to enhance GIS support for complex spatiotemporal queries. Dynamic GIS representations (as opposed to static, map‐based representations) that can integrate proper data elements in the production of geographic information are required. This paper demonstrates the use of a three‐domain representation that facilitates compilation of higher‐level information (such as frequency and rate) from preliminary data records (such as time and location) stored in a database. The three‐domain representation is compared with snapshot, space‐time composite, and spatiotemporal object models using a sample data set for forest transitions. While the three‐domain representation is a normalization of these data models, it offers a conceptual alternative that enables GIS to represent spatiotemporal behaviors of geographic entities, in addition to entities as well as histories at locations as emphasized in most GIS data models. The comparison shows that the three‐domain representation has combined the strengths of the space‐time composite and spatiotemporal object models. Moreover, it enables aggregations of analytical use along with dynamic mappings between geographic concepts and locations, a distinct capability that takes GIS query processing beyond the level of information support offered by static map‐based data models.

. The article is devoted to the study of the possibilities of using geographic information systems (GIS) for monitoring the state of groundwater, which is important for ensuring environmental safety and sustainable management of water resources. Groundwater is an important source of fresh water for the population, industry and agriculture, but it is vulnerable to various pollutions caused by both natural and anthropogenic factors. Therefore, the implementation of reliable monitoring systems that allow timely detection, assessment and forecasting of changes in water quality is necessary to prevent the deterioration of the ecological situation and prevent the spread of pollution. Geographic information systems (GIS ) offer significant opportunities for the collection and integration of large volumes of spatial data, including satellite images, remote sensing results, automated sensor data, and field observations. GIS allow creating multi-layered maps reflecting the ecological state of underground aquifers, modeling pollution processes and predicting their possible impact on nearby ecosystems and water sources. Thanks to the use of GIS, it is possible to combine data from different sources in a single interactive system, which simplifies analysis and provides the ability to quickly respond to any detected deviations. The article examines in detail the key methods used within GIS for groundwater monitoring, including satellite sounding to analyze changes in the earth's surface, spectral indices to assess the state of vegetation and soil salinity, and radar methods to determine soil electrical conductivity. Particular attention is paid to the application of three-dimensional modeling, which allows visualization of the spread of pollution in underground aquifers, which facilitates decision-making regarding the optimal location of monitoring stations and the design of protective structures. In combination with automated sensors recording water parameters in real time, GIS provides continuous monitoring of the physico-chemical indicators of groundwater and allows for prompt assessment of their condition. The article also analyzes the possibilities of using predictive modeling in GIS to assess the further spread of pollution and develop environmental risk management measures. It is described how mathematical models in GIS can help predict the impact of anthropogenic and natural factors on groundwater, which ensures the development of scientifically based solutions to reduce pollution and maintain water quality at a safe level. The implementation of GIS in groundwater monitoring is an important step towards increasing the efficiency of water resources management, ensuring their protection from pollution and preserving the ecological stability of the regions.

Geographic information system is built on the basis of spatial data model, no GIS system can get rid of the data model and exist alone. Essentially, some of the key functions and data management of GIS are determined by the spatial data model. The most important point of the GIS driving system is that it can achieve the visualization of geographic information with the help of the GIS topsoil rendering, which greatly improves the data application rate of spatial mapping and makes the design of object model of spatial mapping gradually precise and scientific. Especially in recent years, due to the continuous improvement of spatial data collection methods and technologies, the efficiency of data collection has been greatly improved. How to use a large amount of data in spatial mapping and how to build models and preserve information has gradually become the focus of research. This paper, based on GIS drive, USES the basic mathematical algorithm to design and apply the object model of spatial cartography, and puts forward the advantages of the object model of spatial cartography in cartography. It provides some reference for the later related research.

Insights into NoSQL databases using financial data: A comparative analysis

Modern information needs have resulted in very large amounts of data being used in geographic information systems. Problems arise when trying to project these data in a reasonable amount of time and accuracy, however. Current single-threaded methods can suffer from two problems: fast projection with poor accuracy, or accurate projection with long processing time. A possible solution may be to combine accurate interpolation methods and distributed processing algorithms to quickly and accurately convert digital geospatial data between coordinate systems. Modern technology has made it possible to construct systems, such as Beowulf clusters, for a low cost and provide access to supercomputer-class technology. Combining these techniques may result in the ability to use large amounts of geographic data in time-critical situations.

A Geographic Information System (GIS) is a technological tool that allows for the capture, storage, analysis, and visualization of geographically referenced data. These systems integrate various forms of spatial and non-spatial data, facilitating the analysis of geographic phenomena and patterns.The integration of Machine Learning (ML) into Geographic Information Systems (GIS) has revolutionized the way geospatial data is analyzed and used. Machine Learning, with its ability to learn from large volumes of data and make accurate predictions, complements the analytical capabilities of GIS, allowing for the extraction of complex patterns and the performance of advanced predictions that were not previously possible. The purpose of this chapter is to explore the applications of Geographic Information Systems (GIS) empowered by the use of machine learning, highlighting their impact on spatial analysis and environmental management.

Interaction with geographic information systems via spatial queries

Chapter 21 - Big Data Integration