Geographic Information Systems

Computational science has significantly advanced theory and experiment over many decades. Specifically, computational geography emerged in the 1980s in response to the reductionist limitations of early GIS software, which hindered deeper analyses of complex geographic data. The advent of relational databases further facilitated the use of computational methods in spatial data analysis. This study discusses the application of computational techniques and geocomputation in the spatial analysis of geographical phenomena. A literature search and data synthesis were conducted, followed by an exploration of computational methods, geocomputation, spatial data representation, storage and organization, spatial analytics, and GeoAI. Geocomputation is defined as the application of computer- intensive approaches, particularly those employing non-conventional data clustering and analysis methods, to discern knowledge. At present, these computational techniques enable the integration of diverse fields, supporting spatial analyses that require resources or ontological frameworks beyond the capabilities of traditional GIS software.

Spatial big data architecture: From Data Warehouses and Data Lakes to the LakeHouse

In this paper we present a Spatial Data Warehouse system that we use for aggregation and analysis of huge amounts of spatial data. The data is generated by utilities meters communicating via radio. In order to provide sufficient efficiency for our system we propose data and workload distribution as well as advanced indexing techniques. The system is based on a cascaded star model, which is a spatial development of a standard star schema and contains interconnected and often nested star schemas. The cascaded star allows efficient storage and analysis of spatial data, whose range extends from meter measurement values to weather information. The indexing tree structure and operation is tightly integrated with the spatial character of the data. Thanks to an available memory evaluating mechanism the system is very flexible in the field of aggregates accuracy. We also implemented indexing structure updating mechanism. The system is written in Java; for the data base we use Oracle 9i. Basing on the wide variety of tests results, we prove that a distributed system significantly surpasses the centralized version in terms of efficiency. We also show that a selective materialization of indexing structure fragments strongly increases system efficiency.

Microsoft SQL Server 2008 introduces new geography and geometry spatial datatypes that enable the storage of structured data describing the shape and position of objects in space. This is an interesting and exciting new feature, with many potentially useful applications. Beginning Spatial with SQL Server 2008 covers everything you need to know to begin using these new spatial datatypes, and explains how to apply them in practical situations involving the spatial relationships of people, places, and things on the earth. All of the spatial concepts introduced are explained from the ground up, so you need not have any previous knowledge of working with spatial data. Every section is illustrated with code examples that you can use directly in SQL Server. All of the topics covered in this book apply to all versions of SQL Server 2008, including the freely available SQL Server 2008 Express. What youll learn Understand the fundamental concepts involved in working with spatial data, including spatial references and coordinate systems. Apply these concepts in the collection and storage of spatial data in SQL Server 2008, using the new geometry and geography field types. Create different types of spatial data objectspoints, lines, and polygonsand use these to describe realworld objects. Learn how to analyze spatial data using a range of supported methods, and be aware of a number of different practical applications for these methods. Be shown how to integrate SQL Server with other tools, such as Microsoft Virtual Earth, to display a visual representation of spatial data. Know how to ensure the performance of spatially enabled databases by creating appropriate spatial indexes. Who is this book for? SQL Server developers who wish to use spatial data in Microsoft SQL Server 2008. About the Apress Beginning Series The Beginning series from Apress is the right choice to get the information you need to land that crucial entrylevel job. These books will teach you a standard and important technology from the ground up because they are explicitly designed to take you from novice to professional. Youll start your journey by seeing what you need to knowbut without needless theory and filler. Youll build your skill set by learning how to put together realworld projects step by step. So whether your goal is your next career challenge or a new learning opportunity, the Beginning series from Apress will take you thereit is your trusted guide through unfamiliar territory!

GIS is a geo-referenced data based system for manipulating digital spatial and thematic data. Because of its unique properties of spatial data (spatial dependence and spatial heterogeneity), there is an urgent need to investigate the mechanisms of spatial processes through exploring spatial data. The techniques of spatial analysis can be categorized into six groups: (1) generation of spatial data and its preliminary treatment; (2) spatialization of attribute data and scale transformation of spatial data; (3) visualization and exploration of spatial data; (4) geo-statistics; (5) analysis of lattice data; and (6) reversing information from complicated spatial data. To meet the real world solutions, a procedural diagram of spatial data analysis is suggested as below: (1) preliminary data treatment; (2) modeling; (3) solution; and (4) interpretation. Various techniques are being employed to investigate spatial processes, the identical study objective and some common characteristics of the study object may lead to the integration and convergence of the various approaches, and further the introduction of a general science: spatial mathematics. A same important issue is to develop robust spatial analysis computer packages for public uses.

Based on GIS technology, a spatial data analysis method for blocked road information data is presented in this paper. First, blocked road information data is introduced. Second, based on GIS technology, the blocked road information data analysis process is proposed. In which, simplify the processing of spatial data, isometric transformation based GIS platform, segment split processing, stake assignment, space matching of blocked road information data, and so on, are included. Finally, the method proposed in the paper is validated by blocked road information data of a province in eastern China from 2010 to 2013. The results show that the method is feasible and effectively, the analysis results can provide support for choosing highway network monitoring sites position, emergency material reserves and management.

3D spatial data acquired from aerial and remote sensing images by photogrammetric techniques is one of the most accurate and economic data sources for GIS, map production, and spatial data updating. However, there are still many problems concerning storage, structuring and appropriate management of spatial data obtained using these techniques. According to the capabilities of spatial database management systems (SDBMSs); direct integration of photogrammetric and spatial database management systems can save time and cost of producing and updating digital maps. This integration is accomplished by replacing digital maps with a single spatial database. Applying spatial databases overcomes the problem of managing spatial and attributes data in a coupled approach. This management approach is one of the main problems in GISs for using map products of photogrammetric workstations. Also by the means of these integrated systems, providing structured spatial data, based on OGC (Open GIS Consortium) standards and topological relations between different feature classes, is possible at the time of feature digitizing process. In this paper, the integration of photogrammetric systems and SDBMSs is evaluated. Then, different levels of integration are described. Finally design, implementation and test of a software package called Integrated Photogrammetric and Oracle Spatial Systems (IPOSS) is presented.

Aiming at the disadvantages in spatial data management of traditional spatial data record system on the global scale, such as the lack of large-scale or global standard management for data mapping range and spanning mapping junction, a new set of space-time record system, which is based on spatial location and time as an attribute, as well as computer cluster as system storage model, is designed in this paper. On the basis of analyzing the traditional spatial data record mode, the connotation and advantages of the space-time record system are discussed, and the system architecture and functions of the space-time record system are designed in detail based on the global subdivision model and GeoID coding so that it provides effective technical support for the management and organization of the global massive spatial data.

This paper describes how the semantic binary database model [Rishe-92-DDS] and its implementation [Rishe-91-FS] are extended to represent spatial data: functions over multi-dimensional space, e.g. the ocean temperature as a function of the four-dimensional space-time. The implementation, utilizing extended linear quad-trees, is compact in storage and allows efficient resolution of queries.

Geographic Artificial Intelligence (GeoAI) and Natural Language Processing (NLP) are two rapidly advancing technologies in spatial data analysis. GeoAI integrates artificial intelligence with geographic data to identify patterns, make predictions, and support decision-making. Meanwhile, NLP enables the processing and analysis of textual data related to spatial information, such as documents, reports, or geographic descriptions. The objective of this research is to obtain a representation of spatial data patterns through a series of processes, including problem identification, needs analysis, data collection and processing, document representation, and the application of Geographic Artificial Intelligence (GeoAI), Natural Language Processing (NLP), and Fuzzy Similarity methods to spatial or textual tax data and textual land data to identify data similarities. The research explores the integration of GeoAI and NLP in spatial data analysis to enhance the efficiency and accuracy of geographic data interpretation. The methods used in this study are based on artificial intelligence, which extracts spatial information from text and performs machine learning-based spatial analysis. The results demonstrate that the combination of GeoAI and NLP can improve the understanding of spatial patterns in unstructured data and support location-based decision-making processes. This research contributes to the development of more accurate spatial data analysis techniques that can be applied in various fields, such as urban planning, disaster management, and environmental analysis.

Potential meets reality: GIS and public health research in Australia

GIS is primarily associated with spatial data. In the most GIS, the spatial data as a file, together with the attribute data, is stored in relational database. So it is difficult to keep data security data management and data consistency. PostgreSQL and PostGIS can store spatial data and attribute data in one database; this makes it easy to manage and use data. In this paper, the features of spatial data, the shortcomings of relational database and how to create spatial database by PostGIS are analyzed, and the management methods of spatial data in PostGIS are introduced.

A WebGis tool for seismic hazard scenarios and risk analysis

The stakeholders involved in a dredging operation are many and varied, some specifically involved in the technical aspects (client and dredging operators, organisations involved in legislative matters and/or environmental monitoring), and others less so (general public).These different kinds of stakeholders need different information and consequently different kind of data need to be produced (description of dredging objectives and scope, technical dredges characteristics, related legislation, environmental monitoring data, work progress, etc.) that need to be managed to share dredging information and knowledge. Here, we illustrate two examples of communication strategy adopted in the case of the dredging of the Port of Genoa (Italy) to ensure the correct data dissemination among the project partners and to the public. The first realized with a WebGIS (MArine Coastal Information SysTEm—Port Section, MACISTE-PS), a technology composed of data handling tools for storage, recovery, management and analysis of spatial data, and the second with a set of different communication products (posters, article publications, congress participations, website, etc.). The adoption of these two strategies assures us the dissemination of dredging information to a broad spectrum of people.

Spatial omics technologies are producing an unprecedented amount of ultra-high plex in situ data that are promising to revolutionize cancer prognoses and treatments. Analytical solutions to integrate big spatial data are, however, lagging relative to the rapid technology development and this hinders discoveries into the pathological processes underlying cancer initiation and progression. Here we present STimage, a machine learning approach to flexibly combine transcriptome-wide spatial sequencing data with single-cell protein-based spatial phenotyping (Phenocycler-Fusion), generated from same tissue samples. As a ground-truth for cell typing, Akoya’s single-cell spatial phenotyping technology enabled us to precisely define cell types and cell states that were then used to evaluate and deploy the data integration pipeline. With these data, STimage first maps cells onto tissue sections with reference an H&E image. This way multiple layers of molecular data are transferred into one common framework, which can then be analyzed together. Traditional pathology annotations on the H&E images are also integrated to add human understanding of morphological patterns in a cancer tissue. The integrated analysis improves cell neighborhood identification, which allows cell-cell interaction analysis based on spatial co-localization between cell types (using single-cell resolution protein data) and locally co-expressing ligand-receptor pairs (using transcriptome-wide spatial data). We applied STimage to head and neck and skin cancer samples, in order to demonstrate the broad applicability of this analysis pipeline for various cancer types. We demonstrate applications for both, diagnoses and prognoses. For diagnosis, ST image identified and cross-validated cell types whilst assessing the expression of markers for drug targets. We also present an in-depth case study for an oropharyngeal squamous cell carcinoma patient not responding to Nivolumab treatment (anti-PD-1). Based on Visium and PhenoCycler-Fusion data, we discovered a spatial signature for non-responsiveness. Furthermore, we used the predicted ligand-receptor interactions to rank the patient’s response potential to currently available drugs, with the top target being TF-TFRC. STimage is thus able to integrate multiple layers of spatial omics data to improve and solidify prognostic biomarkers for cancer treatments. This study highlights the power of machine learning integration to combine multiple spatial multi-omics data, in particular PhenoCycler-Fusion and Visium, for improving diagnosis, prognosis and treatment of diverse cancers. Citation Format: Xiao Tan, Andrew Causer, Jazmina Gonzales-Cruz, Ning Ma, Bassem Ben Cheikh, Oliver Braubach, Quan Nguyen. Machine learning integration of transcriptome-wide spatial sequencing data and ultra-high plex spatial proteomic data enables the prioritization of cancer drug targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2059.