The e-government interface analysis interface is a web service provided via the e-government basic component Geodata (GeoBAK). The analysis interface returns property data for one or more graphic objects (point, line, polygon, circle as polygon) after a selectable spatial assignment to various, mainly area-based object types (e.g. districts/circle-free cities, parcels). The web service is optimised in terms of performance and therefore transfers no geometry in contrast to a WFS. The document “GeoBAK Analysis Interface — Overview Geodata Topics” provides an overview of the object types that can be retrieved from the analysis interface and their attribute structure. The focus is on INSPIRE topics. Previously, intersect, within, nearestneighbour and valuesatpoint have been set up as spatial assignments (operators). The operations intersect and within are defined in such a way that both are excluded. Intersect (default value): Returns all features that are touched by the passed geometries. Within: Returns all features in which the passed geometries are fully included. Nearestneighbour: Return features with the shortest possible distance. Use for determining the nearest address (reverse geocoding). Valuesatpoint: The values are returned at the given point. There is a overlap with raster data. In response, the analysis interface provides a JSON array with several objects combined for the different object types. A numbering of the objects (objectNo) is done in an analog way as the objects have been passed over. This allows applications and electronic technical procedures to transfer coordinates and geo-objects and retrieve numerous information from an object.

The Saudi Arabian geospatial analytics market, valued at $400 million in 2025, is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 9.22% from 2025 to 2033. This expansion is driven by several key factors. Firstly, substantial investments in infrastructure development, including smart city initiatives and digital transformation across various sectors, are fueling the demand for sophisticated geospatial analytics solutions. Secondly, the Kingdom's strategic focus on Vision 2030, which emphasizes diversification and technological advancement, is creating a favorable environment for the adoption of geospatial technologies across sectors such as agriculture, utilities, defense, and real estate. The increasing availability of high-resolution satellite imagery, coupled with advancements in data analytics and artificial intelligence (AI), further enhances the market's growth trajectory. Government initiatives promoting data sharing and open data platforms are also playing a crucial role. Segmentation reveals that network analysis and geovisualization are experiencing the fastest growth, driven by their applications in urban planning, resource management, and emergency response. Key players, including established technology giants like Microsoft and Esri, as well as specialized geospatial firms, are actively competing in this dynamic market, contributing to innovation and service diversification. Despite the promising outlook, challenges remain. Data security and privacy concerns related to handling sensitive geospatial data pose a significant restraint. Furthermore, the lack of skilled professionals proficient in geospatial analytics and data interpretation could hinder market growth in the short term. Nevertheless, ongoing investments in education and training programs should mitigate this issue. The overall market landscape indicates substantial potential for growth, particularly in leveraging geospatial analytics for sustainable development and effective resource allocation across Saudi Arabia's diverse sectors. The forecast period, spanning from 2025 to 2033, projects substantial market expansion, driven by consistent technological innovation and governmental support for digital transformation. Recent developments include: May 2023: Microsoft introduced three new functions for geospatial analysis in Azure Data Explorer, geo_point_buffer, geo_line_buffer, and geo_polygon_buffer. These functions allow users to create polygonal buffers around geospatial points, lines, or polygons, respectively, and return the resulting geometry. Users can use these functions to perform spatial operations such as intersection, containment, distance, or proximity on user geospatial data or to visualize data on maps., October 2022: ROSHN, the Kingdom of Saudi Arabia's nationwide real estate developer, backed by the government's Public Investment Fund (PIF), supported government efforts to improve homeownership rates while delivering sophisticated living standards. The Saudi Arabia designer built communities that looked to the nation's heritage and evolving resident aspirations. To support its vision and ongoing regional projects, ROSHN signed a memorandum of understanding (MOU) with Esri, the global player in location intelligence., . Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Notable trends are: Geovisualization is Expected to Hold Significant Share of the Market.

Australia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC - the Spatial Information Council and the Intergovernmental …Show full descriptionAustralia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC - the Spatial Information Council and the Intergovernmental Committee on Surveying and Mapping (ICSM) as a nationally consistent and topologically correct representation of the land borders published by the Australian states and territories. The purpose of this product is to provide: (i) a building block which enables development of other national datasets; (ii) integration with other geospatial frameworks in support of data analysis; and (iii) visualisation of these borders as cartographic depiction on a map. Although this dataset depicts land borders, it is not nor does it suggests to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context. This product is constructed by Geoscience Australia (GA), on behalf of the ICSM, from authoritative open data published by the land mapping agencies in their respective Australian state and territory jurisdictions. Construction of a nationally consistent dataset required harmonisation and mediation of data issues at abutting land borders. In order to make informed and consistent determinations, other datasets were used as visual aid in determining which elements of published jurisdictional data to promote into the national product. These datasets include, but are not restricted to: (i) PSMA Australia's commercial products such as the cadastral (property) boundaries (CadLite) and Geocoded National Address File (GNAF); (ii) Esri's World Imagery and Imagery with Labels base maps; and (iii) Geoscience Australia's GEODATA TOPO 250K Series 3. Where practical, Land Borders do not cross cadastral boundaries and are logically consistent with addressing data in GNAF. It is important to reaffirm that although third-party commercial datasets are used for validation, which is within remit of the licence agreement between PSMA and GA, no commercially licenced data has been promoted into the product. Australian Land Borders are constructed exclusively from published open data originating from state, territory and federal agencies. This foundation dataset consists of edges (polylines) representing mediated segments of state and/or territory borders, connected at the nodes and terminated at the coastline defined as the Mean High Water Mark (MHWM) tidal boundary. These polylines are attributed to convey information about provenance of the source. It is envisaged that land borders will be topologically interoperable with the future national coastline dataset/s, currently being built through the ICSM coastline capture collaboration program. Topological interoperability will enable closure of land mass polygon, permitting spatial analysis operations such as vector overly, intersect, or raster map algebra. In addition to polylines, the product incorporates a number of well-known survey-monumented corners which have historical and cultural significance associated with the place name. This foundation dataset is constructed from the best-available data, as published by relevant custodian in state and territory jurisdiction. It should be noted that some custodians - in particular the Northern Territory and New South Wales - have opted out or to rely on data from abutting jurisdiction as an agreed portrayal of their border. Accuracy and precision of land borders as depicted by spatial objects (features) may vary according to custodian specifications, although there is topological coherence across all the objects within this integrated product. The guaranteed minimum nominal scale for all use-cases, applying to complete spatial coverage of this product, is 1:25 000. In some areas the accuracy is much better and maybe approaching cadastre survey specification, however, this is an artefact of data assembly from disparate sources, rather than the product design. As the principle, no data was generalised or spatially degraded in the process of constructing this product. Some use-cases for this product are: general digital and web map-making applications; a reference dataset to use for cartographic generalisation for a smaller-scale map applications; constraining geometric objects for revision and updates to the Mesh Blocks, the building blocks for the larger regions of the Australian Statistical Geography Standard (ASGS) framework; rapid resolution of cross-border data issues to enable construction and visual display of a common operating picture, etc. This foundation dataset will be maintained at irregular intervals, for example if a state or territory jurisdiction decides to publish or republish their land borders. If there is a new version of this dataset, past version will be archived and information about the changes will be made available in the change log.

Dr. Kevin Bronson provides this dataset representing the first of three consecutive years of cotton and nitrogen management experimentation in Field 113. Included, is an intermediate analysis mega-table of correlated and calculated parameters, laboratory analysis results generated during the experimentation, plus high-resolution plot level intermediate data analysis tables of SAS process output, as well as the complete raw data sensor recorded logger outputs.

See included README file for operational details and further description of the measured data signals.

Summary - Active optical proximal cotton canopy sensing spatial data and including additional related metrics are presented. Agronomic nitrogen and irrigation management related field operations are listed. Unique research experimentation intermediate analysis table is made available, along with raw data. The raw data recordings, and annotated table outputs with calculated VIs are made available. Plot polygon coordinate designations allow a re-intersection spatial analysis. Data was collected in the 2016 cotton season at Maricopa Agricultural Center, Arizona, USA. High throughput proximal plant phenotyping via electronic sampling and data processing method approach is exampled using a modified high-clearance Hamby spray-rig. Acquired data conforms to location standard methodologies of high-throughput plant phenotyping. The weekly proximal sensing data collected include the primary canopy reflectance at six wavelengths. Lint and seed yields, first open boll biomass, and nitrogen uptake was also determined. Soil profile nitrate to 1.8 m depth was determined in 30-cm increments, before planting and after harvest. Nitrous oxide emissions were determined with 1-L vented chambers (samples taken at 0, 12, and 24 minutes). Nitrous oxide was determined by gas chromatography (electron detection detector).