The SCAR Spatial Data Model has been developed for Geoscience Standing Scientific Group (GSSG). It was presented to XXVII SCAR, 15-26 July 2002, in Shanghai, China.

The Spatial Data Model is one of nine projects of the Geographic Information Program 2000-2002. The goal of this project is 'To provide a SCAR standard spatial data model for use in SCAR and national GIS databases.'

Activities within this project include:

1. Continue developing the SCAR Feature Catalogue and the SCAR Spatial Data Model
2. Provide SCAR Feature Catalogue online
3. Creation and incorporation of symbology
4. Investigate metadata / data quality requirements
5. Ensure compliance to ISO TC211 and OGC standards

Source: http://www.geoscience.scar.org/geog/geog.htm#stds

Spatial data are increasingly being available in digital form, managed in a GIS and distributed on the web. More data are being exchanged between nations/institutions and used by a variety of disciplines. Exchange of data and its multiple use makes it necessary to provide a standard framework. The Feature Catalogue is one component of the Spatial Data Model, that will provide the platform for creating understandable and accessible data to users. Care has been taken to monitor the utility of relevant emerging ISO TC211 standards.

The Feature Catalogue provides a detailed description of the nature and the structure of GIS and map information. It follows ISO/DIS 19110, Geographic Information - Methodology for feature cataloguing. The Feature Catalogue can be used in its entirety, or in part. The Feature Catalogue is a dynamic document, that will evolve with use over time. Considerable effort has gone into ensuring that the Feature Catalogue is a unified and efficient tool that can be used with any GIS software and at any scale of geographic information.

The structure includes data quality information, terminology, database types and attribute options that will apply to any GIS. The Feature Catalogue is stored in a database to enable any component of the information to be easily viewed, printed, downloaded and updated via the Web.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Golden eagle (Aquila chrysaetos) nest site model spatial data and maps as described in Dunk JR, Woodbridge B, Lickfett TM, Bedrosian G, Noon BR, LaPlante DW, et al. (2019) Modeling spatial variation in density of golden eagle nest sites in the western United States. PLoS ONE 14(9): e0223143. https://doi.org/10.1371/journal.pone.0223143

The Arctic Slope Shoreline Change Risk Spatial Data Model consists of shoreline change susceptibility information from the Arctic Slope Shoreline Change Susceptibility Spatial Data Model (Brady 2017) summarized at asset locations defined by a local community-verified Arctic Slope at-risk asset spatial data model. Specifically, this data product is shoreline change susceptibility information added to attribute tables of a variety of asset spatial data for at-risk coastal Arctic Slope assets. The purpose of the data is to identify shoreline change risk to assets of interest to stakeholders selected during an Instructional Systems Design (ISD) process to develop a shoreline change risk WebGIS in collaboration with the North Slope Borough. Data are forthcoming and inquiries concerning the data should be directed to Michael Brady.

To achieve true data interoperability is to eliminate format and data model barriers, allowing you to seamlessly access, convert, and model any data, independent of format. The ArcGIS Data Interoperability extension is based on the powerful data transformation capabilities of the Feature Manipulation Engine (FME), giving you the data you want, when and where you want it.In this course, you will learn how to leverage the ArcGIS Data Interoperability extension within ArcCatalog and ArcMap, enabling you to directly read, translate, and transform spatial data according to your independent needs. In addition to components that allow you to work openly with a multitude of formats, the extension also provides a complex data model solution with a level of control that would otherwise require custom software.After completing this course, you will be able to:Recognize when you need to use the Data Interoperability tool to view or edit your data.Choose and apply the correct method of reading data with the Data Interoperability tool in ArcCatalog and ArcMap.Choose the correct Data Interoperability tool and be able to use it to convert your data between formats.Edit a data model, or schema, using the Spatial ETL tool.Perform any desired transformations on your data's attributes and geometry using the Spatial ETL tool.Verify your data transformations before, after, and during a translation by inspecting your data.Apply best practices when creating a workflow using the Data Interoperability extension.

These data (vector and raster) were compiled for spatial modeling of salinity yield sources in the Upper Colorado River Basin (UCRB) and describe different scales of watersheds in the Upper Colorado River Basin (UCRB) for use in salinity yield modeling. Salinity yield refers to how much dissolved salts are picked up in surface waters that could be expected to be measured at the watershed outlet point annually. The vector polygons are small catchments developed originally for use in SPARROW modeling that break up the UCRB into 10,789 catchments linked together through a synthetic stream network. The catchments were used for a machine learning based salinity model and attributed with the new results in these vector GIS datasets. Although all of these feature classes include the same polygons, the attribute tables for each include differing outputs from new salinity models and a comparison with SPARROW model results from previous research. The new model presented in these datasets utilizes new predictive soil maps and a more flexible random forest function to improve on previous UCRB salinity spatial models. The raster data layers represent aspects of soils, topography, climate, and runoff characteristics that have hypothesized influences on salinity yields.

Geoscience Australia has produced free Web-viewable 3D models of coastal data, for sharing data and information with project partners and coastal zone stakeholders. The models integrate a range of spatial data (including DEMs, multibeam bathymetry, sediment samples, benthic habitats and satellite imagery) within an easy to use interface. The models use the open source and ISO standard Virtual Reality Modelling Language (VRML) file format. The model described in this paper is for the Keppel Bay and Fitzroy River area in Queensland, Australia. These 3D VRML models are a good method for integrating coastal data, for better interpretation, and are easily transferred to end users via the Web.

Australia has a three-tiered hierarchal model of government. A single Federal government, eight State/Territory governments and approximately seven hundred municipal councils make up the three tiers. Each of these tiers, and the separate jurisdictions within the tiers, can have their own standards and arrangements for managing information useful for Emergency Management (EM). Other information resources are held by private organisations. The business drivers for a co-ordinated national approach to data collection, research and analysis?? was identified by the Council of Australian Governments (COAG) review and documented in their reportNatural Disasters in Australia ? Reforming mitigation, relief, and recovery arrangements? in 2001 (released in August 2002). Representatives of all tiers of governments were signatories to this report. Later in 2001 the events in New York on September 11 reinforced the business drivers for access to data that transcends jurisdictional boundaries, as did the 2003 bushfires in Canberra. Against this backdrop there are several projects that are addressing the infrastructure and data requirements at the state/territory level. The LIST? in Tasmania.VicMap? in Victoria, the EICU? project in NSW, theSIS? project in Queensland, the SLIP? project in Western Australia and the ESA CAD system in the ACT are examples of spatial information Infrastructure initiatives that partially support EM at the jurisdictional level. At the national level the Australian & New Zealand Land Information Council (ANZLIC) proposed a national Distributed Spatial Data Library in 2003. Previous attempts to create centralised repositories have failed but maturing web services and the ability to produce hard-copy maps on-demand have moved this concept to a practical reality. Underpinning the distributed library is the development of a communityAll Hazards? Data Taxonomy/Model for the EM community. The majority of the state jurisdictions provided input to the taxonomy, while additional expertises in the modelling and socio-economic domains were provided by Geoscience Australia (GA). The data identified by the taxonomy is sourced from varied and complex sources and formatted into a simplified, coherent form suitable for Emergency Management. The benefits of sharing data through a standardised framework are being progressively demonstrated to organisations through the ability to provide early warning of threats to their assets and services, while ensuring they maintain control of their data.

There are still many hurdles to overcome before an infrastructure to support a Distributed Spatial Data Library can be realised. These hurdles can be broadly categorised as technological and cultural. The technological hurdles are no longer a significant barrier as bandwidth steadily increases, and major GIS systems support web service based data integration. It is arguably the cultural hurdles that are the most difficult. The process of consultation and review used in creating the `All Hazards? taxonomy has created a realisation among the jurisdictions of the benefits of closer ties and co-operation in data sharing and delivery arrangements. There is still some distance to travel but the implementation of an Australian Distributed Spatial Data Library for Emergency Management is moving closer to reality.

Spatial data layers of stream crossing point locations, cross-section polyline, centerline polyline, and bank polyline shapefiles have been developed for selected stream crossings in the Squannacook River basin, Massachusetts. The spatial data and calculated attribute values are model input data for U.S. Army Corps of Engineer’s Hydrologic Engineering Center’s River Analysis System (HEC-RAS) hydraulic models. The stream crossing point locations were derived from the North Atlantic Aquatic Connectivity Collaboration (NAACC) database. The stream channel cross-sections, centerlines, and bank polylines were derived using automated methods in a Geographic Information System (GIS) using ArcGIS Pro and Python programming language. The polyline shapefiles are Z-enabled and have elevation data derived from Light Detection and Ranging (lidar) Digital Elevation Models (DEM) for Z-coordinate vertex values in units of feet. The polyline shapefiles are also M-enabled and have profile stationing values for the M-coordinate vertex values in units of feet. The automated GIS processes delineated a series of stream channel cross-sections along lidar-derived stream centerlines and have stream channel bathymetry estimated from Massachusetts bankfull channel geometry equations (Bent and Waite, 2013). The bankfull equations were also used to derive stream bank polylines. This data release contains the following shapefiles in the Spatial_Data_Layers.zip file: 1. Stream_Crossing_Locations.shp - Esri point shapefile derived from the NAACC stream crossing database. 2. Stream_Crossing_Watersheds.shp - Esri polygon shapefile of lidar-derived watershed boundaries that estimate the upstream drainage area for each stream crossing location. 3. Model_Cross_Sections.shp - Esri Z- and M-enabled polyline shapefile of the cross-section data used for hydraulic model input. 4. Model_Flowpaths.shp - Esri Z- and M-enabled polyline shapefile of the stream centerline and stream bank line data used for hydraulic model input. References: Bent, G.C., and Waite, A.M., 2013, Equations for estimating bankfull channel geometry and discharge for streams in Massachusetts: U.S. Geological Survey Scientific Investigations Report 2013–5155, 62 p., http://dx.doi.org/10.3133/sir20135155

This dataset provides shapefile of outlines of the 68 lakes where temperature was modeled as part of this study. The format is a shapefile for all lakes combined (.shp, .shx, .dbf, and .prj files). This dataset is part of a larger data release of lake temperature model inputs and outputs for 68 lakes in the U.S. states of Minnesota and Wisconsin (http://dx.doi.org/10.5066/P9AQPIVD).

The use of spatial econometric models in political science has steadily risen in recent years. However, the interpretation of these models has generally ignored the important substantive, and even spatial, nature of the estimated effects. This leaves many papers with a (non-spatial) interpretation of coefficients on the covariates and a brief discussion of the sign and strength of the spatial parameter. We introduce a general approach to interpreting spatial models and provide several avenues for an exposition of substantive spatial effects. Our approach can be generalized to most models in the spatial econometric taxonomy. Building on the example of the diffusion of democracy, we elucidate how our approach can be applied to modern political science problems.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

A set of employment forecasts which reflect the Brisbane City Council view of the likely SEQ Regional Employment patterns in the period between 2011 and 2041. These were prepared by the National Institute of Economic and Industrial Research.

Brisbane City Council forecasts of employment by ANZSIC 2 at SA2 geography. Figures are for Place of work employment.

Accurately predicting spatially-continuous daily air temperature (Ta) is critical for agriculture, environmental management, and ecology. While meteorological stations provide precise Ta data, their spatial coverage is limited. Remotely-sensed Land Surface Temperature (LST), often fused with meteorological data, offers broader spatial coverage but struggles due to complex relationships between Ta and LST, influenced by factors like topography and human activities. Traditional supervised learning methods often fail to capture the spatial autocorrelation and heterogeneity inherent in the relationships, indicating the need for a more robust approach that integrates geographic knowledge. This study proposes the Spatially-Varying Coefficients Random Forest (SVCRF) model, to integrate Exploratory Spatial Data Analysis (ESDA) into Random Forest(RF) to capture spatially non-stationary relationships. It first stratifies the study area based on bivariate Local Indicators of Spatial Association and geographical detector,then builds several spatial RFs with specific spatial position and extent. In each spatial RF, the distance from observation/prediction sites to its position are added as a key predictor variable, to model the local spatial variations of the relationships within the spatial extent. Applied to daily Ta mapping at 1 km resolution across China using data from 5,425 meteorological stations, the SVCRF model demonstrated superior accuracy, achieving RMSE of 1.315 °C and MAE of 1.014 °C. Compared to RF, regression kriging, and geographically weighted regression, it reduced MAE by 0.351 °C, 0.786 °C, and 0.831 °C, respectively. The model also offers high interpretability, with uncertainty estimates aligning with actual errors and spatially-resolved variable importance highlighting spatial patterns.

This dataset consists of current and future species distribution models generated using 4 Representative Concentration Pathways (RCPs) carbon emission scenarios, 18 global climate models (GCMs), and 8 time steps between 2015 and 2085, for Grey-headed Honeyeater (Lichenostomus (Ptilotula) keartlandi).

This layer shows the 3D infrastructure models of Hong Kong. The 3D models were converted from infrastructure models. It is a subset of 3D-BIT00 3D Spatial Data made available by Lands Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://www.hkmapservice.gov.hk/ (“HKMS 2.0”). The source data is in 3DS format and uploaded to Esri’s ArcGIS Online platform for sharing and referencing purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.

Geospatial Solutions Market size was valued at USD 282.75 Billion in 2024 and is projected to reach USD 650.14 Billion by 2031, growing at a CAGR of 12.10% during the forecast period 2024-2031.

Geospatial Solutions Market: Definition/ Overview

Geospatial solutions are applications and technologies that use spatial data to address geography, location, and Earth’s surface problems. They use tools like GIS, remote sensing, GPS, satellite imagery analysis, and spatial modelling. These solutions enable informed decision-making, resource allocation optimization, asset management, environmental monitoring, infrastructure planning, and addressing challenges in sectors like urban planning, agriculture, transportation, disaster management, and natural resource management. They empower users to harness spatial information for better understanding and decision-making in various contexts.

Geospatial solutions are technologies and methodologies used to analyze and visualize spatial data, ranging from urban planning to agriculture. They use GIS, remote sensing, and GNSS to gather, process, and interpret data. These solutions help users make informed decisions, solve complex problems, optimize resource allocation, and enhance situational awareness. They are crucial in addressing challenges and unlocking opportunities in today’s interconnected world, such as mapping land use patterns, monitoring ecosystem changes, and real-time asset tracking.

This digital dataset was created as part of a U.S. Geological Survey study, done in cooperation with the Monterey County Water Resource Agency, to conduct a hydrologic resource assessment and develop an integrated numerical hydrologic model of the hydrologic system of Salinas Valley, CA. As part of this larger study, the USGS developed this digital dataset of geologic data and three-dimensional hydrogeologic framework models, referred to here as the Salinas Valley Geological Framework (SVGF), that define the elevation, thickness, extent, and lithology-based texture variations of nine hydrogeologic units in Salinas Valley, CA. The digital dataset includes a geospatial database that contains two main elements as GIS feature datasets: (1) input data to the 3D framework and textural models, within a feature dataset called “ModelInput”; and (2) interpolated elevation, thicknesses, and textural variability of the hydrogeologic units stored as arrays of polygonal cells, within a feature dataset called “ModelGrids”. The model input data in this data release include stratigraphic and lithologic information from water, monitoring, and oil and gas wells, as well as data from selected published cross sections, point data derived from geologic maps and geophysical data, and data sampled from parts of previous framework models. Input surface and subsurface data have been reduced to points that define the elevation of the top of each hydrogeologic units at x,y locations; these point data, stored in a GIS feature class named “ModelInputData”, serve as digital input to the framework models. The location of wells used a sources of subsurface stratigraphic and lithologic information are stored within the GIS feature class “ModelInputData”, but are also provided as separate point feature classes in the geospatial database. Faults that offset hydrogeologic units are provided as a separate line feature class. Borehole data are also released as a set of tables, each of which may be joined or related to well location through a unique well identifier present in each table. Tables are in Excel and ascii comma-separated value (CSV) format and include separate but related tables for well location, stratigraphic information of the depths to top and base of hydrogeologic units intercepted downhole, downhole lithologic information reported at 10-foot intervals, and information on how lithologic descriptors were classed as sediment texture. Two types of geologic frameworks were constructed and released within a GIS feature dataset called “ModelGrids”: a hydrostratigraphic framework where the elevation, thickness, and spatial extent of the nine hydrogeologic units were defined based on interpolation of the input data, and (2) a textural model for each hydrogeologic unit based on interpolation of classed downhole lithologic data. Each framework is stored as an array of polygonal cells: essentially a “flattened”, two-dimensional representation of a digital 3D geologic framework. The elevation and thickness of the hydrogeologic units are contained within a single polygon feature class SVGF_3DHFM, which contains a mesh of polygons that represent model cells that have multiple attributes including XY location, elevation and thickness of each hydrogeologic unit. Textural information for each hydrogeologic unit are stored in a second array of polygonal cells called SVGF_TextureModel. The spatial data are accompanied by non-spatial tables that describe the sources of geologic information, a glossary of terms, a description of model units that describes the nine hydrogeologic units modeled in this study. A data dictionary defines the structure of the dataset, defines all fields in all spatial data attributer tables and all columns in all nonspatial tables, and duplicates the Entity and Attribute information contained in the metadata file. Spatial data are also presented as shapefiles. Downhole data from boreholes are released as a set of tables related by a unique well identifier, tables are in Excel and ascii comma-separated value (CSV) format.

Excel spreadsheet which only contains numeric data from a set of confusion matrices (one sheet per matrix).It is the same quantitative data stored in a field of a table in the database. Only is provided as a complement to the database in order to access to the quantitative data in a more convenient format.

A set of employment forecasts which reflect the Brisbane City Council view of the likely SEQ Regional Employment patterns in the period between 2011 and 2041. These were prepared by the National Institute of Economic and Industrial Research.Brisbane City Council forecasts of employment by ANZSIC 2 at SA2 geography. Figures are for Place of work employment.

Graffiti presents serious urban concerns, often signaling urban decay. This study uses open spatial data to analyze and model graffiti occurrences in terms of street network centrality measures. In particular, betweenness centrality, closeness centrality, and degree centrality are evaluated using San Francisco, California, as the case study area, with data from OpenStreetMap and reported graffiti from 2008 to 2023 from the San Francisco nonemergency municipal service (denoted as 311) as the data sets. The spatial error model was found to outperform both ordinary least squares tests and the spatial lag model. The model could further explain graffiti spatiality. Graffiti writers were observed to favor street segments that are close to the downtown and well-connected to other streets, often having high accessibility, visibility, and accommodating street furniture. The results indicate that bridges and highway segments that are difficult to stop and tag were typically avoided. In addition, for a given street, the model error in adjacent streets significantly (p