This Data Release provides spatial data to support analysis of land cover change and channel width change in the Bogachiel and Calawah River basins, Washington. This supports a larger analysis that quantifies suspended-sediment yields for the two basins for water years 1977-1978 and more recently, for water years 2019-2021. Collectively the study evaluates influences of hydrology, geology, fire, and land cover change on suspended-sediment yields. Data Release Directory Structure: \Bogachiel_Calawah_Imagery.zip \Bogachiel_Calawah_Imagery.xml: metadata file to describe all imagery files in this folder \1939_mosaic.tif \1952_mosaic.tif \1955_mosaic.tif \1977_mosaic.tif \1990_mosaic.tif \1939_1_manual_georef.tif \1939_2_manual_georef.tif \1939_3_manual_georef.tif \1939_4_manual_georef.tif \1939_5_manual_georef.tif \1939_6_manual_georef.tif \1939_7_manual_georef.tif \1939_8_manual_georef.tif \ActiveChannel_Centerlines_ClearedAreas.zip: data and metadata for digitized active channel, centerlines, and cleared areas for 1939, 1952, 1955, 1977, 1990, 2006, and 2017 for the Bogachiel and Calawah Rivers. \Active_channel.shp \Active_channel.xml \Centerlines.shp \Centerlines.xml \ClearedAreas.shp \ClearedAreas.xml \ForksFire.zip: data and metadata for 1951 Great Forks Fire \1951_GreatForks_Fire.shp \1951_GreatForks_Fire.xml \DigitizedSubreaches.zip: data and metadata for digitized active channels and centerlines for select subreaches in the Bogachiel and Calawah Rivers by four individual digitizers for 1939, 1952, 1955, 1977, 1990, 2006, and 2017. \AC_Digitized_Subreaches.shp \AC_Digitized_Subreaches.xml \CL_Digitized_Subreaches.shp \CL_Digitized_Subreaches.xml Anderson, SW, Jaeger, KL, Rasmussen, N, Seguin, CM, Wilkerson, OA, and Curran, CA, 2022, Suspended-Sediment Data for the Bogachiel and Calawah Rivers, WA for Water Years 2019-2021: U.S. Geological Survey Data Release, https://doi.org/10.5066/P9YT9CN2.

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

As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

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

A communication channel should be built to transmit information from one place to another. Imaging is 2 or higher dimensional information communication. Conventionally, an imaging channel comprises a lens with free space at its both sides, whose transfer function is usually known and hence the response of the imaging channel can be well defined. Replacing the lens with a thin scattering medium, the image can still be extracted from the detected optical field, suggesting that the scattering medium retains or reconstructs not only energy but also information transmission channels. Aided by deep learning, we find that unlike the lens system, there are different channels in a scattering medium: the same scattering medium can construct different channels to match the manners of source coding. Moreover, it is found that without a valid channel, the convolution law for a spatial shift-invariant system (the output is the convolution of the point spread function and the input object) is broken, and in this scenario, information cannot be transmitted onto the detection plane. Therefore, valid channels are essential to transmit information through even a spatial shift-invariant system. These findings may intrigue new adventures in imaging through scattering media and reevaluation of the known spatial shift-invariance in various areas.

The National Aggregates of Geospatial Data Collection: Population, Landscape, And Climate Estimates, Version 3 (PLACE III) data set contains estimates of national-level aggregations in urban, rural, and total designations of territorial extent and population size by biome, climate zone, coastal proximity zone, elevation zone, and population density zone, for 232 statistical areas (countries and other UN recognized territories). This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The USDA Long-Term Agroecosystem Research was established to develop national strategies for sustainable intensification of agricultural production. As part of the Agricultural Research Service, the LTAR Network incorporates numerous geographies consisting of experimental areas and locations where data are being gathered. Starting in early 2019, two working groups of the LTAR Network (Remote Sensing and GIS, and Data Management) set a major goal to jointly develop a geodatabase of LTAR Standard GIS Data Layers. The purpose of the geodatabase was to enhance the Network's ability to utilize coordinated, harmonized datasets and reduce redundancy and potential errors associated with multiple copies of similar datasets. Project organizers met at least twice with each of the 18 LTAR sites from September 2019 through December 2020, compiling and editing a set of detailed geospatial data layers comprising a geodatabase, describing essential data collection areas within the LTAR Network.
The LTAR Standard GIS Data Layers geodatabase consists of geospatial data that represent locations and areas associated with the LTAR Network as of late 2020, including LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This geodatabase was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. The creation of the geodatabase began with initial requests to LTAR site leads and data managers for geospatial data, followed by meetings with each LTAR site to review the initial draft. Edits were documented, and the final draft was again reviewed and certified by LTAR site leads or their delegates. Revisions to this geodatabase will occur biennially, with the next revision scheduled to be published in 2023. Resources in this dataset:Resource Title: LTAR Standard GIS Data Layers, 2020 version, File Geodatabase. File Name: LTAR_Standard_GIS_Layers_v2020.zipResource Description: This file geodatabase consists of authoritative GIS data layers of the Long-Term Agroecosystem Research Network. Data layers include: LTAR site locations, LTAR site points of contact and street addresses, LTAR experimental boundaries, LTAR site "legacy region" boundaries, LTAR eddy flux tower locations, and LTAR phenocam locations.Resource Software Recommended: ArcGIS,url: esri.com Resource Title: LTAR Standard GIS Data Layers, 2020 version, GeoJSON files. File Name: LTAR_Standard_GIS_Layers_v2020_GeoJSON_ADC.zipResource Description: The contents of the LTAR Standard GIS Data Layers includes geospatial data that represent locations and areas associated with the LTAR Network as of late 2020. This collection of geojson files includes spatial data describing LTAR site locations, addresses, experimental plots, fields and watersheds, eddy flux towers, and phenocams. There are six data layers in the geodatabase available to the public. This dataset was created in 2019-2020 by the LTAR network as a national collaborative effort among working groups and LTAR sites. Resource Software Recommended: QGIS,url: https://qgis.org/en/site/

HEPGIS is a web-based interactive geographic map server that allows users to navigate and view geo-spatial data, print maps, and obtain data on specific features using only a web browser. It includes geo-spatial data used for transportation planning. HEPGIS previously received ARRA funding for development of Economically distressed Area maps. It is also being used to demonstrate emerging trends to address MPO and statewide planning regulations/requirements , enhanced National Highway System, Primary Freight Networks, commodity flows and safety data . HEPGIS has been used to help implement MAP-21 regulations and will help implement the Grow America Act, particularly related to Ladder of Opportunities and MPO reforms.

Spatial layers are provided in GDA2020 Map Grid of Australia (MGA) 56 coordinate reference system, which include an orthomosaic RGB tiff in cloud optimised geotiff (COG) format; a paddock fence line vector in geojson format; a digital elevation model (DEM) generated from Lidar, a 1m contour vector and a digitised soil map generated from a paper map. All data have been generated under the Strategic Investment Process (SIP): Leveraging the Research Farms. Lineage: Orthomosaic cloud optimised geotiff (COG) was produced by an external provider with full rights and ownership by CSIRO. Data was captured in GDA2020 MGA 56 via a DJI Matrice 300 with a P1 camera at 45 megapixels. A digital elevation model (DEM) tiff image was generated from drone mounted Lidar source. A field spatial survey utilising AusCORS corrections via a rover survey tool was carried out to mark fence strainer post in order to create a highly accurate fence line spatial layer. The digitised soil map was created by georeferencing the paper map produced for the report, 'Schafer, B. M. A description of the soils on the CSIRO pastoral research laboratory property, Chiswick, Armidale, N.S.W. (Animal Research Laboratories technical paper; no. 8).

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Utility GIS Data Quality Services Market Outlook

According to our latest research, the global Utility GIS Data Quality Services market size reached USD 1.29 billion in 2024, with a robust growth trajectory marked by a CAGR of 10.7% from 2025 to 2033. By the end of the forecast period, the market is projected to attain a value of USD 3.13 billion by 2033. This growth is primarily driven by the increasing need for accurate spatial data, the expansion of smart grid initiatives, and the rising complexity of utility network infrastructures worldwide.

The primary growth factor propelling the Utility GIS Data Quality Services market is the surging adoption of Geographic Information Systems (GIS) for utility asset management and network optimization. Utilities are increasingly relying on GIS platforms to ensure seamless operations, improved decision-making, and regulatory compliance. However, the effectiveness of these platforms is directly linked to the quality and integrity of the underlying data. With the proliferation of IoT devices and the integration of real-time data sources, the risk of data inconsistencies and inaccuracies has risen, making robust data quality services indispensable. Utilities are investing heavily in data cleansing, validation, and enrichment to mitigate operational risks, reduce outages, and enhance customer satisfaction. This trend is expected to continue, as utilities recognize the strategic importance of data-driven operations in an increasingly digital landscape.

Another significant driver is the global movement towards smart grids and digital transformation across the utility sector. As utilities modernize their infrastructure, they are deploying advanced metering infrastructure (AMI) and integrating distributed energy resources (DERs), which generate vast volumes of spatial and non-spatial data. Ensuring the accuracy, consistency, and completeness of this data is crucial for optimizing grid performance, minimizing losses, and enabling predictive maintenance. The need for real-time analytics and advanced network management further amplifies the demand for high-quality GIS data. Additionally, regulatory mandates for accurate reporting and asset traceability are compelling utilities to prioritize data quality initiatives. These factors collectively create a fertile environment for the growth of Utility GIS Data Quality Services, as utilities strive to achieve operational excellence and regulatory compliance.

Technological advancements and the rise of cloud-based GIS solutions are also fueling market expansion. Cloud deployment offers utilities the flexibility to scale data quality services, access advanced analytics, and collaborate across geographies. This has democratized access to sophisticated GIS data quality tools, particularly for mid-sized and smaller utilities that previously faced budgetary constraints. Moreover, the integration of artificial intelligence (AI) and machine learning (ML) in data quality solutions is enabling automated data cleansing, anomaly detection, and predictive analytics. These innovations are not only reducing manual intervention but also enhancing the accuracy and reliability of utility GIS data. As utilities continue to embrace digital transformation, the demand for cutting-edge data quality services is expected to surge, driving sustained market growth throughout the forecast period.

Utility GIS plays a pivotal role in supporting the digital transformation of the utility sector. By leveraging Geographic Information Systems, utilities can achieve a comprehensive understanding of their network infrastructures, enabling more efficient asset management and network optimization. The integration of Utility GIS with advanced data quality services ensures that utilities can maintain high standards of data accuracy and integrity, which are essential for effective decision-making and regulatory compliance. As utilities continue to modernize their operations and embrace digital technologies, the role of Utility GIS in facilitating seamless data integration and real-time analytics becomes increasingly critical. This not only enhances operational efficiency but also supports the strategic goals of sustainability and resilience in utility management.

Regionally, North America leads the Utility GIS Data Quality Services market, accounting for the largest share in 2024, followed closely by

This data release contains information to support water quality modeling in the Delaware River Basin (DRB). These data support both process-based and machine learning approaches to water quality modeling, including the prediction of stream temperature. This section provides spatial data files that describe the rivers, reservoirs, and observational data in the Delaware River Basin included in this release. One shapefile of polylines describes the 459 river reaches that define the modeling network, and another shapefile of polygons includes the three reservoirs (Pepacton, Cannonsville, and Neversink) for which data are included in this release. Additionally, a point shapefile contains locations of monitoring sites along the reaches with supporting attributes that describe the monitoring location.

The WMS interface includes the following raster map layers: base map, guide map, up-to-date station plan, ortho-air picture 2014, solar energy map, geoenergy potential map and master plan combination

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.

More and more, social scientists are using (big) digital behavioral data for their research. In this context, the social network and microblogging platform Twitter is one of the most widely used data sources. In particular, geospatial analyses of Twitter data are proving to be fruitful for examining regional differences in user behavior and attitudes. However, ready-to-use spatial information in the form of GPS coordinates is only available for a tiny fraction of Twitter data, limiting research potential and making it difficult to link with data from other sources (e.g., official statistics and survey data) for regional analyses. We address this problem by using the free text locations provided by Twitter users in their profiles to determine the corresponding real-world locations. Since users can enter any text as a profile location, automated identification of geographic locations based on this information is highly complicated. With our method, we are able to assign over a quarter of the more than 866 million German tweets collected to real locations in Germany. This represents a vast improvement over the 0.18% of tweets in our corpus to which Twitter assigns geographic coordinates. Based on the geocoding results, we are not only able to determine a corresponding place for users with valid profile locations, but also the administrative level to which the place belongs. Enriching Twitter data with this information ensures that they can be directly linked to external data sources at different levels of aggregation. We show possible use cases for the fine-grained spatial data generated by our method and how it can be used to answer previously inaccessible research questions in the social sciences. We also provide a companion R package, nutscoder, to facilitate reuse of the geocoding method in this paper.

Utility Network GIS Migration Market Outlook

According to our latest research, the global Utility Network GIS Migration market size reached USD 2.04 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.2% projected for the period from 2025 to 2033. By 2033, the market is anticipated to attain a value of USD 5.67 billion. The primary growth factor driving this surge is the increasing need for utilities to modernize legacy Geographic Information Systems (GIS) and integrate advanced digital mapping, asset management, and real-time data analytics to enhance operational efficiency and regulatory compliance.

One of the key growth drivers for the Utility Network GIS Migration market is the accelerating pace of digital transformation across utility sectors such as electricity, water, gas, and telecommunications. Utilities are under immense pressure to improve service reliability, reduce operational costs, and comply with evolving regulatory frameworks. The migration from traditional GIS platforms to next-generation utility network GIS solutions enables organizations to leverage spatial analytics, automate workflows, and support the integration of smart grid technologies. The proliferation of distributed energy resources, IoT devices, and the need for advanced outage management systems have further intensified the demand for robust and scalable GIS migration strategies. Utilities are increasingly prioritizing the modernization of their spatial data infrastructure to ensure seamless data flow, improve asset tracking, and enhance customer engagement, thereby fueling market expansion.

Another significant growth factor is the rising adoption of cloud-based GIS solutions, which offer utilities unparalleled flexibility, scalability, and cost-effectiveness. Cloud deployment models enable utilities to efficiently manage and analyze vast volumes of spatial and non-spatial data without the burden of maintaining on-premises infrastructure. This shift not only reduces capital expenditure but also accelerates the deployment of new functionalities and ensures rapid disaster recovery. Moreover, cloud-based GIS platforms facilitate real-time collaboration among field and office teams, enabling faster decision-making and improving response times during emergencies. The growing emphasis on sustainability, grid modernization, and the integration of renewable energy sources is prompting utilities to invest in cloud-enabled GIS migration projects to future-proof their operations and achieve long-term operational excellence.

The increasing regulatory focus on data accuracy, cybersecurity, and interoperability is also propelling the Utility Network GIS Migration market. Regulatory bodies worldwide are mandating utilities to maintain precise and up-to-date spatial data for effective asset management, outage response, and infrastructure planning. As a result, utilities are compelled to migrate from outdated GIS systems to advanced platforms that offer robust data governance, security, and integration capabilities. The need to comply with standards such as the Common Information Model (CIM) and industry-specific regulations is driving utilities to adopt sophisticated GIS migration strategies. Furthermore, the emergence of advanced technologies such as artificial intelligence, machine learning, and big data analytics is enabling utilities to extract deeper insights from spatial data, optimize maintenance schedules, and proactively address infrastructure vulnerabilities, thereby fostering market growth.

From a regional perspective, North America continues to dominate the Utility Network GIS Migration market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The rapid modernization of utility infrastructure, extensive deployment of smart grids, and the presence of leading GIS solution providers have positioned North America at the forefront of market growth. In Europe, stringent regulatory mandates and the push for sustainable energy transition are driving significant investments in GIS migration projects. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by large-scale infrastructure development, urbanization, and increasing government initiatives to improve utility services. The Middle East & Africa and Latin America are also emerging as promising markets, supported by ongoing digitalization efforts and investments in utility infrastructure upgrades.

Component Analysis

The Watershed Boundary Dataset (WBD) from The National Map (TNM) defines the perimeter of drainage areas formed by the terrain and other landscape characteristics. The drainage areas are nested within each other so that a large drainage area, such as the Upper Mississippi River, is composed of multiple smaller drainage areas, such as the Wisconsin River. Each of these smaller areas can further be subdivided into smaller and smaller drainage areas. The WBD uses six different levels in this hierarchy, with the smallest averaging about 30,000 acres. The WBD is made up of polygons nested into six levels of data respectively defined by Regions, Subregions, Basins, Subbasins, Watersheds, and Subwatersheds. For additional information on the WBD, go to https://nhd.usgs.gov/wbd.html. The USGS National Hydrography Dataset (NHD) service is a companion dataset to the WBD. The NHD is a comprehensive set of digital spatial data that encodes information about naturally occurring and constructed bodies of surface water (lakes, ponds, and reservoirs), paths through which water flows (canals, ditches, streams, and rivers), and related entities such as point features (springs, wells, stream gages, and dams). The information encoded about these features includes classification and other characteristics, delineation, geographic name, position and related measures, a "reach code" through which other information can be related to the NHD, and the direction of water flow. The network of reach codes delineating water and transported material flow allows users to trace movement in upstream and downstream directions. In addition to this geographic information, the dataset contains metadata that supports the exchange of future updates and improvements to the data. The NHD is available nationwide in two seamless datasets, one based on 1:24,000-scale maps and referred to as high resolution NHD, and the other based on 1:100,000-scale maps and referred to as medium resolution NHD. Additional selected areas in the United States are available based on larger scales, such as 1:5,000-scale or greater, and referred to as local resolution NHD. For more information on the NHD, go to https://nhd.usgs.gov/index.html. Hydrography data from The National Map supports many applications, such as making maps, geocoding observations, flow modeling, data maintenance, and stewardship. Hydrography data is commonly combined with other data themes, such as boundaries, elevation, structures, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain WBD and NHD data in either Esri File or Personal Geodatabase, or Shapefile formats. The Watershed Boundary Dataset is being developed under the leadership of the Subcommittee on Spatial Water Data, which is part of the Advisory Committee on Water Information (ACWI) and the Federal Geographic Data Committee (FGDC). The USDA Natural Resources Conservation Service (NRCS), along with many other federal agencies and national associations, have representatives on the Subcommittee on Spatial Water Data. As watershed boundary geographic information systems (GIS) coverages are completed, statewide and national data layers will be made available via the Geospatial Data Gateway to everyone, including federal, state, local government agencies, researchers, private companies, utilities, environmental groups, and concerned citizens. The database will assist in planning and describing water use and related land use activities. Resources in this dataset:Resource Title: Watershed Boundary Dataset (WBD). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/national/water/watersheds/dataset/?cid=nrcs143_021630 Web site for the Watershed Boundary Dataset (WBD), including links to:

Review Data Availability (Status Maps) Obtain Data by State, County, or Other Area Obtain Seamless National Data offsite link image
Geospatial Data Tools National Technical and State Coordinators Information about WBD dataset

This feature class shows Brisbane City Council LGIP Stormwater infrastructure (map references starting with SW).This feature class is shown on the Plans for Trunk Infrastructure - Stormwater network mapping.This feature class includes the following categories:(a) Pipe - new;(b) Pipe - relief;(c) Natural channel;(d) Bioretention swale;(e) Culvert;(f) Stormwater Quality Improvement Device (SQID);(g) Concrete lined channelFor more information about the PFTI - stormwater infrastructure and how it is applied, please refer to the Brisbane City Plan 2014 document.

1. This dataset is compiled from data provided by various government agencies and commissioned projects for public reference in mapping river resources. The boundaries of central and inter-provincial rivers should still be based on the central and inter-provincial river boundary points announced by the Ministry of Economic Affairs.2. River (watercourse) data is provided for use by various government agencies and private organizations commissioned by government agencies, groups, or academic units. The fields display the Chinese names of the rivers, and the form of geographic spatial data is a total of 13,262 records.3. Rivers have their channels, and the bottom of the channel is called the riverbed.4. When opening this file using Google Earth software, some errors may occur in layer overlay due to the lack of precise orthorectification in the base image provided by Google.

The USDA Forest Service (USFS) builds two versions of percent tree canopy cover data, in order to serve needs of multiple user communities. These datasets encompass conterminous United States (CONUS), Coastal Alaska, Hawaii, and Puerto Rico and U.S. Virgin Islands (PRUSVI). The two versions of data within the v2023-5 TCC product suite include: The initial model outputs referred to as the Science data; And a modified version built for the National Land Cover Database and referred to as NLCD data. The NLCD product suite includes data for years 1985 through 2023. The NCLD data are processed to mask TCC from non-treed features such as water and non-tree crops, and to reduce interannual noise and smooth the NLCD time series. TCC pixel values range from 0 to 100 percent. The non-processing area is represented by value 254, and the background is represented by the value 255. The Science and NLCD tree canopy cover data are accessible for multiple user communities, through multiple channels and platforms. For information on the Science data and processing steps see the Science metadata. Information on the NLCD data and processing steps are included here. Data Download and Methods Documents: - https://data.fs.usda.gov/geodata/rastergateway/treecanopycover/ This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.