This data was developed to represent city of cape coral citizen action center issues and their associated attributes for the purpose of mapping, analysis, and planning. The accuracy of this data varies and should not be used for precise measurements or calculations.

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

Cloud GIS Market size was valued at USD 890.81 Million in 2024 and is projected to reach USD 2298.38 Million by 2032, growing at a CAGR of 14.5% from 2026 to 2032.

Key Market Drivers

• Increased Adoption of Cloud Computing: Cloud computing provides scalable resources that can be adjusted based on demand, making it easier for organizations to manage and process large GIS datasets. The pay-as-you-go pricing models of cloud services reduce the need for significant upfront investments in hardware and software, making GIS more accessible to small and medium-sized enterprises.

• Growing Need for Spatial Data Integration: The ability to integrate and analyze large volumes of spatial and non-spatial data helps organizations make more informed decisions. The proliferation of Internet of Things (IoT) devices generates massive amounts of spatial data that can be processed and analyzed using Cloud GIS.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This database is an initial Asset database for the Central West subregion on 29 April 2015. This dataset contains the spatial and non-spatial (attribute) components of the Central West subregion Asset List as one .mdb files, which is readable as an MS Access database and a personal geodatabase. Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. All reports received associated with the WAIT process for Central West are included in the zip file as part of this dataset. Elements are initially included in the preliminary assets database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet the second Materiality Test (M2). Assets meeting both Materiality Tests comprise the water dependent asset list. Descriptions of the assets identified in the Central West subregion are found in the "AssetList" table of the database. In this version of the database only M1 has been assessed. Assets are the spatial features used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy, as described in Appendix A of "CEN_asset_database_doc_20150429.doc ", located in the zip file as part of this dataset. The "Element_to_Asset" table contains the relationships and identifies the elements that were grouped to create each asset. Detailed information describing the database structure and content can be found in the document "CEN_asset_database_doc_20150429.doc" located in the zip file. Some of the source data used in the compilation of this dataset is restricted.

Dataset History

This is initial asset database.

The Bioregional Assessments methodology (Barrett et al., 2013) defines a water-dependent asset as a spatially distinct, geo-referenced entity contained within a bioregion with characteristics having a defined cultural indigenous, economic or environmental value, and that can be linked directly or indirectly to a dependency on water quantity and/or quality.

Under the BA program, a spatial assets database is developed for each defined bioregional assessment project. The spatial elements that underpin the identification of water dependent assets are identified in the first instance by regional NRM organisations (via the WAIT tool) and supplemented with additional elements from national and state/territory government datasets. Elements are initially included in database if they are partly or wholly within the subregion's preliminary assessment extent (Materiality Test 1, M1). Elements are then grouped into assets which are evaluated by project teams to determine whether they meet materiality test 2 (M2) - assets considered to be water dependent.

Elements may be represented by a single, discrete spatial unit (polygon, line or point), or a number of spatial units occurring at more than one location (multipart polygons/lines or multipoints). Spatial features representing elements are not clipped to the preliminary assessment extent - features that extend beyond the boundary of the assessment extent have been included in full. To assist with an assessment of the relative importance of elements, area statements have been included as an attribute of the spatial data. Detailed attribute tables contain descriptions of the geographic features at the element level. Tables are organised by data source and can be joined to the spatial data on the "ElementID" field

Elements are grouped into Assets, which are the objects used by project teams to model scenarios under the BA program. Detailed attribution does not exist at the asset level. Asset attribution includes only the core set of BA-derived attributes reflecting the BA classification hierarchy.

The "Element_to_asset" table contains the relationships and identifies the elements that were grouped to create each asset.

Following delivery of the first pass asset list, project teams make a determination as to whether an asset (comprised of one or more elements) is water dependent, as assessed against the materiality tests detailed in the BA Methodology. These decisions are provided to ERIN by the project team leader and incorporated into the Assetlist table in the Asset database. The Asset database is then re-registered into the BA repository.

The Asset database dataset (which is registered to the BA repository) contains separate spatial and non-spatial databases.

Non-spatial (tabular data) is provided in an ESRI personal geodatabase (.mdb - doubling as a MS Access database) to store, query, and manage non-spatial data. This database can be accessed using either MS Access or ESRI GIS products. Non-spatial data has been provided in the Access database to simplify the querying process for BA project teams. Source datasets are highly variable and have different attributes, so separate tables are maintained in the Access database to enable the querying of thematic source layers.

Spatial data is provided as an ESRI file geodatabase (.gdb), and can only be used in an ESRI GIS environment. Spatial data is represented as a series of spatial feature classes (point, line and polygon layers). Non-spatial attribution can be joined from the Access database using the AID and ElementID fields, which are common to both the spatial and non-spatial datasets. Spatial layers containing all the point, line and polygon - derived elements and assets have been created to simplify management of the Elementlist and Assetlist tables, which list all the elements and assets, regardless of the spatial data geometry type. i.e. the total number of features in the combined spatial layers (points, lines, polygons) for assets (and elements) is equal to the total number of non-spatial records of all the individual data sources.

Dataset Citation

Department of the Environment (2013) Asset database for the Central West subregion on 29 April 2015. Bioregional Assessment Derived Dataset. Viewed 08 February 2017, http://data.bioregionalassessments.gov.au/dataset/5c3f9a56-7a48-4c26-a617-a186c2de5bf7.

Dataset Ancestors

• Derived From Macquarie Marshes Vegetation 1991-2008 VIS_ID 3920

• Derived From NSW Office of Water GW licence extract linked to spatial locations NIC v2 (28 February 2014)

• Derived From NSW Office of Water Surface Water Entitlements Locations v1_Oct2013

• Derived From Travelling Stock Route Conservation Values

• Derived From NSW Wetlands

• Derived From Communities of National Environmental Significance Database - RESTRICTED - Metadata only

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas

• Derived From Birds Australia - Important Bird Areas (IBA) 2009

• Derived From Environmental Asset Database - Commonwealth Environmental Water Office

• Derived From NSW Office of Water Surface Water Offtakes - NIC v1 20131024

• Derived From National Groundwater Dependent Ecosystems (GDE) Atlas (including WA)

• Derived From Species Profile and Threats Database (SPRAT) - Australia - Species of National Environmental Significance Database (BA subset - RESTRICTED - Metadata only)

• Derived From Ramsar Wetlands of Australia

• Derived From Native Vegetation Management (NVM) - Manage Benefits

• Derived From Key Environmental Assets - KEA - of the Murray Darling Basin

• Derived From National Heritage List Spatial Database (NHL) (v2.1)

• Derived From Climate Change Corridors (Dry Habitat) for North East NSW

• Derived From Great Artesian Basin and Laura Basin groundwater recharge areas

• Derived From NSW Office of Water combined geodatabase of regulated rivers and water sharing plan regions

• Derived From [New South Wales NSW Regional CMA Water Asset

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

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.

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.The statewide layer is only provided as a map image layer service. The data is available as feature layer services by Regional Board extract. To view all regional board feature layer extracts go to the Basin Plan GIS Data Library Group here.

Avalanches represent a very high risk in residential areas, road infrastructure, environment, and economy, and can have fatal consequences if the human factors do not take any action. Advances in geospatial technology and access to spatial data have enabled spatial analysis to assist in decision-making regarding spatial planning in avalanche-prone locations. Determining locations with snow avalanche discharge potential is a crucial step in the avalanche zoning process.

This research deals with areas with snow avalanche potential disjunction, based mainly on topographic factors followed by meteorological ones. Topographic factors were mainly determined according to morphometric techniques, which are achieved through geographic information systems (GIS), as well as meteorological ones from statistical data and various processing of spatial and non-spatial data. Spatial analysis are also supported by geostatistical methods Fuzzy Logic and AHP, which in interaction with GIS have enabled the achievement of the purpose of this paper. The results from the spatial analysis have been verified based on comparison methods, such as the ROC method which was used during this final phase, in which the analysis has shown that the methods used in this research have given satisfactory results. As the main result, we obtained maps of areas with snow avalanche potential discharge in the study area relating to two geostatistical methods.

This dataset is a non-spatial table that identifies the Invasive Species of NWSS.The aim of the Native Woodland Survey of Scotland (NWSS) was to undertake a baseline survey of all native woodlands, nearly native woodlands and PAWS sites in Scotland in order to create a woodland map linked to a dataset showing type, extent and condition of those woods. The objectives were to:Identify the location, type, extent and condition of all native and nearly native woodlands and Plantations on Ancient Woodland Sites (PAWS - as identified from the Ancient Woodland Inventory) in Scotland.Produce a baseline survey map of all native woodland, nearly native woodland and PAWS in Scotland.Collect baseline information to enable future monitoring of the extent and condition of the total Scottish native woodland resource.Provide information to support policy development and the delivery of social, environmental and development forestry.The following NWSS datasets are available from Scottish Forestry.Native Woodland Survey of Scotland (base map and polygon level attributes)NWSS Canopy StructureNWSS Habitat ComponentsNWSS Herbivore ImpactNWSS InvasivesNWSS Other TraitsNWSS Species StructuresThe following describes the layers available from Scottish Forestry and also gives an indication of the nature of the spatial data and the related component non-spatial data. (N.B. Every table contains a SCPTDATA_I field. This is a unique field which is used to link all other component tables). If you wish to carry out complex analysis, particularly involving elements of the components tables, e.g. species selection, you should do so using GIS software.NWSS Map:This is a straightforward view of the data which describes the type of NWSS polygon based on the following categories:Native woodland: >50% native species in the canopyNearly-native woodland: >=40% and <=50% native species in the canopyOpen land habitat: <20% canopy cover, usually 100% surrounded by woodland and adjoining a native woodlandPAWS: A woodland area wholly or partially identified in the Ancient Semi-natural Woodland Inventory as ancient semi-natural but currently not semi-natural.NWSS Nativeness:Displays the percentage share of native species in the total canopy. This ranges from 0% to 100% in 5% classes.NWSS Habitat:This view of the data shows the priority woodland type and National Vegetation Classification (NVC) woodland community. Open land habitat is defined by UK Biodiversity Action Plan (BAP) type.A dominant habitat is recorded for each polygon, however some polygons have habitats of equal dominance. In this case only one of the habitats is recorded in the top level spatial data. To identify all of the habitats in a particular polygon please refer to the NWSS Habitat Components table.Plantations on Ancient Woodland Sites (PAWS) may not display in the Habitat layer if a surveyor has not recorded a native priority habitat type for the site. This will happen when a site is non-native.NWSS Canopy Cover:Displays as a percentage, an assessment of the area covered by trees/shrubs. Values range from 0% to 100% in 10% classes. A minimum of 20% canopy cover is required to define woodland, so the 10% and 20% bands are skewed to allow for this.NWSS Canopy Structures:This displays the number of different structures recorded in a polygon (ranging from 0 to 6). The types of recorded structures are veteran, mature, pole immature, shrub, established regeneration or visible regeneration.A dominant structure is recorded for each polygon, however some polygons have structures of equal dominance. In this case only one of the structures is recorded in the top level spatial data. To identify all of the structures in a particular polygon please refer to the NWSS Canopy Structures.Information on the species identified in each polygon is also in the NWSS Canopy Structures layer and table.* indicates a species which is classed as native for the purpose of the survey.+ indicates a species is a shrub not a tree.NWSS Semi-naturalness:This view of the data shows the percentage of the polygon that is semi-natural. Values range from 0% to 100% in 10% bands.NWSS Maturity:This indicates the approximate stage of woodland development as either: mature, young, regenerating, mixed or shrub. The value is based on the dominance of the structures recorded; a mixed maturity means that none of the others values are dominant.NWSS Other Traits:This layer records whether or not there are any other attributes which have been recorded in the polygon. The details of any other traits that have been found can be accessed by viewing the related information attached to a polygon.NWSS Herbivore Impact:This view of the data shows the overall impact that herbivores have had on a polygon.Summary of AttributesSCPTDATA_I Polygon ID (Unique identifier)PAWS_SURVY Surveyed as PAWSTYPE TypeCANOPY_PCT Canopy cover percentageNATIVE_PCT Native species percentageDOM_HABITA Dominant habitat typeDOM_HB_PCT Dominant habitat type percentageSEMINT_PCT Semi-natural percentageSTRUCT_NUM Number of structuresMATURITY MaturityDOM_STRUCT Dominant structureHERBIVORE Herbivore impactER_NAT_PCT Percentage of establish regeneration of native speciesINVASV_PCT Invasive species percentageINVASV_NUM Number of invasive speciesOTHR_TRAIT Other traits recordedHECTARES Area in hectaresFor more detailed information please see the metadata record on Scotland"s SpatialData.gov.scot Metadata Portal

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.The statewide layer is only provided as a map image layer service. The data is available as feature layer services by Regional Board extract. To view all regional board feature layer extracts go to the Basin Plan GIS Data Library Group here.

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.

Background: Uncontrolled hazardous wastes sites have the potential to adversely impact human health and damage or disrupt ecological systems and the greater environment. Four decades have passed since the Superfund law was enacted, allowing increased exposure time to these potential health hazards while also allowing advancement of analysis techniques. Florida has the sixth highest number of Superfund sites in the US and, in 2016, Florida was projected to have the second largest number of new cancer cases in the US. Objectives: We explore statewide cancer incidence in Florida from 1986 to 2010 to determine if differences or associations exist in counties containing Superfund sites compared to counties that do not. Methods: To investigate potential environmental associations with cancer incidence; results using spatial and non-spatial mixed models were compared. Results: Using a Poisson-Gamma generalized linear mixture model, our results provide some evidence of an association between cancer incidence rates and Superfund site hazard levels, as well as proxy measures of water contamination around Superfund sites. In addition, results build upon previously observed gender differences in cancer incidence rates and further indicate spatial differences for cancer incidence. Conclusions: Results indicate strong evidence of heterogeneity among cancer incidence rates across Florida with some mild association with Superfund exposure proxies.

Efficient Regionalization for Spatially Explicit Neighborhood Delineation## AbstractNeighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. In problems of this type, the true number of neighborhoods (typically called the k parameter) is unknown and analysts often require algorithmic approaches determine kendogenously. Existing approaches for neighborhood delineation that do not require pre-specification ofa k-parameter, however, are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency.## InstructionsThe files in this archive demonstrate the code for the new max-p algorithm.To run the demonstration, please do the following:1. extract the archive unzip maxp.zip2. Install Anaconda python distribution3. conda env create -f environment.yml4. conda activate maxp5. jupyter notebook6. Select the notebook demo.ipynb

This layer shows a hosted table detailing information relating to the City of Grande Prairie's Capital Projects budget for 2019, including categories such as funding amount and project description. This information was downloaded from the city's open data portal for use in visualizing spatial and non-spatial data using GIS tools. It is used in an associated map and dashboard. All data is maintained by the City of Grande Prairie GIS department.

A feature class depicting geographic locations where permanent water quality monitoring locations have been established in Great Smoky Mountains National Park. This includes monitoring location sites established by the National Park Service and other state and federal agencies responsible for water quality monitoring and reporting. Agencies responsible for a monitoring location are listed in the attributes ORGANIZATIONIDENTIFIER and ORGANIZATIONFORMALNAME. For the display, query, and analysis of legacy and current hydrology spatial and tabular data; Consolidate and centralize a very diverse range and quantity of monitoring location site data from numerous programs and protocols; Mitigate the duplication of monitoring location data across shared systems; Allow for single-source identification and management of monitoring location sites that are "co-located"; Provide a single point of data entry, management, query, analysis, and display of water quality data from numerous sources, including STORET which are sourced from an accurate monitoring location database; Enable spatial relationship of water quality monitoring data to High-Resolution USGS NHD Reaches through the use of modern GIS, database, and statistics software; Support USGS and EPA standards for spatial and non-spatial hydrology and water quality data exchange and sharing. Very important details are included in the attached metadata document and should be read thoroughly before these data are used.

Geomatics Services for Pipeline Routing Market Outlook

According to our latest research, the global geomatics services for pipeline routing market size reached USD 3.2 billion in 2024, driven by the increasing demand for accurate, efficient, and cost-effective pipeline planning and management solutions. The market is experiencing robust growth, registering a CAGR of 8.1% over the forecast period. By 2033, the market is expected to attain a value of USD 6.2 billion, reflecting the growing adoption of advanced geomatics technologies across various pipeline applications. Key growth factors include the surge in infrastructure investments, stricter regulatory requirements for environmental protection, and the need for enhanced operational efficiency in pipeline projects.

One of the primary growth drivers for the geomatics services for pipeline routing market is the rapid expansion of the oil and gas industry, particularly in developing regions. As global energy demand continues to rise, oil and gas companies are investing heavily in new pipeline projects to ensure secure and efficient transportation of resources. Geomatics services, such as surveying, mapping, and geographic information systems (GIS), play a pivotal role in optimizing pipeline routing, minimizing environmental impacts, and reducing project costs. The integration of remote sensing technologies and high-resolution satellite imagery has further revolutionized the planning and monitoring of pipeline networks, enabling stakeholders to make data-driven decisions and improve project outcomes.

The growing emphasis on environmental sustainability and regulatory compliance has also significantly contributed to the market's expansion. Governments and regulatory bodies worldwide are imposing stringent regulations to mitigate the environmental risks associated with pipeline construction and operation. This has prompted pipeline operators to adopt advanced geomatics solutions for environmental impact assessments, route optimization, and real-time monitoring. By leveraging geomatics services, companies can ensure compliance with regulatory standards, minimize ecological disruption, and enhance public safety. Additionally, the increasing adoption of digital technologies and automation in pipeline management has accelerated the demand for integrated data management and analytics solutions, further fueling market growth.

Another key factor propelling the geomatics services for pipeline routing market is the rising need for infrastructure modernization and asset management across utility and water pipeline sectors. Aging infrastructure, coupled with the growing urban population, has led to increased investments in utility pipelines for water supply, sewage, and energy distribution. Geomatics services enable utility companies to efficiently plan, design, and maintain pipeline networks, ensuring optimal resource allocation and reducing operational risks. The use of GIS and data management tools facilitates seamless integration of spatial and non-spatial data, enhancing decision-making processes and supporting predictive maintenance strategies. As a result, the market is witnessing a surge in demand from utility companies and municipal authorities aiming to improve the reliability and sustainability of their pipeline infrastructure.

The integration of a Pipeline Geospatial Information System (GIS) is becoming increasingly vital in the geomatics services for pipeline routing. This system allows for the comprehensive management and analysis of spatial data, which is crucial for optimizing pipeline routes and ensuring environmental compliance. By leveraging GIS, pipeline operators can visualize complex data sets, monitor pipeline conditions in real-time, and make informed decisions that enhance project efficiency and safety. The ability to integrate various data sources, such as satellite imagery and remote sensing, into a unified platform enables stakeholders to assess potential risks and opportunities effectively. As the demand for more sophisticated data management solutions grows, the role of GIS in pipeline projects is expected to expand, offering significant benefits in terms of cost savings and operational excellence.

Regionally, North America continues to dominate the geomatics services for pipeline routing market, accounting for the largest revenue share in 2024. The region's leadership is attrib

Use this layer to join non-spatial data: https://ph-lacounty.hub.arcgis.com/datasets/3e38574c3d31477d908c8028fb864ca4/aboutFor more information about the Community Health Profiles data initiative, please see the initiative homepage.

Road centerlines with road names and generalized classifications is a snapshot from our spatial roads (addressing) dataset. Maintenance data was pulled from the Borough asset management software, Cartegraph, which is non-spatial. The non-spatial maintenance data was then tied to the spatial roads data through a series of joins and analyses.Roads with multiple maintenance groups listed have shared maintenance responsibilities; for example 1/2 the road may be maintained by the Borough and the other 1/2 maintained by a city. More detailed information regarding the distances each maintenance group is responsible for can be looked up in the Cartegraph database. This more detailed information can not currently be mapped due to differences in design between the spatial roads (911 addressing) dataset and the Cartegraph database.This dataset does not have a scheduled update cycle and should be viewed as just a snapshot in time. It was last updated in Sept 2017.

The California State Water Resources Control Board is currently in the process of improving the functionality and accessibility of information residing in their Water Quality Control Plans (aka Basin Plans). In order to achieve this, the data (i.e. statewide water quality objectives, beneficial uses, applicable TMDLs, etc.), are being transferred to a standardized digital format and linked to applicable surface water features. This dataset is limited to the beneficial uses data, while the water quality objectives, applicable TMDLs, etc. will be released at a later date. Data formats will include GIS data layers and numerous nonspatial data tables. The GIS layers contain hydrography features derived from a 2012 snapshot of the high-resolution (1:24000 scale) National Hydrography Dataset with added attribution. Nonspatial tables will contain various textual and numeric data from the Regional Basin and State Plans. The extent of the dataset covers the state of California and the non-spatial tables reflect the information and elements from the various plans used up to 2020. The GIS layers and associated attribution will enable the future integration of the various elements of the Basin Plans to ensure that all applicable Basin Plan requirements for a particular waterbody can be determined in a quick and precise manner across different modern mediums. The data are being managed and the project implemented by State and Regional Water Board staff using ESRI's ArcGIS Server and ArcSDE technology.

Under the direction and funding of the National Cooperative Mapping Program with guidance and encouragement from the United States Geological Survey (USGS), a digital database of three-dimensional (3D) vector data, displayed as two-dimensional (2D) data-extent bounding polygons. This geodatabase is to act as a virtual and digital inventory of 3D structure contour and isopach vector data for the USGS National Geologic Synthesis (NGS) team. This data will be available visually through a USGS web application and can be queried using complimentary nonspatial tables associated with each data harboring polygon. This initial publication contains 60 datasets collected directly from USGS specific publications and federal repositories. Further publications of dataset collections in versioned releases will be annotated in additional appendices, respectfully. These datasets can be identified from their specific version through their nonspatial tables. This digital dataset contains spatial ex ...