This is a guide that describes how to interact with pop ups and the attribute tables in web maps where that functionality is available. Not all widgets or functionality is available in every web map.

The CDDA is a data bank for officially designated protected areas such as nature reserves, protected landscapes, National Parks, etc. in Europe. The CDDA is run by the European Environment Agency(EEA). This access database includes only data for National Designations, the main ones being Sites of Special Scientific Interest, National Nature Reserves, Local Nature Reserves, National Parks, Areas of Outstanding Natural Beauty and a variety of Marine Protected Areas. The data are updated annually in March. Further details are available from the EEA's EIONET portal http://rod.eionet.europa.eu/obligations/32. This provides data for all members states in the EU and also describes the data model with descriptions of each table and attribute. The two most important tables in the data schema are the sites table (one row of data for each site) and the designations table (one row for each type of designation). These two tables can be joined on the field DESIG_ABBR. Other tables in the schema are included mainly for EEAs internal purposes. The annual submission of the CDDA

This dataset contains all of the attribute data. This includes RXNORM provided attributes, such as normalized 11-digit National Drug Codes (NDCs), UNII codes, and human or veterinary usage markers, and source-provided attributes, such as labeler, definition, and imprint information. Each attribute has an 'Attribute Name' (ATN) and 'Attribute Value' (ATV) combination. For example, NDCs have an ATN of 'NDC' and an ATV of the actual NDC value.

Road Attribute Data Market Outlook

According to our latest research, the global road attribute data market size reached USD 4.2 billion in 2024, reflecting the increasing integration of advanced data analytics and geospatial technologies in the transportation sector. The market is projected to expand at a robust CAGR of 15.7% from 2025 to 2033, with the total market value expected to reach USD 14.1 billion by 2033. This impressive growth is primarily driven by the surging demand for high-precision data in navigation systems, autonomous vehicle development, and smart city initiatives, as per our latest research findings.

One of the central growth factors for the road attribute data market is the rapid evolution of connected and autonomous vehicles. As automotive manufacturers and technology firms race to bring self-driving cars and advanced driver-assistance systems (ADAS) to the mainstream, the need for detailed, real-time, and accurate road attribute data has never been greater. This data, encompassing geometric details, traffic patterns, and road conditions, is essential for enabling safe navigation and decision-making by both human drivers and AI algorithms. The proliferation of IoT sensors and the integration of edge computing further enhance the granularity and timeliness of road data, making it indispensable for next-generation mobility solutions.

Another significant driver is the growing emphasis on intelligent transportation systems (ITS) and urban planning. Governments and municipalities worldwide are investing heavily in digital infrastructure to optimize traffic flow, reduce congestion, and improve road safety. Road attribute data plays a pivotal role in these efforts by providing actionable insights for real-time traffic management, infrastructure maintenance, and future city planning. The adoption of big data analytics and machine learning in transportation management systems allows stakeholders to predict traffic patterns, identify accident-prone zones, and implement targeted interventions, thereby increasing the overall efficiency and safety of urban mobility networks.

Additionally, the insurance and risk assessment sectors are increasingly leveraging road attribute data to refine their underwriting processes and claims management. By integrating granular environmental and road condition data, insurers can more accurately assess risk profiles, set premiums, and expedite claims settlements. This data-driven approach not only enhances customer satisfaction but also reduces operational costs and fraud. Moreover, the integration of satellite imagery, aerial surveys, and ground-based sensors ensures a comprehensive and up-to-date view of road networks, further driving the adoption of road attribute data solutions across diverse end-user industries.

From a regional perspective, North America currently leads the global road attribute data market, fueled by early adoption of autonomous vehicle technology and significant investments in smart infrastructure. However, Asia Pacific is emerging as the fastest-growing region, supported by rapid urbanization, expanding transportation networks, and government initiatives aimed at developing smart cities. Europe also holds a substantial share, driven by stringent road safety regulations and a strong focus on sustainable urban mobility. The Middle East & Africa and Latin America are gradually catching up, with increasing investments in digital mapping and infrastructure modernization projects.

Data Type Analysis

The data type segment in the road attribute data market is highly diverse, encompassing geometric data, traffic data, environmental data, road condition data, and other specialized datasets. Geometric data, which includes information on road geometry, lane markings, and intersections, forms the backbone of digital maps and navigation systems. This data is critical for both human-driven and autonomous vehicles, enabling accurate route planning and real-time navigation. The continuous improvement in data collection methods, such as LiDAR and high-resolution satellite imagery, has significantly enhanced the precision of geometric data, making it a vital component for advanced mobility applications.

Traffic data is another crucial sub-segment, providing insights into vehicle flow, congestion points, and average speeds across different road segments. The integration of

Road Attribute Data Market Outlook

According to our latest research, the global road attribute data market size reached USD 5.4 billion in 2024, driven by rapid advancements in geospatial technologies and the growing adoption of intelligent transportation systems worldwide. The market is experiencing robust expansion, with a recorded CAGR of 13.2% during the forecast period. By 2033, the market is projected to attain a value of USD 15.2 billion, reflecting the surging demand for high-quality, real-time road attribute data across various industry verticals. The growth of this market is primarily fueled by the proliferation of connected vehicles, the increasing implementation of smart city initiatives, and the critical role of accurate road data in enhancing navigation, safety, and traffic management solutions.

The growth trajectory of the road attribute data market is underpinned by a multitude of technological and societal drivers. One of the most significant growth factors is the rapid expansion of the autonomous vehicle industry, which necessitates granular, up-to-date road attribute data to enable safe and efficient vehicle navigation. As original equipment manufacturers (OEMs) and technology firms race to perfect self-driving technologies, the demand for comprehensive datasets encompassing geometric, surface, and environmental road attributes is intensifying. Additionally, the integration of artificial intelligence and machine learning algorithms into mapping and navigation platforms is further amplifying the need for rich, high-resolution road data, as these systems rely on precise contextual information to make real-time driving decisions.

Another major catalyst for the market's growth is the widespread adoption of smart city initiatives by governments and municipalities worldwide. Urban planners and policymakers are increasingly leveraging road attribute data to optimize traffic flows, reduce congestion, and enhance public safety. The deployment of intelligent transportation systems (ITS) that utilize real-time road data for dynamic traffic signal control, incident detection, and infrastructure management is becoming commonplace in major metropolitan areas. Moreover, the integration of Internet of Things (IoT) devices and sensor networks into road infrastructure is generating a continuous stream of valuable data, further fueling the demand for advanced road attribute data solutions across both public and private sectors.

The digital transformation of the transportation and logistics industry is also playing a pivotal role in propelling the road attribute data market forward. Logistics providers and fleet operators are increasingly relying on detailed road attribute datasets to optimize route planning, improve delivery efficiency, and minimize operational costs. The rise of e-commerce and last-mile delivery services has heightened the need for accurate, real-time road information to navigate complex urban environments and ensure timely deliveries. Furthermore, advancements in satellite imagery, aerial surveys, and ground-based data collection technologies are enhancing the accuracy and granularity of road attribute datasets, enabling new applications and business models across the transportation ecosystem.

Regionally, North America and Europe continue to dominate the road attribute data market, driven by early adoption of advanced transportation technologies, strong regulatory frameworks, and significant investments in smart infrastructure. However, the Asia Pacific region is emerging as the fastest-growing market, fueled by rapid urbanization, increasing vehicle ownership, and ambitious government initiatives aimed at modernizing transportation networks. Countries such as China, India, and Japan are witnessing a surge in demand for high-quality road attribute data to support large-scale infrastructure projects and address the challenges of urban mobility. Meanwhile, the Middle East & Africa and Latin America are gradually embracing road data solutions, albeit at a slower pace, as they seek to improve road safety and support economic development.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition.; It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

Serial number, area code, area name, location code, location name, location number, Longitude, Latitude

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition.; It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

The multiple attribute mapping process provides a vector based inventory of the landscape in terms of slope, terrain, landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey, soil erosion condition, and rockiness. Mass movement and soil conservation measures are mapped where they exist, as is a selected range of weed species. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying of different data sets. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes The information can, however, be useful as a first level of information in property planning exercises.

These data were compiled to support stranding risk modeling of young-of-year rainbow trout in Glen Canyon resulting from fluctuating flows from Glen Canyon Dam, called Trout Management Flows (TMFs). The objective of our study was to evaluate the stranding risk associated with different TMFs. We used the results of a 2-dimensional hydrodynamic model (Wright and others, 2024) as input to a rainbow trout stranding model and described in the associated Larger Work Citation. This data table was developed to link the data output tables of Wright and others (2024) and the trout stranding model to a commonly used and standardized measure of distance along the Colorado River between Glen Canyon Dam and Lees Ferry, AZ (Gushue 2019). The data are organized based on the spatial coordinates of the center point of cells in the hydrodynamic model (Wright and others 2024). The data table represents spatial grid coordinates and locations to the nearest hundredth mile for each row and column of a bathymetry grid that are closest to the Colorado River centerline. The data table includes additional data values that represent bed elevations and distances of the center point for each selected bathymetry grid cell to the nearest Colorado River mile point location. Distances in miles follow a standard referencing system where miles are measured as negative values upstream from Lees Ferry (river mile 0) to Glen Canyon Dam (river mile -15.85). The 2-dimensional hydrodynamic model and trout stranding model described in the associated Larger Work Citation extend from river mile -0.02 to river mile -15.71. This data table provides a way to assign a Colorado River mile to each computational grid cell of Wright and others (2024) and the trout stranding risk model in the associated Larger Work Citation that we used to evaluate how flow influences depth, velocity, and stranding risk of Trout Management Flows. This table can be used by other studies to link the hydrodynamic model to the standard Colorado River reference system on the Colorado River in Glen Canyon.

This data was scraped from the Detroit Parks Master plan https://detroitmi.gov/sites/detroitmi.localhost/files/2023-03/AppendixE_Metric%26Inventory.pdf and reformatted in Google Sheets for ease of use.

.pdf file. Visit https://dataone.org/datasets/doi%3A10.6067%3AXCV82N51GF_meta%24v%3D1336050512504 for complete metadata about this dataset.

This dataset provides information on the enforcement of pollution source inspections and regulations by the Taipei City Environmental Protection Bureau.

The data in this map service is updated every weekend.Note: This data includes all activities regardless of whether there is a spatial feature attached.Note: This is a large dataset. Metadata and Downloads are available at: https://data.fs.usda.gov/geodata/edw/datasets.php?xmlKeyword=FACTS+common+attributesTo download FACTS activities layers, search for the activity types you want, such as timber harvest or hazardous fuels treatments. The Forest Service's Natural Resource Manager (NRM) Forest Activity Tracking System (FACTS) is the agency standard for managing information about activities related to fire/fuels, silviculture, and invasive species. This feature class contains the FACTS attributes most commonly needed to describe FACTS activities.

The mapping process as applied in this dataset provides a vector based inventort of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition. Mass movement is mapped where it exists, as is a selected range of weed species in pasture areas. These characteristics of the land are part of the larger set of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation’s full set of attribute codes. This set of codes are termed the Standard Classification for Attributes of Land (SCALD). The value of the attribute mapping is that the data objectively characterises the land and can be used for a range of land uses and land management purposes. This system of mapping maximises the efficiency of GIS operation by describing a number of attributes into one polygon, avoiding problems caused by overlaying go different data sets. The full SCALD programme permits the coding of slope, terrain, land use, vegetation community, vegetation regeneration, tree and shrub canopy density, understorey status, projective foliage cover (McDonald et al. 1990), Western Region vegetation, soil erosion, mass movement, soil conservation earthworks, extent of rock outcrops, geology and Great soil groups., geology, great soil group, soil landscapes, physical limitations, land capability, soil depth, user defined attributes and Northwest vegetation associations. Soil landscapes information from the DLWC mapping program of the same name can be incorporated into the SCALD code set. Mapping is carried out at 1:25000 scale using base maps from the NSW Land Information Centre medium scale topographic series. Outputs are most useful at the sub-catchment or regional scale but not at property level. The data are extremely valuable at the river basin scale for integrated catchment planning programmes.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy …Show full descriptionThe multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition.; It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

The multiple attribute mapping process as applied in this dataset provides a vector based inventory of the landscape in terms of landuse, vegetation, presence of tree regrowth, tree and shrub canopy density, presence of understorey and soil erosion condition. It is referred to as Land Condition Mapping. Mass movement is mapped where it exists as is a selected range of weed species. These characteristics of the land are part of the larger dataset of characteristics that can be mapped using the NSW Dept. of Land and Water Conservation's full set of attribute codes. Multi Attribute Data is a vector-based inventory of the landscape comprising polygon and linear features. This system of mapping can describe a number of attributes (such as slope, terrain, landuse, vegetation community, presence of tree regrowth, soil erosion, rock outcrops, geology, Great Soil Groups, weed species and soil conservation measures) in to one polygon. The value of attribute mapping lies in the fact that the data, which objectively characterises the land, can be used for a variety of purposes and is only limited by the scale of mapping and the classification used. This translates into the availability of a range of derivative products. Mapping is typically carried out at 1:25 000 scale using topographic maps as a base. Outputs are most useful at a sub- catchment or regional scale but not generally at property level.

Hard rock is basement rock or bedrock that is too hard to dig with hand tools (and does not include hardpans). Mapping shows the average estimated depth to hard rock, while detailed proportion data are supplied for calculating respective areas of each depth to hard rock class (spatial data statistics).

This layer contains attribute Opening information only and can be used in conjunction with other layers. RESULTS Openings is an administrative boundary that has been harvested with reforestation obligations or natural disturbance with intended forest management on Crown Land. This is a part of the Silviculture and Land Status dataset, which includes tracking achievmeent of silviculture obligations on Crown Land. Non-spatial attribute data delivered in CSV format