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.

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

A dataset of well information and geospatial data was developed for 426 U.S. Geological Survey (USGS) observation wells in Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. An extensive list of attributes is included about each well, its location, and water-level history to provide the public and water-resources community with comprehensive information on the USGS well network in New England and data available from these sites. These data may be useful for evaluating groundwater conditions and variability across the region. The well list and site attributes, which were extracted from USGS National Water Information System (NWIS), represent all of the active wells in the New England network up to the end of 2017, and an additional 45 wells that were inactive (discontinued or replaced by a nearby well) at that time. Inactive wells were included in the database because they (1) contain periods of water-level record that may be useful for groundwater assessments, (2) may become active again at some point, or (3) are being monitored by another agency (most discontinued New Hampshire wells are still being monitored and the data are available in the National Groundwater Monitoring Network (https://cida.usgs.gov/ngwmn/index.jsp). The wells in this database have been sites of water-level data collection (periodic levels and/or continuous levels) for an average of 31 years. Water-level records go back to 1913. The groundwater-level statistics included in the dataset represent hydrologic conditions for the period of record for inactive wells, or through the end of water year 2017 (September 30, 2017) for active wells. Geographic Information Systems (GIS) data layers were compiled from various sources and dates ranging from 2003 to 2018. These GIS data were used to calculate attributes related to topographic setting, climate, land cover, soil, and geology giving hydrologic and environmental context to each well. In total, the data include 90 attributes for each well. In addition to site number and station name, attributes were developed for site information (15 attributes); groundwater-level statistics through water year 2017 (16 attributes); well-construction information (9 attributes); topographic setting (11 attributes); climate (2 attributes); land use and cover (17 attributes); soils (4 attributes); and geology (14 attributes). Basic well and site information includes well location, period of record, well-construction details, continuous versus intermittent data collection, and ground altitudes. Attributes that may influence groundwater levels include: well depth, location of open or screened interval, aquifer type, surficial and bedrock geology, topographic position, flow distance to surface water, land use and cover near the well, soil texture and drainage, precipitation, and air temperature.

1990 US Census Data

Abstract The USCensus1990 data set is a discretized version of the USCensus1990raw data set. Many of the less useful attributes in the original data set have been dropped, the few continuous variables have been discretized and the few discrete variables that have a large number of possible values have been collapsed to have fewer possible values.

Sources The USCensus1990raw data set was obtained from the (U.S. Department of Commerce) Census Bureau website using the Data Extraction System. This system can be found at http://www.census.gov/DES/www/d es.html.

Donor of database Chris Meek Bo Thiesson David Heckerman

Data Characteristics The data was collected as part of the 1990 census.

There are 68 categorical attributes. This data set was derived from the USCensus1990raw data set. The attributes are listed in the file USCensus1990.attributes.txt (repeated below) and the coding for the values is described below. Many of the less useful attributes in the original data set have been dropped, the few continuous variables have been discretized and the few discrete variables that have a large number of possible values have been collapsed to have fewer possible values.

More specifically the USCensus1990 data set was obtained from the USCensus1990raw data set by the following sequence of operations;

Randomization: The order of the cases in the original USCensus1990raw data set were randomly permuted. Selection of attributes: The 68 attributes included in the data set are given below. In the USCensus1990 data set we have added a single letter prefix to the original name. We add the letter 'i' to indicate that the original attribute values are used and 'd' to indicate that original attribute values for each case have been mapped to new values (the precise mapping is described below).

Other Relevant Information Hierarchies of values are provided in the file USCensus1990raw.coding.htm and the mapping functions used to transform the USCensus1990raw to the USCensus1990 data sets are giving in the file USCensus1990.mapping.sql.

Data Format The data is contained in a file called USCensus1990.data.txt. The first row contains the list of attributes. The first attribute is a caseid and should be ignored during analysis. The data is comma delimited with one case per row.

References & Further Information The U.S. Department of Commerce Bureau of Census website Data Extraction System Meek, Thiesson, and Heckerman (2001), "The Learning Curve Method Applied to Clustering", to appear in The Journal of Machine Learning Research. MSR-TR-2001-34 The UCI KDD Archive Information and Computer Science University of California, Irvine Irvine, CA 92697-3425 Last modified: 6 Nov 2001

A combination of discrete and daily-aligned groundwater levels for the Mississippi River Valley alluvial aquifer clipped to the Mississippi Alluvial Plain, as defined by Painter and Westerman (2018), with corresponding metadata are based on processing of U.S. Geological Survey National Water Information System (NWIS) (U.S. Geological Survey, 2020) data. The processing was made after retrieval using aggregation and filtering through the infoGW2visGWDB software (Asquith and Seanor, 2019). The nomenclature GWmaster mimics that of the output from infoGW2visGWDB. Two separate data retrievals for NWIS were made. First, the discrete data were retrieved, and second, continuous records from recorder sites with daily-mean or other daily statistics codes were retrieved. Each dataset was separately passed through the infoGW2visGWDB software to create a "GWmaster discrete" and "GWmaster continuous" and these tables were combined and then sorted on the site identifier and date to form the data ...

The primary article (cited below under "Related works") introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. The article includes an online supplement with a worked example demonstrating DCE design and analysis with realistic simulated data. The worked example focuses on caregivers' priorities in choosing treatment for children with attention deficit hyperactivity disorder. This dataset includes the scripts (and, in some cases, Excel files) that we used to identify appropriate experimental designs, simulate population and sample data, estimate sample size requirements for the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models, estimate parameters using the MNL and RPL models, and analyze attribute importance, willingness to pay, and predicted uptake. It also includes the associated data files (experimental designs, data generation parameters, simulated population data and parameters, simulated choice data, MNL and RPL results, RPL sample size simulation results, and willingness-to-pay results) and images. The data could easily be analyzed using other software, and the code could easily be adapted to analyze other data. Because this dataset contains only simulated data, we are not aware of any legal or ethical considerations. Methods In the worked example, we used simulated data to examine caregiver preferences for 7 treatment attributes (medication administration, therapy location, school accommodation, caregiver behavior training, provider communication, provider specialty, and monthly out-of-pocket costs) identified by dosReis and colleagues in a previous DCE. We employed an orthogonal design with 1 continuous variable (cost) and 12 dummy-coded variables (representing the levels of the remaining attributes, which were categorical). Using the parameter estimates published by dosReis et al., with slight adaptations, we simulated utility values for a population of 100,000 people, then selected a sample of 500 for analysis. Relying on random utility theory, we used the mlogit package in R to estimate the MNL and RPL models, using 5,000 Halton draws for simulated maximum likelihood estimation of the RPL model. In addition to estimating the utility parameters, we measured the relative importance of each attribute, estimated caregivers’ willingness to pay (WTP) for differences in attributes (e.g., how much they would be willing to pay for their child to see one type of provider versus another) with bootstrapped 95% confidence intervals, and predicted the uptake of three treatment packages with different sets of attributes. This submission includes both the simulated source data and the processed results. The online supplement of the primary article describes the methods in greater detail.

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Our commitment to quality begins with our data collection process, which is rooted in a robust and reliable approach: - Dynamic Publication Sites: We draw data from ten dynamic publication sites, serving as rich sources for the continuous and real-time creation of our global database. - Contact Discovery Team: Complementing this is our dedicated research powerhouse, the Contact Discovery Team, which conducts extensive investigations to ensure the accuracy and relevance of each contact. This dual-sourcing strategy guarantees that our Global B2B Contact Data is not only comprehensive but also trustworthy, offering you the reliability you need to make informed business decisions.

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We understand that privacy and security are of utmost importance when it comes to handling data. That’s why we uphold the highest standards of privacy and security, ensuring that all data is managed ethically and in full compliance with global privacy regulations. Businesses can confidently leverage our data, knowing that it is handled with the utmost care and respect for legal requirements.

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Adaptability and continuous improvement are at the core of our ethos. We are committed to consistently enhancing our B2B Contact Data solutions by: - Refining Data C...

Provides comprehensive information on real estate, including administrative district units designated by law, continuous GIS data from cadastral map computer files, land-related attribute information, and cadastral survey reference point information that serves as the basis for surveying.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

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This dataset, termed "GAGES II", an acronym for Geospatial Attributes of Gages for Evaluating Streamflow, version II, provides geospatial data and classifications for 9,322 stream gages maintained by the U.S. Geological Survey (USGS). It is an update to the original GAGES, which was published as a Data Paper on the journal Ecology's website (Falcone and others, 2010b) in 2010. The GAGES II dataset consists of gages which have had either 20+ complete years (not necessarily continuous) of discharge record since 1950, or are currently active, as of water year 2009, and whose watersheds lie within the United States, including Alaska, Hawaii, and Puerto Rico. Reference gages were identified based on indicators that they were the least-disturbed watersheds within the framework of broad regions, based on 12 major ecoregions across the United States. Of the 9,322 total sites, 2,057 are classified as reference, and 7,265 as non-reference. Of the 2,057 reference sites, 1,633 have (through 2009) 20+ years of record since 1950. Some sites have very long flow records: a number of gages have been in continuous service since 1900 (at least), and have 110 years of complete record (1900-2009) to date.

The geospatial data include several hundred watershed characteristics compiled from national data sources, including environmental features (e.g. climate – including historical precipitation, geology, soils, topography) and anthropogenic influences (e.g. land use, road density, presence of dams, canals, or power plants). The dataset also includes comments from local USGS Water Science Centers, based on Annual Data Reports, pertinent to hydrologic modifications and influences. The data posted also include watershed boundaries in GIS format.

This overall dataset is different in nature to the USGS Hydro-Climatic Data Network (HCDN; Slack and Landwehr 1992), whose data evaluation ended with water year 1988. The HCDN identifies stream gages which at some point in their history had periods which represented natural flow, and the years in which those natural flows occurred were identified (i.e. not all HCDN sites were in reference condition even in 1988, for example, 02353500). The HCDN remains a valuable indication of historic natural streamflow data. However, the goal of this dataset was to identify watersheds which currently have near-natural flow conditions, and the 2,057 reference sites identified here were derived independently of the HCDN. A subset, however, noted in the BasinID worksheet as “HCDN-2009”, has been identified as an updated list of 743 sites for potential hydro-climatic study. The HCDN-2009 sites fulfill all of the following criteria: (a) have 20 years of complete and continuous flow record in the last 20 years (water years 1990-2009), and were thus also currently active as of 2009, (b) are identified as being in current reference condition according to the GAGES-II classification, (c) have less than 5 percent imperviousness as measured from the NLCD 2006, and (d) were not eliminated by a review from participating state Water Science Center evaluators.

The data posted here consist of the following items:- This point shapefile, with summary data for the 9,322 gages.- A zip file containing basin characteristics, variable definitions, and a more detailed report.- A zip file containing shapefiles of basin boundaries, organized by classification and aggregated ecoregion.- A zip file containing mainstem stream lines (Arc line coverages) for each gage.

This dataset represents the road density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds riparian buffers. Attributes of the landscape layer were calculated for every local NHDPlusV2 catchment and accumulated to provide watershed-level metrics. (See Supplementary Info for Glossary of Terms) This data set is derived from TIGER/Line Files of roads in the conterminous United States. Road density describes how many kilometers of road exist in a square kilometer. A raster was produced using the ArcGIS Line Density Tool to form the landscape layer for analysis. (see Data Sources for links to NHDPlusV2 data and Census Data) The (kilometer of road/square kilometer) was summarized by local catchment and by watershed to produce local catchment-level and watershed-level metrics as a continuous data type (see Data Structure and Attribute Information for a description).

Context The data is technical spec of cars. The dataset is downloaded from UCI Machine Learning Repository

Content Title: Auto-Mpg Data

Sources: (a) Origin: This dataset was taken from the StatLib library which is maintained at Carnegie Mellon University. The dataset was used in the 1983 American Statistical Association Exposition. (c) Date: July 7, 1993

Past Usage:

See 2b (above) Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning. In Proceedings on the Tenth International Conference of Machine Learning, 236-243, University of Massachusetts, Amherst. Morgan Kaufmann. Relevant Information:

This dataset is a slightly modified version of the dataset provided in the StatLib library. In line with the use by Ross Quinlan (1993) in predicting the attribute "mpg", 8 of the original instances were removed because they had unknown values for the "mpg" attribute. The original dataset is available in the file "auto-mpg.data-original".

"The data concerns city-cycle fuel consumption in miles per gallon, to be predicted in terms of 3 multivalued discrete and 5 continuous attributes." (Quinlan, 1993)

Number of Instances: 404

Number of Attributes: 10 including the class attribute ** Attribute Information:**

mpg: continuous cylinders: continuous displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: continuous origin: continuous car name: string (unique for each instance) brands : string (unique for each instance) Missing Attribute Values: horsepower has 6 missing values

Acknowledgements Dataset: UCI Machine Learning Repository Data link : https://archive.ics.uci.edu/ml/datasets/auto+mpg

description: This dataset represents the characterization of global forest extent and change by year from 2001 through 2013 within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the Global Forest Change 2000 2013 (See Supplementary Info for Glossary of Terms). These data are based on global tree cover loss for the period from 2001 to 2013 at a spatial resolution of 30m. The analysis used to create the landscape layer is based on Landsat data. Forest loss was defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale. This landscape layer is a disaggregation of total forest loss to annual time scales. Encoded as either 0 (no loss) or else a value in the range 1 13, representing loss detected primarily in the year 2001 2013, respectively. The forest loss by year characteristics (%) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type (see Data Structure and Attribute Information for a description).; abstract: This dataset represents the characterization of global forest extent and change by year from 2001 through 2013 within individual local NHDPlusV2 catchments and upstream, contributing watersheds based on the Global Forest Change 2000 2013 (See Supplementary Info for Glossary of Terms). These data are based on global tree cover loss for the period from 2001 to 2013 at a spatial resolution of 30m. The analysis used to create the landscape layer is based on Landsat data. Forest loss was defined as a stand-replacement disturbance or the complete removal of tree cover canopy at the Landsat pixel scale. This landscape layer is a disaggregation of total forest loss to annual time scales. Encoded as either 0 (no loss) or else a value in the range 1 13, representing loss detected primarily in the year 2001 2013, respectively. The forest loss by year characteristics (%) were summarized to produce local catchment-level and watershed-level metrics as a continuous data type (see Data Structure and Attribute Information for a description).

Data Set Information:

This file concerns credit card applications. All attribute names and values have been changed to meaningless symbols to protect confidentiality of the data.

This dataset is interesting because there is a good mix of attributes -- continuous, nominal with small numbers of values, and nominal with larger numbers of values. There are also a few missing values.

Attribute Information:

A1: b, a. A2: continuous. A3: continuous. A4: u, y, l, t. A5: g, p, gg. A6: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff. A7: v, h, bb, j, n, z, dd, ff, o. A8: continuous. A9: t, f. A10: t, f. A11: continuous. A12: t, f. A13: g, p, s. A14: continuous. A15: continuous. A16: +,- (class attribute)

Source:

(confidential source)

Submitted by quinlan '@' cs.su.oz.au

Attribute-Based Access Control for Gov Data Market Outlook

According to our latest research, the global Attribute-Based Access Control (ABAC) for Government Data market size reached USD 2.47 billion in 2024, with a robust CAGR of 14.2% expected through the forecast period. By 2033, the market is projected to attain a value of USD 7.6 billion. This significant growth is primarily driven by increasing government digitalization initiatives, the proliferation of sensitive data, and stringent regulatory mandates for data privacy and security across federal, state, and defense sectors.

A primary factor fueling the expansion of the Attribute-Based Access Control (ABAC) for Government Data market is the escalating complexity and volume of data managed by government agencies. As governments worldwide digitize their services, the need for dynamic, fine-grained access control mechanisms becomes paramount. ABAC offers a flexible and scalable solution, enabling agencies to define access policies based on user attributes, context, and resource sensitivity. This adaptability significantly reduces insider threats and unauthorized access, ensuring compliance with evolving data protection regulations such as GDPR, CCPA, and FISMA. The increasing frequency of cyberattacks targeting government databases further amplifies the demand for robust ABAC solutions, as traditional role-based access control models are often insufficient to address modern security challenges.

Another crucial growth driver is the integration of ABAC with emerging technologies such as cloud computing, artificial intelligence, and the Internet of Things (IoT). Governments are rapidly adopting cloud-based infrastructures to enhance operational efficiency and service delivery. However, this transition introduces new security risks, necessitating advanced access control frameworks. ABAC's policy-driven approach enables seamless integration with cloud platforms, providing granular control over who can access what data, when, and under what circumstances. The synergy between ABAC and AI-driven analytics further empowers agencies to automate access decisions, detect anomalies, and respond proactively to potential threats. As digital transformation accelerates, the adoption of ABAC is anticipated to become a standard practice in government cybersecurity strategies.

Regulatory compliance and risk management are also pivotal in driving the ABAC market's growth. Government agencies are subject to rigorous audits and must demonstrate adherence to a myriad of data protection laws. ABAC facilitates continuous compliance by offering transparent, auditable access policies and real-time monitoring capabilities. This not only streamlines regulatory reporting but also enhances accountability and trust among citizens. The growing emphasis on zero-trust security architectures, especially in defense and intelligence sectors, further underscores the importance of ABAC. By enforcing least-privilege principles and contextual access controls, ABAC minimizes the attack surface and mitigates the risk of data breaches, making it an indispensable tool for modern government operations.

Regionally, North America dominates the Attribute-Based Access Control for Government Data market, owing to substantial investments in cybersecurity, advanced IT infrastructure, and stringent data privacy regulations. The United States, in particular, leads the adoption curve, driven by federal mandates and high-profile cyber incidents. Europe follows closely, benefiting from robust regulatory frameworks such as GDPR and significant government digitalization efforts. The Asia Pacific region is witnessing rapid growth, propelled by smart city initiatives and increasing cyber threats targeting public sector data. Latin America and the Middle East & Africa are gradually embracing ABAC solutions, although budget constraints and limited technical expertise remain challenges. Overall, the global outlook for ABAC in government data management is highly optimistic, with sustained growth expected across all major regions.

Kinder Residential Real Estate Assessor & Recorder + Tier 1 Contact Dataset

The KinderData Residential Real Estate Assessor & Recorder + Tier 1 Contact Dataset is a nationwide U.S. property intelligence platform covering 160 million residential and commercial parcels across all 50 states. Updated daily from county assessor, recorder, and verified contact sources, it delivers unmatched accuracy and depth for real estate, financial services, government, marketing, and analytics applications.

What Makes KinderData Different

Integrated Tier 1 Homeowner Contact Data: verified phone, email, and mailing addresses linked to each parcel for marketing, real estate investing, underwriting, and risk use cases.

Nationwide coverage across all 50 states with millions of verified residential and commercial parcels.

Daily refresh cycles ensuring the most current tax, ownership, and transaction records.

Flexible access: bulk delivery, API, or tailored extracts.

Data standardization: normalized formats for seamless use in analytics platforms, CRMs, and enterprise systems.

AI-Driven Property Scores: confidence ratings that help users prioritize the most accurate and relevant records.

Property Intelligence Included

Identification: parcel/APN numbers

Characteristics: lot size, living area, building class, year built, structural details, amenities

Ownership & Contacts: current owners, corporate ownership flags, owner-occupied status, Tier 1 verified phones, emails, and mailing addresses, cash buyer indicators

Valuations: assessed values, tax assessments, exemptions, market value estimates

Transactions: historical and recent sales, buyer/seller records, deeds, mortgage and lien data

Legal & Zoning: subdivision and lot details, zoning codes, land use categories

Status Flags: foreclosure indicators, vacancy signals, current market position

Designed For

Real Estate & PropTech: portfolio analysis, real estate investing, AVMs, comps, listing and disposition platforms

Financial Services: mortgage lending, insurance, investment due diligence, risk modeling

Government & Planning: taxation, zoning, urban development, policy research

Marketing & Lead Generation: targeted homeowner audiences, property-linked campaigns, verified direct-mail and digital or direct outreach

Data & Analytics Teams: demographic overlays, contact enrichment, predictive modeling

Quality & Delivery

KinderData’s property and contact records undergo continuous validation and normalization, including geocoding checks, duplicate removal, and schema standardization.

This is Version 1 of the Soil Depth product of the Soil and Landscape Grid of Australia. The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. This depth …Show full descriptionThis is Version 1 of the Soil Depth product of the Soil and Landscape Grid of Australia. The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. This depth product estimates the depth of soil down to 2 metres. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). The soil attribute products are provided as continuous maps that represent each of six depth intervals to a maximum depth of 2 metres. We acknowledge that soil depth is variable across Australia, and in some landscapes there might be no soil or soil might be shallower than 2 metres. We have provided continuous maps because of the relative unavailability of data on soil depth. Further, existing data on depth is biased to near surface layers and there are few records that extend beyond 1.5m. Therefore, we provide here, our best estimate of soil depth to allow users to generate masks, which might be used together with the attribute maps to approximate the presence of areas with no soil or areas with shallow soil. We encourage users to draw on local data and expertise for such assessments. Attribute Definition: Depth of soil profile (A & B horizons); Units: metres; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 3; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF. The metadata and files (if any) are available to the public.

Abstract

Few datasets contain self-identified sensitive attributes, inferring attributes risks introducing additional biases, and collecting attributes can carry legal risks. Besides, categorical labels can fail to reflect the continuous nature of human phenotypic diversity, making it difficult to compare the similarity between same-labeled faces. To address these issues, we present A View From Somewhere (AVFS)---a dataset of 638,180 human judgments of face similarity. We demonstrate the utility of AVFS for learning a continuous, low-dimensional embedding space aligned with human perception. Our embedding space, induced under a novel conditional framework, not only enables the accurate prediction of face similarity, but also provides a human-interpretable decomposition of the dimensions used in the human decision-making process, and the importance distinct annotators place on each dimension. We additionally show the practicality of the dimensions for collecting continuous attributes, performing classification, and comparing dataset attribute disparities.

ICLR 2023 Paper

arxiv.org/abs/2303.17176

Code

github.com/SonyResearch/a_view_from_somewhere

Citing A View From Somewhere

@inproceedings{ andrews2023avfs, title={A View From Somewhere: Human-Centric Face Representations}, author={Jerone T A Andrews and Przemyslaw Joniak and Alice Xiang}, booktitle={ICLR}, year={2023} }

This is Version 1 of the Soil Depth product of the Soil and Landscape Grid of Australia.

It is superseded by the Release 2 product that can be found at: Soil and Landscape Grid National Soil Attribute Maps - Soil Depth (3" resolution) - Release 2.

The Soil and Landscape Grid of Australia has produced a range of digital soil attribute products. This depth product estimates the depth of soil down to 2 metres. The digital soil attribute maps are in raster format at a resolution of 3 arc sec (~90 x 90 m pixels). The soil attribute products are provided as continuous maps that represent each of six depth intervals to a maximum depth of 2 metres. We acknowledge that soil depth is variable across Australia, and in some landscapes there might be no soil or soil might be shallower than 2 metres. We have provided continuous maps because of the relative unavailability of data on soil depth. Further, existing data on depth is biased to near surface layers and there are few records that extend beyond 1.5m. Therefore, we provide here, our best estimate of soil depth to allow users to generate masks, which might be used together with the attribute maps to approximate the presence of areas with no soil or areas with shallow soil. We encourage users to draw on local data and expertise for such assessments. Attribute Definition: Depth of soil profile (A & B horizons); Units: metres; Period (temporal coverage; approximately): 1950-2013; Spatial resolution: 3 arc seconds (approx 90m); Total number of gridded maps for this attribute: 3; Number of pixels with coverage per layer: 2007M (49200 * 40800); Total size before compression: about 8GB; Total size after compression: about 4GB; Data license : Creative Commons Attribution 4.0 (CC BY); Target data standard: GlobalSoilMap specifications; Format: GeoTIFF.

The approximately 44,000 land names of Vorarlberg were collected and mapped by Werner Vogt. The state of Vorarlberg has positioned these names as points and, in cooperation with Werner Vogt, updated them in 2014. More information is stored in the attribute table. These include: the actual field name, type of field name (mountain, waters...), information on size and orientation on the map, change compared to the 1st edition and if so, type of change. Point layer. Important fields and data types of the attribute table: FID (double) Continuous ID - 1st Stand FID_2014 (double) Continuous ID - ID after update NAME (string) original floor name (1st edition) (string) GROESSE (string) describes the relevance of the field name for the area Content (string) Type of field name ROTATION (double) is used to represent the floor name on the map Content (string) Type of field name NAME_2014 (string) Floor name after update Change2014 (integer) describes whether a change has been made to the original dataset Comment (string) describes the type of change Data source: namengut.shp