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

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

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, ..., 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, esti..., , # Data from: How to Use Discrete Choice Experiments to Capture Stakeholder Preferences in Social Work Research

Access this dataset on Dryad

This dataset supports the worked example in:

Ellis, A. R., Cryer-Coupet, Q. R., Weller, B. E., Howard, K., Raghunandan, R., & Thomas, K. C. (2024). How to use discrete choice experiments to capture stakeholder preferences in social work research. Journal of the Society for Social Work and Research. Advance online publication. https://doi.org/10.1086/731310

The referenced article introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. In a DCE, researchers ask participants to complete a series of choice tasks: hypothetical situations in which each participant is presented with alternative scenarios and selects one or more. For example, social work researchers may want to know how parents and other caregivers pr...

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 ...

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.

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

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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

Attribute-Based Access Control for Gov Data Market Outlook

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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).

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1. Title: SPAM E-mail Database

2. Sources: - Creators: Mark Hopkins, Erik Reeber, George Forman, Jaap Suermondt (Hewlett-Packard Labs, 1501 Page Mill Rd., Palo Alto, CA 94304) - Donor: George Forman (gforman at nospam hpl.hp.com, 650-857-7835) - Generated: June-July 1999

3. Past Usage: - Hewlett-Packard Internal-only Technical Report. External forthcoming. - Used to determine whether a given email is spam or not. - Approximately 7% misclassification error. - Emphasis on minimizing false positives (marking good mail as spam) due to their undesirability. - Even with the insistence on zero false positives in the training/testing set, 20-25% of the spam passed through the filter.

4. Relevant Information: - The concept of "spam" is diverse, encompassing advertisements, make-money-fast schemes, chain letters, and pornography. - Spam emails were collected from the postmaster and individuals who reported spam. - Non-spam emails were collected from work and personal sources, with the words 'george' and the area code '650' indicating non-spam. - Non-spam indicators like 'george' and '650' need to be handled carefully or require a broad collection of non-spam for a general-purpose spam filter. - Background information on spam: Cranor, Lorrie F., LaMacchia, Brian A. "Spam!" Communications of the ACM, 41(8):74-83, 1998.

5. Number of Instances: 4601 (1813 Spam = 39.4%)

6. Number of Attributes: 58 (57 continuous, 1 nominal class label)

7. Attribute Information: - 48 continuous real [0,100] attributes of type word_freq_WORD: Percentage of words in the email that match the specified word. - 6 continuous real [0,100] attributes of type char_freq_CHAR: Percentage of characters in the email that match the specified character. - 1 continuous real [1,...] attribute of type capital_run_length_average: Average length of uninterrupted sequences of capital letters. - 1 continuous integer [1,...] attribute of type capital_run_length_longest: Length of the longest uninterrupted sequence of capital letters. - 1 continuous integer [1,...] attribute of type capital_run_length_total: Sum of the length of uninterrupted sequences of capital letters. - 1 nominal {0,1} class attribute of type spam: Denotes whether the email was considered spam (1) or not (0), i.e., unsolicited commercial e-mail.

8. Missing Attribute Values: None

9. Class Distribution: - Spam: 1813 (39.4%) - Non-Spam: 2788 (60.6%)

10. Attribute Statistics (Min, Max, Average, Std.Dev, Coeff.Var_%): - Detailed statistics provided for each of the 58 attributes.

11. Additional Information: - Documentation available in the file 'spambase.DOCUMENTATION' at the UCI Machine Learning Repository: Link

In this project, I have done exploratory data analysis on the UCI Automobile dataset available at https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

This dataset consists of data From the 1985 Ward's Automotive Yearbook. Here are the sources

1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook. 2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038 3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037

Number of Instances: 398 Number of Attributes: 9 including the class attribute

Attribute Information:

mpg: continuous cylinders: multi-valued discrete displacement: continuous horsepower: continuous weight: continuous acceleration: continuous model year: multi-valued discrete origin: multi-valued discrete car name: string (unique for each instance)

This data set consists of three types of entities:

I - The specification of an auto in terms of various characteristics

II - Tts assigned an insurance risk rating. This corresponds to the degree to which the auto is riskier than its price indicates. Cars are initially assigned a risk factor symbol associated with its price. Then, if it is riskier (or less), this symbol is adjusted by moving it up (or down) the scale. Actuaries call this process "symboling".

III - Its normalized losses in use as compared to other cars. This is the relative average loss payment per insured vehicle year. This value is normalized for all autos within a particular size classification (two-door small, station wagons, sports/specialty, etc...), and represents the average loss per car per year.

The analysis is divided into two parts:

Data Wrangling

1. Pre-processing data in python
2. Dealing with missing values
3. Data formatting
4. Data normalization
5. Binning

6. Exploratory Data Analysis

7. Descriptive statistics

8. Groupby

9. Analysis of variance

10. Correlation

11. Correlation stats

Acknowledgment Dataset: UCI Machine Learning Repository Data link: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data

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2. Extensive Global Coverage

   • Includes profiles of companies from North America, Europe, Asia-Pacific, and beyond.
   • Scale your strategies by tapping into emerging markets, niche sectors, and diverse geographies.

3. Continuous Data Updates

   • Receive real-time updates to keep pace with changing organizational structures, market expansions, and acquisitions.
   • Always rely on current data to guide product roadmaps, growth plans, and strategic partnerships.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible, lawful data usage for every query.

Data Highlights:

• 70M+ Verified Company Profiles: Gain clarity on businesses spanning all major industries and regions.
• Industry Classifications: Filter companies by their sector focus, from manufacturing to technology.
• Revenue and Employee Counts: Understand company sizes, growth potential, and market reach.
• Global Market Insights: Use firmographic data to inform product launches, expansions, and strategic alliances.

Key Features of the Firmographic Data API:

1. Real-Time Company Enrichment

   • Enhance your CRM or analytics platforms with verified firmographic data, eliminating guesswork and manual data imports.
   • Update records automatically as companies grow, diversify, or shift their market focus.

2. Advanced Filtering and Query Capabilities

   • Query the API for specific parameters like industry vertical, company size, or geographic location.
   • Zero in on opportunities aligned with your business goals, improving efficiency and outcomes.

3. Scalability and Flexibility

   • Seamlessly integrate the API into existing workflows, CRM systems, or marketing automation tools.
   • Adjust parameters as markets evolve, ensuring that you always have the intelligence needed to adapt and thrive.

4. AI-Validated Accuracy and Reliability

   • Rely on an AI-powered validation process that continually verifies data integrity.
   • Increase confidence in strategic decisions backed by accurate, current, and relevant information.

Strategic Use Cases:

1. Market Analysis and Competitive Benchmarking

   • Identify industries poised for growth, evaluate emerging markets, and benchmark against competitor profiles.
   • Refine go-to-market strategies and product launches based on solid data rather than assumptions.

2. Strategic Partnering and M&A Efforts

   • Explore potential partners, suppliers, or acquisition targets that match your criteria, from revenue tiers to geographic presence.
   • Shorten due diligence cycles with reliable, on-demand firmographic insights.

3. Sales and Account-Based Marketing

   • Segregate target accounts by industry, size, and region to tailor outreach and messaging.
   • Personalize campaigns, improve lead quality, and increase win rates through better audience alignment.

4. Product Roadmapping and Portfolio Management

   • Inform product development by identifying high-growth verticals or underpenetrated regions.
   • Allocate resources effectively and prioritize product enhancements based on firmographic-driven insights.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality firmographic data at competitive prices, ensuring optimal ROI for your research and strategic planning.

2. Seamless Integration

   • Easily incorporate the API into existing workflows, eliminating data silos and manual data management tasks.

3. Data Accuracy with AI Validation

   • Depend on 99% accuracy to guide data-driven decisions, refine targeting, and boost strategic outcomes.

4. Customizable and Scalable Solutions

   • Tailor the dataset to specific industries, regions, or revenue segments as your business evolves and market conditions shift.

Additional APIs for Enhanced Functionality:

1. Data Enrichment API...

SPAM E-mail Database

The “spam” concept is diverse: advertisements for products/websites, make money fast schemes, chain letters, pornography… Our collection of spam e-mails came from our postmaster and individuals who had filed spam. Our collection of non-spam e-mails came from filed work and personal e-mails, and hence the word ‘george’ and the area code ‘650’ are indicators of non-spam. These are useful when constructing a personalized spam filter. One would either have to blind such non-spam indicators or get a very wide collection of non-spam to generate a general purpose spam filter.

Attribute Information:

The last column denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail. Most of the attributes indicate whether a particular word or character was frequently occurring in the e-mail. The run-length attributes (55-57) measure the length of sequences of consecutive capital letters.

For the statistical measures of each attribute, see the end of this file. Here are the definitions of the attributes:

48 continuous real [0,100] attributes of type word_freq_WORD = percentage of words in the e-mail that match WORD, i.e. 100 * (number of times the WORD appears in the e-mail) / total number of words in e-mail. A “word” in this case is any string of alphanumeric characters bounded by non-alphanumeric characters or end-of-string.

6 continuous real [0,100] attributes of type char_freq_CHAR = percentage of characters in the e-mail that match CHAR, i.e. 100 * (number of CHAR occurrences) / total characters in e-mail

1 continuous real [1,…] attribute of type capital_run_length_average = average length of uninterrupted sequences of capital letters

1 continuous integer [1,…] attribute of type capital_run_length_longest = length of longest uninterrupted sequence of capital letters

1 continuous integer [1,…] attribute of type capital_run_length_total = sum of length of uninterrupted sequences of capital letters = total number of capital letters in the e-mail

1 nominal {0,1} class attribute of type spam = denotes whether the e-mail was considered spam (1) or not (0), i.e. unsolicited commercial e-mail.

This dataset represents the population and housing unit density within individual, local NHDPlusV2 catchments and upstream, contributing watersheds riparian buffers based on 2010 US Census data. Densities are calculated for every block group and watershed averages are calculated for every local NHDPlusV2 catchment(see Data Sources for links to NHDPlusV2 data and Census Data). This data set is derived from The TIGER/Line Files and related database (.dbf) files for the conterminous USA. It was downloaded as Block Group-Level Census 2010 SF1 Data in File Geodatabase Format (ArcGIS version 10.0). The landscape raster (LR) was produced based on the data compiled from the questions asked of all people and about every housing unit. The (block-group population / block group area) and (block-group housing units / block group area) were 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).

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.

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).

Leverage high-quality B2B data with 468 enriched attributes, covering firmographics, financial stability, and industry classifications. Our AI-optimized dataset ensures accuracy through advanced deduplication and continuous updates. With 30+ years of expertise and 1,100+ trusted sources, we provide fully compliant, structured business data to power lead generation, risk assessment, CRM enrichment, market research, and more.

Key use cases of B2B Data have helped our customers in several areas :

1. Boost Lead Generation & Sales Outreach : Target the right businesses with precise, segmented contact lists for cold calling, email marketing, and industry-specific campaigns.
2. Enhance CRM & Web Data for Smarter Engagement : Enrich CRM records with instant access to detailed company profiles, visitor identification, and continuous data updates.
3. Strengthen Risk Assessment & Fraud Prevention : Evaluate supplier reliability, assess credit risk, and prevent fraud with deep firmographic and financial insights.
4. Gain a Competitive Edge with Market Research : Analyse industry trends, benchmark competitors, and identify automation-ready sectors for strategic positioning.
5. Optimize B2B Strategies with AI-Powered Insights : Leverage structured, compliant data to drive smarter business decisions across sales, marketing, and operations.