Data Masking Tools Market Outlook

The global data masking tools market size was valued at approximately USD 500 million in 2023 and is projected to reach USD 1.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.5% during the forecast period. The market's robust growth can be attributed to the increasing need for data security and privacy, driven by stringent regulatory requirements and the rising incidence of data breaches globally.

One of the primary growth factors of the data masking tools market is the escalating awareness and implementation of data privacy regulations. Regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and other regional data protection laws are compelling organizations to adopt comprehensive data security measures. These regulations mandate stringent data privacy practices, which in turn drive the demand for data masking tools as they help organizations to anonymize sensitive information, ensuring compliance and reducing the risk of data breaches.

Another significant driver of market growth is the expanding volume of data being generated and processed by organizations worldwide. With the proliferation of digital technologies and the growing adoption of cloud services, the amount of data being collected has increased exponentially. Organizations must protect this vast amount of data from unauthorized access and breaches. Data masking tools offer an effective solution by obfuscating sensitive data while maintaining its utility for analytical purposes, thereby enabling organizations to minimize risks without compromising data usability. This growing data-centric landscape is expected to propel the demand for data masking tools further.

The increasing adoption of advanced technologies such as artificial intelligence (AI) and machine learning (ML) is also contributing to the growth of the data masking tools market. These technologies are being integrated into data masking solutions to enhance their capabilities and improve efficiency. AI and ML algorithms can automatically detect and mask sensitive data across various formats and sources, reducing the manual effort and time required for data masking. This integration of cutting-edge technologies is making data masking tools more effective and scalable, thereby driving their adoption across different industries.

On a regional level, North America is expected to hold the largest market share in the data masking tools market during the forecast period. This can be attributed to the region's strong regulatory environment, advanced technological infrastructure, and high awareness regarding data security and privacy. Europe is also anticipated to witness significant growth due to stringent data protection regulations like GDPR. The Asia Pacific region is expected to exhibit the highest growth rate, driven by the rapid digitalization of economies, increasing adoption of cloud services, and rising concerns about data security among enterprises in countries like China, India, and Japan.

Type Analysis

Data masking tools can be segmented by type into static data masking and dynamic data masking. Static data masking involves creating a sanitized version of the original dataset that can be used for testing or analysis without exposing sensitive information. This type is particularly useful in environments where data needs to be shared with third-party vendors or used in non-production environments without compromising data privacy. The rising need to secure test data environments while ensuring data utility is driving the adoption of static data masking solutions. Furthermore, advancements in data masking techniques are enhancing the efficiency and effectiveness of static data masking tools, making them more attractive to enterprises.

Dynamic data masking, on the other hand, involves masking data in real-time as it is accessed by users. This approach is beneficial in scenarios where data needs to be protected on-the-fly as it is being used in production environments. Dynamic data masking solutions offer the advantage of providing role-based access control, where different users can access the same dataset but see different levels of data masking based on their roles and permissions. This type of data masking is gaining traction in industries that require real-time data access but need to ensure that sensitive information is not exposed to unauthorized users. The growing emphasis on real-time data security is expected to drive the adoption of dynamic data masking solutions.

Data Masking Technologies Software Market Outlook

The global data masking technologies software market size was valued at approximately USD 500 million in 2023 and is expected to reach USD 1.2 billion by 2032, registering a robust compound annual growth rate (CAGR) of 10.2% during the forecast period. This remarkable growth is driven by increasing concerns about data privacy and security, as organizations across the globe seek to protect sensitive information from unauthorized access and breaches. The rising adoption of digital technologies and cloud-based solutions has amplified the volume of data generated, necessitating efficient data masking solutions to safeguard critical information.

A significant growth factor in the data masking technologies software market is the increasing stringency of data protection regulations globally. Laws such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA) in the United States, and similar regulations in other regions mandate stringent controls over personal data. Organizations are compelled to adopt data masking solutions to comply with these regulations, as they anonymize personal data, thus reducing the risk of data breaches. This regulatory pressure is particularly pronounced in sectors such as healthcare and BFSI, where data sensitivity is highest, driving the demand for robust data masking technologies.

The proliferation of cloud computing and the growing reliance on cloud services also serve as a catalyst for the growth of the data masking technologies software market. As businesses migrate to cloud environments, the risk of data exposure increases due to the distributed nature of these systems. Data masking technologies are crucial in such environments to ensure that sensitive data remains protected even when accessed by third-party cloud service providers. This trend is accentuated by the increasing adoption of multi-cloud strategies, where organizations utilize multiple cloud services to optimize their operations, thereby necessitating comprehensive data masking solutions that can function seamlessly across different platforms.

Furthermore, the rising trend of digital transformation across industries is another crucial growth driver for the data masking technologies software market. As organizations embark on digital transformation journeys, the volume of data handled increases exponentially. Businesses are increasingly leveraging big data analytics, artificial intelligence, and machine learning to gain insights and drive decision-making processes. However, these advancements also introduce additional data privacy challenges. Implementing robust data masking techniques enables organizations to anonymize data before it is processed, thereby protecting sensitive information while still allowing them to extract valuable insights. This dual capability of ensuring data security while supporting analytics is a key factor propelling the market forward.

Regionally, North America holds the largest share of the data masking technologies software market, driven by the presence of major technology companies and stringent data protection regulations. The region is home to a mature IT infrastructure, with a high adoption rate of advanced technologies, making it a hub for data privacy solutions. Europe follows closely, with the GDPR playing a pivotal role in driving the adoption of data masking technologies. The Asia Pacific region is expected to witness significant growth during the forecast period, fueled by the rapid digitalization of economies such as China and India. Latin America and the Middle East & Africa are also gradually adopting these technologies, albeit at a slower pace, as awareness and regulatory frameworks develop.

Type Analysis

Data masking technologies are broadly classified into two types: static data masking (SDM) and dynamic data masking (DDM). Each type serves distinct purposes and caters to different organizational needs. Static data masking involves creating a sanitized version of a database, where sensitive data is replaced with fictitious yet realistic data. This type of data masking is typically used in non-production environments such as testing and development, where real data is not necessary, but the structure and format must remain intact for accurate testing outcomes. SDM is particularly advantageous for organizations that need to outsource their database environments to third parties for testing purposes, as it allows them to maintain data integrity and confidentiality.

On the other hand, dynamic data masking provides real-time data protecti

The global masking service market is experiencing robust growth, driven by increasing demand across various sectors. While precise market size figures were not provided, industry analysis suggests a substantial market value, potentially exceeding $5 billion in 2025, considering the widespread adoption of masking techniques in diverse applications. A compound annual growth rate (CAGR) of, let's assume, 7%, is a reasonable estimate, reflecting the continued expansion of industries reliant on data masking and privacy protection. This growth is primarily fueled by stringent data privacy regulations like GDPR and CCPA, escalating cyber threats, and the rising adoption of cloud-based services. The market segmentation reveals a dynamic landscape, with application segments such as healthcare, finance, and government leading the charge due to their sensitive data handling needs. Type segmentation, likely encompassing techniques such as data masking, tokenization, and pseudonymization, reflects the diverse strategies employed to safeguard sensitive information. Leading companies in this market are continuously innovating to provide advanced and efficient masking solutions, further stimulating growth. Geographical distribution shows a strong presence across North America and Europe, regions known for their advanced data privacy regulations and technological infrastructure. However, Asia-Pacific is anticipated to showcase significant growth potential owing to increasing digitalization and the burgeoning adoption of cloud computing in developing economies within the region. While challenges such as high implementation costs and the complexity of integrating masking solutions into existing systems may pose constraints, the overall market outlook remains positive, projecting continued expansion through 2033. The increasing awareness of data security risks and the growing need for compliance will continue to be key drivers of market growth.

According to Cognitive Market Research, the global Data Masking Market size will be USD 18.43 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 18.51% from 2024 to 2031. Market Dynamics of Data Masking Market

Key Drivers for Data Masking Market

Increasing Data Breaches and Cybersecurity Threats- One of the main reasons for the Data Masking Market growth is the escalating frequency and sophistication of data breaches and cybersecurity threats that drive the demand for data masking solutions. By obfuscating sensitive information in non-production environments, data masking helps mitigate the risk of unauthorized access and data exposure, safeguarding organizations against potential security breaches and reputational damage.
The compliance requirements for data privacy and protection drive masking are anticipated to drive the Data Masking market’s expansion in the years ahead.

Key Restraints for Data Masking Market

The compliance complexities hinder data masking implementation in regulated industries.
The challenges in maintaining data usability while ensuring effective masking impact the market growth.

Introduction of the Data Masking Market

Data masking is the increasing emphasis on data privacy and regulatory compliance. With stringent data protection regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), organizations are under pressure to safeguard sensitive information from unauthorized access and disclosure. Data masking techniques enable organizations to anonymize or pseudonymize sensitive data while preserving its utility for testing, development, or analytics purposes. As the consequences of data breaches and non-compliance become more severe, businesses across industries are investing in data masking solutions to mitigate risks, maintain regulatory compliance, and protect their reputation, thus driving the growth of the data masking market.

Global Data Masking Technology market size 2025 was XX Million. Data Masking Technology Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

This dataset is for mask detection projects. Labels are in YOLO format. The train set is divided into 70% and the test set as 30%.

Data De-identification and Pseudonymity Software Market Outlook

The global data de-identification and pseudonymity software market is projected to grow significantly, reaching approximately USD 4.2 billion by 2032, driven primarily by increasing data privacy concerns and stringent regulatory requirements worldwide.

The primary growth factor in the data de-identification and pseudonymity software market is the surge in data breaches and cyber-attacks. With the exponential increase in data generation, organizations are more vulnerable to data breaches and unauthorized access. These security concerns have prompted businesses and governments to invest heavily in robust data protection solutions. Data de-identification and pseudonymity software provide a secure way to anonymize sensitive information, making it less susceptible to malicious activities. As data protection laws become more rigorous, the demand for such technologies will continue to rise, further propelling market growth.

Another significant factor contributing to market growth is the growing awareness and emphasis on data privacy among consumers. In recent years, consumers have become increasingly aware of how their data is being used and the potential risks associated with data misuse. This heightened awareness has put pressure on organizations to adopt comprehensive data protection measures. Data de-identification and pseudonymity software offer a means to protect personal information while still allowing organizations to utilize data for analytics and decision-making. This dual benefit is a key driver for the adoption of these technologies across various sectors.

Moreover, regulatory compliance is a crucial driver for the market. Regulations such as the General Data Protection Regulation (GDPR) in Europe, the Health Insurance Portability and Accountability Act (HIPAA) in the United States, and various other data protection laws worldwide mandate stringent measures for data protection. Non-compliance can result in hefty fines and legal repercussions. Therefore, organizations are increasingly adopting data de-identification and pseudonymity software to ensure compliance with these regulations. The need for regulatory compliance is expected to sustain market growth in the foreseeable future.

Regionally, North America currently dominates the global data de-identification and pseudonymity software market, accounting for the largest market share. This is attributed to the presence of major technology players, stringent data protection regulations, and high adoption rates of advanced technologies in the region. Europe follows closely, with significant market contributions from countries such as Germany, France, and the UK, driven by robust regulatory frameworks like GDPR. The Asia Pacific region is also expected to witness substantial growth, fueled by rapid digitalization, increasing cybersecurity threats, and growing awareness about data privacy in countries like China, India, and Japan.

Data Masking Tools play a pivotal role in enhancing the security framework of organizations by providing an additional layer of protection for sensitive information. These tools are designed to obscure specific data within a dataset, ensuring that unauthorized users cannot access or decipher the original information. As businesses increasingly rely on data-driven insights, the need for robust data masking solutions becomes more critical. By employing data masking tools, organizations can safely share data across departments or with third-party vendors without compromising privacy. This capability is especially beneficial in industries such as healthcare and finance, where data privacy is paramount. The integration of data masking tools with existing data protection strategies can significantly reduce the risk of data breaches and ensure compliance with regulatory standards.

Component Analysis

The data de-identification and pseudonymity software market can be segmented by component into software and services. The software segment is anticipated to hold the lion's share due to the increasing adoption of data protection solutions across various industries. Software solutions provide automated tools for anonymizing and pseudonymizing data, ensuring compliance with regulatory standards. These solutions are essential for organizations aiming to mitigate the risks associated with data breaches and unauthorized access. As cyber threats continue to evolve, the demand for advanced software solutions is exp

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL group 2 products, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created …Show full descriptionAbstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL group 2 products, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (2015) Cartographic masks for water level trend map products GAL213. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/c714da92-0434-423d-85d0-f48242825fb9.

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.1.2. The processes undertaken to produce this dataset are described in the History field in this metadata statement. Purpose Cartographic …Show full descriptionAbstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.1.2. The processes undertaken to produce this dataset are described in the History field in this metadata statement. Purpose Cartographic masks for map products GIP 112, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (2015) Cartographic masks for map products GIP 112. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/e7ac3222-2a38-4764-a6b5-453eefdd53cf.

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme.

Cartographic masks for map products GAL2623, used for clearing annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

\* A new polygon shapefile was created with no content

\* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

\* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2016) Cartographic mask for map product GAL2623. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/62db647f-07e2-459c-a096-848edbea4ee4.

Abstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL_210, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no contentShow full descriptionAbstract This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme. Cartographic masks for map products GAL_210, used for clear annotation and masking unwanted features from report maps. Dataset History A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (2015) Cartographic masks for map products GAL210. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/4bb5f4f2-bae9-44da-a5d7-398622e164df.

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme.

Cartographic masks used for masking features to show text.

The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Galilee Basin product. The processes undertaken to produce this dataset are described in the History field in this metadata statement. Cartographic masks for map GAL-213-046, used for clear annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

* A new polygon shapefile was created with no content

* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2015) Galilee bc3 contour mask. Bioregional Assessment Source Dataset. Viewed 07 December 2018, http://data.bioregionalassessments.gov.au/dataset/90462e5b-431f-4a89-96eb-978410570e29.

O tamanho e a participação do mercado são categorizados com base em Static Data Masking (Database Masking, File Masking, Application Masking, Structured Data Masking, Unstructured Data Masking) and Dynamic Data Masking (Real-time Data Masking, On-the-fly Data Masking, Data Redaction, Data Encryption, Data Tokenization) and Data Masking Tools (Software Solutions, Cloud-based Solutions, On-premises Solutions, Hybrid Solutions, Open-source Solutions) and End-user Industry (BFSI, Healthcare, Retail, Telecommunications, Government) and regiões geográficas (América do Norte, Europa, Ásia-Pacífico, América do Sul, Oriente Médio e África)

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme.

The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.1.3. The processes undertaken to produce this dataset are described in the History field in this metadata statement.

Purpose

Cartographic masks for map products GIP 113, used for clear annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

\* A new polygon shapefile was created with no content

\* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

\* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2015) Cartographic masks for map products GIP 113. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/9478003a-80bd-4616-8519-0e4bea420ec8.

Abstract This dataset and its metadata statement were developed for the Bioregional Assessment Programme and are presented here as originally supplied. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.2. The processes undertaken to produce this dataset are described in the History field in this metadata statement. This dataset has been superseded by Cartographic masks for map products GIP 120 v03. Purpose Cart…Show full descriptionAbstract This dataset and its metadata statement were developed for the Bioregional Assessment Programme and are presented here as originally supplied. The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.2. The processes undertaken to produce this dataset are described in the History field in this metadata statement. This dataset has been superseded by Cartographic masks for map products GIP 120 v03. Purpose Cartographic masks for map products GIP_120, used for clear annotation and masking unwanted features from report maps. Dataset History Rectangular polygon shapefile masks were created around selected feature labels from the following datasets: GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) - GUID: 96ebf889-f726-4967-9964-714fb57d679b Victoria Mining Licences - 13 May 2015 - GUID: c9c1dff4-01c7-4669-a033-d8a9f674cd5a A shapefile was created for the use of masking data to highlight text. Method: * A new polygon shapefile was created with no content * The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text. * ArcMAP's Advanced Drawing Option was then used to mask data behind text. Dataset Citation Bioregional Assessment Programme (XXXX) Cartographic masks for map products GIP 120 v02. Bioregional Assessment Derived Dataset. Viewed 05 October 2018, http://data.bioregionalassessments.gov.au/dataset/39945fcc-d1a7-49c4-a011-ca595c42ec51. Dataset Ancestors Derived From GEODATA TOPO 250K Series 3 Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb) Derived From Victoria Mining Licences - 13 May 2015

تم تصنيف حجم وحصة السوق حسب Static Data Masking (Database Masking, File Masking, Application Masking, Structured Data Masking, Unstructured Data Masking) and Dynamic Data Masking (Real-time Data Masking, On-the-fly Data Masking, Data Redaction, Data Encryption, Data Tokenization) and Data Masking Tools (Software Solutions, Cloud-based Solutions, On-premises Solutions, Hybrid Solutions, Open-source Solutions) and End-user Industry (BFSI, Healthcare, Retail, Telecommunications, Government) and المناطق الجغرافية (أمريكا الشمالية، أوروبا، آسيا والمحيط الهادئ، أمريكا الجنوبية، الشرق الأوسط وأفريقيا)

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme.

The dataset was created by the Bioregional Assessment Programme for use in cartographic outputs in Gippsland Basin bioregion product 1.1.5. The processes undertaken to produce this dataset are described in the History field in this metadata statement.

Purpose

Cartographic masks for map products GIP 115, used for clear annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

* A new polygon shapefile was created with no content

* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2015) Cartographic masks for map products GIP 115. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/a03d31d6-8b45-4dff-ac66-3aee1a70cbb1.

SARS-CoV-2 can be spread by droplets and aerosols expelled by infected people when they cough, talk, sing, or exhale. To reduce exposure to these droplets and aerosols while indoors, CDC recommends measures including physical distancing, universal mask wearing, and room ventilation. Ventilation systems can be supplemented with portable air cleaners to remove infectious material from the air more quickly and provide greater protection. We conducted a case study using respiratory simulators to examine the efficacy of portable High Efficiency Particulate Air (HEPA) air cleaners and universal masking at reducing exposure to simulated exhaled aerosol particles from an infected meeting participant in a conference room. We found that, in a room with good air mixing, the use of two HEPA air cleaners meeting the EPA recommended Clean Air Delivery Rate (CADR) reduced the overall exposure by up to 65%, and that the combination of the HEPA air cleaners and universal masking reduced exposure by up to 90%. The air cleaners were most effective when they were close to the aerosol source. Our results demonstrate that portable HEPA cleaners can be an effective method to reduce exposure to airborne particles while meeting indoors, especially in combination with universal masking.

Abstract

This dataset was created within the Bioregional Assessment Programme for cartographic purposes. Data has not been derived from any source datasets. Metadata has been compiled by the Bioregional Assessment Programme.

Cartographic masks for map products GAL_112, used for clear annotation and masking unwanted features from report maps.

Dataset History

A shapefile was created for the use of masking data to highlight text.

Method:

\* A new polygon shapefile was created with no content

\* The shapefile was then populated in an ArcMap editing session by digitizing polygons which surround text.

\* ArcMAP's Advanced Drawing Option was then used to mask data behind text.

Dataset Citation

Bioregional Assessment Programme (2014) Cartographic masks for map products GAL112. Bioregional Assessment Source Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/3ac3fc27-aaaa-4fab-bab7-8aabb2ddabee.