Specialized collection of 0 free data visualization SVG illustrations from the technology & electronics category. Data visualization illustrations including bar charts, network graphs, and information graphics Examples include: bar chart, network graph.

The vector graphics software market is booming, projected to reach $1734 million by 2033 with an 8.1% CAGR. Explore key trends, drivers, restraints, and leading players like Adobe Illustrator and CorelDRAW in this comprehensive market analysis. Discover the future of vector graphics design!

Specialized collection of 0 free growth charts SVG illustrations from the business & finance category. Business growth illustrations featuring line graphs, pie charts, and data visualization elements Examples include: line graph, pie chart.

Graph Database Vector Search Market Outlook

According to our latest research, the global Graph Database Vector Search market size reached USD 2.35 billion in 2024, exhibiting robust growth driven by the increasing demand for advanced data analytics and AI-powered search capabilities. The market is expected to expand at a CAGR of 21.7% during the forecast period, propelling the market size to an anticipated USD 16.8 billion by 2033. This remarkable growth trajectory is primarily fueled by the proliferation of big data, the widespread adoption of AI and machine learning, and the growing necessity for real-time, context-aware search solutions across diverse industry verticals.

One of the primary growth factors for the Graph Database Vector Search market is the exponential increase in unstructured and semi-structured data generated by enterprises worldwide. Organizations are increasingly seeking efficient ways to extract meaningful insights from complex datasets, and graph databases paired with vector search capabilities are emerging as the preferred solution. These technologies enable organizations to model intricate relationships and perform semantic searches with unprecedented speed and accuracy. Additionally, the integration of AI and machine learning algorithms with graph databases is enhancing their ability to deliver context-rich, relevant results, thereby improving decision-making processes and business outcomes.

Another significant driver is the rising adoption of recommendation systems and fraud detection solutions across various sectors, particularly in BFSI, retail, and e-commerce. Graph database vector search platforms excel at identifying patterns, anomalies, and connections that traditional relational databases often miss. This capability is crucial for detecting fraudulent activities, building sophisticated recommendation engines, and powering knowledge graphs that underpin intelligent digital experiences. The growing need for personalized customer engagement and proactive risk mitigation is prompting organizations to invest heavily in these advanced technologies, further accelerating market growth.

Furthermore, the shift towards cloud-based deployment models is catalyzing the adoption of graph database vector search solutions. Cloud platforms offer scalability, flexibility, and cost-effectiveness, making it easier for organizations of all sizes to implement and scale graph-powered applications. The availability of managed services and API-driven architectures is reducing the complexity associated with deployment and maintenance, enabling faster time-to-value. As more enterprises migrate their data infrastructure to the cloud, the demand for cloud-native graph database vector search solutions is expected to surge, driving sustained market expansion.

Geographically, North America currently dominates the Graph Database Vector Search market, owing to its advanced IT infrastructure, high adoption rate of AI-driven technologies, and presence of leading technology vendors. However, rapid digital transformation initiatives across Europe and the Asia Pacific are positioning these regions as high-growth markets. The increasing focus on data-driven decision-making, coupled with supportive regulatory frameworks and government investments in AI and big data analytics, is expected to fuel robust growth in these regions over the forecast period.

Component Analysis

The Component segment of the Graph Database Vector Search market is broadly categorized into software and services. The software sub-segment commands the largest share, driven by the relentless innovation in graph database technologies and the integration of advanced vector search functionalities. Organizations are increasingly deploying graph database software to manage complex data relationships, power semantic search, and enhance the performance of AI and machine learning applications. The software market is characterized by the proliferation of both open-source and proprietary solutions, with vendors

Like the subtitle says, this is an upload of all downloadable materials from Our World In Data's Article on Plastic Pollution, by Dr. Hannah Ritchi and Prof. Max Roser. Fully cited and totally unedited from the source, I claim no credit for any of the contents of this dataset, I only put it here to bypass arbitrary data sourcing restrictions for a grad school project, and I hope this assists any other students with similar issues in the future.

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

The scientific illustration software market is experiencing robust growth, driven by the increasing need for high-quality visuals in scientific publications, presentations, and educational materials. The market's expansion is fueled by several key factors: the rising adoption of digital tools within research institutions and universities, the growing complexity of scientific data requiring sophisticated visualization techniques, and the increasing demand for visually engaging content to disseminate research findings effectively to broader audiences. While precise market sizing data is unavailable, a reasonable estimation based on comparable software markets and the reported CAGR would suggest a 2025 market value around $300 million, projecting to over $500 million by 2033. This growth trajectory is likely to continue, driven by the ongoing integration of AI-powered features into scientific illustration software, which streamlines workflows and enhances the creation of complex diagrams and illustrations. Furthermore, the increasing affordability and accessibility of powerful software coupled with cloud-based solutions will further democratize access and fuel wider adoption across diverse scientific disciplines. Despite the positive outlook, certain challenges might hinder the market's growth. High initial costs for premium software packages could pose a barrier for individual researchers and smaller institutions. Moreover, the need for specialized training and a certain level of technical proficiency can limit adoption among users who lack prior experience with vector graphics or specialized scientific illustration tools. However, the growing availability of free or open-source alternatives, along with user-friendly interfaces and comprehensive tutorials, are mitigating these challenges. The market is also witnessing an increasing trend towards collaborative software and the integration of scientific illustration tools into larger research platforms, further enhancing the workflow and fostering greater team synergy. The segmentation of the market across various software categories (e.g., general-purpose vector graphics, specialized tools for chemistry, biology, etc.) reflects the diverse needs of scientific illustration in various fields.

VectorEdits: A Dataset and Benchmark for Instruction-Based Editing of Vector Graphics

Paper (Soon) We introduce a large-scale dataset for instruction-guided vector image editing, consisting of over 270,000 pairs of SVG images paired with natural language edit instructions. Our dataset enables training and evaluation of models that modify vector graphics based on textual commands. We describe the data collection process, including image pairing via CLIP similarity and instruction… See the full description on the dataset page: https://huggingface.co/datasets/authoranonymous321/VectorEdits.

Use of vectors in financial graphs By using mathematical vectors calculations as financial modeling then further into a new form of quantitative analysis instrument for linear financial computation graphs. A new tool in financial data analysis as an indicator.

This dataset constructs a multi-level, fine-grained, lossless knowledge graph and its accompanying vector data files based on the full text of a monograph. Firstly, the entire book's content is systematically decomposed and encoded according to a hierarchical structure of ‘chapter–paragraph–sentence–keyword’. Within the CSV-formatted knowledge graph, each chapter title, paragraph position, sentence text, along with its core concepts and keywords, is fully preserved. Concurrently, each image and table within the book is individually catalogued, with precise annotation of page numbers, corresponding textual descriptions, and key information/data fields contained therein. This ensures lossless representation of textual, visual, and tabular information at the structural level, guaranteeing no loss of individual entries. Building upon this foundation, the dataset concurrently generates a JSON-formatted vector data file. This file performs embedding computations on each record within the knowledge graph, associating each structured knowledge entry in the CSV with its corresponding vector representation in JSON via a unified unique identifier (ID). This enables researchers within the Harvard Dataverse environment to perform graph retrieval, semantic vector retrieval, and advanced analytical tasks such as RAG/GraphRAG. This provides a high-precision, reproducible foundational dataset for subsequent text mining, knowledge discovery, and visualisation modelling.

The dataset consists of two sets of files in JSON format. Each file consists of an array of elements and each element is presented by a vector (it can be a bi-vector in the case of the first set or a tri-vector in the latter case). This vector is followed by the Euler characteristic of surface and a set of Edmonds’ realizations, described as a concatenation of two incidence matrices. Such bi-matrix gives rise to the corresponding graph, its dual, as well as one-to-one correspondence between their edges.

The first set consists of 7 files, each containing all possible bi-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 7).

The bi-vectors are stored in files with the value of ell at the end:

• edmonds-bi-vector-realization-ell2.json
• edmonds-bi-vector-realization-ell3.json
• edmonds-bi-vector-realization-ell4.json
• edmonds-bi-vector-realization-ell5.json
• edmonds-bi-vector-realization-ell6.json
• edmonds-bi-vector-realization-ell7.json

The second set consists of 9 files, each containing all possible tri-vectors with all Edmonds’ realizations in a form of bi-matrices for a fixed ell (ranging from 2 to 8). The last file (for ell 9, contains only one Edmond’s realization per vector (if existing) due to time complexity).

The tri-vectors are stored in files with the ell mentioned at the end:

• edmonds-tri-vector-realization-ell2.json
• edmonds-tri-vector-realization-ell3.json
• edmonds-tri-vector-realization-ell4.json
• edmonds-tri-vector-realization-ell5.json
• edmonds-tri-vector-realization-ell6.json
• edmonds-tri-vector-realization-ell7.json
• edmonds-tri-vector-realization-ell8.json
• edmonds-tri-vector-realization-ell9.json

A set of utilities show use cases:

• Parsing functions with examples implemented in Sage:

edmonds_import.sage

• Parsing functions with examples implemented in Java:

EdmondsReaderBivector.java

EdmondsReader.java

Vectorfusion: Text-to-svg by abstracting pixel-based diffusion models.

Resource2Vec is a provider of knowledge graph embeddings of RDF datasets. It can be used through a portable open-source RESTful API which uses state-of-the-art methods to represent RDF instances as vectors in a common space model.

Digital line graph (DLG) data are digital representations of cartographic information. DLG's of map features are converted to digital form from maps and related sources. Intermediate-scale DLG data are derived from USGS 1:100,000-scale 30- by 60-minute quadrangle maps. If these maps are not available, Bureau of Land Management planimetric maps at a scale of 1: 100,000 are used. Intermediate-scale DLG's are sold in five categories: (1) Public Land Survey System; (2) boundaries (3) transportation; (4) hydrography; and (5) hypsography. All DLG data distributed by the USGS are DLG - Level 3 (DLG-3), which means the data contain a full range of attribute codes, have full topological structuring, and have passed certain quality-control checks.

Answering complex logical queries on large-scale incomplete knowledge graphs (KGs) is a fundamental yet challenging task. Recently, a promising approach to this problem has been to embed KG entities as well as the query into a vector space such that entities that answer the query are embedded close to the query.

Context

When I started exploring how to create interactive maps (using the leaflet() package in R) I come across this free data set (shapefile format) that contains the geographical coordinates (polygons) for all the countries in the world. I thought it would be nice to share this with the Kaggle community.

Content

The .zip folder contains all the necessary files needed for the shapefile data to work properly on your computer. If you are new to using the shapefile format, please see the information provided below:

https://en.wikipedia.org/wiki/Shapefile "The shapefile format stores the data as primitive geometric shapes like points, lines, and polygons. These shapes, together with data attributes that are linked to each shape, create the representation of the geographic data. The term "shapefile" is quite common, but the format consists of a collection of files with a common filename prefix, stored in the same directory. The three mandatory files have filename extensions .shp, .shx, and .dbf. The actual shapefile relates specifically to the .shp file, but alone is incomplete for distribution as the other supporting files are required. "

Acknowledgements

Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com.

The Vector Graphics Software market has emerged as a crucial segment within the broader software industry, catering to the evolving needs of designers, artists, and businesses alike. By enabling the creation and manipulation of scalable graphics, vector graphics software plays a pivotal role across various sectors,

These maps are based on vector map data (IHO S-57 format) containing the detailed description of each object (marking mark, wreckage, submarine cables, restricted areas, probes, etc.). They are the digital equivalent of paper charts.

The Inland Electronic Navigational Charts (IENC) dataset is updated bi-monthly by the US Army Corps (USACE)/Army Geospatial Center (AGC), and part of U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). There is a partial delivery on the 1st and 15th business day of a month. If there is a need for a special chart delivery, occasionally there will be updates outside of the delivery cycle. The charts with data changes are released either as new editions or updates to existing IENCs on a regular delivery cycle. These IENCs were developed from available data used in maintenance of Navigation channels. These vector data, that make up the charts, can be downloaded as a geodatabase here: https://ienccloud.us/ienc/products/files/U37/ienc_master_dataset_gdb/USACE_IENC_Master_Service_gdb.zip. In addition, web mapping services of the feature classes/datasets can be found here: https://ienccloud.us/arcgis/rest/services/IENC_Feature_Classes. Users of these IENCs should be aware that some features and attribute information could have significant inaccuracies due to changing waterway conditions, inaccurate source data, or approximations introduced during chart compilation. Caution is urged in use of these IENCs or derived products for navigation planning or operation, or any decisions pertaining to or affecting safety of vessel operation. Only charts downloaded from the USACE chart server, https://ienccloud.us, and used in an Electronic Chart Display Information System (ECDIS) or Electronic Chart System (ECS), or official government chart books are suitable for navigation.