The compressed package (Study_code.zip) contains the code files implemented by an under review paper ("What you see is what you get: Delineating urban jobs-housing spatial distribution at a parcel scale by using street view imagery based on deep learning technique").The compressed package (input_land_parcel_with_attributes.zip) is the sampled mixed "jobs-housing" attributes data of the study area with multiple probability attributes (Only working, Only living, working and living) at the land parcel scale.The compressed package (input_street_view_images.zip) is the surrounding street view data near sampled land parcels (input_land_parcel_with_attributes.zip) with the pixel size of 240*160 obtained from Tencent map (https://map.qq.com/).The compressed package (output_results.zip) contains the result vector files (Jobs-housing pattern distribution and error distribution) and file description (Readme.txt).This project uses some Python open source libraries (Numpy, Pandas, Selenium, Gdal, Pytorch and sklearn). This project complies with the GPL license.Numpy (https://numpy.org/) is an open source numerical calculation tool developed by Travis Oliphant. Used in this project for matrix operation. This library complies with the BSD license.Pandas (https://pandas.pydata.org/) is an open source library, providing high-performance, easy-to-use data structures and data analysis tools. This library complies with the BSD license.Selenium(https://www.selenium.dev/) is a suite of tools for automating web browsers.Used in this project for getting street view images.This library complies with the BSD license.Gdal(https://gdal.org/) is a translator library for raster and vector geospatial data formats.Used in this project for processing geospatial data.This library complies with the BSD license.Pytorch(https://pytorch.org/) is an open source machine learning framework that accelerates the path from research prototyping to production deployment.Used in this project for deep learning.This library complies with the BSD license.sklearn(https://scikit-learn.org/) is an open source machine learning tool for python.Used in this project for comparing precision metrics.This library complies with the BSD license.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 4.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS4_0_VectorAnalysis_Script_Python3.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). Vector Analysis ("PADUS4_0VectorAnalysis_GAP_PADUS_Only_ClipCENSUS.zip") data was created by clipping the PAD-US 4.0 Spatial Analysis and Statistics results to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS4_0_VectorAnalysisFile_OtherExtents_ClipCENSUS2022.zip"). Comma-separated Value (CSV) tables ("PADUS4_0_SummaryStatistics_TabularData_CSV.zip") provided as an alternative format and enable users to explore and download summary statistics of interest from the PAD-US Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 4.0 combined file without other extent boundaries ("PADUS4_0VectorAnalysis_GAP_PADUS_Only_ClipCENSUS.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS4_0VectorAnalysis_State_Clip_CENSUS2022" feature class ("PADUS4_0_VectorAnalysisFile_OtherExtents_ClipCENSUS2022.gdb") is the source of the PAD-US 4.0 Raster Analysis child item. Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://ngda-portfolio-community-geoplatform.hub.arcgis.com/pages/portfolio ), agencies are the best source of their lands data.

Background Data from thousands of transcription-profiling experiments in organisms ranging from yeast to humans are now publicly available. How best to analyze these data remains an important challenge. A variety of tools have been used for this purpose, including hierarchical clustering, self-organizing maps and principal components analysis. In particular, concepts from vector algebra have proven useful in the study of genome-wide expression data. Results Here we present a framework based on vector algebra for the analysis of transcription profiles that is geometrically intuitive and computationally efficient. Concepts in vector algebra such as angles, magnitudes, subspaces, singular value decomposition, bases and projections have natural and powerful interpretations in the analysis of microarray data. Angles in particular offer a rigorous method of defining 'similarity' and are useful in evaluating the claims of a microarray-based study. We present a sample analysis of cells treated with rapamycin, an immunosuppressant whose effects have been extensively studied with microarrays. In addition, the algebraic concept of a basis for a space affords the opportunity to simplify data analysis and uncover a limited number of expression vectors to span the transcriptional range of cell behavior. Conclusions This framework represents a compact, powerful and scalable construction for analysis and computation. As the amount of microarray data in the public domain grows, these vector-based methods are relevant in determining statistical significance. These approaches are also well suited to extract biologically meaningful information in the analysis of signaling networks.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

This dataset is part of a published paper. The data contained includes both Movie Review and Survey Data.

Analysis of ‘[DEPRECATED] Corine Land Cover 2000 seamless vector data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/data_corine-land-cover-2000-clc2000-seamless-vector-database on 11 January 2022.

--- Dataset description provided by original source is as follows ---

A service allowing for DIRECT DOWNLOAD of the seamless vector database is available HERE

--- Original source retains full ownership of the source dataset ---

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and outdoor recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 4.0 protection status (by GAP Status Code) and public access status for various land unit boundaries (PAD-US 4.0 Vector Analysis and Summary Statistics). Summary statistics are also available to explore and download from the PAD-US Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary Statistics Tabular Data (CSV) are included in this data release. Raster analysis files are also available for combination with other raster data (PAD-US 4.0 Raster Analysis). The PAD-US Combined Fee, Designation, Easement feature class in the Full Inventory Database, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class, was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.

Inputs and output files for the regional model used for the air quality conformity analysis approved in March 2020. They were developed by the Chicago Metropolitan Agency for Planning and cover the modeled region, including portions of Wisconsin, Illinois and Indiana.

The geospatial data provider market, currently valued at $3788 million in 2025, is poised for significant growth, exhibiting a Compound Annual Growth Rate (CAGR) of 6.1% from 2025 to 2033. This expansion is driven by the increasing adoption of location intelligence across diverse sectors. Enterprises leverage geospatial data for optimizing logistics, enhancing customer experiences, and improving operational efficiency. Government agencies utilize it for infrastructure planning, resource management, and disaster response. The rising prevalence of IoT devices and the demand for precise location-based services are further fueling market growth. The market is segmented by application (Enterprises, Government, Others) and data type (Vector Data, Raster Data), with the enterprise segment expected to dominate due to high investments in technology and data analytics. The increasing availability of high-resolution satellite imagery and advancements in data processing technologies are key trends shaping the market. However, challenges such as data security concerns, high initial investment costs, and the need for specialized expertise could potentially restrain market growth. The North American region, particularly the United States, is expected to hold a substantial market share due to the presence of major geospatial data providers and high technological advancements. Europe and Asia Pacific are also projected to witness significant growth, driven by increasing government initiatives and private sector investments in digital infrastructure. The competitive landscape is characterized by a mix of established players like Esri and emerging companies offering innovative solutions. The market will likely witness increased mergers and acquisitions, strategic partnerships, and technological innovations in the coming years, focusing on areas like AI-powered geospatial analytics and the integration of geospatial data with other data sources to deliver actionable insights. The continued evolution of cloud computing and advancements in big data analytics will significantly impact the market's growth trajectory in the forecast period.

Market Analysis for Distributed Vector Search Systems The global distributed vector search system market is projected to witness substantial growth, with a CAGR of XX% during the forecast period (2025-2033). The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies for data processing, retrieval, and analysis is a primary driver of this growth. Additionally, the increasing volume of unstructured data generated across various industries, such as e-commerce, healthcare, and finance, is further propelling market demand. Distributed vector search systems provide efficient and scalable solutions for handling large-scale data, making them an essential component of modern data infrastructure. Key trends shaping the market include the growing popularity of cloud-based distributed vector search platforms, the integration of vector search with other AI technologies such as natural language processing (NLP), and the adoption of open-source distributed vector search frameworks. The market is segmented by type (centralized vector search and distributed vector search) and application (enterprise and individual). Major players in the market include Pinecone, Vespa, Zilliz, Weaviate, Elastic, Meta, Microsoft, Qdrant, and Spotify. Geographically, North America and Europe are expected to lead the market, followed by Asia Pacific and the Middle East & Africa.

Inputs and output files for the regional model used for the air quality conformity analysis approved in October 2014. They were developed by the Chicago Metropolitan Agency for Planning and cover the modeled region, including portions of Wisconsin, Illinois and Indiana.

Time cost analysis of rendering tiles for the three models (in seconds).

Time cost analysis of generating tiles for the three models (in seconds).

Vector Data of the Santa Monica Mountains and Griffith Park Linkage Analysis that is derived from a variety of data sources.

Microbial enzyme data that were collected during the 2004-2006 EMAP-GRE program. These data were then used by Moorhead et al (2016) in their ecoenzyme vector analysis paper. This dataset is associated with the following publication: Moorhead, D., R. Sinsabaugh, B. Hill , and M. Weintraub. Vector analysis of ecoenzyme activities reveal constraints on coupled C, N and P dynamics. SOIL BIOLOGY AND BIOCHEMISTRY. Elsevier Science Ltd, New York, NY, USA, 93: 1-7, (2016).

AMERICAN FORESTS conducted an urban ecosystem analysis of the Delaware Valley region to provide community leaders with detailed information about the region's tree cover and its environmental and economic impacts. The analysis documents what landscape changes have occurred over time and how these changes have impacted the environmental services the urban forest provides to the region. The study used Geographic Information Systems (GIS) technology to connect image analysis of the area to ecological assessment of tree cover change trends over the last 15 years. This file represents a reclassification of 30-meter resolution Landsat Thematic Mapper imagery.

The datasets consist of vectors in .shp format with attributes of the surface affected by wildfire or burned area. All the data have resulted from the Deep Learning U-Net approach based on Sentinel 2 MSI images. The satellite images are publicly available on the Copernicus Data Space Ecosystem website https://dataspace.copernicus.eu. The analysis covers the period from January to April 2024, identified as the early spring of 2024.

The supporting datasets consist of:

1. Polygon vector data for the wildfire resulted from the DL pixel classification on Sentinel 2 images: Fire_S2_ro_poly.shp
2. Point vector data for the wildfire - centroid of the above polygons with the surface affected: Wildfire_ro.shp
3. Polygon vector data delineating the burned area resulted from DL U-Net pixel classification on Sentinel 2: Burned_area.shp
4. Point vector data for the burnt area - centroid of the polygons with the surface affected: Burned_area_point_centroid.shp

Metadata:

• Spatial Reference: Stereo 70; EPSG 3844
• Spatial Resolution: 10 meters
• Attribute field: Surface (hectares)
• Source: Pixel classification on Sentinel 2 MSI; © 2025 The authors
• Geometry: vector polygon and vector point

We hereby confirm that all vector data regarding the wildfire and burned area are coming from our analysis based on Sentinel 2 MSI covering the time interval January to April 2024 for Romania.

© 2025 The authors

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

The global vector database market size was valued at USD XX million in 2023 and is projected to expand at a CAGR of XX% from 2023 to 2033. Vector databases are specialized database management systems (DBMS) designed to handle and process large volumes of vector data, which is characterized by its high dimensionality and sparsity. The growing adoption of machine learning (ML), natural language processing (NLP), and computer vision (CV) applications is driving the demand for vector databases as they offer efficient storage, indexing, and retrieval of vector data, which is essential for training and deploying these AI models. The market is segmented based on type (open source and commercial), application (NLP, CV, recommender systems, etc.), and region (North America, Europe, Asia Pacific, etc.). Key players in the market include Shanghai Yirui Information Technology, Qdrant, Milvus, Weaviate, Pinecone, Vespa, pgvector, opensearch, Alibaba Cloud, cVector, Vearch, Troy Information Technology, Actionsky, Facebook, Tencent Cloud, and others. The market is expected to witness significant growth in the coming years due to the increasing adoption of AI applications across various industries, including healthcare, finance, and manufacturing.

Time cost analysis of rendering tiles for the three models (in seconds).