1. Lublin 1944 aerial image overlay index [.geojson or .gml file] is a vectorized, digital form of selected overlay indexes for degree square 51N022E (https://catalog.archives.gov/id/44241929) of German Flown Aerial Photographs,1939-1945 (https://catalog.archives.gov/id/306065) archived in National Archives and Records Administration, College Park, MD.

2. The vectorized index contains geometries, attributes and other metadata of 104 aerial images of Lublin [Poland] captured by Luftwaffe reconaissance from 10th May 1944 to 6th December 1944.

3. The dataset contains the archive of 104 digital copies of aerial images, scaned with A2-3050-Sharp363N. The images are .jpg files with 24-bit colour depth and resolution 600 dpi. File size: from 7,5 MB to 20 MB.

4. The mosaic of aerial images is uploaded in Ortofotomapa_1944_modificado_3.tif - 0,8GB file. TFW, AUX and OVR files added for GIS users. TPK file with ESRI tiled package is added. This is also available via spatial data services (TMS and WMTS):

• XYZ/TMS layers available at //ortolub.umcs.pl/data/tiles_3857/{z}/{x}/{y}.png or via https://ortolub.umcs.pl/map_en.html

• WMTS layer available at //tiles.arcgis.com/tiles/STaxETJ8DGWEoQ8D/arcgis/rest/services/Ortolub_1944/MapServer or via ArcGIS Online https://www.arcgis.com/home/item.html?id=d5dd97b49b014d6ea0e6bc221fc37668

1. The mosaic cropped to 1931-1947 city boundaries are added: Lublin_1944_aerial_10k_600dpi_gsc.jpg and Lublin_1944_aerial_adm_10k_600dpi_gsc.jpg with area outside the boundaries masked. This is also available at Wikimedia Commons, https://commons.wikimedia.org/wiki/File:Lublin_1944_aerial_image.jpg

2. The project and the platform https://ortolub.umcs.pl was developed under the Polish National Science Centre grant programme - Miniatura 4.0. ref. no. 2020/04/X/HS4/00382. I hereby share my work under Creative Commons license CC BY-SA 4.0 (Attribution - ShareAlike).

Contingency tables from spatial overlay analyses.

The Bayinguoleng Mongolian Autonomous Prefecture of Xinjiang is a continental arid climate with abundant light and heat resources.“Korla Fragrant Pear”has become a pillar industry of economic forest and fruit in Bazhou. With the continuous expansion of planting scale, the disadvantages of industrial planting have become increasingly prominent, which has caused great obstacles to the sustainable green development of fragrant pear. In this study, GIS spatial overlay analysis and three-phase fruit resource data were used to explore the industrial resources of“Korla Fragrant Pear”in Bazhou. This data set is composed of six types of data : resource data, meteorological data, pest and disease data, elevation data, soil data and planting management data of“Korla Fragrant Pear”in Bazhou area. In order to ensure the accuracy of the data, the field personnel have been organized to use satellite images and ArcGIS to check and verify the survey data, and to revise the non-standard, incorrect and missing information to ensure that the inspection pass rate is more than 95 %. This data set provides a scientific theoretical basis for exploring the current situation of“Korla Fragrant Pear”industry, promoting the quality and efficiency of forest and fruit industry, and realizing the high-quality development of digital management of forest and fruit industry in Xinjiang.

Local Area Overlay Zones (forming part of the CTZS Overlay Areas) that are subject to specific provisions under the Local Area Overlays in the CTZS.All spatial layers are served live from internal systems, an item's "Last Updated" or "Publish Date" refers to the Metadata only.

Using the Overlay toolset in ArcGIS Pro, GIS professionals can easily perform analysis to discover and quantify the spatial relationships between and among features. This web course will introduce you to spatial relationships and the tools available for describing them.GoalsDescribe the basic methods available for analyzing relationships between features.

A. SUMMARY This dataset is derived from parcels and several other overlay administrative boundaries (listed below). The dataset was developed by DataSF as a convenience for matching parcels to districts where appropriate. This can be simpler than running a geospatial process every time you want to join parcels to a boundary. The districts provided here run along streets and are non-overlapping so that the parcels will be contained within a single district. The boundaries included are: 1. Analysis Neighborhoods 2. Supervisor Districts 3. Police Districts 4. Planning Districts B. HOW THE DATASET IS CREATED A script runs daily that overlays parcels with each of the boundaries to produce the composite dataset. C. UPDATE PROCESS Updated daily by a script based on the upstream parcels dataset which is also updated daily. D. HOW TO USE THIS DATASET You can use this dataset to match to administrative districts provided here to datasets that contain a parcel number. This can be a simpler process than running these joins spatially. In short, we pre-process the spatial overlays to make joins simpler and more performant.

This dataset was created in conjunction with PLM25, to represent the management overlays. The attributes are based on the PLM25 structure. The overlays have been mapped at 1:25 000, using VicMap topographic data to create more accurate and identifiable boundaries.

PLM25_OVERLAYS is located under the CROWNLAND schema. It has been created in conjunction with PLM25 to ensure the overlays match the PLM25 land management categories.

PLEASE NOTE: This dataset now replaces the PLM100 overlays.

PLM25_OVERLAYS have been created by loading Reference areas, wilderness zones, heritage rivers, remote and natural areas and natural catchment areas into one dataset. They are also available as separate datasets.

This dataset is a representation of the certified plans - the gazettal and certified plans are the official boundaries.

Currently the creation process is not automated or synchronised with PLM25 updates. For more information please contact the Information Services Division.

This data set provides eight feature classes. The base feature class is called CensusTracts_tr and isn't generalized. The weighted centroids feature class is called CensusTracts_tr_cent. The centroids are weighted by the U.S. Block Centroids population distribution. Use the weighted centroids in report aggregation and spatial overlay operations. The CensusTracts_tr and CensusTracts_tr_cent feature classes contain all the attributes. There are six generalized boundaries feature classes and called: CensusTracts_tr_gen2, CensusTracts_tr_gen3, CensusTracts_tr_gen4, CensusTracts_tr_gen5, CensusTracts_tr_gen6 and CensusTracts_tr_gen7. Use the generalized boundaries when creating study areas.

This dataset contains abundance data for nine species of reef fish observed at 40 reef sites between Mobile Bay, Alabama and Saint Andrews Bay, Florida, during research cruises in April 2011, August 2011, and April 2012. It also contains gut content analysis data for two of these species - red snapper (Lutjanus campechanus) and vermilion snapper (Rhomboplites aurorubens) - sampled from half of the sites during each cruise.

General information of different soil types.

SOCD descriptive statistics for 1985 and 2015.

A parcel based layer providing an address based on spatial overlay where available, and a range of other property related attributes. A single address is assigned to each parcel record (based solely on what is determined as a primary address for a parcel in address_point).

PLEASE NOTE: If choosing the Download option of "Spreadsheet" the field PIN is reformatted to a number - you will need to format it as a 10 character text string with leading zeros to join this data with data from King County.King County Assessor data has been summarized to the tax parcel identification number (PIN) and City of Seattle spatial overlay data has been assigned through geographic overlay processes. This data is updated periodically and is used to support the analytical and reporting functions of the City of Seattle long-range and policy planning office.The table includes attribute data from the King County Assessor as well as spatial overlay data for various City of Seattle reporting geographies. These geographic attributes are assigned as "majority rules" by land area in cases where multiple geographies span a single tax parcel.KCA tax parcels are created by King County for property tax assessment and collection and may not match development sites as defined by the City of Seattle (single buildings may span multiple tax parcels), may be stacked on top of each other to represent undivided interest and vertical parcels, or may be made up of several sites that are not contiguous. Every effort is made to accurately summarize key tax parcel attributes to a single PIN. Attributes include parcel centroid locations in latitude/longitude and Washington State Plane X,Y. To get polygon representation of the data please see King County's open data page for parcels and join this table through the PIN field. Please be aware that the King County Assessor site address is not a postal address and may not match other address sources for the same property such as postal, utility billing, and permitting.See the detailed data dictionary for more information.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

The Guide to Crown Land Use Planning Atlas (CLUPA) is the authoritative source for information on overlays. The dataset maintains a spatial record for all geographic areas of Ontario affected by the designations that modify area-specific land use policy. To be used as an overlay to CLUPA Provincial data class.Official LIO title: CLUPA Overlay Additional Documentation
CLUPA Overlay - Data Description (PDF) CLUPA Overlay - Documentation (Word)

Status On going: data is being continually updated Maintenance and Update Frequency Continual: data is repeatedly and frequently updated Contact Nicole Mokrey, Data Analyst, nicole.mokrey@ontario.ca

Site characteristics (0–20 cm) for the field-validation site in Jilin Province.

Overlap is expressed by quartiles (very low, low, moderate and high) of the median and range (minimum to maximum) of the number of existing uses in 2 km2 cells (see Figure 6) that overlap with areas of positive net present value for wave energy. See text for further explanation and consult [30] for the full list for each category.

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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 and sho

This feature class is shown on the Regional infrastructure corridors and substations overlay map (map reference: OM-018.1).This feature class includes the following sub-category: major transport infrastructure