Databases (for SQLite SpatiaLite) were created from publicly available OpenStreetMap data for Poland (https://www.openstreetmap.org/copyright). The db_small database comprises data for the area of the city of Kraków in the Małopolskie Province. The db_medium database comprises data from the entire Małopolskie Province. The db_large database, in addition to the Małopolskie Province, covers the Podkarpackie and Dolnośląskie Provinces. The db_v_large database covers the entire country.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Progress Needed on Identifying Expenditures, Building and Utilizing a Data Infrastructure, and Reducing Duplicative Efforts The federal government collects, maintains, and uses geospatial information—data linked to specific geographic locations—to help support varied missions, including national security and natural resources conservation. To coordinate geospatial activities, in 1994 the President issued an executive order to develop a National Spatial Data Infrastructure—a framework for coordination that includes standards, data themes, and a clearinghouse. GAO was asked to review federal and state coordination of geospatial data. GAO’s objectives were to (1) describe the geospatial data that selected federal agencies and states use and how much is spent on geospatial data; (2) assess progress in establishing the National Spatial Data Infrastructure; and (3) determine whether selected federal agencies and states invest in duplicative geospatial data. To do so, GAO identified federal and state uses of geospatial data; evaluated available cost data from 2013 to 2015; assessed FGDC’s and selected agencies’ efforts to establish the infrastructure; and analyzed federal and state datasets to identify duplication. What GAO Found Federal agencies and state governments use a variety of geospatial datasets to support their missions. For example, after Hurricane Sandy in 2012, the Federal Emergency Management Agency used geospatial data to identify 44,000 households that were damaged and inaccessible and reported that, as a result, it was able to provide expedited assistance to area residents. Federal agencies report spending billions of dollars on geospatial investments; however, the estimates are understated because agencies do not always track geospatial investments. For example, these estimates do not include billions of dollars spent on earth-observing satellites that produce volumes of geospatial data. The Federal Geographic Data Committee (FGDC) and the Office of Management and Budget (OMB) have started an initiative to have agencies identify and report annually on geospatial-related investments as part of the fiscal year 2017 budget process. FGDC and selected federal agencies have made progress in implementing their responsibilities for the National Spatial Data Infrastructure as outlined in OMB guidance; however, critical items remain incomplete. For example, the committee established a clearinghouse for records on geospatial data, but the clearinghouse lacks an effective search capability and performance monitoring. FGDC also initiated plans and activities for coordinating with state governments on the collection of geospatial data; however, state officials GAO contacted are generally not satisfied with the committee’s efforts to coordinate with them. Among other reasons, they feel that the committee is focused on a federal perspective rather than a national one, and that state recommendations are often ignored. In addition, selected agencies have made limited progress in their own strategic planning efforts and in using the clearinghouse to register their data to ensure they do not invest in duplicative data. For example, 8 of the committee’s 32 member agencies have begun to register their data on the clearinghouse, and they have registered 59 percent of the geospatial data they deemed critical. Part of the reason that agencies are not fulfilling their responsibilities is that OMB has not made it a priority to oversee these efforts. Until OMB ensures that FGDC and federal agencies fully implement their responsibilities, the vision of improving the coordination of geospatial information and reducing duplicative investments will not be fully realized. OMB guidance calls for agencies to eliminate duplication, avoid redundant expenditures, and improve the efficiency and effectiveness of the sharing and dissemination of geospatial data. However, some data are collected multiple times by federal, state, and local entities, resulting in duplication in effort and resources. A new initiative to create a national address database could potentially result in significant savings for federal, state, and local governments. However, agencies face challenges in effectively coordinating address data collection efforts, including statutory restrictions on sharing certain federal address data. Until there is effective coordination across the National Spatial Data Infrastructure, there will continue to be duplicative efforts to obtain and maintain these data at every level of government.https://www.gao.gov/assets/d15193.pdfWhat GAO Recommends GAO suggests that Congress consider assessing statutory limitations on address data to foster progress toward a national address database. GAO also recommends that OMB improve its oversight of FGDC and federal agency initiatives, and that FGDC and selected agencies fully implement initiatives. The agencies generally agreed with the recommendations and identified plans to implement them.

MAPDAT is a program for plotting spatial data held in the ORACLE relational database onto any map within the Australian region at any scale. MAPDAT also includes a system for defining geological structures, thus any geological structure can be stored in the database and plotted. The program enables the plotting of sample locations along with infomration specific to each location. The information can be displayed beside each point or in a list to the side of the map. The symbols can be sized proportionally to the value of a column in a table or a SQL expression. Town locations, survey paths, gridlines, survey areas, coastlines and other geographical lines can be plotted. The program does not compete with geographical information systems but fills a niche at a much lower level of complexity. As a result of its simplicity a minimum in setting up of data is required and using the program is very straight forward with the user always aware of the database operations being performed.

Market Overview:

The Vietnam geospatial analytics market size is projected to exhibit a growth rate (CAGR) of 8.90% during 2024-2032. The increasing product utilization by government authorities in various sectors, various technological advancements in satellite technology, remote sensing, and data collection methods, and the rising development of smart cities represent some of the key factors driving the market.

Geospatial analytics is a field of data analysis that focuses on the interpretation and analysis of geographic and spatial data to gain valuable insights and make informed decisions. It combines geographical information systems (GIS), advanced data analysis techniques, and visualization tools to analyze and interpret data with a spatial or geographic component. It also enables the collection, storage, analysis, and visualization of geospatial data. It provides tools and software for managing and manipulating spatial data, allowing users to create maps, perform spatial queries, and conduct spatial analysis. In addition, geospatial analytics often involves integrating geospatial data with other types of data, such as demographic data, environmental data, or economic data. This integration helps in gaining a more comprehensive understanding of complex phenomena. Moreover, geospatial analytics has a wide range of applications. For example, it can be used in urban planning to optimize transportation routes, in agriculture to manage crop yield and soil quality, in disaster management to assess and respond to natural disasters, in wildlife conservation to track animal migrations, and in business for location-based marketing and site selection.

Vietnam Geospatial Analytics Market Trends:

The Vietnamese government has recognized the importance of geospatial analytics in various sectors, including urban planning, agriculture, disaster management, and environmental monitoring. Initiatives to develop and utilize geospatial data for public projects and policy-making have spurred demand for geospatial analytics solutions. In addition, Vietnam is experiencing rapid urbanization and infrastructure development. Geospatial analytics is critical for effective urban planning, transportation management, and infrastructure optimization. This trend is driving the adoption of geospatial solutions in cities and regions across the country. Besides, Vietnam's agriculture sector is a significant driver of its economy. Geospatial analytics helps farmers and agricultural businesses optimize crop management, soil health, and resource allocation. Consequently, precision farming techniques, enabled by geospatial data, are becoming increasingly popular, which is also propelling the market. Moreover, the development of smart cities in Vietnam relies on geospatial analytics for various applications, such as traffic management, public safety, and energy efficiency. Geospatial data is central to building the infrastructure needed for smart city initiatives. Furthermore, advances in satellite technology, remote sensing, and data collection methods have made geospatial data more accessible and affordable. This has lowered barriers to entry and encouraged the use of geospatial analytics in various sectors. Additionally, the telecommunications sector in Vietnam is expanding, and location-based services, such as navigation and advertising, rely on geospatial analytics. This creates opportunities for geospatial data providers and analytics solutions in the telecommunications industry.

Vietnam Geospatial Analytics Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the market, along with forecasts at the country level for 2024-2032. Our report has categorized the market based on component, type, technology, enterprise size, deployment mode, and vertical.

Component Insights:

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• Solution
• Services
  

The report has provided a detailed breakup and analysis of the market based on the component. This includes solution and services.

Type Insights:

• Surface and Field Analytics
• Network and Location Analytics
• Geovisualization
• Others
  

A detailed breakup and analysis of the market based on the type have also been provided in the report. This includes surface and field analytics, network and location analytics, geovisualization, and others.

Technology Insights:

• Remote Sensing
• GIS
• GPS
• Others
  

The report has provided a detailed breakup and analysis of the market based on the technology. This includes remote sensing, GIS, GPS, and others.

Enterprise Size Insights:

• Large Enterprises
• Small and Medium-sized Enterprises
  

A detailed breakup and analysis of the market based on the enterprise size have also been provided in the report. This includes large enterprises and small and medium-sized enterprises.

Deployment Mode Insights:

• On-premises
• Cloud-based
  

The report has provided a detailed breakup and analysis of the market based on the deployment mode. This includes on-premises and cloud-based.

Vertical Insights:

• Automotive
• Energy and Utilities
• Government
• Defense and Intelligence
• Smart Cities
• Insurance
• Natural Resources
• Others
  

A detailed breakup and analysis of the market based on the vertical have also been provided in the report. This includes automotive, energy and utilities, government, defense and intelligence, smart cities, insurance, natural resources, and others.

Regional Insights:

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• Northern Vietnam
• Central Vietnam
• Southern Vietnam
  

The report has also provided a comprehensive analysis of all the major regional markets, which include Northern Vietnam, Central Vietnam, and Southern Vietnam.

Competitive Landscape:

The market research report has also provided a comprehensive analysis of the competitive landscape in the market. Competitive analysis such as market structure, key player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles of all major companies have been provided.

Vietnam Geospatial Analytics Market Report Coverage:

Overview

Empower your location data visualizations with our edge-matched polygons, even in difficult geographies.

Our self-hosted GIS data cover administrative and postal divisions with up to 6 precision levels: a zip code layer and up to 5 administrative levels. All levels follow a seamless hierarchical structure with no gaps or overlaps.

The geospatial data shapes are offered in high-precision and visualization resolution and are easily customized on-premise.

Use cases for the Global Boundaries Database (GIS data, Geospatial data)

• In-depth spatial analysis

• Clustering

• Geofencing

• Reverse Geocoding

• Reporting and Business Intelligence (BI)

Product Features

• Coherence and precision at every level

• Edge-matched polygons

• High-precision shapes for spatial analysis

• Fast-loading polygons for reporting and BI

• Multi-language support

For additional insights, you can combine the GIS data with:

• Population data: Historical and future trends

• UNLOCODE and IATA codes

• Time zones and Daylight Saving Time (DST)

Data export methodology

Our geospatial data packages are offered in variable formats, including - .shp - .gpkg - .kml - .shp - .gpkg - .kml - .geojson

All GIS data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Why companies choose our map data

• Precision at every level

• Coverage of difficult geographies

• No gaps, nor overlaps

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

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 triad of host, agent, and environment has become a widely accepted framework for understanding infectious diseases and human health. While modern medicine has traditionally focused on the individual, there is a renewed interest in the role of the environment. Recent studies have shifted from an early-twentieth-century emphasis on individual factors to a broader consideration of contextual factors, including environmental, climatic, and social settings as spatial determinants of health. This shifted focus has been particularly relevant in the context of the COVID-19 pandemic, where the built environment in urban settings is increasingly recognized as a crucial factor influencing disease transmission. However, operationalizing the complexity of associations between the built environment and health for empirical analyses presents significant challenges. This study aims to identify key caveats in the operationalization of spatial determinants of health for empirical analysis and proposes guiding principles for future research. We focus on how the built environment in urban settings was studied in recent literature on COVID-19. Based on a set of criteria, we analyze 23 studies and identify explicit and implicit assumptions regarding the health-related dimensions of the built environment. Our findings highlight the complexities and potential pitfalls, referred to as the ‘spatial trap,' in the current approaches to spatial epidemiology concerning COVID-19. We conclude with recommendations and guiding questions for future studies to avoid falsely attributing a built environment impact on health outcomes and to clarify explicit and implicit assumptions regarding the health-related dimensions.

The Montana Structures/Addresses Framework is a statewide spatial database of structure and address points in the State of Montana. The Montana Structures/Addresses Framework is part of the Montana Spatial Data Infrastructure (MSDI). The goal of the Montana Structures/Addresses Framework is to have a routinely updated statewide database of primary structures/buildings and addresses. Through a federated approach, the project integrates structures and address data from local, state, federal and private data providers into a standardized database.Structures are grouped into general categories based on the function, or type of structure. For example, the "Emergency services and law enforcement" category includes law enforcement, fire stations, ambulance services, emergency shelters, EOCs, and search and rescue.This feature service is available for offline use. Data update processes require the Montana State Library to delete replicas created for offline use monthly, which will require users to recreate offline map areas. Users will see an “Update Failed” message when trying to sync to a replica that has been deleted.

The development of climate change adaptation policies must be underpinned by a sound understanding of climate change risk. As part of the Hyogo Framework for Action, governments have agreed to incorporate climate change adaptation into the risk reduction process. This paper explores the nature of climate change risk assessment in the context of human assets and the built environment. More specifically, the paper's focus is on the role of spatial data which is fundamental to the analysis. The fundamental link in all of these examples is the National Exposure Information System (NEXIS) which has been developed as a national database of Australia's built infrastructure and associated demographic information. The first illustrations of the use of NEXIS are through post-disaster impact assessments of a recent flood and bushfire. While these specific events can not be said to be the result of climate change, flood and bushfire risks will certainly increase if rainfall or drought become more prevalent, as most climate change models indicate. The second example is from Australia's National Coastal Vulnerability Assessment which is addressing the impact of sea-level rise and increased storms on coastal communities on a national scale. This study required access to or the development of several other spatial databases covering coastal landforms, digital elevation models and tidal/storm surge. Together, these examples serve to illustrate the importance of spatial data to the assessment of climate change risk and, ultimately, to making informed, cost-effective decisions to adapt to climate change.

Author: A Lisson, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 8Resource type: lessonSubject topic(s): gis, geographic thinkingRegion: united statesStandards: Minnesota Social Studies Standards

Standard 1. People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context.Objectives: Students will be able to:

1. Explain the difference between two types of geospatial technologies - GPS and GIS.
2. Develop basic skills to effectively manipulate and use GPS receivers and ArcGIS software.
3. Explain uses of GPS and GIS.Summary: Students use GPS coordinates to discover geocaches at a local park, and they use ArcGIS to layer maps about the park. Frontenac State park is the example, but any park or area (including school grounds) could be used. Students also investigate careers that use GIS.

The Geopspatial Fabric provides a consistent, documented, and topologically connected set of spatial features that create an abstracted stream/basin network of features useful for hydrologic modeling.The GIS vector features contained in this Geospatial Fabric (GF) data set cover the lower 48 U.S. states, Hawaii, and Puerto Rico. Four GIS feature classes are provided for each Region: 1) the Region outline ("one"), 2) Points of Interest ("POIs"), 3) a routing network ("nsegment"), and 4) Hydrologic Response Units ("nhru"). A graphic showing the boundaries for all Regions is provided at http://dx.doi.org/doi:10.5066/F7542KMD. These Regions are identical to those used to organize the NHDPlus v.1 dataset (US EPA and US Geological Survey, 2005). Although the GF Feature data set has been derived from NHDPlus v.1, it is an entirely new data set that has been designed to generically support regional and national scale applications of hydrologic models. Definition of each type of feature class and its derivation is provided within the

MCGD_Rep is a sample of spatial data for China in the first half of twentieth century (1900-1949). The data was extracted from the MCGD Main Dataset. It is based mostly on the list of xian (county) seats in 1931 [Source: Zang, Lihe 臧励龢, ed. Zhongguo gujin diming da cidian 中国古今地名大辞典. Shanghai 上海: Commercial Press, 1931], with the addition of some external data [Source: Crow Newspaper Directories]. By and large, it presents a list of the major locations in China between 1900 and 1949. It contains 1,977 entries with the following variables: name in Chinese, name in pinyin; name of the province in Chinese and in pinyin; latitude and longitude, and Name ID and Location ID.

This dataset comprises land use maps of Maputo city, with exception of the KaTembe urban district, for the years 1964, 1973, 1982, 1991 and 2001. It is the digital version of the land use maps published by Henriques [1] and revised under the LUCO research project.

The land use of Maputo city was identified from: i) aerial photographs (1964, 1982, 1991), orthophoto maps (1973) and IKONOS images (2001); ii) documentary sources, such as the Urbanization Master Plan (1969) and the Maputo City Addressing (1997); iii) the recognition made during several field survey campaigns. The methodology is described in Henriques [1].

Land use was classified into three levels, resulting from a hierarchical classification system, including descriptive and parametric classes. Levels I and II are available in this repository.

Level I, composed by 10 classes, contains the main forms of occupation: built-up areas (residential, economic activity, equipment, and infrastructure) and non-built-up areas (vacant or "natural"). It is geared towards analyses that serve policymaking and resource management at the regional or national scale [1].

Level II, composed by 31 classes, discriminates the higher hierarchical level according to its functional land use to become useful for municipal planning and management in municipal master plans, for example [1].

Maps are available in shapefile format and include predefined symbology-legend files, for QGIS and ArcGIS (v.10.7 or higher). The urban land use classes are described in Portuguese and English, and their meaning is provided as an accompanying document (ULU_Maputo_Nomenclatura_PT.pdf / ULU_Maputo_Nomenclature_EN.pdf).

Data format: vector (shapefile, polygon)

Reference system: WGS84, UTM 36S (EPSG:32736)

Original minimum mapping unit: 25 m2

Urban Land Use dataset attributes:

[N_I_C] – code of level I

[N_I_D_PT] – name of level I, in Portuguese

[N_I_D_EN] - name of level I, in English

[N_II_C] – code of level II

[N_II_D_PT] - name of level II, in Portuguese

[N_II_D_EN] - name of level II, in English

Funding: this research was supported by national funds through FCT – Fundação para a Ciência e Tecnologia, I.P. Project number: FCT AGA-KHAN/ 541731809 / 2019

[1] Henriques, C.D. (2008). Maputo. Cinco décadas de mudança territorial. O uso do solo observado por tecnologias de informação geográfica [Maputo. Five decades of territorial transformation. Land use assessed by geographical information technologies]. Lisboa, Instituto Português de Apoio ao Desenvolvimento (ISBN: 978-972-8975-22-7).

Snapshot img

What is the GIS In Utility Industry Market Size?

The GIS market in the utility industry size is forecast to increase by USD 3.55 billion at a CAGR of 19.8% between 2023 and 2028. Market expansion hinges on various factors, such as the rising adoption of Geographic Information System (GIS) solutions in the utility sector, the convergence of GIS with Building Information Modeling, and the fusion of Augmented Reality with GIS technology. These elements collectively drive market growth, reflecting advancements in spatial data analytics and technological convergence. The increased adoption of GIS solutions in the utility industry underscores the importance of geospatial data in optimizing infrastructure management. Simultaneously, the integration of GIS with BIM signifies the synergy between spatial and building information for enhanced project planning and management. Additionally, the integration of AR with GIS technology highlights the potential for interactive and interactive visualization experiences in spatial data analysis. Thus, the interplay of these factors delineates the landscape for the anticipated expansion of the market catering to GIS and related technologies.

What will be the size of Market during the forecast period?

Request Free GIS In Utility Industry Market Sample

Market Segmentation

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

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    Canada
    US


  Europe

    Germany
    France


  APAC

    China
    India
    Japan


  Middle East and Africa



  South America

    Brazil

Which is the largest segment driving market growth?

The software segment is estimated to witness significant growth during the forecast period. In the utility industry, the spatial context of geographic information systems (GIS) plays a pivotal role in site selection, land acquisition, planning, designing, visualizing, building, and project management. Utilities, including electricity, gas, water, and telecommunications providers, leverage GIS software to efficiently manage their assets and infrastructure. This technology enables the collection, management, analysis, and visualization of geospatial data, derived from satellite imaging, aerial photography, remote sensors, and artificial intelligence. Geospatial AI, sensor technology, and digital reality solutions are integral components of GIS, enhancing capabilities for smart city planning, urban planning, water management, mapping systems, grid modernization, transportation, and green buildings.

Get a glance at the market share of various regions. Download the PDF Sample

The software segment was valued at USD 541.50 million in 2018. Moreover, the geospatial industry continues to evolve, with startups and software solutions driving innovation in hardware, smart city planning, land use management, smart infrastructure planning, and smart utilities. GIS solutions facilitate 4D visualization, enabling stakeholders to overcome geospatial data barriers and make informed decisions. The utility industry's reliance on GIS extends to building information modeling, augmented reality, and smart urban planning, ultimately contributing to the growth of the geospatial technology market.

Which region is leading the market?

For more insights on the market share of various regions, Request Free Sample

North America is estimated to contribute 37% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

How do company ranking index and market positioning come to your aid?

Companies are implementing various strategies, such as strategic alliances, partnerships, mergers and acquisitions, geographical expansion, and product/service launches, to enhance their presence in the market.

AABSyS IT Pvt. Ltd. - The company offers GIS solutions such as remote sensing and computer aided design and drafting solutions for electric and gas utility.

Technavio provides the ranking index for the top 20 companies along with insights on the market positioning of:

AABSyS IT Pvt. Ltd.
Autodesk Inc.
Avineon Inc.
Bentley Systems Inc.
Blue Marble Geographics
Cadcorp Ltd.
Caliper Corp.
Environmental Systems Research Institute Inc.
General Electric Co.
Hexagon AB
Mapbox Inc.
Maxar Technologies Inc.
Mobile GIS Services Ltd.
NV5 Global Inc.
Orbital Insight Inc.
Pitney Bowes Inc.
Schneider Electric SE
SuperMap Software Co. Ltd.
Trimble Inc.
VertiGIS Ltd.

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How can Technavio assist you in ma

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019.

Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar.

The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are:

• Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM)
• Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif

This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed.

Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range.

This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

ABSTRACT: The U.S. Geological Survey (USGS) Upper Midwest Environmental Sciences Center (UMESC) has produced the Vegetation Spatial Database Coverage (vegetation map) for the Acadia National Park Vegetation Mapping Project, USGS-NPS Vegetation Mapping Program (VMP). The vegetation map is of Acadia National Park (NP) and extended environs, providing 99,693 hectares (246,347 acres) of map data. Of this coverage, 52,872 hectares (130,650 acres) is non-vegetated ocean, bay, and estuary (53% of coverage). Acadia NP comprises 19,276 hectares (47,633 acres) of the total data coverage area (19%, 40% not counting ocean and estuary data). Over 7,120 polygons make up the coverage, each with map class description and, for vegetation classes, physiognomic feature information. The spatial database provides crosswalk information to all National Vegetation Classification System (NVCS) floristic and physiognomic levels, and to other established classification systems (NatureServe's U.S. Terrestrial Ecological System Classification, Maine Natural Community Classification, and the USGS Land Use and Land Cover Classification). This mapping project has identified 53 NVCS associations (vegetation communities) at Acadia National Park through analyses of vegetation sample data. These associations are represented in the map coverage with 33 map classes. With all vegetation types, land use classes, and park specific categories combined, 57 map classes define the ground features within the project area (58 classes including the class for no map data). Each polygon within the spatial database map is identified with one of these map classes. In addition, physiognomic modifiers are added to map classes representing vegetation to describe the vegetation structure within a polygon (density, pattern, and height). The spatial database was produced from the interpretation of spring 1997 1:15,840-scale color infrared aerial photographs. The standard minimum mapping unit (MMU) applied is 0.5 hectares (1.25 acres). The interpreted data were transferred and automated using base maps produced from USGS digital orthophoto quadrangles. The finished spatial database is a single seamless coverage, projected in Universal Transverse Mercator, Zone 19, with datum in North American Datum of 1983. The estimated overall thematic accuracy for vegetation map classes is 80%.

This database was prepared using a combination of materials that include aerial photographs, topographic maps (1:24,000 and 1:250,000), field notes, and a sample catalog. Our goal was to translate sample collection site locations at Yellowstone National Park and surrounding areas into a GIS database. This was achieved by transferring site locations from aerial photographs and topographic maps into layers in ArcMap. Each field site is located based on field notes describing where a sample was collected. Locations were marked on the photograph or topographic map by a pinhole or dot, respectively, with the corresponding station or site numbers. Station and site numbers were then referenced in the notes to determine the appropriate prefix for the station. Each point on the aerial photograph or topographic map was relocated on the screen in ArcMap, on a digital topographic map, or an aerial photograph. Several samples are present in the field notes and in the catalog but do not corresp ...

United States Bureau of Reclamation lands within the Rio Grande River Basin. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.