Focus on Geodatabases in ArcGIS Pro introduces readers to the geodatabase, the comprehensive information model for representing and managing geographic information across the ArcGIS platform.Sharing best practices for creating and maintaining data integrity, chapter topics include the careful design of a geodatabase schema, building geodatabases that include data integrity rules, populating geodatabases with existing data, working with topologies, editing data using various techniques, building 3D views, and sharing data on the web. Each chapter includes important concepts with hands-on, step-by-step tutorials, sample projects and datasets, 'Your turn' segments with less instruction, study questions for classroom use, and an independent project. Instructor resources are available by request.AUDIENCEProfessional and scholarly.AUTHOR BIODavid W. Allen has been working in the GIS field for over 35 years, the last 30 with the City of Euless, Texas, and has seen many versions of ArcInfo and ArcGIS come along since he started with version 5. He spent 18 years as an adjunct professor at Tarrant County College in Fort Worth, Texas, and now serves as the State Director of Operations for a volunteer emergency response group developing databases and templates. Mr. Allen is the author of GIS Tutorial 2: Spatial Analysis Workbook (Esri Press, 2016).Pub Date: Print: 6/17/2019 Digital: 4/29/2019 Format: PaperbackISBN: Print: 9781589484450 Digital: 9781589484467 Trim: 7.5 x 9.25 in.Price: Print: $59.99 USD Digital: $59.99 USD Pages: 260

The air quality in Beijing, especially its PM2.5 level, has become of increasing public concern because of its importance and sensitivity related to health risks. A set of monitored PM2.5 data from 31 stations, released for the first time by the Beijing Environmental Protection Bureau, covering 37 days during autumn 2012, was processed using spatial interpolation and overlay analysis. Following analyses of these data, a distribution map of cumulative exceedance days of PM2.5 and a temporal variation map of PM2.5 for Beijing have been drawn. Computational and analytical results show periodic and directional trends of PM2.5 spreading and congregating in space, which reveals the regulation of PM2.5 overexposure on a discontinuous medium-term scale. With regard to the cumulative effect of PM2.5 on the human body, the harm from lower intensity overexposure in the medium term, and higher overexposure in the short term, are both obvious. Therefore, data of population distribution were integrated into the aforementioned PM2.5 spatial spectrum map. A spatial statistical analysis revealed the patterns of PM2.5 gross exposure and exposure probability of residents in the Beijing urban area. The methods and conclusions of this research reveal relationships between long-term overexposure to PM2.5 and people living in high-exposure areas of Beijing, during the autumn of 2012.

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

This project consists of 11 files: 1) a zipped folder with a geodatabase containing seven raster files and two shapefiles, 2) a zipped folder containing the same layers found in the geodatabase, but as standalone files, 3) 9 .xml files containing the metadata for the spatial datasets in the zipped folders. These datasets were generated in ArcPro 3.0.3. (ESRI). Six raster files (drainaged, geology, nlcd, precipitation, slope, solitexture) present spatially distributed information, ranked according to the relative importance of each class for groundwater recharge. The scale used for these datasets is 1-9, where low scale values are assigned to datasets with low relative importance for groundwater recharge, while high scale values are assigned to datasets with high relative importance for groundwater recharge. The seventh raster file contains the groundwater recharge potential map for the Anchor River Watershed. This map was calculated using the six raster datasets mentioned previously. Here, the values assigned represent Very Low to Very High groundwater recharge potential (scale 1 - 5, 1 being Very Low and 5 being Very High). Finally, the two shapefiles represent the groundwater wells and the polygons used for model validation. This data is part of the manuscript titled: Mapping Groundwater Recharge Potential in High Latitude Landscapes using Public Data, Remote Sensing, and Analytic Hierarchy Process, published in the journal remote sensing.

The full dataset comes from CA Department of Fish and Wildlife’s Areas of Conservation Emphasis (ACE) project. ACE Terrestrial Climate Change Resilience incorporates statewide information about lands that have a higher probability of serving as refugia for species adapting to climate change. Based on projections from climate models, this dataset indicates the relative likelihood that an area will experience shifts in temperature, precipitation, or other important climate variables that would negatively impact the current array of plants (and by extension animals) that can thrive under those future conditions. Ranks 4 and 5 are used as an exclusion in the SB 100 Terrestrial Climate Resilience Study Screen. This allows areas of lower climate resilience rank to be considered for exploration of renewable resource technical potential, while keeping areas of higher resilience rank for conservation planning.This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer. For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.

The full Terrestrial Irreplaceability dataset is part of the Areas of Conservation Emphasis (ACE) project from CA Department of Fish and Wildlife (CDFW). ACE Terrestrial Irreplaceability provides a measure of the relative importance of an area based on the number of California endemic, special-status species found there, and the breadth of their distribution, so that species which are more geographically constrained produce a higher score. This geographic weighting system makes a site more ‘irreplaceable’ because it supports species which are found in fewer locations. Rare endemic species are identified by the California Species of Special Concern reports, the California Rare Plant Rank 1B plants, State or Federally-listed species (threatened, endangered, or candidate species), and fully-protected species. Ranks 4 and 5 are used as an exclusion in the biological planning priorities component of the Core and SB 100 Terrestrial Climate Resilience Study Screens. This ensures that areas of technical resource potential identified through screening avoid lands with higher conservation value for irreplaceability.This layer is featured in the CEC 2023 Land-Use Screens for Electric System Planning data viewer. For more information about this layer and its use in electric system planning, please refer to the Land Use Screens Staff Report in the CEC Energy Planning Library.

Resilience—the keen ability of people to adapt to changing physical environments—is essential in today's world of unexpected changes.Resilient Communities across Geographies edited by Sheila Lakshmi Steinberg and Steven J. Steinberg focuses on how applying GIS to environmental and socio-economic challenges for analysis and planning helps make communities more resilient.A hybrid of theory and action, Resilient Communities across Geographies uses an interdisciplinary approach to explore resilience studied by experts in geography, social sciences, planning, landscape architecture, urban and rural sociology, economics, migration, community development, meteorology, oceanography, and other fields. Geographies covered include urban and rural, coastal and mountainous, indigenous areas in the United State and Australia, and more. Geographical Information Systems (GIS) is the unifying tool that helped researchers understand resilience.This book shows how GIS:integrates quantitative, qualitative, and spatial data to produce a holistic view of a need for resilience.serves as a valuable tool to capture and integrate knowledge of local people, places, and resources.allows us to visualize data clearly as portrayed in a real-time map or spatial dashboard, thus leading to opportunities to make decisions.lets us see patterns and communicate what the data means.helps us see what resources they have and where they are located.provides a big vision for action by layering valuable pieces of information together to see where gaps are located, where action is needed, or how policies can be instituted to manage and improve community resilience.Resilience is not only an ideal; it is something that people and communities can actively work to achieve through intelligent planning and assessment. The stories shared by the contributing authors in Resilient Communities across Geographies help readers to develop an expanded sense of the power of GIS to address the difficult problems we collectively face in an ever-changing world.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOSSheila Lakshmi Steinberg is a professor of Social and Environmental Sciences at Brandman University and Chair of the GIS Committee, where she leads the university to incorporate GIS across the curriculum. Her research interests include interdisciplinary research methods, culture, community, environmental sociology, geospatial approaches, ethnicity, health policy, and teaching pedagogy.Steven J. Steinberg is the Geographic Information Officer for the County of Los Angeles, California. Throughout his career, he has taught GIS as a professor of geospatial sciences for the California State University and, since 2011, has worked as a geospatial scientist in the public sector, applying GIS across a wide range of both environmental and human contexts.Pub Date: Print: 11/24/2020 Digital: 10/27/2020ISBN: Print: 9781589484818 Digital: 9781589484825Price: Print: $49.99 USD Digital: $49.99 USDPages: 350 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1. Conceptualizing spatial resilience Dr. Sheila Steinberg and Dr Steven J. SteinbergChapter 2. Resilience in coastal regions: the case of Georgia, USAChapter 3. Building resilient regions: Spatial analysis as a tool for ecosystem-based climate adaptationChapter 4. The mouth of the Columbia River: USACE, GIS and resilience in a dynamic coastal systemChapter 5. Urban resilience: Neighborhood complexity and the importance of social connectivityChapter 6. Mapping Indigenous LAChapter 7. Indigenous Martu knowledge: Mapping place through song and storyChapter 8. Developing resiliency through place-based inquiry in CanadaChapter 9. Engaging Youth in Spatial Modes of Thought toward Social and Environmental ResilienceChapter 10. Health, Place, and Space: Public Participation GIS for Rural Community PowerChapter 11. Best Practices for Using Local KnowledgeContributorsIndex

It is about updating to GIS information database, Decision Support Tool (DST) in collaboration with IWMI. With the support of the Fish for Livelihoods field team and IPs (MFF, BRAC Myanmar, PACT Myanmar, and KMSS) staff, collection of Global Positioning System GPS location data for year-1 (2019-20) 1,167 SSA farmer ponds, and year-2 (2020-21) 1,485 SSA farmer ponds were completed with different GPS mobile applications: My GPS Coordinates, GPS Status & Toolbox, GPS Essentials, Smart GPS Coordinates Locator and GPS Coordinates. The Soil and Water Assessment Tool (SWAT) model that integrates climate change analysis with water availability will provide an important tool informing decisions on scaling pond adoption. It can also contribute to a Decision Support Tool to better target pond scaling. GIS Data also contribute to identify the location point of the F4L SSA farmers ponds on the Myanmar Map by fiscal year from 1 to 5.

Two detailed geomorphological maps (1:2000) depicting landscape changes as a result of a glacial lake outburst flood were produced for the 2.1-km-long section of the Zackenberg river, NE Greenland. The maps document the riverscape before the flood (5 August 2017) and immediately after the flood (8 August 2017), illustrating changes to the riverbanks and morphology of the channel. A series of additional maps (1:800) represent case studies of different types of riverbank responses, emphasising the importance of the lateral thermo-erosion and bank collapsing as significant immediate effects of the flood. The average channel width increased from 40.75 m pre-flood to 44.59 m post-flood, whereas the length of active riverbanks decreased from 1729 to 1657 m. The new deposits related to 2017 flood covered 93,702 m2. The developed maps demonstrated the applicability of small Unmanned Aerial Vehicles (UAVs) for investigating the direct effects of floods, even in the harsh Arctic environment.

These are the main layers that were used in the mapping and analysis for the Santa Monica Mountains Local Coastal Plan, which was adopted by the Board of Supervisors on August 26, 2014, and certified by the California Coastal Commission on October 10, 2014. Below are some links to important documents and web mapping applications, as well as a link to the actual GIS data:

Plan Website – This has links to the actual plan, maps, and a link to our online web mapping application known as SMMLCP-NET. Click here for website. Online Web Mapping Application – This is the online web mapping application that shows all the layers associated with the plan. These are the same layers that are available for download below. Click here for the web mapping application. GIS Layers – This is a link to the GIS layers in the form of an ArcGIS Map Package, click here (LINK TO FOLLOW SOON) for ArcGIS Map Package (version 10.3). Also, included are layers in shapefile format. Those are included below.

Below is a list of the GIS Layers provided (shapefile format):

Recreation (Zipped - 5 MB - click here)

Coastal Zone Campground Trails (2012 National Park Service) Backbone Trail Class III Bike Route – Existing Class III Bike Route – Proposed

Scenic Resources (Zipped - 3 MB - click here)

Significant Ridgeline State-Designated Scenic Highway State-Designated Scenic Highway 200-foot buffer Scenic Route Scenic Route 200-foot buffer Scenic Element

Biological Resources (Zipped - 45 MB - click here)

National Hydrography Dataset – Streams H2 Habitat (High Scrutiny) H1 Habitat H1 Habitat 100-foot buffer H1 Habitat Quiet Zone H2 Habitat H3 Habitat

Hazards (Zipped - 8 MB - click here)

FEMA Flood Zone (100-year flood plain) Liquefaction Zone (Earthquake-Induced Liquefaction Potential) Landslide Area (Earthquake-Induced Landslide Potential) Fire Hazard and Responsibility Area

Zoning and Land Use (Zipped - 13 MB - click here)

Malibu LCP – LUP (1986) Malibu LCP – Zoning (1986) Land Use Policy Zoning

Other Layers (Zipped - 38 MB - click here)

Coastal Commission Appeal Jurisdiction Community Names Santa Monica Mountains (SMM) Coastal Zone Boundary Pepperdine University Long Range Development Plan (LRDP) Rural Village

Contact the L.A. County Dept. of Regional Planning's GIS Section if you have questions. Send to our email.

This dataset represents a unique compiled environmental data set for the circumpolar Arctic ocean region 45N to 90N region. It consists of 170 layers (mostly marine, some terrestrial) in ArcGIS 10 format to be used with a Geographic Information System (GIS) and which are listed below in detail. Most layers are long-term average raster GRIDs for the summer season, often by ocean depth, and represent value-added products easy to use. The sources of the data are manifold such as the World Ocean Atlas 2009 (WOA09), International Bathimetric Chart of the Arctic Ocean (IBCAO), Canadian Earth System Model 2 (CanESM2) data (the newest generation of models available) and data sources such as plankton databases and OBIS. Ocean layers were modeled and predicted into the future and zooplankton species were modeled based on future data: Calanus hyperboreus (AphiaID104467), Metridia longa (AphiaID 104632), M. pacifica (AphiaID 196784) and Thysanoessa raschii (AphiaID 110711). Some layers are derived within ArcGIS. Layers have pixel sizes between 1215.819573 meters and 25257.72929 meters for the best pooled model, and between 224881.2644 and 672240.4095 meters for future climate data. Data was then reprojected into North Pole Stereographic projection in meters (WGS84 as the geographic datum). Also, future layers are included as a selected subset of proposed future climate layers from the Canadian CanESM2 for the next 100 years (scenario runs rcp26 and rcp85). The following layer groups are available: bathymetry (depth, derived slope and aspect); proximity layers (to,glaciers,sea ice, protected areas, wetlands, shelf edge); dissolved oxygen, apparent oxygen, percent oxygen, nitrogen, phosphate, salinity, silicate (all for August and for 9 depth classes); runoff (proximity, annual and August); sea surface temperature; waterbody temperature (12 depth classes); modeled ocean boundary layers (H1, H2, H3 and Wx).This dataset is used for a M.Sc. thesis by the author, and freely available upon request. For questions and details we suggest contacting the authors. Process_Description: Please contact Moritz Schmid for the thesis and detailed explanations. Short version: We model predicted here for the first time ocean layers in the Arctic Ocean based on a unique dataset of physical oceanography. Moreover, we developed presence/random absence models that indicate where the studied zooplankton species are most likely to be present in the Arctic Ocean. Apart from that, we develop the first spatially explicit models known to science that describe the depth in which the studied zooplankton species are most likely to be at, as well as their distribution of life stages. We do not only do this for one present day scenario. We modeled five different scenarios and for future climate data. First, we model predicted ocean layers using the most up to date data from various open access sources, referred here as best-pooled model data. We decided to model this set of stratification layers after discussions and input of expert knowledge by Professor Igor Polyakov from the International Arctic Research Center at the University of Alaska Fairbanks. We predicted those stratification layers because those are the boundaries and layers that the plankton has to cross for diel vertical migration and a change in those would most likely affect the migration. I assigned 4 variables to the stratification layers. H1, H2, H3 and Wx. H1 is the lower boundary of the mixed layer depth. Above this layer a lot of atmospheric disturbance is causing mixing of the water, giving the mixed layer its name. H2, the middle of the halocline is important because in this part of the ocean a strong gradient in salinity and temperature separates water layers. H3, the isotherm is important, because beneath it flows denser and colder Atlantic water. Wx summarizes the overall width of the described water column. Ocean layers were predicted using machine learning algorithms (TreeNet, Salford Systems). Second, ocean layers were included as predictors and used to predict the presence/random absence, most likely depth and life stage layers for the zooplankton species: Calanus hyperboreus, Metridia longa, Metridia pacifica and Thysanoessa raschii, This process was repeated for future predictions based on the CanESM2 data (see in the data section). For zooplankton species the following layers were developed and for the future. C. hyperboreus: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100.For parameters: Presence/random absence, most likely depth and life stage layers M. longa: Best-pooled model as well as future predictions (rcp26 including ocean layer(also excluding), rcp85 including oocean layers (also excluding) for 2010 and 2100. For parameters: Presence/rand... Visit https://dataone.org/datasets/f63d0f6c-7d53-46ce-b755-42a368007601 for complete metadata about this dataset.

Digital representation of areas that met Eugene's Goal 5 Significance criteria for riparian corridors as adopted in paper map format by the Eugene City Council in July 2003. Site boundaries were originally drawn based on air photo interpretation in the late 1980's and early 1990's and were subsequently corrected and updated several times prior to final adoption of paper maps on November 14, 2005. Also see Water Resource Conservation Maps.Terms of UseThis product is for informational purposes and may not have been prepared for, or be suitable for legal, engineering, or surveying purposes. This layer does not constitute the adopted Goal 5 resources, and users of this information should review or consult the primary data and information sources to ascertain the usability of this information. To verify information presented in this layer, contact the Planner-on-Duty at 541-682-5377.The maps and data available for access from the City of Eugene are provided "as is" without warranty or any representation of accuracy, timeliness or completeness. The burden for determining accuracy, completeness, timeliness, merchantability and fitness for or the appropriateness for use rests solely on the user accessing this information. The City of Eugene makes no warranties, expressed or implied, as to the use of the maps and data available for access at this website. There are no implied warranties of merchantability or fitness for a particular purpose. The user acknowledges and accepts all inherent limitations of the maps and data, including the fact that the maps and data are dynamic and in a constant state of maintenance, correction and revision. Any maps and associated data for access do not represent a survey. No liability is assumed for the accuracy of the data delineated on any map, either expressed or implied.

The Australian Proterozoic Large Igneous Provinces GIS Dataset is designed for display at a nominal 1:5 000 000 scale, showing the time-space distribution of Proterozoic Large Igneous Provinces (LIPs) in Australia. Large Igneous Provinces are relatively rare magmatic events distinguished by exceptionally large volumes of mafic dominated magma emplaced over short geological periods of a few millions years or less. Five major LIPs have been recognised, or proposed, so far in Australia, beginning with the ~1780 Ma Hart LIP, followed by the ~1210 Ma Marnda Moorn LIP, the ~1070 Ma Warakurna LIP, the ~825 Ma Gairdner LIP, and the ~510 Ma Kalkarindji LIP. The early Cambrian Kalkarindji LIP is included in this Proterozoic compilation because of its size and importance. Only the youngest two of these LIPs (Gairdner and Kalkarindji) are established as comagmatic provinces based on both time correlation and geochemical equivalence. The other proposed LIPs (Hart, Marnda Moorn and Warakurna) are based on time equivalence alone. For further information on the five proposed Proterozoic LIPs refer to the guide to using the map of Australian Proterozoic Large Igneous Provinces (Geoscience Australia Record 2009/44).

Earlier released extracts include two pdf maps of Australian Proterozoic Large Igneous Provinces and an accompanying Geoscience Australia Record. This release presents the Australian Proterozoic Large Igneous Provinces as a GIS dataset and it should be used in conjunction with the Australian Mafic Ultramafic Magmatic Events GIS Dataset released by Geoscience Australia in 2014 (http://www.ga.gov.au/metadata-gateway/metadata/record/82166/). This file geodatabase that contains points, lines and polygons representing mafic and ultramafic rocks in Australia which have been placed in a magmatic event framework in time and space, primarily based on geochronological data. Together, these datasets provide comprehensive information on the evolution of mafic-ultramafic magmatism associated with the Australian continent, and will be of interest to explorers in the search of magmatic ore deposits of nickel, platinum-group elements, chromium, titanium, and vanadium.

This exploratory study investigated endurance athletes self-reported exercise-associated gastrointestinal symptoms (Ex-GIS) and associated strategies to manage symptomology. Adult endurance athletes with a history of Ex-GIS (n = 137) participating in events ≥ 60 min completed an online validated questionnaire. Respondents included runners (55%, n = 75), triathletes (22%, n = 30), and non-running sports (23%, n = 32), participating at a recreationally competitive (37%, n = 51), recreationally non-competitive (32%, n = 44), and competitive regional/national/international (31%, n = 42) levels. Athletes identified when Ex-GIS developed most frequently either around training (AT), around competitions (AC), or equally around both training (ET) and competitions (EC). Athletes reported the severity of each symptom before, during, and after exercise. Athletes predominantly categorized Ex-GIS severity as mild (< 5/10) on a 0 (no symptoms) to 10 (extremely severe symptoms) visual analog symptomology scale. The Friedman test and post hoc analysis with Wilcoxon signed rank test was conducted with a Bonferroni correction applied to determine differences between repeated measures. The only severe symptom of significance was the urge to defecate during training in the ET group (Z = –0.536, p = 0.01). Ex-GIS incidence was significantly higher during training and competitions in all categories. A content review of self-reported strategies (n = 277) to reduce Ex-GIS indicated popular dietary strategies were dietary fiber reduction (15.2%, n = 42), dairy avoidance (5.8%, n = 16), and a low fermentable oligosaccharides, monosaccharides, and polyols (FODMAP) diet (5.4%, n = 15). In contrast, non-dietary strategies included the use of medications (4.7%, n = 13) and relaxation/meditation (4.0%, n = 11). On a Likert scale of 1–5, the most successful dietary strategies implemented were dietary fiber reduction (median = 4, IQR = 4, 5), low FODMAP diets (median = 4, IQR = 4, 5), dairy-free diets (median = 4, IQR = 4, 5), and increasing carbohydrates (median = 4, IQR = 3, 4). Accredited practicing dietitians were rated as the most important sources of information for Ex-GIS management (n = 29). Endurance athletes use a variety of strategies to manage their Ex-GIS, with dietary manipulation being the most common.

According to Cognitive Market Research, the global Geospatial Solutions market size is USD 508421.2million in 2024 and will expand at a compound annual growth rate (CAGR) of 16.50% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 203368.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.7% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 152526.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 116936.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 18.5% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 25421.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 15.9% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 10168.42 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.2% from 2024 to 2031.
The hospitals held the highest Geospatial Solutions market revenue share in 2024.

Key Driver of the Geospatial Solutions Market

Growing Demand for Location-based Data and Insights to Increase the Demand Globally

Businesses and organizations prioritize making well-informed decisions, driving demand for location-based data and insights. Having accurate and comprehensive information about people, places, and things is becoming increasingly important. Geospatial solutions play a crucial role in gathering, evaluating, and presenting this data, which drives market growth. These technologies help with resource allocation, market targeting, and strategy planning by providing advanced tools for interpreting spatial data. Businesses use geospatial data to improve customer experiences, optimize operations, and gain competitive advantages due to the development of GPS, remote sensing, and GIS. Because of this, the geospatial industry is expanding rapidly and satisfying the changing demands of various industries looking for useful location-based insights.

Advancements in Technology to Propel Market Growth

The geospatial industry is expanding significantly due to technological advancements, including aerial images, remote sensing, GNSS (Global Navigation Satellite Systems), and LiDAR (Light Detection and Ranging). These developments provide ever-more accurate, affordable, and easily accessible ways to collect geospatial data. While GNSS offers precise global location data, remote sensing technologies allow data collection from inaccessible or remote areas. LiDAR and aerial images improve data resolution and detail, allowing for more complex analysis and visualization. The geospatial market is growing due to the ongoing development of these technologies, which enables businesses and organizations in various industries to make wise decisions, maximize operations, and seize new possibilities.

Restraints factor of the Geospatial Solutions Market

Data Privacy and Security Concerns to Limit the Sales

The widespread use of geographical data gives rise to serious privacy and security problems. The increasing accessibility and utilization of location-based data across many businesses underscores the need for strong data governance frameworks to preserve individuals' privacy and prevent potential compromises of sensitive data. Furthermore, upholding moral principles and legal compliance depends on gaining users' trust via open data policies and permission procedures. Companies may promote the responsible and ethical use of location-based information by addressing these concerns and fostering better stakeholder confidence. Additionally, companies should limit risks connected with gathering, sharing, and utilizing geospatial data.

Impact of COVID-19 on the Geospatial Solutions Market

The geospatial solutions market has experienced varying effects from the COVID-19 epidemic. Due to supply chain disruptions and economic uncertainty, several industries faced a brief pause. Still, others saw faster development as a result of the pressing need for location-based data to solve pandemic-related issues. Geospatial technologies are increasingly used in industries, including healthcare, logistics, and urban planning, to track the virus's spread, allocate resourc...

DefinitionThis indicator identifies the quality and availability of surface drinking water for people within the Midwest Landscape. It prioritizes areas based on supply and demand of drinking water, plus the presence of threats to the water supply. Pixels can take the following values:1 – Less important watersheds, not a natural asset2 – Less important watersheds, natural assets3 – Median importance watersheds, not a natural asset4 – Median importance watersheds, natural assets5 – More important watersheds, not a natural asset6 – More important watersheds, natural assetsSelectionThis indicator was chosen as a targetable, important feature of the MLI goals that will be used to track conditions over time and prioritize areas for conservation. Indicators were defined through elicitation and prioritization exercises with federal and state participants. Criteria for the indicators includes 1) actionable, 2) measurable, 3) relevant to multiple groups across the region, and/or 4) representative of other social and/or environmental values.Input Data & Mapping StepsThis indicator originates from the USDA Forest Service National Forests to Faucets 2.0 Assessment. To create this layer, MLI partners, members, and staff completed the following mapping steps: projected all input data to NAD83 (2011) UTM Zone 15N, converted the input data to a 30m raster based on the importance score of each watershed, classified the raster into three categories, and emphasized natural assets within each watershed, leading to a raster with the following categories: 1 – Less important watersheds, not a natural asset, 2 – Less important watersheds, natural assets, 3 – Median importance watersheds, not a natural asset, 4 – Median importance watersheds, natural assets, 5 – More important watersheds, not a natural asset, 6 – More important watersheds, natural assets. Finally, we removed highly altered areas from this layer using our Highly Altered Areas mask.For full mapping details, please refer to the Midwest Conservation Blueprint 2024 Development Process. For a complete download of all Blueprint input and output data, visit the Midwest Conservation Blueprint 2024 Data Download.

Reporter for MRGPThe Reporter for MRGP doesn't require you to download any apps to complete an inventory; all you need is an internet connection and web browser. The Reporter includes culverts and bridges from VTCULVERTS, town highways from Vtrans and the current status of the MRGP segments and outlets on the map.MRGP Fieldworker SolutionNotes on MRGP fieldworker solution: July 12, 2021. The MRGP map now displays the current status of road segments and outlets. Fieldworkers using the MRGP solution should remove the offline map area(s) from their device, and keep their new offline map current, by syncing their map. Enabling auto-sync will get you the current segment or outlet status automatically. See FAQ section below for more information. Road Erosion Inventory forms are available and have a new look and feel this year. The drainage ditch survey is broken out into three pages for a better user experience. The first page contains survey and segment information, the second; the inventory, and the third; barriers to implementation. You will notice the questions are outlined by section so it’s easier to follow along too. The questions have remained the same. Survey123 has a new option requiring users to update surveys on their mobile device. That option has been enabled for the two MRGP Survey123 forms. Step 1: Download the free mobile appsFor fieldworkers to collect and submit data to VT DEC, two free apps are required: ArcGIS Collector or Field Maps and Survey123. ArcGIS Collector or Field Maps is used first to locate the segment or outlet for inventory, and Survey123, for completing the Road Erosion Inventory. ArcGIS Field Maps is ESRI’s new all-in-one app for field work and will replace ArcGIS Collector. You can download ArcGIS Collector or ArcGIS Fields Maps and Survey123 from the Google Play Store.You can download ArcGIS Collector or ArcGIS Field Maps and Survey123 from Apple Store.

Step 2: Sign into the mobile appYou will need appropriate credentials to access fieldworker solution, please contact your Regional Planning Commission’s Transportation Planner or Jim Ryan (MRGP Program Lead) at (802) 490-6140.Open Collector for ArcGIS, select ‘ArcGIS Online’ as shown below, and enter the user name and password. The credential is saved unless you sign out. Step 3: Open the MRGP Mobile MapIf you’re working in an area that has a reliable data connection (e.g. LTE or 4G), open the map below by selecting it.Step 4: Select a road segment or outlet for inventoryUse your location, button circled in red below, select the segment or outlet you need to inventory, and select 'Update Road Segment Status' from the pop-up to launch Survey123.

Step 5: Complete the Road Erosion Inventory and submit inventory to DECSelecting 'Update Road Segment Status' opens Survey123, downloads the relevant survey and pre-populates the REI with important information for reporting to DEC. You will have to enter the same username and password to access the REI forms. The credential is saved unless you sign out of Survey123.Complete the survey using the appropriate supplement below and submit the assessment directly to VT DEC.Paved Roads with Catch Basin SupplementPaved and Gravel Roads with Drainage Ditches Supplement

Step 6: Repeat!Go back to the ArcGIS Collector or Field Maps and select the next segment for inventory and repeat steps 1-5.

If you have question related to inventory protocol reach out to Jim Ryan, MRGP Program Lead, at jim.ryan@vermont.gov, (802) 490-6140If you have questions about implementing the mobile data collection piece please contact Ryan Knox, ADS-ANR IT, at ryan.knox@vermont.gov, (802) 793-0297

The location where I'm doing inventory does not have a data coverage (LTE or 4G). What can I do?ArcGIS Collector allows you take map areas offline when you think there will be spotty or no data coverage. I made a video to demonstrate the steps for taking map areas offline - https://youtu.be/OEsJrCVT8BISurvey123 operates offline by default but you need to download the survey. My recommendation is to test the fieldworker solution (Steps 1-5) before you go into the field but don't submit the test survey.Where can I download the Road Erosion Scoring shown on the the Atlas? You can download the scoring for both outlets and road segments through the VT Open Geodata Portal.https://geodata.vermont.gov/maps/VTANR::mrgp-scoring-open-data/aboutHow do I use my own ArcGIS Collector map for launching the official MRGP REI survey form? You can use the following custom url for launching Survey123, open the REI and prepopulate answers in the form. More information is here. TIP: add what's below directly in the HTML view of the popup not the link as described in the post I provided.

Hydrologically connected segments (lines):Update Road Segment Status

Segment ID: {SegmentID}
Segment Status: {SegmentStatus}
{RoadName}, {Municipality}
Outlets: {Outlets}
Hydrologically connected outlets (points):Update Outlet Status

Outlet ID: {OutletID}
Municipality: {Municipality}
Erosion: {ErosionValue}

How do I save my name and organization information used in subsequent surveys? Watch this short video or execute the steps below:

Open Survey123 and open a blank REI form (Collect button) Note: it's important to open a blank form so you don't save the same segment id for all your surveys Fill-in your 'Name' and 'Organization' and clear the 'Date of Assessment field' (x button). Using the favorites menu in the top-right corner you can use the current state of your survey to 'Set as favorite answers.' Close survey and 'Save this survey in Drafts.' Use Collector to launch survey from selected feature (segment or outlet). Using the favorites menu again, 'Paste answers from favorite.

What if the map doesn't have the outlet or road segment I need to inventory for the MRGP? Go Directly to Survey123 and complete the appropriate Road Erosion Inventory and submit the data to DEC. The survey includes a Geopoint (location) that we can use to determine where you completed the inventory.

Where can I view the Road Erosion Inventories completed with Survey123? Using the MRGP credentials you have access to another map that shows completed REIs.Web map - Completed Road Erosion Inventories for MRGPWhere can I download the 2020-2021 data collected with Survey123?Road Segments (lines) - https://vtanr.maps.arcgis.com/home/item.html?id=f8a11de8a5a0469596ef11429ab49465Outlets (points) - https://vtanr.maps.arcgis.com/home/item.html?id=ae13a925a662490184d5c5b1b9621672Where can I download the 2019 data collected with Survey123?

Road Segments (lines) - https://vtanr.maps.arcgis.com/home/item.html?id=f60050c6f3c04c60b053470483acb5b1 Outlets (points) - https://vtanr.maps.arcgis.com/home/item.html?id=753006f9ecf144ccac8ce37772bb2c03 Where can I download the 2018 data collected with Survey123?Outlets (points) - https://vtanr.maps.arcgis.com/home/item.html?id=124b617d142e4a1dbcfb78a00e8b9bc5Road Segments (lines) - https://vtanr.maps.arcgis.com/home/item.html?id=8abcc0fcec0441ce8ae6cd38e3812b1b Where can I download the Hydrologically Connected Road Segments and Outlets?Vermont Open Data Geoportal - https://geodata.vermont.gov/datasets/VTANR::hydrologically-connected-road-segments-1/about

This 2019 version of the MRGP Outlets is based on professional mapping completed using DEC's Stormwater Infrastructure dataset. In catch basin systems, work was completed to match outlets to road segments that drain to them. The outlets here correspond to Outlet IDs identified in the Hydrologically connected roads segments layer. For outlets that meet standard, road segments will also meet the standard for MRGP compliance.

The SESMAR dataset offers readily available maps and raster images tailored for scientists and decision-makers. It is derived from a wealth of remote sensing data covering the period from 2001 to 2023. Operating at a spatial resolution of 500m, this dataset evaluates soil loss susceptibility in the North African region. The application of the Revised Universal Soil Loss Equation (RUSLE) model, originally formulated by Wischmeier and Smith in 1978, was used to enhance the credibility of the dataset's computational methodology.

The dataset lies on the integration of diverse open-source datasets, namely MOD13A2.061 Terra Vegetation Indices for calculating the Cover Management factor, MCD12Q1.006 MODIS Land Cover Type Yearly Global, CHIRPS dataset for precipitation, Shuttle Radar Topography Mission (SRTM) dataset for topography, and Open Land Map Soil Texture Class (USDA System). This multi-source integration enhances the dataset's reliability and applicability for various environmental and agricultural studies.

The SESMAR dataset provides consistent susceptibility maps for major North African basins and offers readily classified raster images, enhancing its usability for researchers and practitioners. The basins are extracted from HydroSHEDS/v1/Basins/hybas dataset based on HYBAS_ID, also provided to ensure the identification of the specific basin for further analysis. The reliance on HydroSHEDS, a robust mapping product by Lehner and Grill (2013), ensures comprehensive hydrographic information across different scales, ranging from coarse to detailed. For the convenience of prospective users, it's noteworthy that the resultant raster datasets cover extensive basins, which can be further partitioned into smaller or medium-sized sub-basins as necessary.

The dataset is splitted into 22 rasters in a compressed format, consisting of a single band each one. It characterizes soil loss susceptibility, categorizing each raster cell into six distinct classes. The classification is based on the estimated annual soil loss rates per hectare, with associated values as follows:

- 0: No Data
This category designates areas where soil loss susceptibility information is unavailable, serving as a placeholder for missing or inaccessible data.

- 1: Very Low (< 5 t/ha/year)
Raster cells in this class represent areas with very low susceptibility to soil loss, indicating an annual rate of less than 5 tons per hectare.

- 2: Low (5 to 15 t/ha/year)
This class characterizes areas with low susceptibility, where the annual soil loss rate falls within the range of 5 to 15 tons per hectare.

- 3: Medium (15 to 50 t/ha/year)
Raster cells categorized as medium susceptibility denote moderate levels of soil loss, with an annual rate ranging from 15 to 50 tons per hectare.

- 4: High (50 to 80 t/ha/year)
This class identifies areas with high susceptibility to soil loss, where the annual rate ranges from 50 to 80 tons per hectare.

- 5: Very High (> 80 t/ha/year)
Raster cells in this category indicate the highest susceptibility to soil loss, with an annual rate exceeding 80 tons per hectare.

This comprehensive classification system is integral to the raster dataset, facilitating a nuanced understanding of soil loss susceptibility across different geographical locations. The dataset serves for environmental and agricultural planning, enabling stakeholders to identify and prioritize areas for targeted soil conservation measures. Continuous efforts to maintain data accuracy through updates and validation processes will ensure the dataset's reliability and relevance over time.