The Bayinguoleng Mongolian Autonomous Prefecture of Xinjiang is a continental arid climate with abundant light and heat resources. ‘Korla fragrant pear’ has become a pillar industry of economic forest and fruit in Bazhou. With the continuous expansion of planting scale, the disadvantages of industrial planting have become increasingly prominent, which has greatly hindered the sustainable green development of fragrant pear. In this study, GIS spatial overlay analysis and three-phase fruit resource data were used to explore the industrial resources of ‘Korla Fragrant Pear’ in Bazhou. This data set consists of six types of data : forest fruit resource data, meteorological data, pest data, elevation data, soil data and planting management data in Bazhou area. This data set provides a scientific theoretical basis for exploring the current situation of ‘Korla Fragrant Pear’ industry, promoting the quality and efficiency of fruit industry, and realizing the high-quality development of digital management of fruit industry in Xinjiang.

Suitability analysis is a landscape modeling process that is used to determine which locations are best suited for certain uses. The landscape planner specifies the environmental and cultural factors considered important to decision making, selects the appropriate data layers, weights them, and uses geoprocessing tools to filter the criteria and identify the best locations. For example, a planner may want to assess where habitats are located for endangered species and how close a site may be to suburban development. Or you may be researching a site for a new petroleum pipeline and need to figure out which sites would be suitable based on elevation, slope, endangered species habitats, and proximity to urban and suburban areas.This can be a time-consuming process with conventional desktop GIS tools. Landscape Modeler is a web-based application that makes the entire process more efficient. It allows you to use raster services to visualize information such as critical habitats, development risk, and fire potential, across the United States. This information can be used to research sites for urban development, housing developments, habitat locations, and other projects that require you to weigh several types of data against each other.

Geographic Information System Software Market Outlook

The global Geographic Information System (GIS) Software market size was valued at approximately USD 7.8 billion in 2023 and is projected to reach USD 15.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.3% during the forecast period. This impressive growth can be attributed to the increasing demand for efficient data management tools across various industries, which rely on spatial data for decision-making and strategic planning. The rapid advancements in technology, such as the integration of AI and IoT with GIS software, have further propelled the market, enabling organizations to harness the full potential of geographic data in innovative ways.

One of the primary growth drivers of the GIS Software market is the burgeoning need for urban planning and smart city initiatives worldwide. As urbanization trends escalate, cities are increasingly relying on GIS technology to manage resources more effectively, optimize transportation networks, and enhance public safety. The ability of GIS software to provide real-time data and spatial analysis is vital for city planners and administrators faced with the challenges of modern urban environments. Furthermore, the trend towards digital transformation in governmental organizations is boosting the adoption of GIS solutions, as they seek to improve operational efficiency and service delivery.

The agricultural sector is also experiencing significant transformations due to the integration of GIS software, which is another pivotal growth factor for the market. Precision agriculture, which involves the use of GIS technologies to monitor and manage farming practices, is enabling farmers to increase crop yields while reducing resource consumption. By leveraging spatial data, farmers can make informed decisions about planting, irrigation, and harvesting, ultimately leading to more sustainable agricultural practices. This trend is particularly prominent in regions where agriculture forms a substantial portion of the economy, encouraging the adoption of advanced GIS tools to maintain competitive advantage.

Another influential factor contributing to the growth of the GIS Software market is the increasing importance of environmental management and disaster response. GIS technology plays a crucial role in assessing environmental changes, managing natural resources, and planning responses to natural disasters. The ability to overlay various data sets onto geographic maps allows for better analysis and understanding of environmental phenomena, making GIS indispensable in tackling issues such as climate change and resource depletion. Moreover, governments and organizations are investing heavily in GIS tools that aid in disaster preparedness and response, ensuring timely and effective action during emergencies.

The evolution of GIS Mapping Software has been instrumental in transforming how spatial data is utilized across various sectors. These software solutions offer robust tools for visualizing, analyzing, and interpreting geographic data, enabling users to make informed decisions based on spatial insights. With the ability to integrate multiple data sources, GIS Mapping Software provides a comprehensive platform for conducting spatial analysis, which is crucial for applications ranging from urban planning to environmental management. As technology continues to advance, the capabilities of GIS Mapping Software are expanding, offering more sophisticated features such as 3D visualization and real-time data processing. These advancements are not only enhancing the utility of GIS tools but also making them more accessible to a wider range of users, thereby driving their adoption across different industries.

Regionally, North America and Europe have traditionally dominated the GIS Software market, thanks to their robust technological infrastructure and higher adoption rates of advanced technologies. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid urbanization, increased government spending on infrastructure development, and the expanding telecommunications sector. The growing awareness and adoption of GIS solutions in countries like China and India are significant contributors to this regional growth. Furthermore, Latin America and the Middle East & Africa regions are slowly catching up, with ongoing investments in smart city projects and infrastructure development driving the demand for GIS software.

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Comparison of municipal NHSs with TRCA’s updated TNHS spatially using a GIS overlay analysis. Identification of distinct classes through mapping including (i) areas of overlap between municipal NHS and TRCA’s updated TNHS (overlapping NHS), (ii) areas present in municipal NHS only (municipal-only NHS), and (iii) areas present in TRCA’s updated TNHS only (TRCA-only NHS)

Data were extracted from the Rwanda soil dataset and analyzed using the geo-spatial tools of ArcGIS.aAND, Andisols; ALF, Alfisols; ENT, Entisols; INCEPT, Inceptisols; HIST, Histosols; MOLL, Mollisols; OX, Oxisols; ULT, Ultisols; VERT, Vertisols.bTotal Rwanda soil and Arabica coffee coverage per agro-ecological zone.cSoil and Arabica coffee coverage in percentage per agro-ecological zone over total Rwanda soil area and Arabica coffee area, respectively.Distribution of soil types (ha) and areas of Arabica coffee cultivation in the ten agro-ecological zones of Rwanda.

The environmental sensitivity index analysis presented here is an overlay analysis based on the cumulative value of various environmental features in Sarpy County, which are weighted for their relative significance or sensitivity. The greater the environmental signifi cance or sensitivity of a given landscape, the higher the ESI score. Although beyond the scope of this study, ecosystem services and material fl ow frameworks are a different set of environmental analysis methods that could also be applied to quantify the fl ows of natural and human systems in Sarpy County. Ecosystem service and fl ow frameworks seek to defi ne fl ows, or temporal fl uxes of materials, through a given systems. The basic difference between overlay analysis and material fl ow analysis for the environment is in the unit of analysis; where the unit of analysis in a fl ow framework is a volume of material over time per area, and the unit of analysis in overlay modeling is an ordinally ranked value of environmental quality for a given purpose. European ecosystem services frameworks (translated as “landscape functions” out of the German Landschaftsfunktionen) have many similar categories to the traditional overlay modeling used here, and includes the added dimension of fl ow over time for materials within the landscape functions factor framework.Created by Vireo & submitted to Sarpy County 9/5/2013.Data current as of the last business day.

The Geographic Information System Analytics Market size was valued at USD 9.1 USD Billion in 2023 and is projected to reach USD 24.80 USD Billion by 2032, exhibiting a CAGR of 15.4 % during the forecast period. Geographic Information System (GIS) Analytics is the process by which quantitative and qualitative information of geographic locales are employed to describe features, conduct statistical analysis, and discover correlations of geographical regions. Some of the key types are referred to as spatial, in which cases the locations and characteristics of features are analyzed to determine their spatial associations, and temporal which investigate variations in the features over time. Network analysis is also compiled within the GIS Analytics; this process is based upon the connection and throughput in networks and the overlay model that overlays data on top of each other for interaction determinations. Some of the features often utilized in GIS include the generation of maps, spatial analysis, and georeferencing amongst others. It has diverse types of applications in civil, planning and development, environment, disaster management, and transport sectors to understand and analyze spatial information and support the organization’s decision-making process. Key drivers for this market are: Increasing Adoption of Cloud-based Managed Services to Drive Market Growth. Potential restraints include: Growing Security Threats to Hamper the Market . Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

Background: Malaria continues to pose a major public health challenge in tropical regions. Despite significant efforts to control malaria in Tanzania, there are still residual transmission cases. Unfortunately, little is known about where these residual malaria transmission cases occur and how they spread. In Tanzania, for example, the transmission is heterogeneously distributed. In order to effectively control and prevent the spread of malaria, it is essential to understand the spatial distribution and transmission patterns of the disease. This study seeks to predict areas that are at high risk of malaria transmission so that intervention measures can be developed to accelerate malaria elimination efforts.

Methods: This study employs a geospatial-based model to predict and map out malaria risk area in Kilombero Valley. Environmental factors related to malaria transmission were considered and assigned valuable weights in the Analytic Hierarchy Process (AHP), an online system using a pairwise comparison technique. The malaria hazard map was generated by a weighted overlay of the altitude, slope, curvature, aspect, rainfall distribution, and distance to streams in Geographic Information Systems (GIS). Finally, the risk map was created by overlaying components of malaria risk including hazards, elements at risk, and vulnerability. Results: The study demonstrates that the majority of the study area falls under the moderate-risk level (61%), followed by the low-risk level (31%), while the high-malaria risk area covers a small area, which occupies only 8% of the total area. Conclusion: The findings of this study are crucial for developing spatially targeted interventions against malaria transmission in residual transmission settings. Predicted areas prone to malaria risk provide information that will inform decision-makers and policymakers for proper planning, monitoring, and deployment of interventions. Methods Data acquisition and description The study employed both primary and secondary data, which were collected from numerous sources based on the input required for the implementation of the predictive model. Data collected includes the locations of all public and private health centers that were downloaded free from the health portal of the United Republic of Tanzania, Ministry of Health, Community Development, Gender, Elderly, and Children, through the universal resource locator (URL) (http://moh.go.tz/hfrportal/). Human population data was collected from the 2012 population housing census (PHC) for the United Republic of Tanzania report. Rainfall data were obtained from two local offices; Kilombero Agricultural Training and Research Institute (KATRIN) and Kilombero Valley Teak Company (KVTC). These offices collect meteorological data for agricultural purposes. Monthly data from 2012 to 2017 provided from thirteen (13) weather stations. Road and stream network shapefiles were downloaded free from the MapCruzin website via URL (https://mapcruzin.com/free-tanzania-arcgis-maps-shapefiles.htm). With respect to the size of the study area, five neighboring scenes of the Landsat 8 OLI/TIRS images (path/row: 167/65, 167/66, 167/67, 168/66 and 168/67) were downloaded freely from the United States Geological Survey (USGS) website via URL: http://earthexplorer.usgs.gov. From July to November 2017, the images were selected and downloaded from the USGS Earth Explorer archive based on the lowest amount of cloud cover coverage as viewed from the archive before downloading. Finally, the digital elevation data with a spatial resolution of three arc-seconds (90m by 90m) using WGS 84 datum and the Geographic Coordinate System were downloaded free from the Shuttle Radar Topography Mission (SRTM) via URL (https://dds.cr.usgs.gov/srtm/version2_1/SRTM3/Africa/). Only six tiles that fall in the study area were downloaded, coded tiles as S08E035, S09E035, S10E035, S08E036, S09E036, S10E036, S08E037, S09E037 and S10E037. Preparation and Creation of Model Factor Parameters Creation of Elevation Factor All six coded tiles were imported into the GIS environment for further analysis. Data management tools, with raster/raster data set/mosaic to new raster feature, were used to join the tiles and form an elevation map layer. Using the spatial analyst tool/reclassify feature, the generated elevation map was then classified into five classes as 109–358, 359–530, 531–747, 748–1017 and >1018 m.a.s.l. and new values were assigned for each class as 1, 2, 3, 4 and 5, respectively, with regards to the relationship with mosquito distribution and malaria risk. Finally, the elevation map based on malaria risk level is levelled as very high, high, moderate, low and very low respectively. Creation of Slope Factor A slope map was created from the generated elevation map layer, using a spatial analysis tool/surface/slope feature. Also, the slope raster layer was further reclassified into five subgroups based on predefined slope classes using standard classification schemes, namely quantiles as 0–0.58, 0.59–2.90, 2.91–6.40, 6.41–14.54 and >14.54. This classification scheme divides the range of attribute values into equal-sized sub-ranges, which allow specifying the number of the intervals while the system determines where the breaks should be. The reclassified slope raster layer subgroups were ranked 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence in the locality. To elaborate, the steeper slope values are related to lesser malaria hazards, and the gentler slopes are highly susceptible to malaria incidences. Finally, the slope map based on malaria risk level is leveled as very high, high, moderate, low and very low respectively. Creation of Curvature Factor Curvature is another topographical factor that was created from the generated elevation map using the spatial analysis tool/surface/curvature feature. The curvature raster layer was further reclassified into five subgroups based on predefined curvature class. The reclassified curvature raster layer subgroups were ranked to 1, 2, 3, 4 and 5 according to their degree of suitability for malaria occurrence. To explain, this affects the acceleration and deceleration of flow across the surface. A negative value indicates that the surface is upwardly convex, and flow will be decelerated, which is related to being highly susceptible to malaria incidences. A positive profile indicates that the surface is upwardly concave and the flow will be accelerated which is related to a lesser malaria hazard, while a value of zero indicates that the surface is linear and related to a moderate malaria hazard. Lastly, the curvature map based on malaria risk level is leveled as very high, high, moderate, low, and very low respectively.
Creation of Aspect Factor As a topographic factor associated with mosquito larval habitat formation, aspect determines the amount of sunlight an area receives. The more sunlight received the stronger the influence on temperature, which may affect mosquito larval survival. The aspect of the study area also was generated from the elevation map using spatial analyst tools/ raster /surface /aspect feature. The aspect raster layer was further reclassified into five subgroups based on predefined aspect class. The reclassified aspect raster layer subgroups were ranked as 1, 2, 3, 4 and 5 according to the degree of suitability for malaria incidence, and new values were re-assigned in order of malaria hazard rating. Finally, the aspect map based on malaria risk level is leveled as very high, high, moderate, low, and very low, respectively. Creation of Human Population Distribution Factor Human population data was used to generate a population distribution map related to malaria occurrence. Kilombero Valley has a total of 42 wards, the data was organized in Ms excel 2016 and imported into the GIS environment for the analysis, Inverse Distance Weighted (IDW) interpolation in the spatial analyst tool was applied to interpolate the population distribution map. The population distribution map was further reclassified into five subgroups based on potential to malaria risk. The reclassified map layer subgroups were ranked according to the vulnerability to malaria incidence in the locality such as areas having high population having the highest vulnerability and the less population having less vulnerable, and the new value was assigned as 1, 2, 3, 4 and 5, and then leveled as very high, high, moderate, low and very low malaria risk level, respectively. Creation of Proximity to Health Facilities Factor The distribution of health facilities has a significant impact on the malaria vulnerability of the population dwellings in the Kilombero Valley. The health facility layer was created by computing distance analysis using proximity multiple ring buffer features in spatial analyst tool/multiple ring buffer. Then the map layer was reclassified into five sub-layers such as within (0–5) km, (5.1–10) km, (10.1–20) km, (20.1–50) km and >50km. According to a WHO report, it is indicated that the human population who live nearby or easily accessible to health facilities is less vulnerable to malaria incidence than the ones who are very far from the health facilities due to the distance limitation for the health services. Later on, the new values were assigned as 1, 2, 3, 4 and 5, and then reclassified as very high, high, moderate, low and very low malaria risk levels, respectively. Creation of Proximity to Road Network Factor The distance to the road network is also a significant factor, as it can be used as an estimation of the access to present healthcare facilities in the area. Buffer zones were calculated on the path of the road to determine the effect of the road on malaria prevalence. The road shapefile of the study area was inputted into GIS environment and spatial analyst tools / multiple ring buffer feature were used to generate five buffer zones with the

"The Land Trust of Santa Cruz County, in cooperation with public and private interests, protects and manages lands of significant natural resource, agricultural, cultural and open space value…We see a future where the wild and working forests, the beaches and coastline, the globally unique biotic habitats and the County’s rich farming and ranching heritage—those things that define the extraordinary place we call Santa Cruz County—are preserved forever, are lovingly cared for by supportive communities and are cherished by all as an extraordinary gift that this generation has made to future generations."Conservation BlueprintThe Conservation Blueprint is the Land Trust’s 2-year assessment of the
natural health of Santa Cruz County – and recommendations for the next 25 years of conservation of our natural world. Financial support was provided by the Gordon and Betty Moore Foundation, the Resources Legacy Fund, and individual donors from the Land Trust of Santa Cruz County. Community forums held as part of the Blueprint process were supported by the Community Foundation of Santa Cruz County.The Conservation Blueprint was guided by a seven- member Steering Committee.-Karen Christensen, Executive Director, Resource Conservation District of Santa Cruz County-Betsy Herbert, Watershed Analyst, San Lorenzo Valley Water District & Sempervirens Fund Board Member-Jim Rider, Apple Grower, Bruce Rider & Sons, and Land Trust of Santa Cruz County Board Member-John Ricker, Water Resources Division Manager, Santa Cruz County Environmental Health Services-Joe Schultz, Director, Santa Cruz County Parks and Recreation Department-Steve Staub, Forester, Staub Forestry and Environmental Consulting-Chris Wilmers, Assistant Professor of Environmental Studies, UC Santa CruzThe primary authors, Andrea MacKenzie, Jodi McGraw and Matt Freeman,
consulted over 110 experts in preparing the report and held four
community forums throughout the county. The resulting 200 page report
includes 24 maps, and is available from Land Trust’s website.
http://www.landtrustsantacruz.org/blueprint/The result is a 200 page document that addresses 4 major categories: Biodiversity, Water
Resources, Agriculture and Recreation. The complete package with all
GIS data and MXD files for 9.3 and 10.0 can be downloaded free from the Bay Area Open Space Council website:
http://www.bayarealands.org/gis/.The Design of these map
services was based on these original MXD's, with cartographic
modifications as needed to allow the use of these layers as a map
overlay.Donate now to help them implement their new blueprint in their current-year drive to protect 10,000 acres of Redwoods and hills:
https://www.landtrustsantacruz.org/webdonation/donationform.htm

This data is the vulnerability assessment result map of the 1:250000 debris flow in the Chenglan Transportation Corridor, which shows the vulnerability assessment results of the entire area. The storage format is JPG. When conducting regional disaster prevention and reduction work on debris flows, the vulnerability assessment results of the entire region and small watersheds can provide relevant information and scientific basis. The evaluation data is obtained through GIS overlay analysis. Based on the grey correlation theory and the main content of vulnerability, six evaluation factors were selected, namely population density (person/km2) and housing density (km) ²/ Km ²）、 The village density (village/km2), land use type, transportation trunk level, and transportation trunk density (km/km2) are sourced from the geographic national monitoring cloud platform. Using GIS analysis to obtain vulnerability assessment results.

This hosted feature layer has been published in RI State Plane Feet NAD 83.THIS IS A FUTURE LAND USE MAP CREATED IN 2006. THIS DOES NOT SHOW CURRENT 2025 LAND USE LAND COVER.The Land Use 2025 dataset was developed for the Division of Planning, RI Statewide Planning Program as part of an update to a state land use plan. It evolved from a GIS overlay analysis of land suitability and availability and scenario planning for future growth. The analysis focused on the 37% of the State identified as undeveloped and unprotected in a land cover analysis from RIGIS 1995 land use land cover data. The project studied areas for suitability for conservation and development, based on the location of key natural resources and public infrastructure. The results identified areas with future use potential, under three categories of development intensity and two categories of conservation.These data are presented in the Plan as Figure 121-02-(01), Future Land Use Map. Land Use 2025: State Land Use Policies and Plan was published by the RI Statewide Planning Program on April 13, 2006. The intent of the Plan is to bring together the elements of the State Guide Plan such as natural resources, economic development, housing and transportation to guide conservation and land development in the State. The Plan directs the state and communities to concentrate growth inside the Urban Services Boundary (USB) and within potential growth centers in rural areas. It establishes different development approaches for urban and rural areas.These data have several purposes and applications: They are intended to be used as a policy guide for directing growth to areas most capable of supporting current and future developed uses and to direct growth away from areas less suited for development. Secondly, these data are a guide to assist the state and communities in making land use policies. It is important to note these data are a generalized portrayal of state land use policy. These are not a statewide zoning data. Zoning matters and individual land use decisions are the prerogative of local governments. The land use element is the over arching element in Rhode Island's State Guide Plan. The Plan articulates goals, objectives and strategies to guide the current and future land use planning of municipalities and state agencies. The purpose of the plan is to guide future land use and to present policies under which state and municipal plans and land use activities will be reviewed for consistency with the State Guide Plan. The Map is a graphical representation of recommendations for future growth patterns in the State. It depicts where different intensities of development (e.g. parks, urban development, non-urban development) should occur by color. The Map contains a USB that shows where areas with public services supporting urban development presently exist, or are likely to be provided, through 2025. Within the USB, most land is served by public water service; many areas also have public sewer service, as well as, public transit. Also included on the map are growth centers which are potential areas for development and redevelopment outside of the USB. Growth Centers are envisioned to be areas that will encourage development that is both contiguous to existing development with low fiscal and environmental impacts.NOTE: These data will be updated when the associated plan is updated or upon an amendment approved by the State Planning Council. NOTE: Wetlands were not categorized within the Land Use 2025 dataset.When using this dataset, the RIGIS wetlands dataset should be overlaid as a mask. Full descriptions of the categories and intended uses can be found within Section 2-4, Future Land Use Patterns, Categories, and Intended Uses, of the Plan. https://www.planning.ri.gov/documents/guide_plan/landuse2025.pdf

This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories. Three are shown here: Excessive Heat Warning, Excessive Heat Watch, and Heat Advisory.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseSample DataSee Sample Layer Item for sample data during Weather inactivity!Update FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.

A global map of anthropogenic transformations of terrestrial biomes has been produced to characterize the ecosystem changes due to anthropogenic influences before and during the Industrial Revolution, from 1700 to 2000. Patterns of anthropogenic transformations were assessed at 5 min resolution by comparing potential natural vegetation maps (Ramankutty and Foley, 1999; Olson et al., 2001) with the anthrome map of Ellis and Ramankutty (2008) circa 2000, global, historical gridded data for human population density and agricultural and urban land use from the HYDE data model (Klein Goldewijk and van Drecht, 2006), ORNL LandScan population data, irrigated land data (Siebert et al., 2007), rice cover data (Monfreda et al., 2008), and other agricultural census data at century intervals (1700, 1800, 1900, and 2000). The investigators used overlay analysis and other analytical geographic information system (GIS) software tools to produce the four data sets and maps. For more information, see Ellis, E.C., Kees Klein Goldewijk, K., Siebert, S., Lightman, D., and Ramankutty, N. 2010. Anthropogenic transformation of the biomes, 1700 to 2000. Global Ecology and Biogeography 19: 589-606. References: Ellis, E.C., and N. Ramankutty. 2008. Putting people in the map: anthropogenic biomes of the world. Frontiers in Ecology and the Environment 6(8): 439-447. Monfreda, C., N. Ramankutty, and J. A. Foley. 2008. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000, Global Biogeochem. Cycles, 22, GB1022, doi:10.1029/2007GB002947; Ramankutty, N., A.T. Evan, C. Monfreda, and J.A. Foleyl. 2008. Farming the planet: 1. Geographic distribution of global agricultural lands in the year 2000. Global Biogeochemical Cycles 22, GB1003, doi:10.1029/2007GB002952; Siebert, S., P. Doll, S. Feick, J. Hoogeveen, and K. Frenken. 2007. Global map of irrigation areas version 4.0.1. Johann Wolfgang Goethe University, Frankfurt am Main, Germany/Food and Agriculture Organization of the United Nations, Rome, Italy; Oak Ridge National Laboratory (2006) LandScan Global Population Database. Oak RidgeNational Laboratory, Oak Ridge, TN. [http://www.ornl.gov/landscan]; and Olson, D.M., E. Dinerstein, E.D. Wikramanayake, N.D. Burgess, G.V.N Powell, E.C. Underwood, J.A. D’Amico, I. Itoua, H.E. Strand, J.C. Morrison, C.J. Loucks, T.F. Allnutt, T.H. Ricketts, Y. Kura, J.F. Lamoreux, W.W. Wettengel, P. Hedao, and K.R. Kassem. 2001. Terrestrial ecoregions of the world: a new map of life on Earth. BioScience 51: 933–938.

This is a saved copy of the NWS Weather Watches and Warning layer, filtered just for wildfire related warnings.Details from the orginal item:https://www.arcgis.com/home/item.html?id=a6134ae01aad44c499d12feec782b386This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseUpdate FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into well over 100 categories. See event descriptions for full details. A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseSample DataSee Sample Layer Item for sample data during Weather inactivity!Update FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.Revisions:Feb 25, 2021: Revised service data upate workflow, improving stability and update interval.Process now checks for data updates every 5 minutes!Mar 3, 2021: Revised data processing to leverage VTEC parameter details to better align Event 'effective' dates with reported dates on Alert pages.Apr 17, 2023: Turned off popups for boundary Layers by default.Feb 1, 2024: Revised to leverage CAP v1.2 source endpoint. Update event link to use alert search.Feb 16, 2024: Revised event link to accomodate change in alert search endpoint.Jan 19, 2025: Added event 'Description' and 'Instructions', updated Pop-up.Jan 22, 2025: Exposed 'Hours Old' fields supporting last 'Updated', 'Effective', and 'Expiration' as +- age values for events.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Evaluation of watersheds and development of a management strategy require accurate measurement of the past and present land cover/land use parameters as changes observed in these parameters determine the hydrological and ecological processes taking place in a watershed. This study applied supervised classification-maximum likelihood algorithm in ERDAS imagine to detect land cover/land use changes observed in Simly watershed, Pakistan using multispectral satellite data obtained from Landsat 5 and SPOT 5 for the years 1992 and 2012 respectively. The watershed was classified into five major land cover/use classes viz. Agriculture, Bare soil/rocks, Settlements, Vegetation and Water. Resultant land cover/land use and overlay maps generated in ArcGIS 10 indicated a significant shift from Vegetation and Water cover to Agriculture, Bare soil/rock and Settlements cover, which shrank by 38.2% and 74.3% respectively. These land cover/use transformations posed a serious threat to watershed resources. Hence, proper management of the watershed is required or else these resources will soon be lost and no longer be able to play their role in socio-economic development of the area.

This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseSample DataSee Sample Layer Item for sample data during Weather inactivity!Update FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.Additional information on Watches and Warnings.

Potential Groundwater Dependent Ecosystems (GDE) are ecosystems identified within the landscape as likely to be at least partly dependent on groundwater. State-wide screening analysis was performed to identify locations of potential terrestrial GDEs, including wetland areas. The GDE mapping was developed utilising satellite remote sensing data, geological data and groundwater monitoring data in a GIS overlay model. Validation of the model through field assessment has not been performed. The method has been applied for all of Victoria and is the first step in identifying potential groundwater dependent ecosystems that may be threatened by activities such as drainage and groundwater pumping. The dataset specifically covers the Goulburn Broken Catchment Management Authority (CMA) area. The method used in this research is based upon the characteristics of a potential GDE containing area as one that: 1. Has access to groundwater. By definition a GDE must have access to groundwater. For GDE occurrences associated with wetlands and river systems the water table will be at surface with a zone of capillary extension. In the case of terrestrial GDE's (outside of wetlands and river systems), these are dependent on the interaction between depth to water table and the rooting depth of the vegetation community. 2. Has summer (dry period) use of water. Due to the physics of root water uptake, GDEs will use groundwater when other sources are no longer available; this is generally in summer for the Victorian climate. The ability to use groundwater during dry periods creates a contrasting growth pattern with surrounding landscapes where growth has ceased. 3. Has consistent growth patterns, vegetation that uses water all year round will have perennial growth patterns. 4. Has growth patterns similar to verified GDEs. The current mapping does not indicate the degree of groundwater dependence, only locations in the landscape of potential groundwater dependent ecosystems. This dataset does not directly support interpretation of the amount of dependence or the amount of groundwater used by the regions highlighted within the maps. Further analysis and more detailed field based data collection are required to support this.

The core data used in the modelling is largely circa 1995 to 2005. It is expected that the methodology used will over estimate the extent of terrestrial GDEs. There will be locations that appear from EvapoTranspiration (ET) data to fulfil the definition of a GDE (as defined by the mapping model) that may not be using groundwater. Two prominent examples are: 1. Riparian zones along sections of rivers and creeks that have deep water tables where the stream feeds the groundwater system and the riparian vegetation is able to access this water flow, as well as any bank storage contained in the valley alluvials. 2. Forested regions that are accessing large unsaturated regolith water stores. The terrestrial GDE layer polygons are classified based on the expected depth to groundwater (ie shallow <5 m or deep >5 m). Additional landscape attributes are also assigned to each mappnig polygon.

In 2011-2012 a species tolerance model was developed by Arthur Rylah Institute, collaborating with DPI, to model landscapes with ability to support GDEs and to provide a relative measure of sensitivity of those ecosystems to changes in groundwater availability and quality. Rev 1 of the GDE mapping incorporates species tolerance model attributes for each potential GDE polygon and attributes for interpreted depth to groundwater.

Separate datasets and associated metadata records have been created for GDE species tolerance.

Data collection and GIS data service to the report Regional baseline assessment (RBA) for South Greenland, Scientific Report from DCE – Danish Centre for Environment and Energy No. 482 edited by Janne Fritt-Rasmussen, Katrine Raundrup and Anders Mosbech. The project was funded by Environmental Agency for Mineral Resource Activities.This regional environmental baseline assessment of mining activities in South Greenland is based on a project idea developed between Environmental Agency for Mineral Resource Activities (EAMRA), Greenland Institute of Natural Resources (GINR) and DCE (AU). The purpose of the project is to provide a basis for supporting environmentally sound planning and regulation of mining activities by summarising existing regional background information supplemented with new studies and making these results operational and easily accessible.Detailed information on each layer can be found in the report. The online map is primarily for display and overview.Citation:Fritt-Rasmussen, J., Raundrup, K. & Mosbech, A. (Eds). 2022. Regional baseline assessment (RBA) for South Greenland. Aarhus University, DCE – Danish Centre for Environment and Energy, 172 pp. Scientific Report No. 482 https://dce2.au.dk/pub/SR482.pdfData service URL for GISCaptions to the layers of the report figures

1.1. Area of interest of the Regional Baseline Assessment of South Greenland.

4.1. Harbour seal Area within which harbour seals are found in the AOI. During breeding and moulting, one or more specific haul-out sites within the marked are used (Rosing-Asvid et al. 2020).

4.2. White-tailed eagle territories Number of white-tailed eagle territories in the central and northern part of the AOI. The species also breeds in the southern part, but the numbers are unknown. The squares are 10x10 km, absence of squares represents “no data available”. Based on data from Frank Wille before 2000, DCE AU Data Center.

4.3. Moulting harlequin ducks observed during a survey in summer 1999. DCE AU Data Center.

4.4. Black-legged kittiwake colony locations and size as number of breeding pairs. Data from Greenland Seabird Colony Register, DCE AU Data Center.

4.5. Arctic tern colony locations and size as number of individuals. Data from Greenland Seabird Colony Register, DCE AU Data Center.

4.6. Razorbill colony locations and size as number of individuals. Data from Greenland Seabird Colony Register, DCE AU Data Center.

4.7. Thick-billed murre colony locations Data from Greenland Seabird Colony Register, DCE AU Data Center.

4.7. Thick-billed murre winter areas Data from Greenland Seabird Colony Register, DCE AU Data Center.

4.8. Atlantic puffin colony locations and size as number of individuals. Data from Greenland Seabird Colony Register, DCE AU Data Center.

4.9. Northern fulmar colony locations and size in number of individuals. Data from Greenland Seabird Colony Register, DCE AU Data Center.

4.10. Common eider colonies Data from Merkel et al. (2019). Many of the smallest dots represent breeding colonies with an unknown number of birds. DCE AU Data Center.

4.10. Common eider wintering areas Data from Merkel et al. (2019), DCE AU Data Center.

4.11. Red listed plants Distribution map of observations of red listed (vulnerable and near threatened) plants in the AOI. GINR Dept. of Environment and Minerals.

4.11. Possible area for red listed plants The geographical precision of the individual observations is fairly low, thus a buffer zone (of 8 km, cut off by coastline and 800 m altitude line) of each observation is included in the map (the possible area for the red listed plants). GINR Dept. of Environment and Minerals.

4.13. Bird protection area Source: NatureMap Protected Areas layer, Executive Order on Birds.

4.13. Natural Protection Act area Source: NatureMap Protected Areas layer, Act on Nature Protection.

4.14. Biological important areas The three ecological and biological important areas located in the AOI marked as 17, 18 and 19 in the report by Christensen et al. (2016). For location names mentioned in the text, please refer to Figure 1.1 and the species-specific figures in this chapter.

5.1. Agricultural fields Data from the Municipality Plan for Kommune Kujalleq.

5.1. Agricultural zones Data from the Municipality Plan for Kommune Kujalleq

5.1. Grazing regions. Reference: Government of Greenland.

5.1. Planning zones Data from the Municipality Plan for Kommune Kujalleq.

5.1. Plantations Source: Katrine Raundrup and Karl Brix Zinglersen review of literature and local’s personal comments.

5.2. Muskox population The muskox introduction area at Nanortalik is marked in yellow. Northwest of the AOI, the Ivittuut muskox population is found (also marked in yellow). Source: Christine Cuyler and Karl Brix Zinglersen, Greenland Institute of Natural Resources.

5.2. Reindeer herding areas Reindeer herding areas (marked in purple). The westernmost area is "Isortoq" and the easternmost "Tuttutooq". Source: Christine Cuyler, Katrine Raundrup, and Karl Brix Zinglersen, Greenland Institute of Natural Resources.

5.4. Arctic char catchment. Source: DCE AU Data Center.

5.4. Arctic char river Source: Christensen et al. (2016).

5.5. Atlantic cod Average catches of Atlantic cod in kilos per year (2014-2019). The squares refer to the individual fishery field codes (statistical catch squares). The individual dots are centered in each of the relevant squares and thus do not necessarily refer to the specific catch position within the field code area. Source: LULI database, Greenland Fishery Licence Control.

5.6. Greenland halibut average Average catches of Greenland halibut in kilos per year (2014-2019). The squares refer to the individual fishery field codes (statistical catch squares). The individual dots are centered in each of the relevant squares and thus do not necessarily refer to the specific catch position within the field code area. Source: LULI database, Greenland Fishery Licence Control.

5.7. Lumpfish roe Average catches of lumpfish roe in tons per year (2014-2018). The squares refer to the individual fishery field codes (statistical catch squares). The colour coding refers to the average catch in each of the squares. Source: LULI database, Greenland Fishery Licence Control.

5.8. Northern shrimp Average catches of northern shrimp in kilos per year (2014-2018). The squares refer to the individual fishery field codes (statistical catch squares). The individual dots are centered in each of the relevant squares and thus do not necessarily refer to the specific catch position within the field code area. Source: LULI database, Greenland Fishery Licence Control.

5.9. Snow crab Average catches of snow crab in kilos per year during the period 2014-2019. The squares refer to the individual fishery field codes (statistical catch squares). The individual dots are centered in each of the relevant squares and do thus not necessarily refer to the specific catch position within the field code area. Source: LULI database, Greenland Fishery Licence Control.

5.11. Driving zone for power line Data from the Municipality Plan for Kommune Kujalleq).

5.11. Driving zone snow mobile Data from the Municipality Plan for Kommune Kujalleq.

5.11. Hiking trails Data from the Municipality Plan for Kommune Kujalleq.

5.12. Oil spill sensitivity Oil spill sensitivity along the coast in South Greenland (from Mosbech et al. (2004)).

7.1. Overlay analysis all layers Result of overlay analysis of all 51 map layers listed in Table 7.1, spanning flora and fauna, human use and cultural heritage interests. The maximum cell values are 14, reflecting that in these cells features from 14 different map layers overlap.

7.2. Larger mining projects

7.3. Overlay analysis biological layers Result of overlay analysis of 34 map layers with mainly biologically relevant information (see column “Sub-analysis, biology” in Table 7.1 for included map layers).

7.4. Overlay analysis human use Result of overlay analysis of 29 map layers with mainly human use and cultural heritage relevant information (see column “Sub-analysis, human use” in Table 7.1 for included map layers).

Fishery field codes Statistical catch squares. Source: LULI database, Greenland Fishery Licence Control.

This is a saved copy of the NWS Weather Watches and Warning layer, filtered just for wildfire related warnings.Details from the orginal item:https://www.arcgis.com/home/item.html?id=a6134ae01aad44c499d12feec782b386This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseUpdate FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.