The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

Analysis of ‘PLACES: Census Tract Data (GIS Friendly Format), 2020 release’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/36454ff3-3bd6-4626-8607-ed62ff3f4619 on 12 February 2022.

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

This dataset contains model-based census tract level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census tract 2015 boundary file in a GIS system to produce maps for 27 measures at the census tract level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

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

This dataset has been published by the Human Resources Department of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.Distributed bydata.virginiabeach.gov2405 Courthouse Dr.Virginia Beach, VA 23456EntityEmployee SalariesPoint of ContactHuman ResourcesSherri Arnold, Human Resources Business Partner IIIsharnold@vbgov.com757-385-8804Elda Soriano, HRIS Analystesoriano@vbgov.com757-385-8597AttributesColumn: DepartmentDescription: 3-letter department codeColumn: Department DivisionDescription: This is the City Division that the position is assigned to.Column: PCNDescription: Tracking number used to reference each unique position within the City.Column: Position TitleDescription: This is the title of the position (per the City’s pay plan).Column: FLSA Status Description: Represents the position’s status with regards to the Fair Labor Standards Act (FLSA) “Exempt” - These positions do not qualify for overtime compensation – Generally, a position is classified as FLSA exempt if all three of the following criteria are met: 1) Paid at least $47,476 per year ($913 per week); 2) Paid on a salary basis - generally, salary basis is defined as having a guaranteed minimum amount of pay for any work week in which the employee performs any work; 3) Perform exempt job duties - Job duties are split between three classifications: executive, professional, and administrative. All three have specific job functions which, if present in the employee’s regular work, would exempt the individual from FLSA. Employees may also be exempt from overtime compensation if they are a “highly compensated employee” as defined by the FLSA or the position meets the criteria for other enumerated exemptions in the FLSA.“Non-exempt” – These positions are eligible for overtime compensation - positions classified as FLSA non-exempt if they fail to meet any of exempt categories specified in the FLSA. Column: Initial Hire DateDescription: This is the date that the full-time employee first began employment with the City.Column: Date in TitleDescription: This is the date that the full-time employee first began employment in their current position.Column: SalaryDescription: This is the annual salary of the full-time employee or the hourly rate of the part-time employee.Frequency of dataset updateMonthly

Analysis of ‘2018 CT Data Catalog (Non GIS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/fe457197-5afe-4a20-a131-1bdcf9bd8ace on 26 January 2022.

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

Catalog of high value data inventories produced by Connecticut executive branch agencies and compiled by the Office of Policy and Management. This catalog does not contain information about high value GIS data, which is compiled in a separate data inventory at the following link: https://data.ct.gov/Government/CT-Data-Catalog-GIS-/p7we-na27

As required by Public Act 18-175, executive branch agencies must annually conduct a high value data inventory to capture information about the high value data that they collect.

High value data is defined as any data that the department head determines (A) is critical to the operation of an executive branch agency; (B) can increase executive branch agency accountability and responsiveness; (C) can improve public knowledge of the executive branch agency and its operations; (D) can further the core mission of the executive branch agency; (E) can create economic opportunity; (F) is frequently requested by the public; (G) responds to a need and demand as identified by the agency through public consultation; or (H) is used to satisfy any legislative or other reporting requirements.

This dataset was last updated 3/4/2019 and will continue to be updated as high value data inventories are submitted to OPM.

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

Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

This dataset represents point locations of cities and towns in Arizona. The data contains point locations for incorporated cities, Census Designated Places and populated places. Several data sets were used as inputs to construct this data set. A subset of the Geographic Names Information System (GNIS) national dataset for the state of Arizona was used for the base location of most of the points. Polygon files of the Census Designated Places (CDP), from the U.S. Census Bureau and an incorporated city boundary database developed and maintained by the Arizona State Land Department were also used for reference during development. Every incorporated city is represented by a point, originally derived from GNIS. Some of these points were moved based on local knowledge of the GIS Analyst constructing the data set. Some of the CDP points were also moved and while most CDP's of the Census Bureau have one point location in this data set, some inconsistencies were allowed in order to facilitate the use of the data for mapping purposes. Population estimates were derived from data collected during the 2010 Census. During development, an additional attribute field was added to provide additional functionality to the users of this data. This field, named 'DEF_CAT', implies definition category, and will allow users to easily view, and create custom layers or datasets from this file. For example, new layers may created to include only incorporated cities (DEF_CAT = Incorporated), Census designated places (DEF_CAT = Incorporated OR DEF_CAT = CDP), or all cities that are neither CDP's or incorporated (DEF_CAT= Other). This data is current as of February 2012. At this time, there is no planned maintenance or update process for this dataset.This data is created to serve as base information for use in GIS systems for a variety of planning, reference, and analysis purposes. This data does not represent a legal record.

Spatially-explicit data is increasingly becoming available across disciplines, yet they are often limited to a specific domain. In order to use such datasets in a coherent analysis, such as to decide where to target specific types of agricultural investment, there should be an effort to make such datasets harmonized and interoperable. For Africa South of the Sahara (SSA) region, the HarvestChoice CELL5M Database was developed in this spirit of moving multidisciplinary data into one harmonized, geospatial database. The database includes over 750 biophysical and socio-economic indicators, many of which can be easily expanded to global scale. The CELL5M database provides a platform for cross-cutting spatial analyses and fine-grain visualization of the mix of farming systems and populations across SSA. It was created as the central core to support a decision-making platform that would enable development practitioners and researchers to explore multi-faceted spatial relationships at the nexus of poverty, health and nutrition, farming systems, innovation, and environment. The database is a matrix populated by over 350,000 grid cells covering SSA at five arc-minute spatial resolution. Users of the database, including those conduct researches on agricultural policy, research, and development issues, can also easily overlay their own indicators. Numerical aggregation of the gridded data by specific geographical domains, either at subnational level or across country borders for more regional analysis, is also readily possible without needing to use any specific GIS software. See the HCID database (http://dx.doi.org/10.7910/DVN/MZLXVQ) for the geometry of each grid cell. The database also provides standard-compliant data API that currently powers several web-based data visualization and analytics tools.

This dataset contains 50-ft contours for the Hot Springs shallowest unit of the Ouachita Mountains aquifer system potentiometric-surface map. The potentiometric-surface shows altitude at which the water level would have risen in tightly-cased wells and represents synoptic conditions during the summer of 2017. Contours were constructed from 59 water-level measurements measured in selected wells (locations in the well point dataset). Major streams and creeks were selected in the study area from the USGS National Hydrography Dataset (U.S. Geological Survey, 2017), and the spring point dataset with 18 spring altitudes calculated from 10-meter digital elevation model (DEM) data (U.S. Geological Survey, 2015; U.S. Geological Survey, 2016). After collecting, processing, and plotting the data, a potentiometric surface was generated using the interpolation method Topo to Raster in ArcMap 10.5 (Esri, 2017a). This tool is specifically designed for the creation of digital elevation models and imposes constraints that ensure a connected drainage structure and a correct representation of the surface from the provided contour data (Esri, 2017a). Once the raster surface was created, 50-ft contour interval were generated using Contour (Spatial Analyst), a spatial analyst tool (available through ArcGIS 3D Analyst toolbox) that creates a line-feature class of contours (isolines) from the raster surface (Esri, 2017b). The Topo to Raster and contouring done by ArcMap 10.5 is a rapid way to interpolate data, but computer programs do not account for hydrologic connections between groundwater and surface water. For this reason, some contours were manually adjusted based on topographical influence, a comparison with the potentiometric surface of Kresse and Hays (2009), and data-point water-level altitudes to more accurately represent the potentiometric surface. Select References: Esri, 2017a, How Topo to Raster works—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/how-topo-to-raster-works.htm. Esri, 2017b, Contour—Help | ArcGIS Desktop, accessed December 5, 2017, at ArcGIS Pro Raster Surface toolset at http://pro.arcgis.com/en/pro-app/tool-reference/3d-analyst/contour.htm. Kresse, T.M., and Hays, P.D., 2009, Geochemistry, Comparative Analysis, and Physical and Chemical Characteristics of the Thermal Waters East of Hot Springs National Park, Arkansas, 2006-09: U.S. Geological Survey 2009–5263, 48 p., accessed November 28, 2017, at https://pubs.usgs.gov/sir/2009/5263/. U.S. Geological Survey, 2015, USGS NED 1 arc-second n35w094 1 x 1 degree ArcGrid 2015, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html. U.S. Geological Survey, 2016, USGS NED 1 arc-second n35w093 1 x 1 degree ArcGrid 2016, accessed December 5, 2017, at The National Map: Elevation at https://nationalmap.gov/elevation.html.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.The model is trained with classified LiDAR that follows the The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

CAP’s Analyst Shopping Center dataset is the most comprehensive resource available for analyzing the Canadian shopping center landscape. Covering over 3,500 shopping centers across the country, this dataset provides a full horizontal and vertical view, enabling analysts, data scientists, solution providers, and application developers to gain unparalleled insights into market trends, tenant distribution, and operational efficiencies.

Comprehensive Data Coverage The Analyst Shopping Center dataset contains everything included in the Premium dataset, expanding to a total of 39 attributes. These attributes enable a deep dive into deriving key metrics and extracting valuable information about the shopping center ecosystem.

Advanced Geospatial Insights A key feature of this dataset is its multi-stage geocoding process, developed exclusively by CAP. This process ensures the most precise map points available, allowing for highly accurate spatial analysis. Whether for market assessments, location planning, or competitive analysis, this dataset provides geospatial precision that is unmatched.

Rich Developer & Ownership Details Understanding ownership and development trends is critical for investment and planning. This dataset includes detailed developer and owner information, covering aspects such as:

Center Type (Operational, Proposed, or Redeveloped) Year Built & Remodeled Owner/Developer Profiles Operational Status & Redevelopment Plans Geographic & Classification Variables

The dataset also includes various geographic classification variables, offering deeper context for segmentation and regional analysis. These variables help professionals in: Identifying prime locations for expansion

Analyzing the distribution of shopping centers across different regions Benchmarking against national and local trends

Enhanced Data for Decision-Making Other insightful elements of the dataset include Placekey integration, which ensures consistency in location-based analytics, and additional attributes that allow consultants, data scientists, and business strategists to make more informed decisions. With the CAP Analyst Shopping Center dataset, users gain a data-driven competitive edge, optimizing their ability to assess market opportunities, streamline operations, and drive strategic growth in the retail and commercial real estate sectors.

The Minnesota DNR Toolbox and Hydro Tools provide a number of convenience geoprocessing tools used regularly by MNDNR staff. Many of these may be useful to the wider public. However, some tools may rely on data that is not available outside of the DNR. All tools require at least ArcGIS 10+.

If you create a GDRS using GDRS Manager and include this toolbox resource and MNDNR Quick Layers, the DNR toolboxes will automatically be added to the ArcToolbox window whenever Quick Layers GDRS Location is set to the GDRS location that has the toolboxes.

Toolsets included in MNDNR Tools V10:
- Analysis Tools
- Conversion Tools
- Division Tools
- General Tools
- Hydrology Tools
- LiDAR and DEM Tools
- Raster Tools
- Sampling Tools

These toolboxes are provided free of charge and are not warrantied for any specific use. We do not provide support or assistance in downloading or using these tools. We do, however, strive to produce high-quality tools and appreciate comments you have about them.

Per California Water Code Section 10609.80 (a), DWR has released an update to the indicators analyzed for the rural communities water shortage vulnerability analysis and a new interactive tool to explore the data. This page remains to archive the original dataset, but for more current information, please see the following pages: - https://water.ca.gov/Programs/Water-Use-And-Efficiency/SB-552/SB-552-Tool - https://data.cnra.ca.gov/dataset/water-shortage-vulnerability-technical-methods - https://data.cnra.ca.gov/dataset/i07-water-shortage-vulnerability-sections - https://data.cnra.ca.gov/dataset/i07-water-shortage-social-vulnerability-blockgroup This dataset is made publicly available pursuant to California Water Code Section 10609.42 which directs the California Department of Water Resources to identify small water suppliers and rural communities that may be at risk of drought and water shortage vulnerability and propose to the Governor and Legislature recommendations and information in support of improving the drought preparedness of small water suppliers and rural communities. As of March 2021, two datasets are offered here for download. The background information, results synthesis, methods and all reports submitted to the legislature are available here: https://water.ca.gov/Programs/Water-Use-And-Efficiency/2018-Water-Conservation-Legislation/County-Drought-Planning Two online interactive dashboards are available here to explore the datasets and findings. https://dwr.maps.arcgis.com/apps/MapSeries/index.html?appid=3353b370f7844f468ca16b8316fa3c7b The following datasets are offered here for download and for those who want to explore the data in tabular format. (1) Small Water Suppliers: In total, 2,419 small water suppliers were examined for their relative risk of drought and water shortage. Of these, 2,244 are community water systems. The remaining 175 systems analyzed are small non-community non-transient water systems that serve schools for which there is available spatial information. This dataset contains the final risk score and individual risk factors for each supplier examined. Spatial boundaries of water suppliers' service areas were used to calculate the extent and severity of each suppliers' exposure to projected climate changes (temperature, wildfire, and sea level rise) and to current environmental conditions and events. The boundaries used to represent service areas are available for download from the California Drinking Water System Area Boundaries, located on the California State Geoportal, which is available online for download at https://gispublic.waterboards.ca.gov/portal/home/item.html?id=fbba842bf134497c9d611ad506ec48cc (2) Rural Communities: In total 4,987 communities, represented by US Census Block Groups, were analyzed for their relative risk of drought and water shortage. Communities with a record of one or more domestic well installed within the past 50 years are included in the analysis. Each community examined received a numeric risk score, which is derived from a set of indicators developed from a stakeholder process. Indicators used to estimate risk represented three key components: (1) the exposure of suppliers and communities to hazardous conditions and events, (2) the physical and social vulnerability of communities to the exposure, and (3) recent history of shortage and drought impacts. The unit of analysis for the rural communities, also referred to as "self-supplied communities" is U.S. Census Block Groups (ACS 2012-2016 Tiger Shapefile). The Census Block Groups do not necessarily represent socially-defined communities, but they do cover areas where population resides. Using this spatial unit for this analysis allows us to access demographic information that is otherwise not available in small geographic units.

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Resource contains an ArcGIS file geodatabase raster for the National Vegetation Information System (NVIS) Major Vegetation Groups - Australia-wide, present extent (FGDB_NVIS4_1_AUST_MVG_EXT).

Related datasets are also included: FGDB_NVIS4_1_KEY_LAYERS_EXT - ArcGIS File Geodatabase Feature Class of the Key Datasets that make up NVIS Version 4.1 - Australia wide; and FGDB_NVIS4_1_LUT_KEY_LAYERS - Lookup table for Dataset Key Layers.

This raster dataset provides the latest summary information (November 2012) on Australia's present (extant) native vegetation. It is in Albers Equal Area projection with a 100 m x 100 m (1 Ha) cell size. A comparable Estimated Pre-1750 (pre-european, pre-clearing) raster dataset is available: - NVIS4_1_AUST_MVG_PRE_ALB. State and Territory vegetation mapping agencies supplied a new version of the National Vegetation Information System (NVIS) in 2009-2011. Some agencies did not supply new data for this version but approved re-use of Version 3.1 data. Summaries were derived from the best available data in the NVIS extant theme as at June 2012. This product is derived from a compilation of data collected at different scales on different dates by different organisations. Please refer to the separate key map showing scales of the input datasets. Gaps in the NVIS database were filled by non-NVIS data, notably parts of South Australia and small areas of New South Wales such as the Curlewis area. The data represent on-ground dates of up to 2006 in Queensland, 2001 to 2005 in South Australia (depending on the region) and 2004/5 in other jurisdictions, except NSW. NVIS data was partially updated in NSW with 2001-09 data, with extensive areas of 1997 data remaining from the earlier version of NVIS. Major Vegetation Groups were identified to summarise the type and distribution of Australia's native vegetation. The classification contains different mixes of plant species within the canopy, shrub or ground layers, but are structurally similar and are often dominated by a single genus. In a mapping sense, the groups reflect the dominant vegetation occurring in a map unit where there are a mix of several vegetation types. Subdominant vegetation groups which may also be present in the map unit are not shown. For example, the dominant vegetation in an area may be mapped as dominated by eucalypt open forest, although it contains pockets of rainforest, shrubland and grassland vegetation as subdominants. The (related) Major Vegetation Subgroups represent more detail about the understorey and floristics of the Major Vegetation Groups and are available as separate raster datasets: - NVIS4_1_AUST_MVS_EXT_ALB - NVIS4_1_AUST_MVS_PRE_ALB A number of other non-vegetation and non-native vegetation land cover types are also represented as Major Vegetation Groups. These are provided for cartographic purposes, but should not be used for analyses. For further background and other NVIS products, please see the links on http://www.environment.gov.au/erin/nvis/index.html.

The current NVIS data products are available from http://www.environment.gov.au/land/native-vegetation/national-vegetation-information-system.

Purpose

For use in Bioregional Assessment land classification analyses

Dataset History

NVIS Version 4.1

The input vegetation data were provided from over 100 individual projects representing the majority of Australia's regional vegetation mapping over the last 50 years. State and Territory custodians translated the vegetation descriptions from these datasets into a common attribute framework, the National Vegetation Information System (ESCAVI, 2003). Scales of input mapping ranged from 1:25,000 to 1:5,000,000. These were combined into an Australia-wide set of vector data. Non-terrestrial areas were mostly removed by the State and Territory custodians before supplying the data to the Environmental Resources Information Network (ERIN), Department of Sustainability Environment Water Population and Communities (DSEWPaC).

Each NVIS vegetation description was written to the NVIS XML format file by the custodian, transferred to ERIN and loaded into the NVIS database at ERIN. A considerable number of quality checks were performed automatically by this system to ensure conformity to the NVIS attribute standards (ESCAVI, 2003) and consistency between levels of the NVIS Information Hierarchy within each description. Descriptions for non-vegetation and non-native vegetation mapping codes were transferred via CSV files.

The NVIS vector (polygon) data for Australia comprised a series of jig-saw pieces, eachup to approx 500,000 polygons - the maximum tractable size for routine geoprocesssing. The spatial data was processed to conform to the NVIS spatial format (ESCAVI, 2003; other papers). Spatial processing and attribute additions were done mostly in ESRI File Geodatabases. Topology and minor geometric corrections were also performed at this stage. These datasets were then loaded into ESRI Spatial Database Engine as per the ERIN standard. NVIS attributes were then populated using Oracle database tables provided by custodians, mostly using PL/SQL Developer or in ArcGIS using the field calculator (where simple).

Each spatial dataset was joined to and checked against a lookup table for the relevant State/Territory to ensure that all mapping codes in the dominant vegetation type of each polygon (NVISDSC1) had a valid lookup description, including an allocated MVG. Minor vegetation components of each map unit (NVISDSC2-6) were not checked, but could be considered mostly complete.

Each NVIS vegetation description was allocated to a Major Vegetation Group (MVG) by manual interpretation at ERIN. The Australian Natural Resources Atlas (http://www.anra.gov.au/topics/vegetation/pubs/native_vegetation/vegfsheet.html) provides detailed descriptions of most Major Vegetation Groups. Three new MVGs were created for version 4.1 to better represent open woodland formations and forests (in the NT) with no further data available. NVIS vegetation descriptions were reallocated into these classes, if appropriate:

• Unclassified Forest

• Other Open Woodlands

• Mallee Open Woodlands and Sparse Mallee Shublands

(Thus there are a total of 33 MVGs existing as at June 2012). Data values defined as cleared or non-native by data custodians were attributed specific MVG values such as 25 - Cleared or non native, 27 - naturally bare, 28 - seas & estuaries, and 99 - Unknown.

As part of the process to fill gaps in NVIS, the descriptive data from non-NVIS sources was also referenced in the NVIS database, but with blank vegetation descriptions. In general. the gap-fill data comprised (a) fine scale (1:250K or better) State/Territory vegetation maps for which NVIS descriptions were unavailable and (b) coarse-scale (1:1M) maps from Commonwealth and other sources. MVGs were then allocated to each description from the available desciptions in accompanying publications and other sources.

Parts of New South Wales, South Australia, QLD and the ACT have extensive areas of vector "NoData", thus appearing as an inland sea. The No Data areas were dealt with differently by state. In the ACT and SA, the vector data was 'gap-filled' and attributed using satellite imagery as a guide prior to rasterising. Most of these areas comprised a mixture of MVG 24 (inland water) and 25 (cleared), and in some case 99 (Unknown). The NSW & QLD 'No Data' areas were filled using a raster mask to fill the 'holes'. These areas were attributed with MVG 24, 26 (water & unclassified veg), MVG 25 (cleared); or MVG 99 Unknown/no data, where these areas were a mixture of unknown proportions.

Each spatial dataset with joined lookup table (including MVG_NUMBER linked to NVISDSC1) was exported to a File Geodatabase as a feature class. These were reprojected into Albers Equal Area projection (Central_Meridian: 132.000000, Standard_Parallel_1: -18.000000, Standard_Parallel_2: -36.000000, Linear Unit: Meter (1.000000), Datum GDA94, other parameters 0).

Each feature class was then rasterised to a 100m raster with extents to a multiple of 1000 m, to ensure alignment. In some instances, areas of 'NoData' had to be modelled in raster. For example, in NSW where non-native areas (cleared, water bodies etc) have not been mapped. The rasters were then merged into a 'state wide' raster. State rasters were then merged into this 'Australia wide' raster dataset.

November 2012 Corrections

Closer inspection of the original 4.1 MVG Extant raster dataset highlighted some issues with the raster creation process which meant that raster pixels in some areas did not align as intended. These were corrected, and the new properly aligned rasters released in November 2012.

Dataset Citation

Department of the Environment (2012) Australia - Present Major Vegetation Groups - NVIS Version 4.1 (Albers 100m analysis product). Bioregional Assessment Source Dataset. Viewed 10 July 2017, http://data.bioregionalassessments.gov.au/dataset/57c8ee5c-43e5-4e9c-9e41-fd5012536374.

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Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion.

Purpose

These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5 Surface water - groundwater interactions report.

Dataset History

Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion.

Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values.

Then added new columns of calculations:

WaterElev = TsRefElev - Water_Leve

DepthWater = WaterElev - Ref_pt_height

Ref_pt_height = TsRefElev - LandElev

Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006

2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source.

12_dw_olp_enf - Select out only those bores that are in both source files.

Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset.

2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion.

selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster.

Then used the alluvium boundary to truncate the raster, to limit to the area of interest.

12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf

Dataset Citation

Bioregional Assessment Programme (2017) Namoi bore analysis rasters. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Upper Namoi groundwater management zones

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

The project lead for the collection of this data was Tom Stephenson. Mule deer (130 adult females) from the Casa Diablo herd were captured and equipped with store-onboard GPS collars (Lotek Pinnacle Pro and Vectronic Survey), transmitting data from 2014-2023. Casa Diablo mule deer are largely traditional migrants, with a winter range stretching between the Benton Range and eastern Inyo National Forest. For summer, individuals move west using a wide range of pathways; however, a concentrated movement corridor passes through Long Valley, across U.S. Highway 395, and into the high mountain Sierra Nevada range. Most deer do not cross into Yosemite National Park. Summer range exists on both sides of U.S. Highway 395, which is a known hotspot for deer-vehicle collisions. Migrants vary in their movements from shorter (4 km) to longer (80 km) distances.GPS locations were fixed between 1-24 hour intervals in the dataset. To improve the quality of the data set as per Bjørneraas et al. (2010), the GPS data were filtered prior to analysis to remove locations which were: i) further from either the previous point or subsequent point than an individual deer is able to travel in the elapsed time, ii) forming spikes in the movement trajectory based on outgoing and incoming speeds and turning angles sharper than a predefined threshold , or iii) fixed in 2D space and visually assessed as a bad fix by the analyst. The methodology used for this migration analysis allowed for the mapping of winter ranges and the identification and prioritization of migration corridors. Brownian Bridge Movement Models (BBMMs; Sawyer et al. 2009) were constructed with GPS collar data from 101 migrating deer, including 445 migration sequences, location, date, time, and average location error as inputs in Migration Mapper. The dataset was divided into two overlapping subgroups based on winter range location (i.e., north, central) and analyzed separately, but visualized together as a final product. The averag

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.

Abstract

The dataset was derived by the Bioregional Assessment Programme from multiple source datasets. The source datasets are identified in the Lineage field in this metadata statement. The processes undertaken to produce this derived dataset are described in the History field in this metadata statement.

This resource contains raster datasets created using ArcGIS to analyse groundwater levels in the Namoi subregion.

This is an update to some of the data that is registered here: http://data.bioregionalassessments.gov.au/dataset/7604087e-859c-4a92-8548-0aa274e8a226

Purpose

These data layers were created in ArcGIS as part of the analysis to investigate surface water - groundwater connectivity in the Namoi subregion. The data layers provide several of the figures presented in the Namoi 2.1.5 Surface water - groundwater interactions report.

Dataset History

Extracted points inside Namoi subregion boundary. Converted bore and pipe values to Hydrocode format, changed heading of 'Value' column to 'Waterlevel' and removed unnecessary columns then joined to Updated_NSW_GroundWaterLevel_data_analysis_v01\NGIS_NSW_Bore_Join_Hydmeas_unique_bores.shp clipped to only include those bores within the Namoi subregion.

Selected only those bores with sample dates between >=26/4/2012 and <31/7/2012. Then removed 4 gauges due to anomalous ref_pt_height values or WaterElev values higher than Land_Elev values.

Then added new columns of calculations:

WaterElev = TsRefElev - Water_Leve

DepthWater = WaterElev - Ref_pt_height

Ref_pt_height = TsRefElev - LandElev

Alternatively - Selected only those bores with sample dates between >=1/5/2006 and <1/7/2006

2012_Wat_Elev - This raster was created by interpolating Water_Elev field points from HydmeasJune2012_only.shp, using Spatial Analyst - Topo to Raster tool. And using the alluvium boundary (NAM_113_Aquifer1_NamoiAlluviums.shp) as a boundary input source.

12_dw_olp_enf - Select out only those bores that are in both source files.

Then using depthwater in Topo to Raster, with alluvium as the boundary, ENFORCE field chosen, and using only those bores present in 2012 and 2006 dataset.

2012dw1km_alu - Clipped the 'watercourselines' layer to the Namoi Subregion, then selected 'Major' water courses only. Then used the Geoprocessing 'Buffer' tool to create a polygon delineating an area 1km around all the major streams in the Namoi subregion.

selected points from HydmeasJune2012_only.shp that were within 1km of features the WatercourseLines then used the selected points and the 1km buffer around the major water courses and the Topo to Raster tool in Spatial analyst to create the raster.

Then used the alluvium boundary to truncate the raster, to limit to the area of interest.

12_minus_06 - Select out bores from the 2006 dataset that are also in the 2012 dataset. Then create a raster using depth_water in topo to raster, with ENFORCE field chosen to remove sinks, and alluvium as boundary. Then, using Map Algebra - Raster Calculator, subtract the raster just created from 12_dw_olp_enf

Dataset Citation

Bioregional Assessment Programme (2017) Namoi bore analysis rasters - updated. Bioregional Assessment Derived Dataset. Viewed 10 December 2018, http://data.bioregionalassessments.gov.au/dataset/effa0039-ba15-459e-9211-232640609d44.

Dataset Ancestors

• Derived From Bioregional Assessment areas v02

• Derived From Gippsland Project boundary

• Derived From Bioregional Assessment areas v04

• Derived From Upper Namoi groundwater management zones

• Derived From Natural Resource Management (NRM) Regions 2010

• Derived From Bioregional Assessment areas v03

• Derived From Victoria - Seamless Geology 2014

• Derived From GIS analysis of HYDMEAS - Hydstra Groundwater Measurement Update: NSW Office of Water - Nov2013

• Derived From Bioregional Assessment areas v01

• Derived From GEODATA TOPO 250K Series 3, File Geodatabase format (.gdb)

• Derived From GEODATA TOPO 250K Series 3

• Derived From NSW Catchment Management Authority Boundaries 20130917

• Derived From Geological Provinces - Full Extent

• Derived From Hydstra Groundwater Measurement Update - NSW Office of Water, Nov2013

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.