This dashboard defaults to a presentation of the crash points that will cluster the crash types and determine a predominant crash type. In the case two crash types have the same number of crashes for that type the predominant type will not be colored to either of the crash types. Clicking on the clusters will include a basic analysis of the cluster. These clusters are dynamic and will change as the user zooms in an out of the map. The clustering of crashes is functionality availalble in ArcGIS Online and the popups for the clusters is based on items that include elements configured with the Arcade language. Users interested in learning more about point clustering and the configuration of popups should read through some of the examples of the following ESRI Article (https://www.esri.com/arcgis-blog/products/arcgis-online/mapping/summarize-and-explore-point-clusters-with-arcade-in-popups/) . The dashboard itself does include a map widget that does allow the user to toggle the visibility of layers and/or click on the crashes within the map. The popups for single crashes can be difficult to see unless the map is expanded (click in upper right of map widget). There is a Review Crashes tab that allows for another display of details of a crash that may be easier for users.This dashboard includes selectors in both the header and sidebar. By default the sidebar is collapsed and would need to be expanded. The crash dataset used in the presentation includes columns with a prefix of the unit. The persons information associated to each unit would be based on the Person that was considered the driver. Crash data can be filtered by clicking on items in chart widgets. All chart widgets have been configured to allow multiple selections and these selections will then filter the crash data accordingly. Allowing for data to be filtered by clicking on widgets is an alternative approach to setting up individual selectors. Selectors can take up a lot of space in the header and sidebar and clicking on the widget items can allow you to explore different scenarios which may ultimately be setup as selectors in the future. The Dashboard has many widgets that are stacked atop each other and underneath these stacked widgets are controls or tabs that allow the user to toggle between different visualizations. The downside to allowing a user to filter based on the output of a widget is the need for the end user to keep track of what has been clicked and the need to go back through and unclick.Many of the Crash Data Elements are based on lookups that have a fairly large range of values to select. This can be difficult sometimes with charts and the fact that a user may be overwhelmed by the number of items be plotted. Some of these values could potentially benefit by grouping similar values. The crash data being used in this dashboard hasn't been post processed to simplify some of the groupings of data and represent the value as it would appear in the Crash System. This dashboard was put together to continue the discussion on what data elements should be included in the GIS Crash Dataset. At the moment there is currently one primary dataset that is used to present crash data in Map Services. There is lots of potential to extend this dataset to include additional elements or it might be beneficial to create different versions of the crash data. Having an examples like this one will hopefully help with the discussion. Workable examples of what works and doesn't work. There are lots of data elements in the Crash System that could allow for an even more detailed safety analysis. Some of the unit items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash.Most Damaged AreaExtent of DamageUnit TypeDirection of Travel (Northbound, Southbound, Eastbound, Westbound)Pre-Crash ActionsSequence of Events 1-4Most Harmful Event Some of the persons items included in the example for Minot Ave in Auburn are the following. This information is included for the first three units associated to any crash and the person would be based on the driver.Condition at Time of CrashDriver Action 1Driver Action 2Driver DistractedAgeSexPerson Type (Driver/Owner(6), Driver(1))In addition to the Units and Persons information included above each crash includes the standard crash data elements which includesDate, Time, Day of Week, Year, Month, HourInjury Level (K,A,B,C,PD)Type of CrashTownname, County, MDOT RegionWeather ConditionsLight ConditionsRoad Surface ConditionsRoad GradeSchool Bus RelatedTraffic Control DeviceType of LocationWork Zone ItemsLocation Type (NODE, ELEMENT) used for LRS# of K, # of A, # of B, # of C, # of PD InjuriesTotal # of UnitsTotal # of PersonsFactored AADT (Only currently applicable for crashes along the roadway (ELEMENT)).Location of First Harmful EventTotal Injury Count for the CrashBoolean Y/N if Pedestrian or Bicycles are InvolvedContributing EnvironmentsContributing RoadRoute Number, Milepoint, Element ID, Node ID

The Inventory of New Jersey’s The Inventory of New Jersey’s Estuarine Shellfish Resources is conducted on a rotating basis throughout the major Atlantic coastal estuaries of New Jersey. The primary purpose of the work is to estimate the standing stock of hard clams (Mercenaria mercenaria) and describe their relative distribution. Additionally, the survey describes the relative distribution of other commercially important bivalve species and vascular submerged aquatic vegetation (“SAV”), also known as seagrasses. Hard Clam: The substrate is sampled with a hydraulic hard clam dredge designed to retain clams sized 30mm and larger. All live clams collected are counted and measured to the nearest millimeter. The density of clams at each station is reported in clams per square foot. The resulting geospatial data represents the relative distribution of hard clams at either “none” (no clams collected), “low” (0.01 to 2), “moderate” (>0.20 - 2), or “high” (>0.50 clams/ft2) densities. Where no category designation is given, the area is considered a “no data” area relative to this survey. This means that the survey did not sample within this area for reasons including shallow water, obstructions, or the presence of shellfish aquaculture leases. The area may or may not be marked formally as such. However, a “no data” area may contain shellfish resources unknown to the Marine Resources Administration (MRA) or the MRA may have data for the area from other investigations. It does not automatically mean that the area is devoid of shellfish resources. This data represents a one point in time documentation of relative abundance of hard clams, and hard clams may be found presently in areas not previously sampled or at stations where they were not historically collected. Complete reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be found on the NJ Fish and Wildlife’s website. The NJ Coastal Zone Management rules at N.J.A.C. 7:7 define shellfish densities of 0.2 clams per square foot or greater as productive shellfish habitat. The Leasing of Atlantic Coast Bottom for Aquaculture regulations discourages establishing leases in productive shellfish habitat (NJAC 7:25-24.6(d)). Note that this layer does not include delineation of shellfish leases or aquaculture development zones. Those data are provided separately. Data from 1980s were digitized based primarily on the georeferenced images of the 1980s’ map series, in combination with usage of the 1986 NJDEP Landuse/Landcover geospatial dataset to more accurately depict shoreline boundaries. Digitizing was completed using freehand and/or copying/pasting/editing waterbody features from the 1986 NJDEP Landuse/Landcover geospatial dataset. Digitizing was completed at a scale between 1:4,000 to 1:12,000. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Data from 2000s to present were created based upon survey station tabular data which was then mapped as a point feature class. Several GIS tools were then used to generate polygon features surrounding the stations to represent hard clam distribution (see Process Steps for more detail). Associated Species: When other commercially or recreationally important bivalve species are retained in the sample, they are documented, along with common invertebrate species. Data from the 1980s documents the presence of all other commercially and recreationally important bivalve species that are regulated by the State of New Jersey as well as common (but not all) shellfish predators that were retrained in the dredge while targeting hard clams. Presence indicates the area is productive for the species. The regulated bivalve species are soft clams (Mya arenaria), bay scallops (Argopecten irradians), surf clam (Spisula solidissima), Eastern oyster (Crassostrea virginica), and blue mussel (Mytilus edulis). This data is a point in time observation of production areas and regulated bivalve species may be found presently in areas not previously sampled or at stations where they were not historically collected. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. It is important to note that this data is not a comprehensive evaluation of Eastern oyster populations in the Mullica River, Great Egg Harbor River, or Delaware Bay, which are surveyed separately and specifically for that species. Similarly, although surf clams are occasionally found in estuarine environments, the species primarily dwells in the Atlantic Ocean and separate comprehensive population surveys of state and federal waters are available. For additional species collected (for example sponges, non-commercial shellfish, etc.) please contact the Bureau of Shellfisheries. Historical reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be requested by contacting the Marine Resources Administration. The features were digitized based primarily on the georeferenced images of the 1980s’ map series, in combination with usage of the 1986 NJDEP Land use/Landcover geospatial dataset in order to more accurately depict shoreline boundaries. Digitizing was completed using freehand and/or copying/pasting/editing waterbody features from the 1986 NJDEP Landuse/Landcover geospatial dataset. Digitizing was completed at a scale between 1:4,000 to 1:12,000. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Data from 2000 to present also documents the presence of all other commercially and recreationally important bivalve species that are regulated by the State of New Jersey as well as common invertebrates, including common bivalve predators. Presence indicates that area is productive for the species listed. The regulated bivalve species are soft clams (Mya arenaria), bay scallops (Argopecten irradians), surf clam (Spisula solidissima), Eastern oyster (Crassostrea virginica), and blue mussel (Mytilus edulis). This data is a one point in time observation of production areas and regulated bivalve species may be found presently in areas not previously sampled or at stations where they were not historically collected. It is important to note that this data is not a comprehensive evaluation of Eastern oyster populations in the Mullica River, Great Egg Harbor River, or Delaware Bay, which are surveyed separately and specifically for that species. Similarly, although surf clams are occasionally found in estuarine environments, the species primarily dwells in the Atlantic Ocean and separate comprehensive population surveys of state and federal waters are available. Further, data on channeled whelk (Busycotypus canaliculatus), knobbed whelk (Busycon carica), Atlantic horseshoe crab (Limulus polyphemus) and blue crab (Callinectes sapidus) are not intended for use in fishery management plans at this time. For additional species collected (for example sponges, non-commercial shellfish, etc.) please contact the Marine Resources Administration. This feature class was created based upon survey station tabular data which was then mapped as a point feature class. Several GIS tools were then used to generate polygon features surrounding the stations to represent each species’ distribution (see Process Steps for more detail). Submerged Aquatic Vegetation: When submerged aquatic vegetation (SAV; seagrass) is retained in the sample, or observed visually from the research vessel, the presence of the vegetation and species is noted. Only presence of the vegetation is provided, without inference regarding coverage, shoot density, or any other characteristic. Only regulated species (per N.J.A.C. 7:7-9.6) of vascular vegetation is presented here. This is primarily eelgrass (Zostera marina) and widgeon grass (Ruppia maritima. However, other regulated species are found in New Jersey. Data from 1980s is a “snapshot in time” of relative distribution of SAV, and SAV may be found presently in areas not previously sampled or at stations where they were not historically collected. Species composition may change over time. This data represents a digital interpretation of the original hard copy charts. Therefore, some anomalies may exist in the line features along the present-day coastline. Users should interpret the mapping to extend to the present-day coastline. Where hard copy charts were not previously created (Shrewsbury, Manasquan, and Metedeconk Rivers), a 1,000ft buffer was placed around the survey station where SAV was found. Historical reports for each surveyed estuary provide methodology, analysis, charts, and additional pertinent information, and can be requested by contacting the Marine Resources Administration. The SAV data from the 1980s can confirm the history of SAV in a given area, corroborating other survey years. However, further investigation is necessary if it is the only dataset available for a project. In such cases, please contact the Marine Resources Administration (MRA) as they may have information on the area that was collected during different surveys or is not yet published. Data from 2000s to present is also a “one point in time” documentation of relative distribution of SAV, and SAV may be found presently in areas not previously sampled or at stations where they were not historically collected. Species composition may change over time. Where SAV was found, a 1,000ft

This dataset is a contribution to the development of a kelp distribution vector dataset. Bull kelp (Nereocystis leutkeana) and giant kelp (Macrocystis pyrifera) are important canopy-forming kelp species found in marine nearshore habitats on the West coast of Canada. Often referred to as a foundation species, beds of kelp form structural underwater forests that offer habitat for fishes and invertebrates. Despite its far-ranging importance, kelp has experienced a decline in the west coast of North America. The losses have been in response to direct harvest, increase in herbivores through the removal of predators by fisheries or diseases, increase in water turbidity from shoreline development as well as sea temperature change, ocean acidification, and increased storm activates. Understanding these impacts and the level of resilience of different kelp populations requires spatiotemporal baselines of kelp distribution. The area covered by this dataset includes the BC coast and extends to portions of the Washington and Alaska coasts. This dataset was created using 137 British Admiralty (BA) charts, including insets, with scales ranging from 1:6,080 to 1:500,000, created between 1858 and 1956. All surveys were based on triangulation, in which a sextant or theodolite was used to determine latitude and angles, while a chronometer was used to help determine longitude. First, each BA chart was scanned by the Canadian Hydrographic Service (CHS) using the CHS Colortrac large format scanner, and saved as a Tagged Image Format at 200 DPI, which was deemed sufficient resolution to properly visualize all the features of interest. Subsequently, the scanned charts were imported into ESRI ArcMap and georeferenced directly to WGS84 using CHS georeferencing standards and principles (charts.gc.ca). In order to minimize error, a hierarchy of control points was used, ranging from high survey order control points to comparing conspicuous stable rock features apparent in satellite imagery. The georeferencing result was further validated against satellite imagery, CHS charts and fieldsheets, the CHS-Pacific High Water Line (charts.gc.ca), and adjacent and overlapping BA charts. Finally, the kelp features were digitized, and corresponding chart information (scale, chart number, title, survey start year, survey end year, and comments) was added as attributes to each feature. Given the observed differences in kelp feature representation at different scales, when digitizing kelp features, polygons were used to represent the discrete observations, and as such, they represent presence of kelp and not kelp area. Polygons were created by tracing around the kelp feature, aiming to keep the outline close to the stipe and blades. The accuracy of the location of the digitized kelp features was defined using a reliability criterion, which considers the location of the digitized kelp feature (polygon) in relation to the local depth in which the feature occurs. For this, we defined a depth threshold of 40 m to represent a low likelihood of kelp habitat in areas deeper than the threshold. An accuracy assessment of the digitized kelp features concluded that 99% of the kelp features occurred in expected areas within a depth of less than 40 m, and only about 1% of the features occurred completely outside of this depth.

Abstract:This service represents a combination of two data products, the DEM_SRTM_1Second dataset and the Australian_Bathymetry_Topography dataset. This service was created to support the CO2SAP (Co2 Storage application) Project to create a transect elevation graph within the application. This data is not available as a dataset for download as a Geoscience Australia product.

The DEM_SRTM_1Second service represents the National Digital Elevation Model (DEM) 1 Second product derived from the National DEM SRTM 1 Second. The DEM represents ground surface topography, with vegetation features removed using an automatic process supported by several vegetation maps. eCat record 72759. The Australian_Bathymetry_Topography service describes the bathymetry dataset of the Australian Exclusive Economic Zone and beyond. Bathymetry data was compiled by Geoscience Australia from multibeam and single beam data (derived from multiple sources), Australian Hydrographic Service (AHS) Laser Airborne Depth Sounding (LADS) data, Royal Australian Navy (RAN) fairsheets, the General Bathymetric Chart of the Oceans (GEBCO) bathymetric model, the 2 arc minute ETOPO (Smith and Sandwell, 1997) and 1 arc minute ETOPO satellite derived bathymetry (Amante and Eakins, 2008). Topographic data (onshore data) is based on the revised Australian 0.0025dd topography grid (Geoscience Australia, 2008), the 0.0025dd New Zealand topography grid (Geographx, 2008) and the 90m SRTM DEM (Jarvis et al, 2008). eCat record 67703.

IMPORTANT INFORMATION For data within this service that lays out of the Australian boundary the following needs to be considered. This grid is not suitable for use as an aid to navigation, or to replace any products produced by the Australian Hydrographic Service. Geoscience Australia produces the 0.0025dd bathymetric grid of Australia specifically to provide regional and local broad scale context for scientific and industry projects, and public education. The 0.0025dd grid size is, in many regions of this grid, far in excess of the optimal grid size for some of the input data used. On parts of the continental shelf it may be possible to produce grids at higher resolution, especially where LADS or multibeam surveys exist. However these surveys typically only cover small areas and hence do not warrant the production of a regional scale grid at less than 0.0025dd. There are a number of bathymetric datasets that have not been included in this grid for various reasons.© Commonwealth of Australia (Geoscience Australia) 2016.Downloads and Links:Web ServicesDEM_SRTM_1Second_over_Bathymetry_Topography MapServerDEM_SRTM_1Second_over_Bathymetry_Topography WMSDEM_SRTM_1Second_over_Bathymetry_Topography WCSDownloads available from the expanded catalogue link, belowMetadata URL:https://pid.geoscience.gov.au/service/ga/100320