Recent baseball scholarship has drawn attention to U.S. professional baseball’s complex twentieth century labor dynamics and expanding global presence. From debates around desegregation to discussions about the sport’s increasingly multicultural identity and global presence, the cultural politics of U.S. professional baseball is connected to the problem of baseball labor. However, most scholars address these topics by focusing on Major League Baseball (MLB), ignoring other teams and leagues—Minor League Baseball (MiLB)—that develop players for Major League teams. Considering Minor League Baseball is critical to understanding the professional game in the United States, since players who populate Major League rosters constitute a fraction of U.S. professional baseball’s entire labor force. As a digital humanities dissertation on baseball labor and globalization, this project uses digital humanities approaches and tools to analyze and visualize a quantitative data set, exploring how Minor League Baseball relates to and complicates MLB-dominated narratives around globalization and diversity in U.S. professional baseball labor. This project addresses how MiLB demographics and global dimensions shifted over time, as well as how the timeline and movement of foreign-born players through the Minor Leagues differs from their U.S.-born counterparts. This project emphasizes the centrality and necessity of including MiLB data in studies of baseball’s labor and ideological significance or cultural meaning, making that argument by drawing on data analysis, visualization, and mapping to address how MiLB labor complicates or supplements existing understandings of the relationship between U.S. professional baseball’s global reach and “national pastime” claims.

The Geographical Names Register (GNR) is a database of the authoritative place names in NSW. Since its inception in 1966 the Geographical Names Board has recorded information in relation to NSW geographical names within the GNR. There are currently over eighty thousand place names recorded in the GNR. On average, 200 new place names are assigned and added to this database every year.

Every record in this database has the provision for over thirty attributes ranging from spatial location information in respect to co-ordinate, map tile, parish etc. to cultural information on history, meaning and origin. The GNR also holds official information such as the name’s status and feature type, and temporal information dealing with the gazettal date of the name.

NSW Coastline data is linear feature class defining the line of contact between a body of water and the land. In particular, the line delineating the interface between land, internal waters and the “Territorial Sea” as defined by the Seas and Submerged Lands Act - No. 161 of 1973.\r \r The coastline defining Mean High Water Mark (MHWM) does not include MHWM within estuaries.\r Features within coastline feature class include: mean high water mark and mean low water mark.

Austro Control SDI provides spatial data by means of metadata for searching, maps for viewing and in some cases also downloads for external usage. Currently some selected aeronautical data as well as charts published in the AIP Austria are provided on a daily basis. Data may be accessed via two different web based applications: * MapStore provides available charts and data as preconfigured layers and maps. The content can be switched on and off and various background layers (Basemap.at or Open Street Map) may be added. It’s also possible to request attribute data (e.g. airspace limits, frequencies, etc.) of the selected features. The Map Client is meant for easy display of charts and geographical information, where you can also add your own data via KML or GPX and add information via annotation functions. * GeoNetwork provides INSPIRE compliant information (metadata) about charts and data available in the ACG SDI. It furthermore provides standardized interfaces (WMS or WFS) and download options (e.g. shapefile) for those who need to integrate data into external systems. Data may be filtered by a search function and visualised textually or via an integrated map viewer.

description: A geospatial interface will be developed using ArcIMS software. The interface will provide a means of accessing information stored in the SOFIA database and the SOFIA data exchange web site through a geospatial query. The spatial data will be served using the ArcSDE software, which provides a mechanism for storing spatial data in a relational database. A spatial database will be developed from existing data sets, including national USGS data sets, the Florida Geographic Digital Library, and other available data sets. Additional data sets will be developed from the published data sets available from PBS and other projects.; abstract: A geospatial interface will be developed using ArcIMS software. The interface will provide a means of accessing information stored in the SOFIA database and the SOFIA data exchange web site through a geospatial query. The spatial data will be served using the ArcSDE software, which provides a mechanism for storing spatial data in a relational database. A spatial database will be developed from existing data sets, including national USGS data sets, the Florida Geographic Digital Library, and other available data sets. Additional data sets will be developed from the published data sets available from PBS and other projects.

Materials related to the data presented in the publication titled "Empirical Evidence for Concepts of Spatial Information as Cognitive Means for Interpreting and Using Maps". The materials include:
- Questionnaire design document
- Raw questionnaire results
- Cleaned questionnaire results
- R script for analysis

This dataset provides watershed delineations for 1,703 U.S. Geological Survey (USGS) streamgaging stations (gages) for geospatial statistical study of peak streamflows in and near Texas. These streamgaging stations are in Texas, Oklahoma, and New Mexico (east of the Great Continental Divide) with some of the watersheds associated with the 1,703 streamgaging stations extending into several surrounding states or into Mexico. Watershed characteristics are indexed by using the National Hydrography Dataset (NHD) version 2.2.1 Indexing was accomplished by using the Permanent Identifier (PERMID; a string that uniquely identifies each feature in the NHD) and by using the USGS identification number for the streamgaging station (gage). The following watershed characteristics are included: watershed centroid, area, perimeter, basin shape index, sinuosity, drainage area, contributing drainage area, functional drainage area, summed values per watershed from the National Inventory of Dams (NID), mean watershed slope, main-channel slope, 10-85 slope, streamgaging station point elevation, mean elevation per watershed, mean annual precipitation per streamgaging station, mean annual and monthly precipitation per watershed, mean annual and monthly solar radiation per streamgaging station, mean annual and monthly solar radiation per watershed, hydrologic soil groups per watershed, land cover per watershed, and multi order hydrologic position of streamgaging stations and stream segments. The watershed characteristics in this dataset are used to describe the point at the USGS streamgaging station, the full watershed that defines each site, and the main channel segment of each watershed.

Main output generated with the custom-region possibility-for-electricity-autarky workflow.

This output provides similar data to https://doi.org/10.5281/zenodo.3246302 (technically eligible land area for renewables and other spatially disaggregated energy system data), but with two key differences:

The spatial extent has been expanded to include Iceland.

Two new spatial resolutions have been added: ehighways and ehighways_disaggregated.

ehighways defines 98 regions based on the result of work undertaken in the European Commission Seventh Framework Programme project e-HIGHWAY 2050 [1]. The regions cover 35 European countries; 19 are described at a national resolution and the rest at a subnational resolution. Those at a subnational resolution are aggregated from NUTS3-2006 statistical units. ehighways_disaggregated provides the data at the resolution of statistical units in Europe, which is then aggregated to produce the data at the ehighways resolution. The mapping from statistical units to ehighways regions is defined in ./ehighways/statistical_units_to_ehighways_regions.csv. ./ehighways/units.png shows a map of the resulting 98 ehighways regions. The region colours are used to help differentiate regions and have no other meaning.

This dataset is used as an input to the Sector-Coupled Euro-Calliope workflow.

[1] Anderski, T., Surmann, Y., Stemmer, S., Grisey, N., Momot, E., Leger, A.-C., Betraoui, B., and van Roy, P. (2014). European cluster model of the Pan-European transmission grid (e-HIGHWAY 2050)

The dataset contains information on European groundwater bodies, monitoring sites, river basin districts, river basin districts sub-units and surface bodies reported to the European Environment Agency between 2001-11-29 and 2020-01-17.

The information was reported to the European Environment Agency under the State of Environment reporting obligations. For the EU27 countries, Iceland, Norway and the United Kingdom, the EIONET spatial data was consolidated with the spatial data reported under the Water Framework Directive reporting obligations. For these countries, the reference spatial data set is the "WISE WFD Reference Spatial Datasets reported under Water Framework Directive".

Relevant concepts:

Groundwater body: 'Body of groundwater' means a distinct volume of groundwater within an aquifer or aquifers. Groundwater: All water which is below the surface of the ground in the saturation zone and in direct contact with the ground or subsoil. Aquifer: Subsurface layer or layers of rock or other geological strata of sufficient porosity and permeability to allow either a significant flow of groundwater or the abstraction of significant quantities of groundwater. Surface water body: Body of surface water means a discrete and significant element of surface water such as a lake, a reservoir, a stream, river or canal, part of a stream, river or canal, a transitional water or a stretch of coastal water. Surface water: Inland waters, except groundwater; transitional waters and coastal waters, except in respect of chemical status for which it shall also include territorial waters. Inland water: All standing or flowing water on the surface of the land, and all groundwater on the landward side of the baseline from which the breadth of territorial waters is measured. River: Body of inland water flowing for the most part on the surface of the land but which may flow underground for part of its course. Lake: Body of standing inland surface water. River basin district: The area of land and sea, made up of one or more neighbouring river basins together with their associated groundwaters and coastal waters, which is the main unit for management of river basins. River basin: The area of land from which all surface run-off flows through a sequence of streams, rivers and, possibly, lakes into the sea at a single river mouth, estuary or delta. Sub-basin: The area of land from which all surface run-off flows through a series of streams, rivers and, possibly, lakes to a particular point in a water course (normally a lake or a river confluence). Sub-unit [Operational definition. Not in the WFD]: Reporting unit. River basin districts larger than 50000 square kilometre should be divided into comparable sub-units with an area between 5000 and 50000 square kilometre. The sub-units should be created using river basins (if more than one river basin exists in the RBD), set of contiguous river basins, or sub-basins, for example. If the RBD area is less than 50000 square kilometre, the RBD itself should be used as a sub-unit.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Encompassing a total area of 113,661 acres (45,997 hectares), BISO is a rugged area carved from sandstone and shale, and filled with river gorges, steep cliffs, and unique sandstone arches. Using the National Vegetation Classification System (NVCS) developed by NatureServe, with additional classes and modifiers developed by CRMS, the vegetation communities for BISO were manually interpreted from 1:16,000 scale stereo color infrared aerial photographs acquired by Air Photographics, Inc. (Martinsburg, WV) in October 2003. Although the legislated area of BISO is 113,661 acres, the total area mapped was 122,368 ac (49,520 ha) and corresponds to the boundary file provided by the park. In addition, a 22,660 ac (9,170 ha) buffer area, extending 400 m outside of the park was mapped using a more general classification system meaning the total area mapped for BISO was 145,028acres (58,793 ha). Using a minimum mapping unit of 0.5 hectares (MMU = 0.5 ha), polygons representing areas of relatively uniform vegetation were delineated and annotated on clear plastic overlays registered to the aerial photographs. Polygons were labeled according to the dominant vegetation community. Where the polygons were not uniform, second and third vegetation classes were added. A number of modifier codes were employed to indicate important aspects of the polygon that could be interpreted from the photograph. The polygons on the overlays were then corrected using photogrammetric procedures and converted to vector format for use in creating a geographic information system (GIS) database for the park.

Daily (1981-2019), monthly (1981-2019) and monthly mean (1981-2010) surfaces of precipitation across Victoria at a spatial resolution of 9 seconds (approx. 250 m). Lineage: A) Data modelling: 1. Weather station observations collected by the Australian Bureau of Meteorology were obtained via the SILO patched point dataset (https://data.qld.gov.au/dataset/silo-patched-point-datasets-for-queensland), followed by the removal of all interpolated records. 2. Climate normals representing the 1981-2010 reference period were calculated for each weather station. A regression patching procedure (Hopkinson et al. 2012) was used to correct for biases arising due to differences in record length where possible. 3. Climate normals for each month were interpolated using trivariate splines (latitude, longitude and elevation as spline variables) using a DEM smoothed (Gaussian filter with a standard deviation of 10 and a search radius of 0.0825°, optimised using cross validation) to account for the lack of strong correlation between elevation and precipitation at short distances (Hutchinson 1998; Sharples et al. 2005). All data was interpolated using ANUSPLIN 4.4 (Hutchinson & Xu 2013). 4. Monthly surfaces were interpolated directly from monthly station records using the methods described in step 3. 5. Daily anomalies were calculated as a proportion of monthly precipitation, and interpolated with full spline dependence on latitude and longitude. 6. Interpolated anomalies (constrained to values between 0 and 1) were multiplied by monthly precipitation to obtain the final daily surfaces. B) Spatial data inputs: 1. Fenner School of Environment and Society and Geoscience Australia. 2008. GEODATA 9 Second Digital Elevation Model (DEM-9S) Version 3. C) Model performance: Accuracy assessment was conducted with leave-one-out cross validation. Mean monthly precipitation: RMSE = 7.65 mm (14.0% relative to mean) Monthly precipitation: RMSE = 13.12 mm (24.7% relative to mean) Daily precipitation: RMSE = 2.21 mm (26.3% relative to mean)

The Surface Management Agency (SMA) Geographic Information System (GIS) dataset depicts Federal land for the United States and classifies this land by its active Federal surface managing agency. The SMA feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details.

The SMA Withdrawals feature class covers the continental United States, Alaska, Hawaii, Puerto Rico, Guam, American Samoa and the Virgin Islands. A Federal SMA Withdrawal is defined by formal actions that set aside, withhold, or reserve Federal land by statute or administrative order for public purposes. A withdrawal creates a title encumbrance on the land. Withdrawals must accomplish one or more of the following: A. Transfer total or partial jurisdiction of Federal land between Federal agencies. B. Close (segregate) Federal land to operation of all or some of the public land laws and/or mineral laws. C. Dedicate Federal land to a specific public purpose. There are four major categories of formal withdrawals: (1) Administrative, (2) Presidential Proclamations, (3) Congressional, and (4) Federal Power Act (FPA) or Federal Energy Regulatory Commission (FERC) Withdrawals. These SMA Withdrawals will include the present total extent of withdrawn areas rather than all of the individual withdrawal actions that created them over time. A Federal SMA agency refers to a Federal agency with administrative jurisdiction over the surface of Federal lands. Jurisdiction over the land is defined when the land is either: Withdrawn by some administrative or legislative action, or Acquired or Exchanged by a Federal Agency. This layer is a dynamic assembly of spatial data layers maintained at various federal and local government offices. The GIS data contained in this dataset represents the polygon features that show the boundaries for Surface Management Agency and the surface extent of each Federal agency’s surface administrative jurisdiction. SMA data depicts current withdrawn areas for a particular agency and (when appropriate) includes land that was acquired or exchanged and is located outside of a withdrawal area for that agency. The SMA data do not illustrate land status ownership pattern boundaries or contain land ownership attribute details.

CrimeStat III is a spatial statistics program for the analysis of crime incident locations, developed by Ned Levine and Associates under the direction of Ned Levine, PhD, that was funded by grants from the National Institute of Justice (grants 1997-IJ-CX-0040, 1999-IJ-CX-0044, 2002-IJ-CX-0007, and 2005-IJ-CX-K037). The program is Windows-based and interfaces with most desktop GIS programs. The purpose is to provide supplemental statistical tools to aid law enforcement agencies and criminal justice researchers in their crime mapping efforts. CrimeStat is being used by many police departments around the country as well as by criminal justice and other researchers. The program inputs incident locations (e.g., robbery locations) in 'dbf', 'shp', ASCII or ODBC-compliant formats using either spherical or projected coordinates. It calculates various spatial statistics and writes graphical objects to ArcGIS, MapInfo, Surfer for Windows, and other GIS packages. CrimeStat is organized into five sections: Data Setup Primary file - this is a file of incident or point locations with X and Y coordinates. The coordinate system can be either spherical (lat/lon) or projected. Intensity and weight values are allowed. Each incident can have an associated time value. Secondary file - this is an associated file of incident or point locations with X and Y coordinates. The coordinate system has to be the same as the primary file. Intensity and weight values are allowed. The secondary file is used for comparison with the primary file in the risk-adjusted nearest neighbor clustering routine and the duel kernel interpolation. Reference file - this is a grid file that overlays the study area. Normally, it is a regular grid though irregular ones can be imported. CrimeStat can generate the grid if given the X and Y coordinates for the lower-left and upper-right corners. Measurement parameters - This page identifies the type of distance measurement (direct, indirect or network) to be used and specifies parameters for the area of the study region and the length of the street network. CrimeStat III has the ability to utilize a network for linking points. Each segment can be weighted by travel time, travel speed, travel cost or simple distance. This allows the interaction between points to be estimated more realistically. Spatial Description Spatial distribution - statistics for describing the spatial distribution of incidents, such as the mean center, center of minimum distance, standard deviational ellipse, the convex hull, or directional mean. Spatial autocorrelation - statistics for describing the amount of spatial autocorrelation between zones, including general spatial autocorrelation indices - Moran's I , Geary's C, and the Getis-Ord General G, and correlograms that calculate spatial autocorrelation for different distance separations - the Moran, Geary, Getis-Ord correlograms. Several of these routines can simulate confidence intervals with a Monte Carlo simulation. Distance analysis I - statistics for describing properties of distances between incidents including nearest neighbor analysis, linear nearest neighbor analysis, and Ripley's K statistic. There is also a routine that assigns the primary points to the secondary points, either on the basis of nearest neighbor or point-in-polygon, and then sums the results by the secondary point values. Distance analysis II - calculates matrices representing the distance between points for the primary file, for the distance between the primary and secondary points, and for the distance between either the primary or secondary file and the grid. 'Hot spot' analysis I - routines for conducting 'hot spot' analysis including the mode, the fuzzy mode, hierarchical nearest neighbor clustering, and risk-adjusted nearest neighbor hierarchical clustering. The hierarchical nearest neighbor hot spots can be output as ellipses or convex hulls. 'Hot spot' analysis II - more routines for conducting hot spot analysis including the Spatial and Temporal Analysis of Crime (STAC), K-means clustering, Anselin's local Moran, and the Getis-Ord local G statistics. The STAC and K-means hot spots can be output as ellipses or convex hulls. All of these routines can simulate confidence intervals with a Monte Carlo simulation. Spatial Modeling Interpolation I - a single-variable kernel density estimation routine for producin

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

In visual narratives like comics, not only do comprehenders need to track shifts in characters, space, and time, but they do so across a spatial layout. While many scholars and comic artists have speculated about connections between meaning and layout in comics, few empirical studies have examined this relationship. We investigated whether situational changes between time, characters, or space interacted with page layouts, by looking at across-page, across-constituent, and within-constituent transitions in a corpus of 134 annotated comics from North America, Europe, and Asia. Panels shifting within constituents (e.g., while moving within a row) changed the situation the least, while those across pages and across constituents (like in a row break) had more situational changes. The boundary of a page especially aligned with changes in spatial location of the scene. In addition, discontinuous changes primarily aligned with across-page transitions. Cross-cultural analyses indicated that Asian comics convey meaning across panels in ways that are relatively less constrained by layouts, while American and European comics use the page as a unit to group and segment spatial information. Such results indicate a partial correspondence between layout and meaning, but with different cultural constraints.

The INSPIRE geoportal provides the means to search for spatial data sets and spatial data services, and subject to access restrictions, to view spatial data sets from the EU Member States within the framework of the INSPIRE Directive. A vast quantity of spatial datasets and held within the portal for a wide variety of different themes

Website: http://inspire-geoportal.ec.europa.eu/discovery/

These geospatial data sets were developed as part of a new analysis of all known current and historical rain gages in the Luquillo Mountains, Puerto Rico published in the journal article Murphy, S.F., Stallard, R.F., Scholl, M.A., Gonzalez, G., and Torres-Sanchez, A.J., 2017, Reassessing rainfall in the Luquillo Mountains, Puerto Rico: Local and global ecohydrological implications: PLOS One 12(7): e0180987, p. 1-26, https://doi.org/10.1371/journal.pone.0180987. That article provides a revised map of mean annual precipitation developed using elevation regression functions and residual interpolation, and that map is presented here in a raster file. Most previous forest- and watershed-wide estimates of precipitation (and evapotranspiration, as inferred by a water balance) have assumed that precipitation increases consistently with elevation in the Luquillo Mountains; therefore, precipitation in leeward Luquillo watersheds has been overestimated by up to 40%.Because the Luquillo Mount ...

This dataset contains multiple themed layers of different types of sporting facilities across Scotland. The facilities are represented as point locations, which have been captured against Google Maps (along with attributes and details about them). Where there are multiple sports facilities at the same location there will be multiple points, meaning that calculations of total numbers of facilities can be calculated easily. Where there were missing coordinates for some facilities, manual lookups of appropriate coordinates have been made. Also, a judgement has been made to delete some provided facilities where they appear to no longer exist (for whatever reason). Provided layers are: Athletics Tracks (incl velodromes, training areas, indoor and outdoor) Bowling Greens (incl croquet, petanque and cricket squares) Fitness Suites Golf Courses Ice Rinks (incl curling rinks) Pitches (incl size, sport and type) Sports Halls (incl gyms and other types) Squash Court Swimming Pools (incl diving and other types) Indoor Tennis Courts Outdoor Tennis Courts Please note that the information provided is provided by third parties and therefore we cannot guarantee its accuracy, but it is the most up to date information we hold. As part of our work to keep this data up to date, we would kindly request that if you identify any issues, you share this with sportscotland at facilities@sportscotland.org.uk

Abstract Australia's Land Borders is a product within the Foundation Spatial Data Framework (FSDF) suite of datasets. It is endorsed by the ANZLIC – the Spatial Information Council and the Intergovernmental Committee on Surveying and Mapping (ICSM) as the nationally consistent representation of the land borders as published by the Australian states and territories. It is topologically correct in relation to published jurisdictional land borders and the Geocoded National Address File (G-NAF). The purpose of this product is to provide:

a building block which enables development of other national datasets; integration with other geospatial frameworks in support of data analysis; and visualisation of these borders as cartographic depiction on a map.

Although this service depicts land borders, it is not nor does it purport to be a legal definition of these borders. Therefore it cannot and must not be used for those use-cases pertaining to legal context. Termination Points are the point at which the state border polylines meet the coastline. For the purpose of this product, the coastline is defined as the Mean High Water Mark (MHWM). In the absence of a new MHWM for NSW, the Jervis Bay termination points are defined by the NSW cadastre. This feature layer is a sub-layer of the Land Borders service. Currency Date modified: 10 November 2021 Modification frequency: None Data extent Spatial extent North: -14.88° South: -38.06° East: 153.55° West: 129.00° Source information Catalog entry: Australia's Land Borders The Land Borders dataset is created using a range of source data including:

Australian Capital Territory data was sourced from the ACT Government GeoHub – ‘ACT Boundary’. No changes have been made to the polylines or vertices of the source data. In the absence of any custodian published border for Jervis Bay – New South Wales, a border has been constructed from the boundary of the NSW cadastre supplied by NSW Spatial Services. Geoscience Australia’s GEODATA TOPO 250K data was considered as an alternative, however, that border terminated short of the coastline as it stops at the shoreline of the major water bodies. Therefore, a decision was made to use the NSW and OT supplied cadastre to create a new representation of the Jervis Bay border that continued to the coastline (MHWM), in place of the TOPO 250K data. In the absence of publicly available data from New South Wales, the land borders for New South Wales have been constructed using the data of adjoining states Queensland, South Australia, Victoria and the Australian Capital Territory. This approach is agreeable to New South Wales Government for this interim product. In the absence of publicly available data from the Northern Territory the land borders for the Northern Territory have been constructed using the data of adjoining states Western Australia, Queensland and South Australia. This approach is agreeable to Northern Territory Government for this interim product. Queensland state border and coastline data have been download from the Queensland Spatial, Catalogue – QSpatial. Publicly available data for the state borders of South Australia was downloaded from data.gov.au and is ‘SA State Boundary - PSMA Administrative Boundaries’. Downloaded as a file geodatabase in GDA2020. Victorian state border data has been downloaded from the Victorian state Government Spatial Datamart, it is titled ‘FR_FRAMEWORK_AREA_LINE’. The Victorian state border data was used for the NSW/VIC section of border due to the absence of any publicly available data from New South Wales for this section of the border. Western Australian state border data was downloaded from the WA Government as publicly available. The Western Australia state border data has been used for the WA/NT section of the border due to the absence of publicly available data from Northern Territory for this section of the border. Selecting the SA data for the WA/SA border would introduce mismatches with the WA cadastre. It would also not improve the SA relationship with the SA cadastre. Using the WA data for the WA/SA section of the border aligns each state with its own cadastre without causing overlaps.

Sources specific to the Termination Points are as follows:

Jurisdictions Coastline data source

NT/QLD Publicly available Queensland Coastline and State Border data

QLD/NSW Publicly available Queensland Coastline and State Border data

NSW/VIC VIC Framework (1:25K) line

VIC/SA Coastline Capture Program (of SA by Tasmania)

SA/WA Coastline Capture Program (of SA by Tasmania)

WA/NT Coastline Capture Program (of NT by Tasmania)

JBT (OT) NSW Cadastre

Lineage statement At the southwest end of the NT/SA/WA border the South Australian data for the border was edited by moving the end vertex ~1.7m to correctly create the intersection of the 3 states (SA/WA/NT). At the southeast end of the NT/QLD/SA border the South Australian data for the border was edited by moving the end vertex ~0.4m to correctly create the intersection of the 3 states (NT/SA/QLD). Queensland data was used for the NT/QLD border and the QLD/NSW border due to the absence of publicly available data from the Northern Territory for these section of the border. Data published by Queensland also included a border sections running westwards along the southern Northern Territory border and southwards along the western New South Wales border. These two sections were excluded from the product as they are not within the state of Queensland. Queensland data was also used in the entirety for the SA/QLD segment of the land borders. Although the maximum overlap between SA and QLD state border data was less than ~5m (and varied along the border), the Queensland data closely matched its own cadastre and that of South Australia. The South Australian data overlapped the Queensland data, it also did not match the South Australian cadastre. Therefore, a decision to use the Queensland data for the QLD/SA section of the border ensured the best possible topological consistency with the published cadastre of each state. The South Australian/Victorian state border, north-south, were generally very similar with some minor deviations from each other from less than 1m to ~60m (there is one instance of deviation of 170m). The section of border that follows the Murray River is matched, for the most part by both states. Over three quarters of the border running along the river is matched with both states. There is a mismatch between the states in the last quarter of the border along the river, the northern section, however, both states still have the border running inside, or along, the river polygon (Surface hydrology), the Victorian data was chosen for this section purely for consistency as the Victorian data was used for the preceding arcs. Overall, the Victorian data was selected for use as the South Australia/Victoria land border. After taking the existing cadastre and GNAF points into account and it did not introduce extra errors into the relationship between the land borders and the cadastre of either state. In parts, it improved the relationship between the South Australian cadastre and the SA/VIC state border. This interim product will be updated when all states and territories have published agreed, authoritative representations of their land borders. This product will also be updated to include land mass polygons at time when the Coastline Capture Program is complete. This dataset is GDA 2020 compliant - transformed into GDA2020 from it's original source datum. Reference System Code 2020.00. Data dictionary All Layers

Attribute name Description

CREATE_DATE Date on which the positional data point was created in the data set

Field All features in this data set are labelled "TERMINATION_POINT"

SOURCE Project from which the data point information is derived

STATEMENT Legal disclaimer for the positional data

STATES Termination points divide at least two states and/or territories

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This dataset summarises 40 years of seagrass data collection (1983-2022) within Torres Strait and the Gulf of Carpentaria into two GIS shapefiles: (1) a point shapefile that includes survey data for 48,612 geolocated sites, and (2) a polygon geopackage describing seagrass at 641 individual or composite meadows.

Managing seagrass resources in northern Australia requires adequate baseline information on where seagrass is (presence/absence), the mapped extent of meadows, what species are present, and date of collection. This baseline is particularly important as a reference point against which to compare seagrass loss or change through time. The scale of northern Australia and the remoteness of many seagrass meadows from human populations present a challenge for research and management agencies reporting on the state of seagrass ecological indicators. Broad-scale and repeated surveys/studies of areas are logistically and financially impractical. However seagrass data is being collected through various projects which, although designed for specific reasons, are amenable to collating a picture of the extent and state of the seagrass resource.

In this project we compiled seagrass spatial data collected during surveys in Torres Strait and the Gulf of Carpentaria into a standardised form with point-specific and meadow-specific spatial and temporal information. We revisited, evaluated, simplified, standardised, and corrected individual records, including those collected several decades ago by drawing on the knowledge of one of our authors (RG Coles) who led the early seagrass data collection and mapping programs. We also incorporate new data, such as from photo records of an aerial assessment of mangroves in the Gulf of Carpentaria in 2017. This project was funded by the National Environmental Science Programme (NESP) Marine and Coastal Hub and Torres Strait Regional Authority (TSRA) in partnership with the Centre for Tropical Water and Aquatic Ecosystem Research (TropWATER), James Cook University. The project follows on from TropWATER’s previous work compiling 35 years of seagrass spatial point data and 30 years of seagrass meadow extent data for the Great Barrier Reef World Heritage Area (GBRWHA) and adjacent estuaries, funded through successive NESP Tropical Water Quality Hub Projects 3.1 (2015-2016) and 5.4 (2018-2020). These data sets are now publicly available through the eAtlas data portal: https://doi.org/10.25909/y1yk-9w85 . In making this data publicly available for management, the authors and data custodians request being contacted and involved in decision making processes that incorporate this data, to ensure its limitations are fully understood.

Methods: The data were collected using a variety of survey methods to describe and monitor seagrass sites and meadows. For intertidal sites/meadows, these include walking, observations from helicopters in low hover, and observations from hovercraft when intertidal banks were exposed. For subtidal sites/meadows, methods included free diving, scuba diving, video transects from towed cameras attached to a sled with/without a sled net, video drops with filmed quadrats, trawl and net samples, and van Veen grab samples. These methods were selected and tailored by the data custodians to the location, habitat surveyed, and technology available. Important site and method descriptions and contextual information is contained in the original trip reports and publications for each data set provided in Table 1 of Carter et al. (2022).

Geographic Information System (GIS) Mapping data for historic records (1980s) were transcribed from original logged and mapped data based on coastal topography, dead reckoning fixes and RADAR estimations. More recent data (1990’s onwards) is GPS located. All spatial data were converted to shapefiles with the same coordinate system (GDA 1994 Geoscience Australia Lambert), then compiled into a single point shapefile and a single polygon shapefile (seagrass meadows) using ArcMap (ArcGIS version 10.8 Redlands, CA: Environmental Systems Research Institute, ESRI). Some early spatial data was offset by several hundred metres and where this occurred data was repositioned to match the current coastline projection. The satellite base map used throughout this report is courtesy ESRI 2022.

Seagrass Site Layer: This layer contains information on data collected at assessment sites, and includes: 1. Temporal survey details – Survey month and year; 2. Spatial position - Latitude/longitude; 3. Survey name; 4. Depth for each subtidal site is m below MSL Depth and was extracted from the Australian Bathymetry and Topography Grid, June 2009 (Whiteway 2009). This approach was taken due to inconsistencies in depth recordings among data sets, e.g., converted to depth below mean sea level, direct readings from depth sounder with no conversion, or no depth recorded. Depth for intertidal sites was recorded as 0 m MSL, with an intertidal site defined as one surveyed by helicopter, walking, or hovercraft when banks were exposed during low tide;
5. Seagrass information including presence/absence of seagrass, and whether individual species were present/absent at a site; 6. Dominant sediment - Sediment type in the original data sets were based on grain size analysis or deck descriptions. For consistency, in this compilation we include only the most dominant sediment type (mud, sand, shell, rock, rubble), removed descriptors such as “fine”, “very fine”, “coarse”, etc., and replaced redundant terms, e.g. “mud” and “silt” are termed “mud”; 7. Survey methods – In this compilation we have updated and standardised the terms used to describe survey methods from the original reports; and
8. Data custodians.

Seagrass Meadow Layer: Polygons in the meadow layer are drawn from extent data collected during some surveys. Not all surveys collected meadow extent data (e.g., Torres Strait lobster surveys). The seagrass meadow layer is a composite of all the spatial polygon data we could access where meadow boundaries were mapped as part of the survey. All spatial layers were compiled into a single spatial layer using the ArcToolbox ‘merge’ function in ArcMap. Where the same meadow was surveyed multiple times as part of a long-term monitoring program, the overlapping polygons were compiled into a single polygon using the ‘merge’ function in ArcMap. Because meadows surveyed more than once were merged, there were some cases where adjacent polygons overlap each other.

Meadow Data Includes: 1. Temporal survey details – Survey month and year, or a list of survey dates for meadows repeatedly sampled; 2. Survey methods; 3. Meadow persistence – Classified into three categories: a. Unknown – Unknown persistence as the meadow was surveyed less than five times; b. Enduring – Seagrass is present in the meadow ≥90% of the surveys; c. Transitory – Seagrass is present in the meadow <90% of the surveys; 4. Meadow depth – Classified into three categories: a. Intertidal – Meadow was mapped on an exposed bank during low tide, e.g. Karumba monitoring meadow; b. Subtidal – Meadow remains completely submerged during spring low tides, e.g. Dugong Sanctuary meadow; c. Intertidal-Subtidal – Meadow includes sections that expose during low tide and sections that remain completely submerged, e.g. meadows adjacent to the Thursday Island shipping channel; 5. Dominant species of the meadow based on the most recent survey; 6. Presence or absence of individual seagrass species in a meadow; 7. Meadow density categories – Seagrass meadows were classified as light, moderate, dense, variable or unknown based on the consistency of mean above-ground biomass of the dominant species among all surveys, or percent cover of all species combined (see Table 2 in Carter et al. 2022). For example, a Halophila ovalis dominated meadow would be classed as “light” if the mean meadow biomass was always <1 gram dry weight m-2 (g DW m-2) among years, “variable” if mean meadow biomass ranged from <1 - >5 g DW m-2, and “dense” if mean meadow biomass was always >5 g DW m-2 among years. For meadows with density assessments based on both percent cover (generally from older surveys) and biomass, we assessed density categories based on the biomass data as this made the assessment comparable to a greater number of meadows, and comparable to the most recent data. Meadows with only one year of data were assigned a density category based on that year but no assessment of variability could be made and these are classified as “unknown”; 8. The minimum and maximum annual mean above-ground biomass measured in g DW m-2 (+ standard error if available) for each meadow is included for meadows with >1 year of biomass data. For meadows that were only surveyed once the mean meadow biomass (+ standard error if available) is presented as the minimum and maximum biomass of the meadow. “-9999” represents meadows where no above-ground biomass data was collected.; 9. The minimum and maximum annual mean percent cover is included for each meadow with >1 year of percent cover data. For meadows that were only surveyed once the mean meadow percent cover is presented as the minimum and maximum percent cover of the meadow. Older surveys (e.g., 1986 Gulf of Carpentaria surveys) used percent cover rather than biomass. For some surveys percent cover was estimated as discrete categories or ‘data binning’ (e.g., <10% - >50%). “-9999” represents meadows where no percent cover data was collected; 10. Meadow area survey details – The minimum, maximum and total area (hectares; ha) for each meadow: a. Total area - Total area of each meadow was estimated in the GDA 1994 Geoscience Australia Lambert projection using the ‘calculate geometry’ function in ArcMap. For meadows that were mapped multiple times, meadow area represents the merged maximum extent for