Public Open Space Geographic Information System data collection for Perth and Peel Metropolitan Areas

The public open space (POS) dataset contains polygon boundaries of areas defined as publicly available and open. This geographic information system (GIS) dataset was collected in 2011/2012 using ArcGIS software and aerial photography dated from 2010-2011. The data was collected across the Perth Metro and Peel Region.

POS refer to all land reserved for the provision of green space and natural environments (e.g. parks, reserves, bushland) that is freely accessible and intended for use for recreation purposes (active or passive) by the general public. Four types of “green and natural public open spaces” are distinguished: (1) Park; (2) Natural or Conservation Area; (3) School Grounds; and (4) Residual. Areas where the public are not permitted except on payment or which are available to limited and selected numbers by membership (e.g. golf courses and sports centre facilities) or setbacks and buffers required by legislation are not included.

Initially, potential POSs were identified from a combination of existing geographic information system (GIS) spatial data layers to create a generalized representation of ‘green space’ throughout the Perth metropolitan and Peel regions. Base data layers include: cadastral polygons, metropolitan and regional planning scheme polygons, school point locations, and reserve vesting polygons. The ‘green’ space layer was then visually updated and edited to represent the true boundaries of each POS using 2010-2011 aerial photography within the ArcGIS software environment. Each resulting ’green’ polygon was then classified using a decision tree into one of four possible categories: park, natural or conservation area, school grounds, or residual green space.

Following the classification process, amenity and other information about each POS was collected for polygons classified as “Park” following a protocol developed at the Centre for the Built Environment and Health (CBEH) called POSDAT (Public Open Space Desktop Auditing Tool). The parks were audited using aerial photography visualized using ArcGIS software. . The presence or absence of amenities such as sporting facilities (e.g. tennis courts, soccer fields, skate parks etc) were audited as well as information on the environmental quality (i.e. presence of water, adjacency to bushland, shade along paths, etc), recreational amenities (e.g. presence of BBQ’, café or kiosks, public access toilets) and information on selected features related to personal safety.

The data is stored in an ArcGIS File Geodatabase Feature Class (size 4MB) and has restricted access.

Data creation methodology, data definitions, and links to publications based on this data, accompany the dataset.

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.

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 Mountains often serve as a wet tropical archetype in global assessments of basic ecohydrological processes, these revised estimates are relevant to regional and global assessments of runoff efficiency, hydrologic effects of reforestation, geomorphic processes, and climate change.

A land parcel is an area of land with defined boundaries, under unique ownership for specific property rights or interests. A property is something that is capable of being owned, in the form of real property (land). The interest can involve physical aspects, such as the use of land, or conceptual rights, such as a right to use the land in the future.

The NSW cadastre is an up to date parcel based land information system which contains a unique identifier which can be linked of interests in land (i.e. rights, restrictions and responsibilities). The cadastre includes a geometric definition of land parcels linked to other records, such as land titles, describing the nature of the interests, the ownership or control of those interests, and often the value of the parcel and its improvements.

A cadastral product or service visualises the boundaries of land parcels, often buildings on land, the parcel identifier, and basic topographic features.

The land parcel and property theme provides the foundation fabric of land ownership. It consists of the digital cadastral database and associated parcel and property information.

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.

Dataset Description:

• This dataset has population data of each Indian district from 2001 and 2011 censuses.
• The special thing about this data is that it has centroids for each district and state.
• Centroids for a district are calculated by mapping border of each district as a polygon of latitude/longitude points in a 2D plane and then calculating their mean center.
• Centroids for a state are calculated by calculating the weighted mean center of all districts that constitutes a state. The population count is the weight assigned to each district.

Example Analysis:

• The complete code for calculating the centroids and web scraping for the data is shared on GitHub.

• The purpose of this project was to map population density center for each state.

• You can also read about the complete project here: https://medium.com/@sumit.arora/plotting-weighted-mean-population-centroids-on-a-country-map-22da408c1397

Output Screenshots: Indian districts mapped as polygons https://i.imgur.com/UK1DCGW.png" alt="Indian districts mapped as polygons">

Mapping centroids for each district https://i.imgur.com/KCAh7Jj.png" alt="Mapping centroids for each district">

Mean centers of population by state, 2001 vs. 2011 https://i.imgur.com/TLHPHjB.png" alt="Mean centers of population by state, 2001 vs. 2011">

National center of population https://i.imgur.com/yYxE4Hc.png" alt="National center of population">

The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) provides a new level of detail in global topographic data. Previously, the best available global DEM was GTOPO30 with a horizontal grid spacing of 30 arc-seconds. The GMTED2010 product suite contains seven new raster elevation products for each of the 30-, 15-, and 7.5-arc-second spatial resolutions and incorporates the current best available global elevation data. The new elevation products have been produced using the following aggregation methods: minimum elevation, maximum elevation, mean elevation, median elevation, standard deviation of elevation, systematic subsample, and breakline emphasis. Metadata have also been produced to identify the source and attributes of all the input elevation data used to derive the output products. Many of these products will be suitable for various regional continental-scale land cover mapping, extraction of drainage features for hydrologic modeling, and geometric and radiomet ...

Download service provided by ARIB. The database shall contain:

"Agricultural parcels" means agricultural parcels for which area payments have been applied for in the last two years. The crop information in the field block data comes from the last area aid application. The latest application information will reach the service on 17 June each year, i.e. after the end of the application.

‘Agricultural parcels’ means the agricultural parcels covered by the last area aid application. The fields requested for the last year will reach the service from 17 June. In addition to land use and culture, the data on fields also reflect the status of mowing detection on grasslands.

Livestock buildings – data on livestock buildings and facilities provided by keepers.

Landscape features – preserved landscape features (field island, forest hedgehog, row of trees, hedge, ditch, rock garden).

PLK 2014-2020 – Suitable communities for applying for support for the maintenance of semi-natural biotic communities. The latest application information will reach the service on 17 June each year, i.e. after the end of the application.

‘Areas applied for by PLK’ means the areas for which aid for the maintenance of semi-natural biotic communities has been applied for in the last area aid application. Areas requested in the last year will reach the service from 17 June.

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.

The coastline defining Mean High Water Mark (MHWM) does not include MHWM within estuaries. Features within coastline feature class include: mean high water mark and mean low water mark.

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)

This file contains the data set used to develop a random forest model predict background specific conductivity for stream segments in the contiguous United States. This Excel readable file contains 56 columns of parameters evaluated during development. The data dictionary provides the definition of the abbreviations and the measurement units. Each row is a unique sample described as R** which indicates the NHD Hydrologic Unit (underscore), up to a 7-digit COMID, (underscore) sequential sample month. To develop models that make stream-specific predictions across the contiguous United States, we used StreamCat data set and process (Hill et al. 2016; https://github.com/USEPA/StreamCat). The StreamCat data set is based on a network of stream segments from NHD+ (McKay et al. 2012). These stream segments drain an average area of 3.1 km2 and thus define the spatial grain size of this data set. The data set consists of minimally disturbed sites representing the natural variation in environmental conditions that occur in the contiguous 48 United States. More than 2.4 million SC observations were obtained from STORET (USEPA 2016b), state natural resource agencies, the U.S. Geological Survey (USGS) National Water Information System (NWIS) system (USGS 2016), and data used in Olson and Hawkins (2012) (Table S1). Data include observations made between 1 January 2001 and 31 December 2015 thus coincident with Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data (https://modis.gsfc.nasa.gov/data/). Each observation was related to the nearest stream segment in the NHD+. Data were limited to one observation per stream segment per month. SC observations with ambiguous locations and repeat measurements along a stream segment in the same month were discarded. Using estimates of anthropogenic stress derived from the StreamCat database (Hill et al. 2016), segments were selected with minimal amounts of human activity (Stoddard et al. 2006) using criteria developed for each Level II Ecoregion (Omernik and Griffith 2014). Segments were considered as potentially minimally stressed where watersheds had 0 - 0.5% impervious surface, 0 – 5% urban, 0 – 10% agriculture, and population densities from 0.8 – 30 people/km2 (Table S3). Watersheds with observations with large residuals in initial models were identified and inspected for evidence of other human activities not represented in StreamCat (e.g., mining, logging, grazing, or oil/gas extraction). Observations were removed from disturbed watersheds, with a tidal influence or unusual geologic conditions such as hot springs. About 5% of SC observations in each National Rivers and Stream Assessment (NRSA) region were then randomly selected as independent validation data. The remaining observations became the large training data set for model calibration. This dataset is associated with the following publication: Olson, J., and S. Cormier. Modeling spatial and temporal variation in natural background specific conductivity. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 53(8): 4316-4325, (2019).

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

This repository contains supporting material for a journal article being submitted to one of the journals published by the American Geophysical Union, titled Earth’s Future. The repository contains the following items: 1. README file of what is in the repository including methods associated with the geodatabase 2. File Geodatabase 1. README file The files collected here relate to a study being submitted to the American Geophysical Union’s journal, Earth’s Future. The title of the paper being submitted is, “The contribution of Microrefugia to landscape thermal inertia for climate-adaptive conservation and adaptation strategies.” The study was conducted across 40,250 km2 of complex mountainous terrain in Northern California. The objective of the study was to consider whether it was possible to identify the relative strength of microrefugia systematically in order to provide conservation and climate-adaptation strategies with information that could help with prioritizing actions. We selected an operational scale of 10 ha (25 acres) as a scale that is suitable for various types of landscape planning exercises, and created a hexagon grid for the region. We calculated the mean value for multiple variables and appended them into the hexagons. For thermal inertia, we calculated the mean elevation per hexagon and then its coolest (highest) point using an environmental lapse rate. We also calculated solar energy loading, calculated the mean solar load per hexagon, and calculated its effect on air temperature. We combined these two temperature metrics to identify how much thermal buffering capacity each hexagon contains, as measured by how much warming it could experience before the mean temperature, as determined from a baseline time period, is no longer found anywhere within the hexagon. We tied the mean annual temperature from 1981–2010 to the mean elevation in each hexagon, as well as a temperature from an earlier period, and from several future periods, based on global circulation models. The study shows how long current (baseline) climate conditions found in each hexagon may persist and shows how the resulting map of landscape thermal inertia can be used when considering natural vegetation types for conservation, identifying which parts of high-priority wildlife corridors have the greatest capacity to retain their current climate conditions, and what the potential for retaining baseline climate conditions is for areas with late-seral forest conditions as represented by forest canopy height. The methods section below describes the data used in the study to create the data in the geodatabase that is posted here. The Geodatabase itself provides all the data needed to replicate the various results presented in the paper. Further information can be found in Thorne et al. 2020. That report is more extensive than the results in our associated paper, but it contains more information on the calculation of various metrics associated with and was the foundation from which we developed this study. The report is provided here in order to keep all the relevant materials compiled for potential use by others. 2. File Geodatabase The geodatabase is provided as a separate file. Name: ThermalInertiaIndex.gdb Contents:

AllHexagons

A feature class containing all 408,948 hexagon grids used in this study Fields within the feature class:

Id

A unique ID for each hexagon

Watershed

Watershed the hexagon falls within

DomWHR

Habitat type (WHR) that had the majority coverage within the hexagon

WHR_Name

Descriptive name of the habitat type

WHR_GroupName

Major vegetation type

CanopyHt_Score

Canopy Height Score ranging from 1 (under 1m) to 5 (over 25m)

CanopyHt_m

Average canopy height within the hexagon (m)

Conn_Score

Connectivity Score ranging from 1 (low) to 5 (high)

dem10m

Average elevation within the hexagon (m)

dem10m_min

Minimum elevation within the hexagon (m)

dem10m_max

Maximum elevation within the hexagon (m)

SRtemp_min

The lowest Solar Radiation load within the hexagon (degree C)

ElevLR_NegEff2

Effect of elevation on air temperature (degree C)

Thermal_Inertia

Hexagon buffering capacity (degree C)

tave_5180

Average temperature 1951-1980

tave_8110

Average temperature 1981-2010

tave_1039mi8

Average temperature 2010-2039 (MIROC-ESM RCP 8.5)

tave_4069mi8

Average temperature 2040-2069 (MIROC-ESM RCP 8.5)

tave_7099mi8

Average temperature 2070-2099 (MIROC-ESM RCP 8.5)

tave_1039cn8

Average temperature 2010-2039 (CNRM-CM5 RCP 8.5)

tave_4069cn8

Average temperature 2040-2069 (CNRM-CM5 RCP 8.5)

tave_7099cn8

Average temperature 2070-2099 (CNRM-CM5 RCP 8.5)

Connectivity_Scores

90m raster containing all 3 connectivity scores Fields within the raster:

TNC_Conn_Score

Connectivity Score from reclassed TNC/Omniscape

CEHC_Score

Connectivity Score from reclassed California Essential Habitat Connectivity

Combined_Score

Overall Connectivity Score

Methods These methods describe the steps taken to calculate the attribute columns in the associated database. Compilations were done on publicly available data such as digital elevation models, climate data and others. For references to the public base data used, please see references in Table 1. There are two sections a. How we processed material into the hexagon framework b. The sequence of steps for each of the analyses presented in the results section of the main report a. How we processed material into the hexagon framework We created a geodatabase of 10 ha hexagons for the region in order to summarize the spatial data in this study into spatial units that are comparable across the region but that also represent an area size that is relevant for site-level plans such as landscape connectivity or forest conservation. The hexagon geodatabase covers 28,269 km2 in within the 5 watersheds in northern California, and 40,895 km2 in the 5 watersheds plus a 10 km buffer area. Integrating data into the hexes Data from a variety of grid scales, including 10, 30, 90, and 270m was added using the ArcGIS sample tool with the Hexagon centroids to sample the 270m resolution data, and the zonal statistics tool within Hexagon boundaries for raster data with smaller grid cell sizes.

This study used four types of data (Table 1):

Air temperature & topographic – Topographic data was used to calculate microrefugia buffering capacity for each hexagon. Temperature data was used to evaluate the effect of historical and projected future warming on the ability of local sites to retain baseline temperature conditions. Habitats / Dominant Vegetation Types – Habitat data was used to profile the presence and extent of microrefugia by habitat type for the region Landscape Connectivity Models – were used to find microrefugia in areas that are highly ranked for landscape connectivity Forest Structure data – was used to identify where large, late seral trees occupy microrefugia sites.

Microrefugia – Air temperature & topographic

National Elevation Dataset

www.usgs.gov/core-science-systems/ngp/tnm-delivery

Raster - 10m

Solar Radiation Model

Developed at UC Davis for this study from 25m DEM

Raster - 25m

Environmental Lapse Rate Model

Developed at UC Davis for this study from 10m DEM

Raster - 10m

Linking Temperature to Hexagons

Downscaled PRISM Tmax & Tmin – BCM – current & historical

http://climate.calcommons.org/dataset/2014-CA-BCM

Raster – 270 m

Downscaled future climate projections MIROC & CNRM RCP8.5

http://climate.calcommons.org/dataset/2014-CA-BCM

Raster – 270 m

Habitats / Dominant Vegetation Types

FVEG - CalFire (FRAP)

https://frap.fire.ca.gov/mapping/gis-data/

Raster - 30m

Vegetation and Climate Refugia

Vegetative Climate Exposure (UCD Modeling)

Raster - 270m

Landscape Connectivity Models

California Essential Connectivity

https://wildlife.ca.gov/Conservation/Planning/Connectivity/CEHC

Polygon

Omniscape Climate Connectivity

https://omniscape.codefornature.org/

90 m

Forest Structure

Canopy Height - SALO Sciences

https://forestobservatory.com/

Raster - 10m

Table 1: Data sources b. The sequence of steps for each of the analyses presented in the results section of the main report Microrefugia – thermal buffering capacity Thermal buffering capacity combined two metrics that represent potential modifications to the air temperature in each 10-ha hexagon. First, a 10m digital elevation model was used to calculate the variation in air temperature within each hexagon due to variations in elevation, using a standard environmental lapse rate. Second, the influence of solar radiation on air temperature was calculated. These two metrics were combined. Elevational Effect on Air Temperature Column: ElevLR_NegEff2 Zonal Statistics was performed on a 10m DEM for each hex. The range of elevation was used with environmental lapse rate to calculate “buffering capacity” within each Hexagon. We used an environmental lapse rate of 0.00649606 C⁰/ meter (International Civil Aviation Organization, 1993) to calculate the range of temperatures within the hexagon. To calculate the effect of elevation on air temperature within each hexagon we used the following equation: (Average Elevation – Maximum Elevation) x 0.00649606

Solar Radiation Effect on Air Temperature: – Column: SRtemp_min We ran the analysis on a 25 m-resolution DEM. We calculated annualized solar radiation via the r.sun model available in GRASS 7.8 (https://grass.osgeo.org/grass70/manuals/r.sun.html) which calculates direct, diffuse, and reflected solar irradiation for a given day, location, topography, and atmospheric conditions. We assumed clear-sky conditions to run this model, and ran the model for 2 days in each month, from which we calculated solar

Number and area of urban parks before and after harmonization obtained by GMaps and OSM tools for the 16 cities with official spatial data.

Esri ArcGIS Online (AGOL) Imagery Layer which includes the MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid geospatial data product.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid consists of a depth grid image service depicting conditions of sea level change based on the 10% annual chance event (10-Year Storm) scenario for coastal areas throughout the State of Maryland in year 2050. This data product supports Maryland Department of Transportation State Highway Administration (MDOT SHA) leadership and planners as they endeavor to mitigate or prevent the impacts of sea level change resulting from land surface subsidence and rising sea levels.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid data was produced as a result of efforts by the Maryland Department of Transportation State Highway Administration (MDOT SHA), Eastern Shore Regional GIS Cooperative (ESRGC), Salisbury University (SU), United States Corps of Engineers (USACE), National Oceanic & Atmospheric Administration (NOAA), and the United States Geological Survey (USGS). The US Army Corps of Engineers provide the sea level change estimate. Sea level change is localized using water elevations collected from a qualifying National Oceanic and Atmospheric Administration (NOAA) tidal reference station - NOAA observations are transformed from tidal datum to North American Vertical Datum of 1988. A final correction for glacial isostatic adjustment and land creates an sea level change value for the official project year, 2050.MDOT SHA 2050 Mean Sea Level 10% Annual Chance (10.Year Storm) - Flood Depth Grid data was task-based, and will only be updated on an As-Needed basis where necessary.Last Updated: 10/07/2019For additional information, contact the MDOT SHA Geospatial Technologies:Email: GIS@mdot.maryland.govFor information related to the data, visit the Eastern Shore Regional GIS Cooperative (ESRGC) websiteWebsite: https:www.esrgc.org/mapServices/MDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.

Jurisdictional Unit, 2022-05-21. For use with WFDSS, IFTDSS, IRWIN, and InFORM.This is a feature service which provides Identify and Copy Feature capabilities. If fast-drawing at coarse zoom levels is a requirement, consider using the tile (map) service layer located at https://nifc.maps.arcgis.com/home/item.html?id=3b2c5daad00742cd9f9b676c09d03d13.OverviewThe Jurisdictional Agencies dataset is developed as a national land management geospatial layer, focused on representing wildland fire jurisdictional responsibility, for interagency wildland fire applications, including WFDSS (Wildland Fire Decision Support System), IFTDSS (Interagency Fuels Treatment Decision Support System), IRWIN (Interagency Reporting of Wildland Fire Information), and InFORM (Interagency Fire Occurrence Reporting Modules). It is intended to provide federal wildland fire jurisdictional boundaries on a national scale. The agency and unit names are an indication of the primary manager name and unit name, respectively, recognizing that:There may be multiple owner names.Jurisdiction may be held jointly by agencies at different levels of government (ie State and Local), especially on private lands, Some owner names may be blocked for security reasons.Some jurisdictions may not allow the distribution of owner names. Private ownerships are shown in this layer with JurisdictionalUnitIdentifier=null,JurisdictionalUnitAgency=null, JurisdictionalUnitKind=null, and LandownerKind="Private", LandownerCategory="Private". All land inside the US country boundary is covered by a polygon.Jurisdiction for privately owned land varies widely depending on state, county, or local laws and ordinances, fire workload, and other factors, and is not available in a national dataset in most cases.For publicly held lands the agency name is the surface managing agency, such as Bureau of Land Management, United States Forest Service, etc. The unit name refers to the descriptive name of the polygon (i.e. Northern California District, Boise National Forest, etc.).These data are used to automatically populate fields on the WFDSS Incident Information page.This data layer implements the NWCG Jurisdictional Unit Polygon Geospatial Data Layer Standard.Relevant NWCG Definitions and StandardsUnit2. A generic term that represents an organizational entity that only has meaning when it is contextualized by a descriptor, e.g. jurisdictional.Definition Extension: When referring to an organizational entity, a unit refers to the smallest area or lowest level. Higher levels of an organization (region, agency, department, etc) can be derived from a unit based on organization hierarchy.Unit, JurisdictionalThe governmental entity having overall land and resource management responsibility for a specific geographical area as provided by law.Definition Extension: 1) Ultimately responsible for the fire report to account for statistical fire occurrence; 2) Responsible for setting fire management objectives; 3) Jurisdiction cannot be re-assigned by agreement; 4) The nature and extent of the incident determines jurisdiction (for example, Wildfire vs. All Hazard); 5) Responsible for signing a Delegation of Authority to the Incident Commander.See also: Unit, Protecting; LandownerUnit IdentifierThis data standard specifies the standard format and rules for Unit Identifier, a code used within the wildland fire community to uniquely identify a particular government organizational unit.Landowner Kind & CategoryThis data standard provides a two-tier classification (kind and category) of landownership. Attribute Fields JurisdictionalAgencyKind Describes the type of unit Jurisdiction using the NWCG Landowner Kind data standard. There are two valid values: Federal, and Other. A value may not be populated for all polygons.JurisdictionalAgencyCategoryDescribes the type of unit Jurisdiction using the NWCG Landowner Category data standard. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State. A value may not be populated for all polygons.JurisdictionalUnitNameThe name of the Jurisdictional Unit. Where an NWCG Unit ID exists for a polygon, this is the name used in the Name field from the NWCG Unit ID database. Where no NWCG Unit ID exists, this is the “Unit Name” or other specific, descriptive unit name field from the source dataset. A value is populated for all polygons.JurisdictionalUnitIDWhere it could be determined, this is the NWCG Standard Unit Identifier (Unit ID). Where it is unknown, the value is ‘Null’. Null Unit IDs can occur because a unit may not have a Unit ID, or because one could not be reliably determined from the source data. Not every land ownership has an NWCG Unit ID. Unit ID assignment rules are available from the Unit ID standard, linked above.LandownerKindThe landowner category value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. There are three valid values: Federal, Private, or Other.LandownerCategoryThe landowner kind value associated with the polygon. May be inferred from jurisdictional agency, or by lack of a jurisdictional agency. A value is populated for all polygons. Valid values include: ANCSA, BIA, BLM, BOR, DOD, DOE, NPS, USFS, USFWS, Foreign, Tribal, City, County, OtherLoc (other local, not in the standard), State, Private.DataSourceThe database from which the polygon originated. Be as specific as possible, identify the geodatabase name and feature class in which the polygon originated.SecondaryDataSourceIf the Data Source is an aggregation from other sources, use this field to specify the source that supplied data to the aggregation. For example, if Data Source is "PAD-US 2.1", then for a USDA Forest Service polygon, the Secondary Data Source would be "USDA FS Automated Lands Program (ALP)". For a BLM polygon in the same dataset, Secondary Source would be "Surface Management Agency (SMA)."SourceUniqueIDIdentifier (GUID or ObjectID) in the data source. Used to trace the polygon back to its authoritative source.MapMethod:Controlled vocabulary to define how the geospatial feature was derived. Map method may help define data quality. MapMethod will be Mixed Method by default for this layer as the data are from mixed sources. Valid Values include: GPS-Driven; GPS-Flight; GPS-Walked; GPS-Walked/Driven; GPS-Unknown Travel Method; Hand Sketch; Digitized-Image; DigitizedTopo; Digitized-Other; Image Interpretation; Infrared Image; Modeled; Mixed Methods; Remote Sensing Derived; Survey/GCDB/Cadastral; Vector; Phone/Tablet; OtherDateCurrentThe last edit, update, of this GIS record. Date should follow the assigned NWCG Date Time data standard, using 24 hour clock, YYYY-MM-DDhh.mm.ssZ, ISO8601 Standard.CommentsAdditional information describing the feature. GeometryIDPrimary key for linking geospatial objects with other database systems. Required for every feature. This field may be renamed for each standard to fit the feature.JurisdictionalUnitID_sansUSNWCG Unit ID with the "US" characters removed from the beginning. Provided for backwards compatibility.JoinMethodAdditional information on how the polygon was matched information in the NWCG Unit ID database.LocalNameLocalName for the polygon provided from PADUS or other source.LegendJurisdictionalAgencyJurisdictional Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.LegendLandownerAgencyLandowner Agency but smaller landholding agencies, or agencies of indeterminate status are grouped for more intuitive use in a map legend or summary table.DataSourceYearYear that the source data for the polygon were acquired.Data InputThis dataset is based on an aggregation of 4 spatial data sources: Protected Areas Database US (PAD-US 2.1), data from Bureau of Indian Affairs regional offices, the BLM Alaska Fire Service/State of Alaska, and Census Block-Group Geometry. NWCG Unit ID and Agency Kind/Category data are tabular and sourced from UnitIDActive.txt, in the WFMI Unit ID application (https://wfmi.nifc.gov/unit_id/Publish.html). Areas of with unknown Landowner Kind/Category and Jurisdictional Agency Kind/Category are assigned LandownerKind and LandownerCategory values of "Private" by use of the non-water polygons from the Census Block-Group geometry.PAD-US 2.1:This dataset is based in large part on the USGS Protected Areas Database of the United States - PAD-US 2.`. PAD-US is a compilation of authoritative protected areas data between agencies and organizations that ultimately results in a comprehensive and accurate inventory of protected areas for the United States to meet a variety of needs (e.g. conservation, recreation, public health, transportation, energy siting, ecological, or watershed assessments and planning). Extensive documentation on PAD-US processes and data sources is available.How these data were aggregated:Boundaries, and their descriptors, available in spatial databases (i.e. shapefiles or geodatabase feature classes) from land management agencies are the desired and primary data sources in PAD-US. If these authoritative sources are unavailable, or the agency recommends another source, data may be incorporated by other aggregators such as non-governmental organizations. Data sources are tracked for each record in the PAD-US geodatabase (see below).BIA and Tribal Data:BIA and Tribal land management data are not available in PAD-US. As such, data were aggregated from BIA regional offices. These data date from 2012 and were substantially updated in 2022. Indian Trust Land affiliated with Tribes, Reservations, or BIA Agencies: These data are not considered the system of record and are not intended to be used as such. The Bureau of Indian Affairs (BIA), Branch of Wildland Fire Management (BWFM) is not the originator of these data. The

This dataset provides the raw anonymised (quantitative) data from the EDSA demand analysis. This data has been gathered from surveys performed with those who identify as data scientists and manages of data scientists in different sectors across Europe. The coverage of the data includes level of current expertise of the individual or team (data scientist and manager respectively) in eight key areas. The dataset also includes the importance of the eight key areas as capabilities of a data scientist. Further the dataset includes a breakdown of key tools, technologies and training delivery methods required to enhance the skill set of data scientists across Europe. The EDSA dashboard provides an interactive view of this dataset and demonstrates how it is being used within the project. The dataset forms part of the European Data Science Academy (EDSA) project which received funding from the European Unions's Horizon 2020 research and innovation programme under grant agreement No 643937. This three year project ran/runs from February 2015 to January 2018. Important note on privacy: This dataset has been collected and made available in a pseudo anonymous way, as agreed by participants. This means that while each record represents a person, no sensitive identifiable information, such as name, email or affiliation is available (we don't even collect it). Pseudo anonymisation is never full proof, however the projects privacy impact assessment has concluded that the risk resulting from the de-anonymisation of the data is extremely low. It should be noted that data is not included of participants who did not explicitly agree that it could be shared pseudo anonymously (this was due to a change of terms after the survey had started gathering responses, meaning any early responses had come from people who didn't see this clause). If you have any concerns please contact the data publisher via the links below.

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.

Description: These Reference grids have been created for the NaturaConnect project and are based on an intersection of the European Coastline delineation and the GADM database. Thee reference grids have been created in a way so that they are fully consistent with the EEA reference grid (https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2), meaning that for example two 5km gridded cells fully match a 10km grid cell in width. Filestructure: ReferenceGrid_Europe_{format}_{grain} format is either "frac" for fractional data (which has been multiplied with 10000 to save in integer format) or binary (0,1). grain is provided as layers in 100m, 1000m, 5000m, 10000m, 50000m spatial resolution. Alternative aggregations can be provided on request. File format: The layers are gridded geoTiff files and can be loaded in any conventional Graphical Information System (GIS) or specific analytical programming languages (e.g. R or python). In addition external pyramids (.tfw) have been precreated to enable faster rendering. Geographic projection: We use the Lamberts-Equal-Area Projection by default for all layers in NaturaConnect. This is an equal-area (but distorted shape) projection and commonly used by European institution with a focus on the European continent. For global layers the equal-area World Mollweide projection is used. Sourcecode: The code to reproduce the layers has been made available in the "code" file.

This data contains the API specifications for each function of NGTN (OpenAPI), and includes detailed technical information such as the name of each API, function description, input/output parameters, and classification code. It is practical linked development data for building spatial information-based services. 1. Format: CSV 2. Summary of contents ■ func_sn: Unique identification number for each function ■ class_cd: API classification code to which the function belongs ■ func_ctgry_cd: Sub-function category code ■ func_nm: API function name (e.g. XDCameraViewSetColor, SetZoomLevel, etc.) ■ func_dc: Function description (e.g. camera color setting, zoom level adjustment, etc.) ■ input_paramtr_dc: Input parameter description (data type + description) ■ return_paramtr_dc: Return value information (data type + success or return value meaning) ■ plugin_ver: API applied plugin version ■ func_se: Function type (e.g. 2D, 3D, data processing, etc.) ■ indict_at: Display status (Y/N) ■ regist_date, updt_date: First registration date and last modification date 3. Usage examples ■ API call configuration and testing required when public institutions or private developers build map services, disaster information display systems, drone control systems, etc. Utilization for automation ■ When designing UI/UX, use it as a standard for designing menu structures or organizing a functional classification system according to API categories by function, such as map control/data search/3D visualization. ■ When developing a system for automatic documentation of system linkage manuals and API specifications, it can be used to create JSON schemas or documentation forms based on the names, descriptions, and input/output items of each API.