This is an Australian extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).AWS data licence is "CC BY-NC-SA 4.0", so use of this data must be:- non-commercial (NC)- reuse must be share-alike (SA)(add same licence).This restricts the standard CC-BY Figshare licence.A world speedtest open data was dowloaded (>400Mb, 7M lines of data). An extract of Australia's location (lat, long) revealed 88,000 lines of data (attached as csv).A Jupyter notebook of extract process is attached.A link to Twitter thread of outputs provided.A link to Data tutorial provided (GitHub), including Jupyter Notebook to analyse World Speedtest data, selecting one US State.Data Shows: (Q2)- 3.1M speedtests- 762,000 devices- 88,000 grid locations (600m * 600m), summarised as a point- average speed 33.7Mbps (down), 12.4M (up)- Max speed 724Mbps- data is for 600m * 600m grids, showing average speed up/down, number of tests, and number of users (IP). Added centroid, and now lat/long.See tweet of image of centroids also attached.Versions:v15/16. Add Hist comparing Q1-21 vs Q2-20. Inc ipynb (incHistQ121, v.1.3-Q121) to calc.v14 Add AUS Speedtest Q1 2021 geojson.(79k lines avg d/l 45.4Mbps)v13 - Added three colour MELB map (less than 20Mbps, over 90Mbps, 20-90Mbps)v12 - Added AUS - Syd - Mel Line Chart Q320.v11 - Add line chart compare Q2, Q3, Q4 plus Melb - result virtually indistinguishable. Add line chart to compare Syd - Melb Q3. Also virtually indistinguishable. Add HIST compare Syd - Melb Q3. Add new Jupyter with graph calcs (nbn-AUS-v1.3). Some ERRATA document in Notebook. Issue with resorting table, and graphing only part of table. Not an issue if all lines of table graphed.v10 - Load AURIN sample pics. Speedtest data loaded to AURIN geo-analytic platform; requires edu.au login.v9 - Add comparative Q2, Q3, Q4 Hist pic.v8 - Added Q4 data geojson. Add Q3, Q4 Hist pic.v7 - Rename to include Q2, Q3 in Title.v6 - Add Q3 20 data. Rename geojson AUS data as Q2. Add comparative Histogram. Calc in International.ipynb.v5 - add Jupyter Notebook inc Histograms. Hist is count of geo-locations avg download speed (unweighted by tests).v4 - added Melb choropleth (png 50Mpix) inc legend. (To do - add Melb.geojson). Posted Link to AURIN description of Speedtest data.v3 - Add super fast data (>100Mbps) less than 1% of data - 697 lines. Includes png of superfast.plot(). Link below to Google Maps version of superfast data points. Also Google map of first 100 data points - sample data. Geojson format for loading into GeoPandas, per Jupyter Notebook. New version of Jupyter Notebook, v.1.1.v2 - add centroids image.v1 - initial data load.** Future Work- combine Speedtest data with NBN Technology by location data (national map.gov.au); https://www.data.gov.au/dataset/national-broadband-network-connections-by-technology-type- combine Speedtest data with SEIFA data - socioeconomic categories - to discuss with AURIN.- Further international comparisons- discussed collaboration with Assoc Prof Tooran Alizadeh, USyd.

This service displays a complete state-wide digital land use map of Queensland. It is based on the Queensland Land Use Mapping Program (QLUMP) data product produced by the Queensland Government. The service presents the most recent mapping of land use features for Queensland. The service is cached to the standard Google / Bing Maps scale levels from 1:591,657,551 to 1:9,028.

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

The Bing Maps team at Microsoft released a U.S.-wide vector building dataset in 2018, which includes over 125 million building footprints for all 50 states in GeoJSON format. This dataset is extracted from aerial images using deep learning object classification methods. Large-extent modelling (e.g., urban morphological analysis or ecosystem assessment models) or accuracy assessment with vector layers is highly challenging in practice. Although vector layers provide accurate geometries, their use in large-extent geospatial analysis comes at a high computational cost. We used High Performance Computing (HPC) to develop an algorithm that calculates six summary values for each cell in a raster representation of each U.S. state: (1) total footprint coverage, (2) number of unique buildings intersecting each cell, (3) number of building centroids falling inside each cell, and area of the (4) average, (5) smallest, and (6) largest area of buildings that intersect each cell. These values are represented as raster layers with 30m cell size covering the 48 conterminous states, to better support incorporation of building footprint data into large-extent modelling. This Project is funded by NASA’s Biological Diversity and Ecological Forcasting program; Award # 80NSSC18k0341

Aqui, são fornecidos dados em tempo real para colocar em bicha o atual estado de ocupação dos lugares de parque para pessoas com deficiência na cidade de Osnabrück.Os dados podem ser acessados usando SensorThingsAPI.

O modelo de dados associado é o seguinte:

Padronização do valor chave da entidade

Nome da coisa Parque lugar Descrição Parque (localização: Localização) & & nbsp; Propriedades/tópicos Gestão de Parque & propriedades/palavras-chave [«parque espaço», «estado de ocupação», «disponibilidade» («espaço de parque desativado»)] & & propriedades/língua de propriedades/proprietário OPG & & nbsp; propriedades/proprietárioOPG Propriedades /ImageLink URL para imagem & & Propriedades/dispositivoIDniota do niota & & Propriedades/navegação http://maps.google.com/maps?q=lat%2Clon & & nbsp; propriedades/parque desativado sim/não

Nome da localização Localização do parque Localização Descrição Aqui está o lugar de parque codificaçãoType application/geo + json Localização GeoJSON Point Geometry

Histórica Localização hora Timestamp UTC Nome Datastream Estado de ocupação no parque Localização & Descrição do Documento observaçãoTipo OpenGIS url OpenGIS unitOfMeasurement/nome Status unidadeMedição/símbolo unidadeMedição/definição observadoArea GeoJSON Point Geometry (automático)

& & nbsp; fenômenoTime Timestamp UTC (automático) & ResultTime Timestamp UTC (automático) & & nbsp; Propriedades/mídia Transporte monitorizado (?)/inspirar Nome do sensor PNI Sensor de parque PlacePod (ou outro)

 Descrição O sensor de parque deteta e reporta a ocupação de lugares de parque eionet
codificaçãoType application/pdf

Metadados & «https://www.pnicorp.com/wp-content/uploads/PNI-PlacePod-Vehicle-Detection-Sensor-User-Manual.pdf»

Nome da propriedade observada Condição & procuring instantâneo, gearedness;Modo de existência de alguém, de uma coisa em certo momento; Constituição, natureza [Fonte:https://www.duden.de/rechtschreibung/Zustand; consultado em 28.06.2021] definição http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_CategoryObservation eionet/OpenGIS Resultado da observação «gratuito»/«ocupado»/«desconhecido» & & nbsp; fenômenoTime Timestamp UTC & ResultTime Timestamp UTC

Característica do nome de interesse FoI para localização location_id

 Descrição Gerada a partir da localização_id

codificaçãoType application/vnd.geo + json

recurso GeoJSON Point Geometry

Documentation at Link:https://developer.entur.org/pages-mobility-docs-mobility-v2 Updated every 5 minutes. Data Formats: JSON: Versatile data exchange format, used for simple and efficient representation of structured data, with broad support in various programming environments. CSV: Common file type for importing and analyzing data in various tools. XLSX: Spreadsheet format, useful for further processing and analysis of the data. GEOJSON: Enables easy visualization and geographic analysis of locations. TopoJSON: Compressed and topologically optimized variant of JSON for efficient visualization and analysis of large geographical datasets. KML: XML-based format for storing and visualizing geographic data in Google Earth and Maps, including points, lines, polygons, and custom annotations.

Čia pateikiami duomenys realiuoju laiku, kad būtų galima nustatyti dabartinę neįgaliųjų stovėjimo vietų padėtį Osnabriuko mieste.Duomenis galima pasiekti naudojant SensorThingsAPI.

Susijęs duomenų modelis atrodo taip:

Subjekto pagrindinės vertės standartizavimas

Daikto pavadinimas Parkavimo vieta aprašymas Automobilių stovėjimo aikštelė (vieta: Vieta) & savybės/tema Parkavimo valdymas & savybės/raktiniai žodžiai [„parko vieta“, „užimtumo statusas“, „prieinamumas“ („neįgaloji stovėjimo vieta“)] savybės/language de savybės/savininkas OPG & savybės/savininkasThing OPG savybės/ImageLink URL į vaizdą & savybės/deviceIDniota deviceID iš niota & & savybės/navigacija http://maps.google.com/maps?q=lat%2Clon & savybės/išjungta automobilių stovėjimo aikštelė taip/ne Vietovės pavadinimas Parkavimo vietos vieta aprašymas Čia yra stovėjimo vieta kodavimoType taikymas/geo + json

Vieta GeoJSON Point geometrija Istorinis Vietos laikas Timestamp UTC Duomenų srauto pavadinimas Užimtumo statusas automobilių stovėjimo aikštelėje Vieta aprašymas Dokumento būsena stebėjimasType OpenGIS url OpenGIS unitOfMeasurement/name Status unitOfMeasurement/simbolis μnbsp; unitOfMeasurement/apibrėžimas stebima vietovė GeoJSON taško geometrija (automatinė) & nbsp; reiškinysTime Timestamp UTC (automatinis) & nbsp; & ResultTime Timestamp UTC (automatinis) Raktiniai žodžiai: apgyvendinimo įstaigos/MediaMonitored transport eionet (?)/Inpire

Jutiklio pavadinimas PNI PlacePod parkavimo jutiklis (ar kita) aprašymas Parkavimo jutiklis aptinka ir praneša apie automobilių stovėjimo vietų eionet užimtumą kodavimasType taikymas/pdf & metaduomenys „https://www.pnicorp.com/wp-content/uploads/PNI-PlacePod-Vehicle-Detection-Sensor-User-Manual.pdf“ Stebimas turto pavadinimas Sąlyga aprašymas momentinis pirkimas, krumpliaratis; Kažkieno egzistavimo būdas, daiktas tam tikru momentu;Konstitucija, gamta [Šaltinis: https://www.duden.de/rechtschreibung/Zustand;nuoroda pateikta 2021 m. birželio 28 d.] apibrėžimas http://www.opengis.net/def/observationType/OGC-OM/2.0/OM_CategoryObservation eionet/OpenGIS

Stebėjimo rezultatas „laisvas“/„užimtas“/„nežinomas“ & nbsp; reiškinysTime Timestamp UTC & nbsp; & ResultTime Timestamp UTC Lankytinos vietos pavadinimas FoI dėl vietovės vietovės_id aprašymas Sugeneruota iš vietos location_id kodavimoType taikymas/vnd.geo + json funkcija GeoJSON Point geometrijaDuomenis galima pasiekti naudojant SensorThingsAPI.

Susijęs duomenų modelis atrodo taip:

Subjekto pagrindinės vertės standartizavimas

Daikto pavadinimas Parkavimo vieta aprašymas Automobilių stovėjimo aikštelė (vieta:Vieta)

& savybės/tema Parkavimo valdymas & savybės/raktiniai žodžiai [„parko vieta“, „užimtumo statusas“, „prieinamumas“ („neįgaloji stovėjimo vieta“)] savybės/language de savybės/savininkas OPG & savybės/savininkasThing OPG

savybės/ImageLink URL į vaizdą

& savybės/deviceIDniota deviceID iš niota

& & savybės/navigacija http://maps.google.com/maps?q=lat%2Clon

& savybės/išjungta automobilių stovėjimo aikštelė taip/ne

Vietovės pavadinimas Parkavimo vietos vieta

 aprašymas Čia yra stovėjimo vieta

kodavimoType taikymas/geo + json

Vieta GeoJSON Point geometrija

Istorinis Vietos laikas Timestamp UTC

Duomenų srauto pavadinimas Užimtumo statusas automobilių stovėjimo aikštelėje Vieta

 aprašymas Dokumento būsena

stebėjimasType OpenGIS url OpenGIS

unitOfMeasurement/name Status

 unitOfMeasurement/simbolis

μnbsp; unitOfMeasurement/apibrėžimas

stebima vietovė GeoJSON taško geometrija (automatinė)

& nbsp; reiškinysTime Timestamp UTC (automatinis)

& nbsp; & ResultTime Timestamp UTC (automatinis)

Raktiniai žodžiai: apgyvendinimo įstaigos/MediaMonitored transport eionet (?)/Inpire

Jutiklio pavadinimas

Im Rahmen der Open-Data-Initiative der Stadtverwaltung Münster erhalten Sie auf dieser Seite eine GeoJSON-Datei mit den Umrissen des Innenstadtbereichs von Münster, in dem Stand 26.11.2021 eine Maskenpflicht gilt.

Dieser Umriss wird in maschinenlesbarem Format als GeoJSON-Datei zum Download zur Verfügung gestellt.

Bitte beachten Sie: Der Umriss wurde sorgfältig erstellt, und enthält die im Amtsblatt vom 26.11.2021 genannten Plätze und Straßenabschnitte. Dennoch kann mit dieser Datei nur einen ungefährer Anhaltspunkt gegeben werden und sie besitzt keine Rechtsverbindlichkeit. Sie können diese GeoJSON-Datei z.B. für Darstellungen in Online-Kartendiensten wie OpenStreetmaps oder Google Maps nutzen.

Weitere Informationen wie z.B. eine textuelle Beschreibung des Bereiches, Infos zur Dauer der Maskenpflicht oder eine Visualisierung des Umrisses im Stadtplan erhalten Sie im Amtsblatt vom 29. November 2021 unter der folgenden Internetadresse:
https://www.stadt-muenster.de/amtsblatt.html

Im Rahmen der Open-Data-Initiative der Stadtverwaltung Münster erhalten Sie auf dieser Seite eine GeoJSON-Datei mit den Umrissen des Innenstadtbereichs von Münster, in dem Stand 21.12.2021 eine Maskenpflicht gilt.

Dieser Umriss wird in maschinenlesbarem Format als GeoJSON-Datei zum Download zur Verfügung gestellt.

Bitte beachten Sie: Der Umriss wurde sorgfältig erstellt, und enthält die in der Allgemeinverfügung der Stadt Münster vom 21.12.2021 genannten Plätze und Straßenabschnitte. Dennoch kann mit dieser Datei nur einen ungefährer Anhaltspunkt gegeben werden und sie besitzt keine Rechtsverbindlichkeit. Sie können diese GeoJSON-Datei z.B. für Darstellungen in Online-Kartendiensten wie OpenStreetmaps oder Google Maps nutzen.

Weitere Informationen wie z.B. eine textuelle Beschreibung des Bereiches, Infos zur Dauer der Maskenpflicht oder eine Visualisierung des Umrisses im Stadtplan erhalten Sie in der Allgemeinverfügung der Stadt Münster vom 21.12.2021 unter der folgenden Internetadresse:
https://www.stadt-muenster.de/amtsblatt.html