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 dataset compares four cities FIXED-line broadband internet speeds: - Melbourne, AU - Bangkok, TH - Shanghai, CN - Los Angeles, US - Alice Springs, AU

ERRATA: 1.Data is for Q3 2020, but some files are labelled incorrectly as 02-20 of June 20. They all should read Sept 20, or 09-20 as Q3 20, rather than Q2. Will rename and reload. Amended in v7.

1. LAX file named 0320, when should be Q320. Amended in v8.

*lines of data for each geojson file; a line equates to a 600m^2 location, inc total tests, devices used, and average upload and download speed - MEL 16181 locations/lines => 0.85M speedtests (16.7 tests per 100people) - SHG 31745 lines => 0.65M speedtests (2.5/100pp) - BKK 29296 lines => 1.5M speedtests (14.3/100pp) - LAX 15899 lines => 1.3M speedtests (10.4/100pp) - ALC 76 lines => 500 speedtests (2/100pp)

Geojsons of these 2* by 2* extracts for MEL, BKK, SHG now added, and LAX added v6. Alice Springs added v15.

This dataset unpacks, geospatially, data summaries provided in Speedtest Global Index (linked below). See Jupyter Notebook (*.ipynb) to interrogate geo data. See link to install Jupyter.

** To Do Will add Google Map versions so everyone can see without installing Jupyter. - Link to Google Map (BKK) added below. Key:Green > 100Mbps(Superfast). Black > 500Mbps (Ultrafast). CSV provided. Code in Speedtestv1.1.ipynb Jupyter Notebook. - Community (Whirlpool) surprised [Link: https://whrl.pl/RgAPTl] that Melb has 20% at or above 100Mbps. Suggest plot Top 20% on map for community. Google Map link - now added (and tweet).

** Python melb = au_tiles.cx[144:146 , -39:-37] #Lat/Lon extract shg = tiles.cx[120:122 , 30:32] #Lat/Lon extract bkk = tiles.cx[100:102 , 13:15] #Lat/Lon extract lax = tiles.cx[-118:-120, 33:35] #lat/Lon extract ALC=tiles.cx[132:134, -22:-24] #Lat/Lon extract

Histograms (v9), and data visualisations (v3,5,9,11) will be provided. Data Sourced from - This is an extract of Speedtest Open data available at Amazon WS (link below - opendata.aws).

**VERSIONS v.24 Add tweet and google map of Top 20% (over 100Mbps locations) in Mel Q322. Add v.1.5 MEL-Superfast notebook, and CSV of results (now on Google Map; link below). v23. Add graph of 2022 Broadband distribution, and compare 2020 - 2022. Updated v1.4 Jupyter notebook. v22. Add Import ipynb; workflow-import-4cities. v21. Add Q3 2022 data; five cities inc ALC. Geojson files. (2020; 4.3M tests 2022; 2.9M tests)

Melb 14784 lines Avg download speed 69.4M Tests 0.39M

SHG 31207 lines Avg 233.7M Tests 0.56M

ALC 113 lines Avg 51.5M Test 1092

BKK 29684 lines Avg 215.9M Tests 1.2M

LAX 15505 lines Avg 218.5M Tests 0.74M

v20. Speedtest - Five Cities inc ALC. v19. Add ALC2.ipynb. v18. Add ALC line graph. v17. Added ipynb for ALC. Added ALC to title.v16. Load Alice Springs Data Q221 - csv. Added Google Map link of ALC. v15. Load Melb Q1 2021 data - csv. V14. Added Melb Q1 2021 data - geojson. v13. Added Twitter link to pics. v12 Add Line-Compare pic (fastest 1000 locations) inc Jupyter (nbn-intl-v1.2.ipynb). v11 Add Line-Compare pic, plotting Four Cities on a graph. v10 Add Four Histograms in one pic. v9 Add Histogram for Four Cities. Add NBN-Intl.v1.1.ipynb (Jupyter Notebook). v8 Renamed LAX file to Q3, rather than 03. v7 Amended file names of BKK files to correctly label as Q3, not Q2 or 06. v6 Added LAX file. v5 Add screenshot of BKK Google Map. v4 Add BKK Google map(link below), and BKK csv mapping files. v3 replaced MEL map with big key version. Prev key was very tiny in top right corner. v2 Uploaded MEL, SHG, BKK data and Jupyter Notebook v1 Metadata record

** LICENCE AWS data licence on Speedtest data 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.

** Other uses of Speedtest Open Data; - see link at Speedtest below.

Banner

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F2a02819343cd77c9e90857bea0b42f84%2F_58981594-9fb4-4081-8bc3-89e8e27076ae-small.jpeg?generation=1738488675620142&alt=media" alt="">

What is Jollibee?

Jollibee is a Filipino chain of fast food restaurants owned by Jollibee Foods Corporation (JFC) which serves as its flagship brand. Established in 1978 by Tony Tan Caktiong, it is the Philippines' top fast food restaurant[3] and is among the world's fastest growing restaurants, expanding its international presence from 2014 to 2024 almost sixfold. As of January 2024, there were over 1,668 Jollibee fast-food branches across 17 countries,[4] with restaurants in Southeast Asia, East Asia (Hong Kong and Macau), the Middle East, North America, and Europe (including Spain, Italy, and the United Kingdom). Jollibee is best known for its bestselling item, the Chickenjoy.

From Wikipedia article

Context

I saw this from Reddit

Decided to post this here because I feel like it's time. I made this during COVID lockdown out of sheer boredom. Made this using MapHub. I simply looked up Jollibee's official store directory, and confirmed them by looking through Facebook (particularly when new stores are opening) and Google Maps. This also includes CityMall branches with the combined Jollibee Foods Corp franchises (Jabee, Mang Inasal, Chowking, Greenwich). Also, like I said, it's in progress. I have yet to include branches in the Middle East, Europe, and North America, but I have been slowly adding new openings. This map is downloadable (through MapHub GeoJSON, KML, GPX, etc.) if anyone wants to update it themselves or use it. I will continue to update it myself slowly, but I have university studies. Link: https://maphub.net/johndotto/jollibee-branches

Source

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fe15632085415bec132441da8c11cffdf%2FScreenshot%20from%202025-02-02%2017-33-14.png?generation=1738488824320643&alt=media" alt="">

https://maphub.net/johndotto/jollibee-branches

Credits

All credits to u/JohnJD1302

Images

• From Wikipedia article
• Created with Bing Image Creator
• Screenshot of online tool by u/JohnJD1302

Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 5 years of road development (46,311 km) from 2019-2023.

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:

• NetworkID: A unique ID for each connected road network.
• SegLenM: The length of the road segment (in meters).
• NetLenM: The length of the connected road network (in meters).
• Month: The road segment opening month.
• Year: The road segment opening year.
• MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.

Additional information

• More information about the forest road mapping project can be found at: https://wur.eu/forest-roads
• Continuously updated road maps can be interactively viewed at: https://nrtwur.users.earthengine.app/view/forest-roads
• The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12')
• The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380" target="_blank" rel="noopener">https://doi.org/10.1016/j.rse.2024.114380

Citation

Please cite the following when referring to this dataset:

This layer contains detailed outlines of Maryland counties. The Maryland land county boundaries were built using political county boundaries and the National Hydrology Data (NHD). Land boundaries are a key geographic featue in our mapping process.This is a MD iMAP hosted service. Find more information at https://imap.maryland.gov.Last Updated: UnknownFeature Service Link:https://mdgeodata.md.gov/imap/rest/services/Boundaries/MD_PhysicalBoundaries/FeatureServer/0

Beach Monitoring and Advisories 2015

Virginia Department of Health (VDH) monitors bacteria levels in beach water at 46 public beaches on the Chesapeake Bay and the Atlantic Ocean during the swimming season (May-September). Weekly water samples are collected by local health departments and analyzed by local laboratories for enterococci bacteria levels. Any bacteria level exceeding Virginia's Water Quality Standard of 104 colony forming units (cfu)/100 mL of water requires the issuance of a swimming advisory. Enterococci bacteria serve as an indicator for fecal contamination in salt/brackish waters; while not harmful themselves, enterococci bacteria indicate that other potentially harmful organisms may be present. High levels of enterococci bacteria indicate an increased health risk to recreational water users. Swallowing water contaminated by these disease-causing organisms can cause the most common recreational water illnesses, which are gastrointestinal and may cause vomiting, diarrhea, nausea, abdominal pain or fever. Contact with contaminated water can also cause upper respiratory (ear, nose, and throat), and wound infections. The elderly and young children, as well as those with weakened immune systems are particularly vulnerable to recreational water illnesses.

Beach Monitoring and Advisory 2015 data consists of CSV datasets parsed from PDF original datasets, and GeoJSON of Monitoring/Advisory locations, converted from Google My Maps KML dataset.

Data Citations, References and Resources:

2015 Beach Monitoring Data for Virginia (PDF)
Virginia's 2015 Beach Monitoring Advisory Data (PDF)
Monitoring and Advisory Data by Year
Beach Monitoring - VDH Environmental Epidemiology
Current Swimming Advisories and Monitored Beaches Map
Beach Monitoring Map, Virginia - Google My Maps

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