Mapping incident locations from a CSV file in a web map (YouTube video).

Content This dataset is composed of 10k images from Google Street Map.

The coords.csv file holds latitude and longitude information for all 10k images. The images themselves have a size of 640x640. All the coordinates come directly from google street map so they are 100% accurate.

Contribute The script to get those image is available as free software a https://github.com/paulchambaz/geotrouvetout.

License This dataset is licensed under the GPLv3 license, feel free to use it however you want.

The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

Context

Full Database of city state country available in CSV format. All Countries, States & Cities are Covered & Populated with Different Combinations & Versions.

Each CSV has the 1. Longitude 2. Latitude

of each location, alongside other miscellaneous country data such as 3. Currency 4. State code 5. Phone country code

Content

Total Countries : 250 Total States/Regions/Municipalities : 4,963 Total Cities/Towns/Districts : 148,061

Last Updated On : 29th January 2022

Source

https://github.com/dr5hn/countries-states-cities-database

In this research, we are interested in the use of what we call pan-scalar maps, i.e. interactive, zoomable, multi-scale maps such as Google Maps. It is frequent to feel disorientation when we use these pan-scalar maps, and the absence of consistent landmarks or anchors across scales can be one of the causes of this disorientation \citep{touya_where_2023}. As a consequence, within the virtual environment of a pan-scalar map, we make the hypothesis that map objects, parts of objects, or groups of objects can comport comparably to the qualities of anchors or landmarks in a real space for spatialization purposes.For that we designed a user study where participants were asked to draw on top of the memorable, salient landmarks they saw on the map.

- RAW_DATA contains 2 CSV files: the first contains all drawings, the second all participations.

- MAP_DRAWING contains all drawings split by view (location, style, zoom).

- DRAWING_ANCHORS split drawings by view into pan-scalar anchors (Location, style, zoom, drawings_anchor).

- ANCHORS contains the vector delineation of pan-scalar anchors (Location, style, zoom,anchor).

-STATISTIC_DRAWING contains anchoress, presence... attribute information in xls of drawings (Location, style, zoom,drawings_statistics)

-BOUNDED_ANCHOR contains vector data for anchor lines that have been drawn in the same hue (Location, style, zoom,bounded_anchor)

-WORFLOW_ANCHOR : Contains all QGIS workflows used for AnchorWhat analysis.

- ILLUSTATIONS contains some illustrations from the AnchorWhat analysis.

You can also access an API version of this dataset.

TMS

(traffic monitoring system) daily-updated traffic counts API

Important note: due to the size of this dataset, you won't be able to open it fully in Excel. Use notepad / R / any software package which can open more than a million rows.

Data reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. 

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

The .csv table is part of a dataset package that was compiled for use as mineral assessment guidance in the Sagebrush Mineral-Resource Assessment project (SaMiRA). Mineral potential maps from previous mineral-resource assessments which included areas of the SaMiRA project areas were georeferenced. The images were clipped to the extent of the map and all explanatory text, gathered from map explanations or report text, was recorded into the All_georef_images_descriptive_information_table.csv table. This table lists and describes the column headings in the All_georef_images_descriptive_information_table.csv table.

This dataset contains identifiers, metadata, and a map of the locations where field measurements have been conducted at the East River Community Observatory located in the Upper Colorado River Basin, United States. This is version 3.0 of the dataset and replaces the prior version 2.0, which should no longer be used (see below for details on changes between the versions). Dataset description: The East River is the primary field site of the Watershed Function Scientific Focus Area (WFSFA) and the Rocky Mountain Biological Laboratory. Researchers from several institutions generate highly diverse hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data at the East River in collaboration with the WFSFA. Thus, the purpose of this dataset is to maintain an inventory of the field locations and instrumentation to provide information on the field activities in the East River and coordinate data collected across different locations, researchers, and institutions. The dataset contains (1) a README file with information on the various files, (2) three csv files describing the metadata collected for each surface point location, plot and region registered with the WFSFA, (3) csv files with metadata and contact information for each surface point location registered with the WFSFA, (4) a csv file with with metadata and contact information for plots, (5) a csv file with metadata for geographic regions and sub-regions within the watershed, (6) a compiled xlsx file with all the data and metadata which can be opened in Microsoft Excel, (7) a kml map of the locations plotted in the watershed which can be opened in Google Earth, (8) a jpeg image of the kml map which can be viewed in any photo viewer, and (9) a zipped file with the registration templates used by the SFA team to collect location metadata. The zipped template file contains two csv files with the blank templates (point and plot), two csv files with instructions for filling out the location templates, and one compiled xlsx file with the instructions and blank templates together. Additionally, the templates in the xlsx include drop down validation for any controlled metadata fields. Persistent location identifiers (Location_ID) are determined by the WFSFA data management team and are used to track data and samples across locations. Dataset uses: This location metadata is used to update the Watershed SFA’s publicly accessible Field Information Portal (an interactive field sampling metadata exploration tool; https://wfsfa-data.lbl.gov/watershed/), the kml map file included in this dataset, and other data management tools internal to the Watershed SFA team. Version Information: The latest version of this dataset publication is version 3.0. The latest version contains a breaking change to the Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml), If you had previously downloaded the map file prior to version 3.0, it will no longer work. Use the updated Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml) in this version of the dataset. This version also contains a total of 51 new point locations, 8 new plot locations, and 1 new geographic region. Additionally, it corrects inconsistencies in existing metadata. Refer to methods for further details on the version history. This dataset will be updated on a periodic basis with new measurement location information. Researchers interested in having their East River measurement locations added in this list should reach out to the WFSFA data management team at wfsfa-data@googlegroups.com. Acknowledgements: Please cite this dataset if using any of the location metadata in other publications or derived products. If using the location metadata for the NEON hyperspectral campaign, additionally cite Chadwick et al. (2020). doi:10.15485/1618130.

The Dataset contains images derived from the Newspaper Navigator (news-navigator.labs.loc.gov/), a dataset of images drawn from the Library of Congress Chronicling America collection (chroniclingamerica.loc.gov/).

[The Newspaper Navigator dataset] consists of extracted visual content for 16,358,041 historic newspaper pages in Chronicling America. The visual content was identified using an object detection model trained on annotations of World War 1-era Chronicling America pages, including annotations made by volunteers as part of the Beyond Words crowdsourcing project.

source: https://news-navigator.labs.loc.gov/

One of these categories is 'maps'. In the original training data for Newspaper Navigator, there were relatively few labelled examples of maps. The predictions for maps have an Average Precision of 69.5%, and 34 images in the validation data.

This dataset contains a sample of these images which have been predicted as 'maps'. It also includes additional labels which indicate whether the predicted map image is a 'map' or 'not a map'.

The data is organised as follows:

• The images themselves can be found in 'newspaper_maps.zip'
• `2020_30_10_13_19_228_sample.json` contains metadata about each image drawn from the Newspaper Navigator Dataset.
• map_labels.csv contains the labels for the images as a CSV file

This is the repository for the results of the 'expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps'.

Note: check the most recent version in the sidebar

Current version v.0.2

Date 2024/01/10

Respondants 30

Available files:

File Type Description

responses_v01_public.csv CSV table Survey raw results (anonymous)

responses_v01_stats.csv CSV table Questions statistics

responses_v01_mean_sd.jpg JPEG Image Illustration of Stats (mean and standard deviation)

responses_v01_bands.jpg JPEG Image Illustration of Stats (uncertainty bands)

The column descriptions in the statistical table are as follows:

Prefixes:

HABITAT: habitat suitability score

WEIGHT: Threat weight

MAX_DIST: Maximum distance of negative influence (impact)

Suffixes:

mean: Average

std: Standard deviation

min: Minimum value

p05: 5th percentile

p25: 25th percentile

p50: 50th percentile (median)

p75: 75th percentile

p95: 95th percentile

max: Maximum value

These prefixes and suffixes describe various statistical measures used to analyze the environmental modeling data.

This data package includes an ArcMap geodatabase for the Chihuahuan Desert Rangeland Research Center (CDRRC) pastures 1, 4, 14, and 15: one polygon feature class, one point feature class, associated attribute tables and metadata. The spatial data, CDRRC1_4_14_15_StateMap_v1.gdb.zip, represents the ecological sites and states on Pastures 1, 4, 14 and 15 on the Chihuahuan Desert Rangeland Research Center, and includes field traverse data. CDRRC1_4_14_15_StateMapMetadata.pdf and TraversePointsMetadata.pdf contain the geospatial metadata provided by ArcMap. CDRRC1_4_14_15_StateMap_v1.csv is the attribute table associated with the state map’s polygon feature class, and TraversePoints.xlsx is the attribute table associated with the traverse points feature class and includes a sheet containing detailed attribute metadata.

Web App. Use the tabs provided to discover information about map features and capabilities. Link to Metadata. A variety of searches can be performed to find the parcel of interest. Use the Query Tool to build searches. Click Apply button at the bottom of the tool.Query by Name (Last First) (e.g. Bond James)Query by Address (e.g. 41 S Central)Query by Locator number (e.g. 21J411046)Search results will be listed under the Results tab. Click on a parcel in the list to zoom to that parcel. Click on the parcel in the map and scroll through the pop-up to see more information about the parcel. Click the ellipse in the Results tab or in the pop-up to view information in a table. Attribute information can be exported to CSV file. Build a custom Filter to select and map properties by opening the Parcels attribute table:1. Click the arrow tab at the bottom middle of the map to expand the attribute table window2. Click on the Parcels tab3. Check off Filter by map extent4. Open Options>Filter5. Build expressions as needed to filter by owner name or other variables6. Select the needed records from the returned list7. Click Zoom to which will zoom to the selected recordsPlease note that as the map zooms out detailed layers, such as the parcel boundaries will not display.In addition to Search capabilities, the following tools are provided:MeasureThe measure tool provides the capabilities to draw a point, line, or polygon on the map and specify the unit of measurement.DrawThe draw tool provides the capabilities to draw a point, line, or polygon on the map as graphics. PrintThe print tool exports the map to either a PDF or image file. Click Settings button to configure map or remove legend.Map navigation using mouse and keyboard:Drag to panSHIFT + CTRL + Drag to zoom outMouse Scroll Forward to zoom inMouse Scroll Backward to zoom outUse Arrow keys to pan+ key to zoom in a level- key to zoom out a levelDouble Click to Zoom inFAQsHow to select a parcel: Click on a parcel in the map, or use Query Tool to search for parcel by owner, address or parcel id.How to select more than one parcel: Go to Select Tool and choose options on Select button.How to clear selected parcel(s): Go to Select Tool and click Clear.

The .csv table is part of a dataset package that was compiled for use as mineral assessment guidance in the Sagebrush Mineral-Resource Assessment project (SaMiRA). Mineral potential maps from previous mineral-resource assessments which included areas of the SaMiRA project areas were georeferenced. The images were clipped to the extent of the map and all explanatory text, gathered from map explanations or report text, was recorded into this table. This table is to be used in conjunction with the individual georeferenced raster images. It includes the image file name, map title and figure caption when appropriate. The images are also classified according to the legal definition of mineral resources: metallic, non-metallic, leasable non-fuel, leasable fuel, geothermal, paleontological, and saleable.

When a natural disaster or disease outbreak occurs there is a rush to establish accurate health care location data that can be used to support people on the ground. This has been demonstrated by events such as the Haiti earthquake and the Ebola epidemic in West Africa. As a result valuable time is wasted establishing accurate and accessible baseline data. Healthsites.io establishes this data and the tools necessary to upload, manage and make the data easily accessible. Global scope The Global Healthsites Mapping Project is an initiative to create an online map of every health facility in the world and make the details of each location easily accessible. Open data collaboration Through collaborations with users, trusted partners and OpenStreetMap the Global Healthsites Mapping Project will capture and validate the location and contact details of every facility and make this data freely available under an Open Data License (ODBL). Accessible The Global Healthsites Mapping Project will make the data accessible over the Internet through an API and other formats such as GeoJSON, Shapefile, KML, CSV. Focus on health care location data The Global Healthsites Mapping Project's design philosophy is the long term curation and validation of health care location data. The healthsites.io map will enable users to discover what healthcare facilities exist at any global location and the associated services and resources.

This .zip file contains a table in .csv format of the places as mentioned in West's book and a table of travel routes between places as described in the book, both in .csv format.

LouVelo is a docked bikeshare program owned by Louisville Metro Government and operated by Cyclehop since May of 2017. The System includes Approximately 250 bikes, 25 Docked Stations in Louisville, and an additional 3 stations owned and operated by the City of Jeffersonville in Partnership with Cyclehop. These data will be updated on a monthly basis to show trends in station use. These data are updated and maintained for use in the Louisville Metro Open Data Portal LouVelo Dashboard to show ridership for the entirety of the program. Some stations have been relocated since the programs founding. For up to date information on dock locations please view the system map on the LouVelo website. This dashboard is maintained by Louisville Metro Public Works.For any questions please contact:James GrahamMobility CoordinatorLouisville Metro Public WorksDivision of Transportation444 S. 5th, St, Suite 400Louisville, KY 40202(502) 574-6473james.graham@louisvilleky.govFor more information about the LouVelo bikeshare program please visit their website.

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.

Financial information is published in the Public Accounts of Canada at the end of each fiscal year, and Open Government publishes this data in CSV format for public use. This dataset maps the tables from the Public Account of Canada to the corresponding Open Government dataset.