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.

Download high-quality, up-to-date shapefile boundaries (SHP, projection system SRID 4326). Our Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

Download high-quality, up-to-date United Arab Emirates shapefile boundaries (SHP, projection system SRID 4326). Our United Arab Emirates Shapefile Database offers comprehensive boundary data for spatial analysis, including administrative areas and geographic boundaries. This dataset contains accurate and up-to-date information on all administrative divisions, zip codes, cities, and geographic boundaries, making it an invaluable resource for various applications such as geographic analysis, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including Shapefile, GeoJSON, KML, ASC, DAT, CSV, and GML, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

Dataset for "Fresh rockfalls near the landing site of ExoMars Rosalind Franklin Rover: drivers, trafficability, and implications".

The catalog contains rockfall locations (shapefiles compatible with QGIS/ArcGIS and a .csv file).

Bairros de São Paulo

Fala rapaziadinha, segue o dataset contendo as coordenadas do perímetro de cada distrito de São Paulo - Capital. Junto com o dataset, tem os arquivos de plot, para você explorar no google earth, arcgis, QGIS ou outro software de geoprocessamento.

Passo a Passo:

-Primeiramente baixei os shapefiles no site de dados abertos da Prefeitura de São Paulo;

-Depois eu importei o arquivo no QGIS e fiz uma conversão para GEOJSON (é melhor para manipular no Python);

-Depois salvei no google drive e fiz as manipulações no google colaboratory com Python;

Utilidade:

-Bem eu estava melhorando um dataset de favelas na cidade de São Paulo, e estava querendo colocar uma coluna de Distrito. Essa já foi uma utilidade;

-Se você trabalha com logística como eu, é importante ter os shapefiles para fazer estudo de região. O arquivo CSV ajuda bastante e é o maior problema, pois converter os pontos de coordenadas em de um shapefile em um CSV, é bem chatinho e provavelmente você só vai conseguir usando alguma linguagem de programação;

-Desenvolvedores. Se você não usar nenhum tipo de API, bem provavelmente você vai precisar de um database com as coordenadas dos perímetros das regiões para colocar o nome do Distrito nos endereços.

Qualquer dúvida é só me chamar!

(11) 94937-0306 |Whatsapp| marcus.rodrigues4003@gmail.com |Email|

This dataset quantifies the extent and annual rate of change in surface water area (SWA) in India's rivers and basins over a period of 30 years from 1991 to 2020. Visit Surface Water Trends - India for an interactive web interface to explore these results, and for additional data and information.

It is derived from the Global Surface Water Explorer which maps terrestrial surface water globally using historical Landsat satellite imagery. (Pekel, J. et al., Nature 540, 418-422 (2016). (doi:10.1038/nature20584)). The data files contain zipped archives of shapefiles and CSV (comma separated values) files.

Shapefiles are one for each season (dry, wet and permanent) and scale (river basin and reach) of our analysis, and contain annual trends in surface water area. To open and explore them in a GIS software (eg. QGIS), un-ZIP them and include them as vector datasets.

CSV files are one for each scale (river basin and reach (transect)) of our analysis, and contain time series of surface water areas from 1991 to 2020. To open and explore them, for analysis or to explore in a table editing software, un-ZIP them and read them in.

Refer to 00_README.txt for details on feature and table attributes in the files.

Update NotesMar 16 2024, remove spaces in the file and folder names.Mar 31 2024, delete the underscore in the city names with a space (such as San Francisco) in the '02_TransCAD_results' folder to ensure correct data loading by TransCAD (software version: 9.0).Aug 31 2024, add the 'cityname_link_LinkFlows.csv' file in the '02_TransCAD_results' folder to match the link from input data and the link from TransCAD results (LinkFlows) with the same Link_ID.IntroductionThis is a unified and validated traffic dataset for 20 US cities. There are 3 folders for each city.01 Input datathe initial network data obtained from OpenStreetMap (OSM)the visualization of the OSM dataprocessed node / link / od data02 TransCAD results (software version: 9.0)cityname.dbd : geographical network database of the city supported by TransCAD (version 9.0)cityname_link.shp / cityname_node.shp : network data supported by GIS software, which can be imported into TransCAD manually. Then the corresponding '.dbd' file can be generated for TransCAD with a version lower than 9.0od.mtx : OD matrix supported by TransCADLinkFlows.bin / LinkFlows.csv : traffic assignment results by TransCADcityname_link_LinkFlows.csv: the input link attributes with the traffic assignment results by TransCADShortestPath.mtx / ue_travel_time.csv : the traval time (min) between OD pairs by TransCAD03 AequilibraE results (software version: 0.9.3)cityname.shp : shapefile network data of the city support by QGIS or other GIS softwareod_demand.aem : OD matrix supported by AequilibraEnetwork.csv : the network file used for traffic assignment in AequilibraEassignment_result.csv : traffic assignment results by AequilibraEPublicationXu, X., Zheng, Z., Hu, Z. et al. (2024). A unified dataset for the city-scale traffic assignment model in 20 U.S. cities. Sci Data 11, 325. https://doi.org/10.1038/s41597-024-03149-8Usage NotesIf you use this dataset in your research or any other work, please cite both the dataset and paper above.A brief introduction about how to use this dataset can be found in GitHub. More detailed illustration for compiling the traffic dataset on AequilibraE can be referred to GitHub code or Colab code.ContactIf you have any inquiries, please contact Xiaotong Xu (email: kid-a.xu@connect.polyu.hk).

This dataset maps the location of anti-social graffiti around the University of Edinburgh's central campus. The data was collected over a 2 week period between the 19th May and the 2nd June 2014. The data was collected using a smartphone through an app called Fieldtrip GB (http://fieldtripgb.blogs.edina.ac.uk/). Multiple asset collectors were deployed to use a pre-defined data collection form which allowed users to log the following attributes: Date / Name of asset collector / Type of graffiti (image/tag/words/advert/.....) / What the graffiti was on (building/wall/lamppost/....) / What medium was used (paint/paper/chalk/....) / Density of graffiti / Photograph / Location. The data is by no means complete and realistically captured only around 50% of the graffiti in the study area. It is hoped that this dataset will be updated every 3 months to chart the distribution of graffiti over time. data was collected using the app Fieldtrip GB Once collected, data from multiple asset collectors was merged in FtGB's authoring tool and exported as a CSV file. This was then imported into QGIS and saved as a vector dataset in ESRI Shapefile format. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2014-06-06 and migrated to Edinburgh DataShare on 2017-02-22.

Ovdje je slika globalnog općinskog poreza (founcier bati + stanovanje). Prosječni porez po imovini Nancy 2014 Da biste to učinili opet, trebat će vam: — Softver QGIS (besplatno: https://www.qgis.org/fr/site/forusers/download.html — qgs datoteka vašeg odjela (http://www.actualitix.com/shapefiles-des-departements-de-france.html)

— izvoz poreznih stopa (https://www.data.gouv.fr/fr/datasets/impots-locaux/ > općinski podaci i podaci među zajednicama > Vaš odjel > Podaci o lokalnom izravnom porezu za 2014. (format XLS)) — podaci (većina dana INSEE-a ovdje 2012. http://www.insee.fr/fr/themes/detail.asp?reg_id=99&ref_id=base-cc-emploi-pop-active-2012)

Način rada: — obradite svoje podatke u omiljenoj proračunskoj tablici (Excel ili OpenOffice Calc) integracijom impot podataka i INSEE-om kako biste izvukli brojeve koji vam se čine otkrivenima. — Instalirajte QGIS — Otvorite.qgs svog odjela

Dodaj stupce — Desni klik nekretnina na glavnom sloju — Idite na izbornik polja (na lijevoj strani) — Dodati (putem olovke) željene stupce (ovdje prosječni porez na stanovanje po imovini, prosječni porez na imovinu po imovini i zbroj oboje) — To su reali preciznosti 2, a dužina 6 — Registriraj se

Umetnuti podatke: — Desnom tipkom miša kliknite na sloj „Otvori tablicu atributa” — Odaberite sve — Preslika — Zalijepite u Excel (ili openOffice kalcs)

— Staviti ad hoc formule u Excel (SOMME.SI.ENS za povrat stope) — Spremite željenu karticu u CSV DOS s novim vrijednostima — U QGIS-u > Izbornik > Sloj > Dodaj razgraničeni sloj teksta — Uvoz CSV-a

Predočiti podatke: — Za pojednostavljenje savjetujem vam da napravite sloj po stopi, a slojevi sume.Tako vas trune u tri klika iz slike željene stope — Za svaki sloj (ili stopu) — Svojstva desnog klika na csv sloj — Oznake za dodavanje naziva grada i željene stope — Stil za fct bojanje csv polja Ispis podataka u pdf-u: Da biste ispisali, morate definirati predložak za ispis — U izborniku odaberite novi brojčanik za ispis — odaberite format (odjel u A0 je prilično čitljiv) — Dodajte vas legendu, ljestvicu i druge Otisak i ovdje... NAPOMENA: ova metoda stvara aberacije: — u slučaju kada INSEE nema broj ili brojeve koji su se puno preselili od tada pretpostavlja se da samo imovina plaća porez (što je pravednije, ali ne i 100 %)Ovdje je slika globalnog općinskog poreza (founcier bati + stanovanje). Prosječni porez po imovini Nancy 2014

Da biste to učinili opet, trebat će vam:

— Softver QGIS (besplatno:https://www.qgis.org/fr/site/forusers/download.html — qgs datoteka vašeg odjela (http://www.actualitix.com/shapefiles-des-departements-de-france.html) — izvoz poreznih stopa (https://www.data.gouv.fr/fr/datasets/impots-locaux/ > općinski podaci i podaci među zajednicama > Vaš odjel > Podaci o lokalnom izravnom porezu za 2014. (format XLS)) — podaci (većina dana INSEE-a ovdje 2012. http://www.insee.fr/fr/themes/detail.asp?reg_id=99&ref_id=base-cc-emploi-pop-active-2012)

Način rada: — obradite svoje podatke u omiljenoj proračunskoj tablici (Excel ili OpenOffice Calc) integracijom impot podataka i INSEE-om kako biste izvukli brojeve koji vam se čine otkrivenima. — Instalirajte QGIS — Otvorite.qgs svog odjela

Dodaj stupce — Desni klik nekretnina na glavnom sloju — Idite na izbornik polja (na lijevoj strani) — Dodati (putem olovke) željene stupce (ovdje prosječni porez na stanovanje po imovini, prosječni porez na imovinu po imovini i zbroj oboje) — To su reali preciznosti 2, a dužina 6 — Registriraj se

Umetnuti podatke: — Desnom tipkom miša kliknite na sloj „Otvori tablicu atributa” — Odaberite sve — Preslika — Zalijepite u Excel (ili openOffice kalcs) — Staviti ad hoc formule u Excel (SOMME.SI.ENS za povrat stope) — Spremite željenu karticu u CSV DOS s novim vrijednostima — U QGIS-u > Izbornik > Sloj > Dodaj razgraničeni sloj teksta — Uvoz CSV-a

Predočiti podatke: — Za pojednostavljenje savjetujem vam da napravite sloj po stopi, a slojevi sume.Tako vas trune u tri klika iz slike željene stope — Za svaki sloj (ili stopu) — Svojstva desnog klika na csv sloj — Oznake za dodavanje naziva grada i željene stope

— Stil za fct bojanje csv polja Ispis podataka u pdf-u: Da biste ispisali, morate definirati predložak za ispis — U izborniku odaberite novi brojčanik za ispis — odaberite format (odjel u A0 je prilično čitljiv) — Dodajte vas legendu, ljestvicu i druge Otisak i ovdje...

NAPOMENA:ova metoda stvara aberacije:

— u slučaju kada INSEE nema broj ili brojeve koji su se puno preselili od tada pretpostavlja se da samo imovina plaća porez (što je pravednije, ali ne i 100 %)Ovdje je slika globalnog općinskog poreza (founcier bati + stanovanje). Prosječni porez po imovini Nancy 2014

Da bist

Punctuele objecten die de exacte posities van de hotels van grootstedelijke en ultramarijnse prefecturen weergeven — met uitzondering van Saint-Pierre-et-Miquelon en Wallis-et-Futuna.

De oorsprong van deze gegevens is te vinden op de Wikipedia pagina „Lijst van hotels in de prefectuur van Frankrijk” waarvan de tabel werd geformatteerd en vervolgens geëxporteerd in CSV-formaat (delimiter: puntkomma) zodat het kan worden geïmporteerd in QGIS („Een afgebakende tekstlaag toevoegen” nauwkeurig van velden X en Y).

De posities van de hotels in de prefecturen Parijs, Dijon, Belfort en Mâcon moesten echter worden gecorrigeerd.Als gevolg hiervan zijn de geografische coördinaten van alle objecten (105) opnieuw berekend en verschijnen ze in decimale graden in twee velden (LonDD en LatDD).

Het volgende wordt verstrekt: — de laag in GeoJSON en SHP formaten (shapefile) — EPSG:4326, — het CSV afgebakende bestand — het spreadsheetbestand dat wordt gebruikt om brongegevens (ODS) te formatteren.

Opmerking:Niet te verwarren met de laag „Prefecturen en subprefecturen (punt)” die op deze site beschikbaar is gesteld door DREAL Poitou-Charentes en bestaande uit centroïden van oppervlakteobjecten die prefecturen en subprefecturen vertegenwoordigen. De oorsprong van deze gegevens is te vinden op de Wikipedia pagina „Lijst van hotels in de prefectuur van Frankrijk” waarvan de tabel werd geformatteerd en vervolgens geëxporteerd in CSV-formaat (delimiter: puntkomma) zodat het kan worden geïmporteerd in QGIS („Een afgebakende tekstlaag toevoegen” nauwkeurig van velden X en Y).

De posities van de hotels in de prefecturen Parijs, Dijon, Belfort en Mâcon moesten echter worden gecorrigeerd.Als gevolg hiervan zijn de geografische coördinaten van alle objecten (105) opnieuw berekend en verschijnen ze in decimale graden in twee velden (LonDD en LatDD).

Het volgende wordt verstrekt: — de laag in GeoJSON en SHP formaten (shapefile) — EPSG:4326, — het CSV afgebakende bestand

— het spreadsheetbestand dat wordt gebruikt om brongegevens (ODS) te formatteren.

Opmerking: Niet te verwarren met de laag „Prefecturen en subprefecturen (punt)” die op deze site beschikbaar is gesteld door DREAL Poitou-Charentes en bestaande uit centroïden van oppervlakteobjecten die prefecturen en subprefecturen vertegenwoordigen.