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.

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).

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.

Introduction This travel time matrix records travel times and travel distances for routes between all centroids (N = 13231) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below. The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region. Data formats The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably. Helsinki_Travel_Time_Matrix_2023.csv.zst: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically. Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip: an OGC GeoPackage standard file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively. Helsinki_Travel_Matrix_2023_travel_times.csv.zip: a set of 13231 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv where 5787545 is replaced by the to_id by which the rows in the file are grouped. Use the from_id column to join with the geometries from one of the files below. Geometry, only: Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively. Helsinki_Travel_Time_Matrix_2023_grid.shp.zip: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the from_id and to_id columns of the data files. Table structure from_id ID number of the origin grid cell to_id ID number of the destination grid cell walk_avg Travel time in minutes from origin to destination by walking at an average speed walk_slo Travel time in minutes from origin to destination by walking slowly bike_avg Travel time in minutes from origin to destination by cycling at an average speed; incl. extra time (1 min) to unlock and lock bicycle bike_fst Travel time in minutes from origin to destination by cycling fast; incl. extra time (1 min) to unlock and lock bicycle bike_slo Travel time in minutes from origin to destination by cycling slowly; incl. extra time (1 min) to unlock and lock bicycle pt_r_avg Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed pt_r_slo Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed pt_m_avg Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed pt_m_slo Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed pt_n_avg Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed pt_n_slo Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed car_r Travel time in minutes from origin to destination by private car in rush hour traffic car_m Travel time in minutes from origin to destination by private car in midday traffic car_n Travel time in minutes from origin to destination by private car in nighttime traffic walk_d Distance from origin to destination, in metres, on foot Data for 2013, 2015, and 2018 At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iter

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.

This dataset contains the results of Food Standards Agency inspections of food outlets in England and Wales. The FSA inspect and rate food outlets, scores of 0-5 are returned with 0/1 indicating urgent and immediate action is required and 5 being exemplary. To pass, an outlet must score 3 or above. Even 0 and 1 scores are allowed to remain open, only outlets that are deemed to pose an immediate risk to health anre closed down. 91% of outlets pass, but that leaves 9% that fail. The dataset contains over 300,000 records. Data source from the Guardian website () who in turn sourced it from the Food Standards Agency. It was downloaded from the Guardian as a CSV and converted to a shp file in QGIS. Data was cleaned and converted from Txt to float using MMQGIS. The original data contains 340,000 records but the shp file has 310,000. The lost records did not contain lat/lon. The most probable reason for this is that they were outside catering or mobile catering and had no fixed address for their kitchens. This data is free to use, but acknowledging The FSA and the Guardian would be courteous. GIS vector data. This dataset was first accessioned in the EDINA ShareGeo Open repository on 2013-08-09 and migrated to Edinburgh DataShare on 2017-02-22.

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.

국토교통부 토지소유정보는 토지대장에 등록된 토지의 상태 및 소유 현황을 제공하는 데이터이다. 시도별로 구축된 자료를 통해 토지의 위치와 경계뿐만 아니라 소유상태를 함께 확인할 수 있으며, 토지 관리, 행정업무, 연구 및 정책 수립 등 다양한 분야의 기초자료로 활용된다.

이 데이터는 SHP(Shapefile)와 CSV 형식으로 제공된다. SHP 형식은 토지의 공간정보와 속성을 함께 담고 있어 GIS 소프트웨어(ArcGIS, QGIS 등)에서 시각화 및 공간분석에 활용하기 적합하다. CSV 형식은 엑셀, 데이터베이스, R·Python 등 범용 분석 환경에서 손쉽게 가공·처리할 수 있어 범용성이 높다. 두 가지 형식이 모두 제공되므로 공간분석 기반 행정업무부터 일반 통계·데이터 분석, 민간 서비스 개발까지 다양한 목적에 맞게 유연하게 활용할 수 있다.

국토교통부 토지특성정보는 필지별로 해당 토지가 가지는 물리적·입지적 특성을 제공하는 데이터이다. 주요 항목에는 토지이용상황, 지형높이, 지형형상, 도로접면 여부 등이 포함되어 있으며, 이를 통해 개별 필지의 입지 조건과 물리적 특성을 종합적으로 파악할 수 있다. 이 정보는 토지 가치 평가, 도시계획, 개발 행위 관리, 연구 및 정책 수립 등 다양한 분야에서 기초 자료로 활용된다.

이 데이터는 SHP(Shapefile)와 CSV 형식으로 제공된다. SHP 형식은 공간좌표와 속성을 함께 담고 있어 GIS 소프트웨어(ArcGIS, QGIS 등)에서 직접 시각화·분석할 수 있으며, 공간데이터와 연계 활용이 가능하다. CSV 형식은 엑셀, 데이터베이스, R·Python 등 통계·분석 환경에서 손쉽게 가공·처리할 수 있어 범용성이 높다. 두 가지 형식이 모두 지원되므로 행정업무, 연구, 민간 서비스 개발 등 다양한 목적에 따라 유연하게 활용할 수 있다.